diff --git a/.gitignore b/.gitignore index 49bd4b4..6a35b7d 100644 --- a/.gitignore +++ b/.gitignore @@ -37,3 +37,6 @@ haystack-test/strands-venv/ __pycache__/ *.pyc node_modules/ + +cookbooks/* +notebooks/* diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/README.md b/README.md index 04b6781..dd00f55 100644 --- a/README.md +++ b/README.md @@ -134,6 +134,17 @@ node build.js # builds all tracks node build.js semantic-caching # build one track ``` +### Generating Jupyter notebooks (optional) + +Every cookbook can be exported as a `.ipynb` notebook for use in JupyterLab, Colab, or VS Code: + +```bash +node build-notebooks.js # all tracks → notebooks/ +node build-notebooks.js semantic-caching # one track +``` + +Notebooks are generated from the same markdown source in `content/`. Python blocks become code cells, bash blocks become `!`-prefixed code cells, and prose stays as markdown cells. + ## Project structure ``` diff --git a/build-notebooks.js b/build-notebooks.js new file mode 100644 index 0000000..d637f15 --- /dev/null +++ b/build-notebooks.js @@ -0,0 +1,204 @@ +#!/usr/bin/env node +/** + * build-notebooks.js - Converts Markdown cookbooks to Jupyter notebooks (.ipynb) + * + * Usage: + * node build-notebooks.js # builds all tracks + * node build-notebooks.js semantic-caching # builds one track + * + * Output goes to notebooks//.ipynb + */ + +const fs = require('fs'); +const path = require('path'); + +const CONTENT_DIR = path.join(__dirname, 'content'); +const OUTPUT_DIR = path.join(__dirname, 'notebooks'); + +/** + * Split markdown into alternating prose / code blocks. + * Returns an array of { type: 'markdown' | 'code', lang?: string, content: string } + */ +function splitMarkdown(md) { + const blocks = []; + const fenceRe = /^```(\w*)\s*$/; + const lines = md.split('\n'); + let i = 0; + + let mdBuf = []; + + function flushMd() { + const text = mdBuf.join('\n').trim(); + if (text) blocks.push({ type: 'markdown', content: text }); + mdBuf = []; + } + + while (i < lines.length) { + const fenceMatch = lines[i].match(fenceRe); + if (fenceMatch) { + flushMd(); + const lang = fenceMatch[1] || ''; + i++; // skip opening fence + const codeBuf = []; + while (i < lines.length && !lines[i].match(/^```\s*$/)) { + codeBuf.push(lines[i]); + i++; + } + i++; // skip closing fence + blocks.push({ type: 'code', lang, content: codeBuf.join('\n') }); + } else { + mdBuf.push(lines[i]); + i++; + } + } + flushMd(); + return blocks; +} + +/** + * Convert a list of blocks into Jupyter notebook cells. + * - python code → code cells + * - bash code → code cells prefixed with ! (so they run in notebook) + * - other fenced code (json, text, pseudo) → markdown cells wrapped in fences + * - prose → markdown cells + */ +function blocksToNotebookCells(blocks) { + const cells = []; + + for (const block of blocks) { + if (block.type === 'markdown') { + cells.push(markdownCell(block.content)); + } else if (block.type === 'code') { + const lang = block.lang.toLowerCase(); + if (lang === 'python' || lang === 'py') { + cells.push(codeCell(block.content)); + } else if (lang === 'mermaid') { + // Skip mermaid diagrams — not supported in Jupyter + } else if (lang === 'bash' || lang === 'sh' || lang === 'shell') { + // Convert each line to a !-prefixed shell command for notebooks + const shellLines = block.content + .split('\n') + .map(line => { + const trimmed = line.trim(); + if (!trimmed || trimmed.startsWith('#')) return trimmed; + return trimmed.startsWith('!') ? trimmed : `!${trimmed}`; + }) + .join('\n'); + cells.push(codeCell(shellLines)); + } else { + // Non-executable code (json, yaml, text, pseudo-code) → markdown fence + cells.push(markdownCell(`\`\`\`${block.lang}\n${block.content}\n\`\`\``)); + } + } + } + return cells; +} + + +function markdownCell(source) { + return { + cell_type: 'markdown', + metadata: {}, + source: sourceLines(source), + }; +} + +function codeCell(source) { + return { + cell_type: 'code', + execution_count: null, + metadata: {}, + outputs: [], + source: sourceLines(source), + }; +} + +/** Jupyter expects source as an array of lines, each ending with \n except the last */ +function sourceLines(text) { + const lines = text.split('\n'); + return lines.map((line, i) => (i < lines.length - 1 ? line + '\n' : line)); +} + +function buildNotebook(cells, title) { + return { + nbformat: 4, + nbformat_minor: 5, + metadata: { + kernelspec: { + display_name: 'Python 3', + language: 'python', + name: 'python3', + }, + language_info: { + name: 'python', + version: '3.11.0', + }, + title, + }, + cells, + }; +} + +function loadMeta(trackDir) { + const metaPath = path.join(trackDir, 'meta.json'); + if (!fs.existsSync(metaPath)) { + console.error(` ⚠️ No meta.json in ${trackDir}`); + return null; + } + return JSON.parse(fs.readFileSync(metaPath, 'utf8')); +} + +function buildTrack(trackName) { + const trackDir = path.join(CONTENT_DIR, trackName); + const outDir = path.join(OUTPUT_DIR, trackName); + + if (!fs.existsSync(trackDir)) { + console.error(`❌ Track not found: ${trackDir}`); + return; + } + + const meta = loadMeta(trackDir); + if (!meta) return; + + fs.mkdirSync(outDir, { recursive: true }); + + console.log(`📓 Building notebooks: ${meta.trackName} (${meta.cookbooks.length} cookbooks)`); + + for (const cookbook of meta.cookbooks) { + const mdPath = path.join(trackDir, cookbook.source); + if (!fs.existsSync(mdPath)) { + console.error(` ⚠️ Missing: ${mdPath}`); + continue; + } + + const md = fs.readFileSync(mdPath, 'utf8'); + + // Add a title cell at the top + const titleMd = `# ${cookbook.title}\n\n**${cookbook.difficulty}** · ~${cookbook.time} · ${meta.trackName}`; + const blocks = splitMarkdown(md); + const cells = [markdownCell(titleMd), ...blocksToNotebookCells(blocks)]; + + const nb = buildNotebook(cells, cookbook.title); + const outName = cookbook.source.replace(/\.md$/, '.ipynb'); + const outPath = path.join(outDir, outName); + fs.writeFileSync(outPath, JSON.stringify(nb, null, 1)); + console.log(` ✅ ${outName}`); + } +} + +// --- Main --- +const targetTrack = process.argv[2]; + +if (targetTrack) { + buildTrack(targetTrack); +} else { + const tracks = fs.readdirSync(CONTENT_DIR).filter(d => + fs.statSync(path.join(CONTENT_DIR, d)).isDirectory() + ); + console.log(`🔨 Building notebooks for ${tracks.length} track(s)...\n`); + for (const track of tracks) { + buildTrack(track); + } +} + +console.log('\n✨ Done!'); diff --git a/build.js b/build.js index b317e07..06eb123 100644 --- a/build.js +++ b/build.js @@ -30,7 +30,14 @@ function loadMeta(trackDir) { } function buildPage(mdContent, cookbook, track, meta) { - const htmlContent = marked.parse(mdContent); + let htmlContent = marked.parse(mdContent); + + // Convert 
...
to 
...
+ // so mermaid.js can find and render them + htmlContent = htmlContent.replace( + /
([\s\S]*?)<\/code><\/pre>/g,
+    (_, code) => `
${code}
`
+  );
   
   const diffClass = { 'Beginner': 'diff-easy', 'Intermediate': 'diff-medium', 'Advanced': 'diff-hard' }[cookbook.difficulty] || 'diff-medium';
   
@@ -83,6 +90,7 @@ ${nextHtml}
 
 
 
+
 
 
 `;
diff --git a/content/betterdb-agent-cache/01-getting-started.md b/content/betterdb-agent-cache/01-getting-started.md
index 892e4d9..a4cd890 100644
--- a/content/betterdb-agent-cache/01-getting-started.md
+++ b/content/betterdb-agent-cache/01-getting-started.md
@@ -17,7 +17,7 @@ Unlike `langgraph-checkpoint-redis` or similar packages, it requires **no Valkey
 No special image needed - the standard Valkey image works:
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 ```bash
diff --git a/content/betterdb-semantic-cache/01-getting-started.md b/content/betterdb-semantic-cache/01-getting-started.md
index 2e16b9a..e2e6860 100644
--- a/content/betterdb-semantic-cache/01-getting-started.md
+++ b/content/betterdb-semantic-cache/01-getting-started.md
@@ -13,7 +13,7 @@ Unlike other semantic cache libraries it is **Valkey-native** (handles `valkey-s
 The `valkey-bundle` image includes the `valkey-search` module required for vector indexing.
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 Verify the module is loaded:
diff --git a/content/context-engineering/01-fundamentals.md b/content/context-engineering/01-fundamentals.md
index 65458b9..201044d 100644
--- a/content/context-engineering/01-fundamentals.md
+++ b/content/context-engineering/01-fundamentals.md
@@ -23,15 +23,28 @@ Every LLM call needs context assembled from up to 5 sources. Valkey can serve as
 ## Prerequisites
 
 - Valkey with the **valkey-search** module (or ElastiCache for Valkey 8.2+)
-- Python 3.9+ with `valkey`
+- Python 3.12+ with `valkey`
+
+## Step 1: Start Valkey
 
 ```bash
-pip install valkey
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
-## Step 1: Store System Instructions
+## Step 2: Install Dependencies
+
+```bash
+uv pip install valkey python-dotenv
+```
+
+## Step 3: Store System Instructions
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import json
 
@@ -47,7 +60,7 @@ client.hset("agent:config:support_bot", mapping={
 print("System instructions stored")
 ```
 
-## Step 2: Manage Conversation History
+## Step 4: Manage Conversation History
 
 ```python
 import time
@@ -76,7 +89,7 @@ for msg in history:
     print(f"  {msg['role']}: {msg['content'][:60]}...")
 ```
 
-## Step 3: Store Tool Outputs
+## Step 5: Store Tool Outputs
 
 ```python
 def store_tool_output(session_id: str, step: int, tool_name: str, result: dict):
@@ -109,7 +122,7 @@ store_tool_output("sess_001", 1, "check_order", {
 })
 ```
 
-## Step 4: Long-term User Memory
+## Step 6: Long-term User Memory
 
 ```python
 def remember(user_id: str, key: str, value: str):
diff --git a/content/context-engineering/02-assembly-pipeline.md b/content/context-engineering/02-assembly-pipeline.md
index 3202126..643a51a 100644
--- a/content/context-engineering/02-assembly-pipeline.md
+++ b/content/context-engineering/02-assembly-pipeline.md
@@ -3,6 +3,11 @@
 Before every LLM call, an agent needs to assemble context from multiple sources. This function is the core of context engineering - it gathers everything the LLM needs into a structured prompt.
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import json
 import struct
diff --git a/content/context-engineering/03-production.md b/content/context-engineering/03-production.md
index 4ead5a8..295d927 100644
--- a/content/context-engineering/03-production.md
+++ b/content/context-engineering/03-production.md
@@ -3,6 +3,11 @@
 The memory layer is the foundation of context engineering at scale. Valkey supports both memory types through TTL management:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import json
 import time
diff --git a/content/conversation-memory/01-getting-started.md b/content/conversation-memory/01-getting-started.md
index 3ba93b1..5354997 100644
--- a/content/conversation-memory/01-getting-started.md
+++ b/content/conversation-memory/01-getting-started.md
@@ -10,12 +10,12 @@ LLMs are stateless - every API call starts from scratch. Conversation memory bri
 ## Prerequisites
 
   * Docker installed (or a running Valkey instance)
-  * Python 3.9+
+  * Python 3.12+
 
 ## Step 1: Start Valkey
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 Verify it's running:
@@ -28,7 +28,7 @@ docker exec valkey valkey-cli PING
 ## Step 2: Install GLIDE
 
 ```bash
-pip install valkey-glide
+uv pip install valkey-glide python-dotenv
 ```
 
 GLIDE is the official Valkey client - Rust core with Python bindings. It works with both standalone Valkey and ElastiCache for Valkey clusters.
@@ -54,6 +54,11 @@ chat:sess_abc123 → [
 ## Step 4: Connect and Store Messages
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import asyncio
 import json
 from glide import GlideClient, GlideClientConfiguration, NodeAddress
diff --git a/content/conversation-memory/02-session-management.md b/content/conversation-memory/02-session-management.md
index 09a1717..56111ec 100644
--- a/content/conversation-memory/02-session-management.md
+++ b/content/conversation-memory/02-session-management.md
@@ -25,6 +25,11 @@ meta:sess_abc123          → {
 ## Pattern 1: Create a Session with Metadata
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import asyncio, json, time
 from glide import GlideClient, GlideClientConfiguration, NodeAddress
 
diff --git a/content/conversation-memory/03-semantic-memory.md b/content/conversation-memory/03-semantic-memory.md
index 7d3fb3b..7273cc8 100644
--- a/content/conversation-memory/03-semantic-memory.md
+++ b/content/conversation-memory/03-semantic-memory.md
@@ -25,7 +25,7 @@ User asks: "How do I deploy?"
 ## Step 1: Start Valkey with Search Module
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 The `valkey-bundle` image includes valkey-search for vector similarity search and valkey-json for JSON document storage.
@@ -33,6 +33,11 @@ The `valkey-bundle` image includes valkey-search for vector similarity search an
 ## Step 2: Create the Vector Index
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from glide import (
     GlideClient, GlideClientConfiguration, NodeAddress,
     ft, TagField, NumericField,
diff --git a/content/conversation-memory/04-semantic-caching.md b/content/conversation-memory/04-semantic-caching.md
index b687214..9eeced1 100644
--- a/content/conversation-memory/04-semantic-caching.md
+++ b/content/conversation-memory/04-semantic-caching.md
@@ -25,6 +25,11 @@ User: "Can you explain Valkey?"
 ## Step 1: Create the Cache Index
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from glide import (
     ft, VectorField, VectorAlgorithm, VectorFieldAttributesHnsw,
     VectorType, DistanceMetricType, NumericField,
diff --git a/content/conversation-memory/05-agent-state.md b/content/conversation-memory/05-agent-state.md
index 8c42390..9fc1043 100644
--- a/content/conversation-memory/05-agent-state.md
+++ b/content/conversation-memory/05-agent-state.md
@@ -28,6 +28,11 @@ AI agents run multi-step workflows: plan → search → analyze → respond. If
 Save the agent's state after each step. If it crashes, resume from the last checkpoint.
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import json
 from glide import GlideClient, GlideClientConfiguration, NodeAddress
 
diff --git a/content/crewai/01-getting-started.md b/content/crewai/01-getting-started.md
index 9fc45b7..b84b383 100644
--- a/content/crewai/01-getting-started.md
+++ b/content/crewai/01-getting-started.md
@@ -14,7 +14,7 @@ CrewAI lets you build teams of AI agents that collaborate on tasks. The problem:
 Docker installed and Python 3.10+ required. AWS credentials needed for Bedrock (later guides).
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 The `valkey-bundle` image includes JSON and Search modules. Verify:
@@ -27,7 +27,7 @@ docker exec valkey valkey-cli PING
 ## Step 2: Install Dependencies
 
 ```bash
-pip install crewai valkey-glide boto3
+uv pip install crewai valkey-glide boto3 python-dotenv
 ```
 
 **Why GLIDE?** Valkey GLIDE is the official Valkey client with a Rust core for high performance. It supports both standalone Valkey and ElastiCache for Valkey clusters. Unlike generic Redis clients, GLIDE is purpose-built for Valkey and supports all Valkey-specific features including `FT.*` search commands.
@@ -35,6 +35,11 @@ pip install crewai valkey-glide boto3
 ## Step 3: Connection Configuration
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from dataclasses import dataclass
 import os
 
diff --git a/content/crewai/02-memory-storage.md b/content/crewai/02-memory-storage.md
index 52ff8c2..b9362d8 100644
--- a/content/crewai/02-memory-storage.md
+++ b/content/crewai/02-memory-storage.md
@@ -3,6 +3,11 @@
 Each memory is a structured record with content, scope, categories, importance, and an embedding vector:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from pydantic import BaseModel, Field
 from datetime import datetime
 from uuid import uuid4
diff --git a/content/crewai/03-agent-memory.md b/content/crewai/03-agent-memory.md
index 92fccb4..c19e8c0 100644
--- a/content/crewai/03-agent-memory.md
+++ b/content/crewai/03-agent-memory.md
@@ -1,6 +1,11 @@
 ## Step 1: Configure Memory with Bedrock
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import boto3
 from crewai.memory.unified_memory import Memory
 from valkey_storage import ValkeyStorage
diff --git a/content/feature-store/01-getting-started.md b/content/feature-store/01-getting-started.md
index 9db9cf5..17aa6c2 100644
--- a/content/feature-store/01-getting-started.md
+++ b/content/feature-store/01-getting-started.md
@@ -10,12 +10,12 @@ ML models need features at inference time. A feature store bridges offline train
 ## Prerequisites
 
   * Docker installed (or a running Valkey instance)
-  * Python 3.9+
+  * Python 3.12+
 
 ## Step 1: Start Valkey
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 Verify it's running:
@@ -28,7 +28,7 @@ docker exec valkey valkey-cli ping
 ## Step 2: Install Dependencies
 
 ```bash
-pip install valkey
+uv pip install valkey python-dotenv
 ```
 
 The `valkey` package is the official Valkey Python client - no special drivers needed.
@@ -56,6 +56,11 @@ fs:v1:user_profile:user_123 → {
 Starting with raw Valkey commands to see what's happening under the hood:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import time
 
diff --git a/content/feature-store/02-online-serving.md b/content/feature-store/02-online-serving.md
index c7fcc29..5db7c10 100644
--- a/content/feature-store/02-online-serving.md
+++ b/content/feature-store/02-online-serving.md
@@ -21,6 +21,11 @@ The simplest pattern - fetch all features for one entity:
 ### Raw Valkey: HGETALL
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
diff --git a/content/feature-store/03-realtime-aggregations.md b/content/feature-store/03-realtime-aggregations.md
index eea2de2..0b1d097 100644
--- a/content/feature-store/03-realtime-aggregations.md
+++ b/content/feature-store/03-realtime-aggregations.md
@@ -13,6 +13,11 @@ Count events in a sliding time window - e.g., "transactions in the last hour."
 We use a **Sorted Set** where each member is a unique event ID and the score is the timestamp:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import time
 import uuid
diff --git a/content/feature-store/04-streaming-updates.md b/content/feature-store/04-streaming-updates.md
index b02dea0..233afbb 100644
--- a/content/feature-store/04-streaming-updates.md
+++ b/content/feature-store/04-streaming-updates.md
@@ -24,6 +24,11 @@ Any service can publish feature updates using `XADD`:
 ### Raw Valkey
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import json
 import time
diff --git a/content/feature-store/05-ml-integration.md b/content/feature-store/05-ml-integration.md
index cd29a4b..b4d07e4 100644
--- a/content/feature-store/05-ml-integration.md
+++ b/content/feature-store/05-ml-integration.md
@@ -15,6 +15,11 @@ The goal: keep feature fetching so fast that it's invisible compared to model in
 A fraud model trained on 4 features. At inference, we fetch them from Valkey and predict:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import numpy as np
 from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
 
diff --git a/content/feature-store/06-production.md b/content/feature-store/06-production.md
index d85e9d9..d4bbb43 100644
--- a/content/feature-store/06-production.md
+++ b/content/feature-store/06-production.md
@@ -3,6 +3,11 @@
 Every feature Hash includes an `_updated_at` timestamp. Use it to detect stale features before they poison your model:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import time
 
diff --git a/content/haystack/01-getting-started.md b/content/haystack/01-getting-started.md
index 2a34d65..0447ddb 100644
--- a/content/haystack/01-getting-started.md
+++ b/content/haystack/01-getting-started.md
@@ -10,7 +10,7 @@ Haystack is a framework for building RAG pipelines and search applications. Valk
 Vector search requires the `valkey-bundle` image, which includes the valkey-search module:
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 Verify it's running and the search module is loaded:
@@ -25,9 +25,9 @@ docker exec valkey valkey-cli MODULE LIST
 ## Step 2: Install Dependencies
 
 ```bash
-pip install haystack-ai
-pip install valkey-haystack
-pip install sentence-transformers
+uv pip install haystack-ai python-dotenv
+uv pip install valkey-haystack python-dotenv
+uv pip install sentence-transformers python-dotenv
 ```
 
 The `valkey-haystack` package provides `ValkeyDocumentStore` and `ValkeyEmbeddingRetriever` as first-class Haystack components.
@@ -35,6 +35,11 @@ The `valkey-haystack` package provides `ValkeyDocumentStore` and `ValkeyEmbeddin
 ## Step 3: Connect to ValkeyDocumentStore
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
 
 document_store = ValkeyDocumentStore(
diff --git a/content/haystack/02-rag-pipeline.md b/content/haystack/02-rag-pipeline.md
index b2c4d64..8e7aa91 100644
--- a/content/haystack/02-rag-pipeline.md
+++ b/content/haystack/02-rag-pipeline.md
@@ -15,6 +15,11 @@ Valkey handles the retrieval step - sub-millisecond KNN over your document embed
 Run this once to embed and store your documents:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from haystack import Pipeline, Document
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder
 from haystack.components.writers import DocumentWriter
diff --git a/content/langchain/01-getting-started.md b/content/langchain/01-getting-started.md
index cac1914..c5e415d 100644
--- a/content/langchain/01-getting-started.md
+++ b/content/langchain/01-getting-started.md
@@ -14,7 +14,7 @@ LangGraph lets you build multi-step AI agents with branching logic and tool use.
 Docker installed and Python 3.10+ required.
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 The `valkey-bundle` image includes JSON and Search modules needed for ValkeyStore. Verify:
@@ -27,7 +27,7 @@ docker exec valkey valkey-cli PING
 ## Step 2: Install the Package
 
 ```bash
-pip install 'langgraph-checkpoint-aws[valkey]' langchain-aws
+uv pip install 'langgraph-checkpoint-aws[valkey]' langchain-aws python-dotenv
 ```
 
 This installs `ValkeySaver`, `ValkeyStore`, `ValkeyCache`, and the Bedrock integrations.
@@ -53,6 +53,11 @@ This installs `ValkeySaver`, `ValkeyStore`, `ValkeyCache`, and the Bedrock integ
 ## Step 4: Persist a LangGraph Agent
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from langgraph.graph import StateGraph, MessagesState
 from langgraph_checkpoint_aws import ValkeySaver
 from langchain_aws import ChatBedrockConverse
diff --git a/content/langchain/02-llm-caching.md b/content/langchain/02-llm-caching.md
index 57470ec..b41d252 100644
--- a/content/langchain/02-llm-caching.md
+++ b/content/langchain/02-llm-caching.md
@@ -13,6 +13,11 @@ Exact-match caching eliminates redundant calls. (For meaning-based matching, see
 ## Step 1: Initialize ValkeyCache
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from langgraph_checkpoint_aws import ValkeyCache
 from valkey import Valkey
 
diff --git a/content/langchain/03-semantic-search.md b/content/langchain/03-semantic-search.md
index 0b320f1..181187c 100644
--- a/content/langchain/03-semantic-search.md
+++ b/content/langchain/03-semantic-search.md
@@ -9,6 +9,11 @@
 ## Step 1: Configure ValkeyStore
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from langgraph_checkpoint_aws import ValkeyStore
 from langchain_aws import BedrockEmbeddings
 
diff --git a/content/langchain/04-full-agent.md b/content/langchain/04-full-agent.md
index 90338cd..92f53a2 100644
--- a/content/langchain/04-full-agent.md
+++ b/content/langchain/04-full-agent.md
@@ -18,6 +18,11 @@ User: "I forgot my password, help!"
 ## Step 1: Shared Valkey Connection
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from valkey import Valkey
 from langgraph_checkpoint_aws import ValkeySaver, ValkeyStore, ValkeyCache
 from langchain_aws import ChatBedrockConverse, BedrockEmbeddings
diff --git a/content/mem0/01-getting-started.md b/content/mem0/01-getting-started.md
index 246666b..f6c535a 100644
--- a/content/mem0/01-getting-started.md
+++ b/content/mem0/01-getting-started.md
@@ -7,21 +7,25 @@
 ## Step 1: Install
 
 ```bash
-pip install mem0ai
+uv pip install mem0ai python-dotenv
 ```
 
 The `valkey` Python client is included as a core dependency - no separate install needed.
 
 ## Step 2: Start Valkey with Search Module
 
-```python
-# Valkey with valkey-search module loaded
-valkey-server --loadmodule /usr/lib/valkey/libsearch.so
+```bash
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 ## Step 3: Configure Mem0 with Valkey
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from mem0 import Memory
 
 config = {
diff --git a/content/mem0/02-multi-user-memory.md b/content/mem0/02-multi-user-memory.md
index feabb10..70906e6 100644
--- a/content/mem0/02-multi-user-memory.md
+++ b/content/mem0/02-multi-user-memory.md
@@ -11,6 +11,11 @@ Session| `run_id`| Per-conversation context (ephemeral)
 ## Step 1: Setup
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from mem0 import Memory
 
 config = {
diff --git a/content/mem0/03-production.md b/content/mem0/03-production.md
index 0215fb9..2beb10b 100644
--- a/content/mem0/03-production.md
+++ b/content/mem0/03-production.md
@@ -3,6 +3,11 @@
 ElastiCache for Valkey 8.2 includes built-in vector search at no additional cost. Create a cluster via the AWS Console or CLI, then use the cluster endpoint as your Valkey URL.
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from mem0 import Memory
 
 config = {
diff --git a/content/pubsub-streaming/01-getting-started.md b/content/pubsub-streaming/01-getting-started.md
index 1d67751..96ed581 100644
--- a/content/pubsub-streaming/01-getting-started.md
+++ b/content/pubsub-streaming/01-getting-started.md
@@ -11,23 +11,28 @@ Valkey Pub/Sub is a fire-and-forget messaging system. Publishers send messages t
 ## Prerequisites
 
   * Docker installed (or a running Valkey instance)
-  * Python 3.9+
+  * Python 3.12+
 
 ## Step 1: Start Valkey
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 ## Step 2: Install Dependencies
 
 ```bash
-pip install valkey
+uv pip install valkey python-dotenv
 ```
 
 ## Step 3: Create a Publisher
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import time
 import json
diff --git a/content/pubsub-streaming/02-streaming-llm-tokens.md b/content/pubsub-streaming/02-streaming-llm-tokens.md
index f5c3166..6079e68 100644
--- a/content/pubsub-streaming/02-streaming-llm-tokens.md
+++ b/content/pubsub-streaming/02-streaming-llm-tokens.md
@@ -12,6 +12,11 @@ When streaming from an LLM (OpenAI, Anthropic, etc.), tokens arrive one at a tim
 ## Step 1: Token Publisher (Server Side)
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import json
 import time
diff --git a/content/pubsub-streaming/03-valkey-streams.md b/content/pubsub-streaming/03-valkey-streams.md
index eb73325..b0b139a 100644
--- a/content/pubsub-streaming/03-valkey-streams.md
+++ b/content/pubsub-streaming/03-valkey-streams.md
@@ -11,6 +11,11 @@ Backpressure| None| MAXLEN trimming
 ## Step 1: Add Messages
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey, json, time
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
diff --git a/content/pubsub-streaming/04-consumer-groups.md b/content/pubsub-streaming/04-consumer-groups.md
index 8f095f9..df75c0e 100644
--- a/content/pubsub-streaming/04-consumer-groups.md
+++ b/content/pubsub-streaming/04-consumer-groups.md
@@ -5,6 +5,11 @@ With plain XREAD, every consumer gets every message. With consumer groups, messa
 ## Step 1: Create a Group
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
diff --git a/content/pubsub-streaming/05-fan-out-patterns.md b/content/pubsub-streaming/05-fan-out-patterns.md
index 55d476b..7ad4a01 100644
--- a/content/pubsub-streaming/05-fan-out-patterns.md
+++ b/content/pubsub-streaming/05-fan-out-patterns.md
@@ -1,6 +1,11 @@
 ## Pattern 1: Pub/Sub Fan-Out (Live)  
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey, json, time
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
diff --git a/content/pubsub-streaming/06-production.md b/content/pubsub-streaming/06-production.md
index 6cfd426..32538e6 100644
--- a/content/pubsub-streaming/06-production.md
+++ b/content/pubsub-streaming/06-production.md
@@ -1,6 +1,11 @@
 ## Pattern 1: Backpressure with MAXLEN  
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey, time
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
diff --git a/content/rag-pipelines/01-getting-started.md b/content/rag-pipelines/01-getting-started.md
index 28d64b7..96483dc 100644
--- a/content/rag-pipelines/01-getting-started.md
+++ b/content/rag-pipelines/01-getting-started.md
@@ -12,7 +12,7 @@ The easiest way to get started is with Docker. Valkey Bundle includes vector sea
 # Pull and run Valkey with search module
 docker run -d --name valkey \
   -p 6379:6379 \
-  valkey/valkey-bundle:latest
+  valkey/valkey-bundle:9-alpine
 
 # Verify it's running
 docker exec -it valkey valkey-cli ping
@@ -42,6 +42,11 @@ Store your document chunks as hashes with their embeddings.
 
 ```python
 # Python example
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import numpy as np
 
diff --git a/content/rag-pipelines/03-vector-search.md b/content/rag-pipelines/03-vector-search.md
index ef090ac..4c928ff 100644
--- a/content/rag-pipelines/03-vector-search.md
+++ b/content/rag-pipelines/03-vector-search.md
@@ -110,6 +110,11 @@ FT.SEARCH idx:docs
 ## Python Implementation
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from valkey.commands.search.query import Query
 
 def hybrid_search(query_text, category=None, date_range=None, k=5):
diff --git a/content/rag-pipelines/05-scaling-production.md b/content/rag-pipelines/05-scaling-production.md
index 3867d34..1d30924 100644
--- a/content/rag-pipelines/05-scaling-production.md
+++ b/content/rag-pipelines/05-scaling-production.md
@@ -26,19 +26,19 @@ Distribute data across multiple nodes for horizontal scaling:
 version: '3.8'
 services:
   valkey-1:
-    image: valkey/valkey-bundle:latest
+    image: valkey/valkey-bundle:9-alpine
     command: valkey-server --cluster-enabled yes --cluster-node-timeout 5000
     ports:
       - "7001:6379"
 
   valkey-2:
-    image: valkey/valkey-bundle:latest
+    image: valkey/valkey-bundle:9-alpine
     command: valkey-server --cluster-enabled yes --cluster-node-timeout 5000
     ports:
       - "7002:6379"
 
   valkey-3:
-    image: valkey/valkey-bundle:latest
+    image: valkey/valkey-bundle:9-alpine
     command: valkey-server --cluster-enabled yes --cluster-node-timeout 5000
     ports:
       - "7003:6379"
@@ -120,6 +120,11 @@ client = valkey.Valkey(
 )
 
 # For cluster mode
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from valkey.cluster import ValkeyCluster
 
 cluster = ValkeyCluster(
diff --git a/content/rag-pipelines/06-monitoring.md b/content/rag-pipelines/06-monitoring.md
index 2dc0947..0964072 100644
--- a/content/rag-pipelines/06-monitoring.md
+++ b/content/rag-pipelines/06-monitoring.md
@@ -40,6 +40,11 @@ FT.INFO idx:docs
 ## Application-Level Metrics
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import time
 from dataclasses import dataclass, field
 
diff --git a/content/rate-limiting/01-getting-started.md b/content/rate-limiting/01-getting-started.md
index 6aed5d4..815efb9 100644
--- a/content/rate-limiting/01-getting-started.md
+++ b/content/rate-limiting/01-getting-started.md
@@ -1,12 +1,12 @@
 ## Prerequisites
 
   * Docker installed (or a running Valkey instance)
-  * Python 3.9+
+  * Python 3.12+
 
 ## Step 1: Start Valkey
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 Verify it's running:
@@ -19,7 +19,7 @@ docker exec valkey valkey-cli ping
 ## Step 2: Install Dependencies
 
 ```bash
-pip install valkey
+uv pip install valkey python-dotenv
 ```
 
 That's it - no special libraries needed. The `valkey` package is the official Valkey Python client.
@@ -27,6 +27,11 @@ That's it - no special libraries needed. The `valkey` package is the official Va
 ## Step 3: Your First Rate Limiter
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import time
 
diff --git a/content/rate-limiting/02-token-aware.md b/content/rate-limiting/02-token-aware.md
index 8fc49d5..891c5e3 100644
--- a/content/rate-limiting/02-token-aware.md
+++ b/content/rate-limiting/02-token-aware.md
@@ -12,6 +12,11 @@ A request-based limiter treats all 3 the same. That's wrong.
 ## Step 1: Count Tokens Before Calling the LLM
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import tiktoken
 
 def count_tokens(text: str, model: str = "gpt-4") -> int:
diff --git a/content/rate-limiting/04-hierarchical.md b/content/rate-limiting/04-hierarchical.md
index 9413e65..64bd9f8 100644
--- a/content/rate-limiting/04-hierarchical.md
+++ b/content/rate-limiting/04-hierarchical.md
@@ -19,6 +19,11 @@ Alice's agent-1 calls GPT-4. We check:
 ## Step 1: Define Tiers
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from dataclasses import dataclass
 
 @dataclass
diff --git a/content/rate-limiting/06-production.md b/content/rate-limiting/06-production.md
index e1879fc..0f18dce 100644
--- a/content/rate-limiting/06-production.md
+++ b/content/rate-limiting/06-production.md
@@ -3,6 +3,11 @@
 Always tell clients **when** to retry:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from fastapi import FastAPI, Response, HTTPException
 
 app = FastAPI()
diff --git a/content/semantic-caching/01-getting-started.md b/content/semantic-caching/01-getting-started.md
index 9f6a7c0..3231fdd 100644
--- a/content/semantic-caching/01-getting-started.md
+++ b/content/semantic-caching/01-getting-started.md
@@ -1,31 +1,63 @@
 ## How Semantic Caching Works
 
-```python
-# Flow:
-# 1. User sends prompt
-# 2. Embed the prompt → vector
-# 3. FT.SEARCH: find similar cached prompts (KNN)
-# 4. If similarity score < threshold → CACHE HIT → return cached response
-# 5. Else → CACHE MISS → call LLM → cache the prompt+response → return
-#
-#  User Prompt → Embed → FT.SEARCH KNN → Hit? → Return cached
-#                                        → Miss? → Call LLM → HSET → Return
+Gist
+
+```
+User Prompt → Embed → FT.SEARCH KNN → Hit?  → Return cached response (~1ms)
+                                      Miss? → Call LLM (~1-3s) → HSET in Valkey → Return
+```
+
+Diagram
+
+```mermaid
+flowchart TD
+    A["User Prompt"] --> B["Embed → Vector"]
+    B --> C["FT.SEARCH KNN\n(find similar cached prompts)"]
+    C --> D{"score < threshold?"}
+    D -- "✅ Cache Hit" --> E["Return cached response\n⚡ ~1ms"]
+    D -- "❌ Cache Miss" --> F["Call LLM\n🕐 ~1-3s"]
+    F --> G["HSET prompt + response\n+ embedding in Valkey"]
+    G --> H["Return LLM response"]
 ```
 
 > **Why semantic, not exact?** "What is Valkey?" and "Can you explain what Valkey is?" are different strings but mean the same thing. Exact-match caching misses these. Semantic caching uses vector similarity to match by *meaning*, dramatically increasing hit rates.
 
 ## Prerequisites
 
-- Valkey with the **valkey-search** module loaded (or ElastiCache for Valkey 8.2+)
-- Python 3.9+ with `valkey`, `openai`, `numpy`
+- Valkey with the **valkey-search** module loaded (or ElastiCache for Valkey 8.2+ or Memorystore for Valkey 8+)
+- Python 3.12+ with `valkey`, `openai`, `numpy` and `python-dotenv`
+
+## Step 1: Start Valkey
+
+1.1 - If you haven't already started a `Valkey` server with the `valkey-search` module you can start with the `valkey-bundle` container.
+
+```bash
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
+```
+
+## Step 2: Setup
 
-## Step 1: Setup
+2.1 - Create a `.env` file with the following environment variables:
 
 ```bash
-pip install valkey openai numpy
+OPENAI_API_KEY=
+OPENAI_MODEL=gpt-5.1
 ```
 
+2.2 - Install dependencies
+
+```bash
+uv pip install valkey openai numpy python-dotenv
+```
+
+2.3 - Connect to Valkey and OpenAI
+
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import numpy as np
 import json
@@ -42,7 +74,7 @@ SIMILARITY_THRESHOLD = 0.15  # COSINE distance: 0=identical, 2=opposite
 CACHE_TTL = 3600  # 1 hour
 ```
 
-## Step 2: Create the Cache Index
+## Step 3: Create the Cache Index
 
 ```python
 def create_cache_index():
@@ -65,7 +97,7 @@ def create_cache_index():
 create_cache_index()
 ```
 
-## Step 3: Embedding Helper
+## Step 4: Embedding Helper
 
 ```python
 def get_embedding(text: str) -> bytes:
@@ -78,7 +110,7 @@ def get_embedding(text: str) -> bytes:
     return np.array(vec, dtype=np.float32).tobytes()
 ```
 
-## Step 4: The Semantic Cache
+## Step 5: The Semantic Cache
 
 ```python
 def semantic_cache_lookup(prompt: str) -> dict:
@@ -162,7 +194,7 @@ def ask_with_cache(prompt: str) -> dict:
     }
 ```
 
-## Step 5: Test It
+## Step 6: Test It
 
 ```python
 # First call - cache MISS (calls LLM)
diff --git a/content/semantic-caching/02-multiturn-caching.md b/content/semantic-caching/02-multiturn-caching.md
index 9adcbad..41a3946 100644
--- a/content/semantic-caching/02-multiturn-caching.md
+++ b/content/semantic-caching/02-multiturn-caching.md
@@ -17,6 +17,11 @@ Instead of embedding only the last user message, we embed a summary of the full
 ## Step 1: Create Index with TAG Filter for User/Session
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import numpy as np
 import hashlib
diff --git a/content/semantic-caching/03-production.md b/content/semantic-caching/03-production.md
index c047430..c776f84 100644
--- a/content/semantic-caching/03-production.md
+++ b/content/semantic-caching/03-production.md
@@ -31,6 +31,11 @@ def evaluate_threshold(test_pairs: list, threshold: float):
 ## Pattern 2: Cache Hit Rate Monitoring
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 
 client = valkey.Valkey(host="localhost", port=6379)
diff --git a/content/strands/01-getting-started.md b/content/strands/01-getting-started.md
index 45e4d2b..6874148 100644
--- a/content/strands/01-getting-started.md
+++ b/content/strands/01-getting-started.md
@@ -21,7 +21,7 @@ The session manager persists three types of data to Valkey:
 ## Step 1: Start Valkey
 
 ```bash
-docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 ```bash
@@ -32,9 +32,9 @@ docker exec valkey valkey-cli PING
 ## Step 2: Install Dependencies
 
 ```bash
-pip install strands-agents strands-agents-tools
-pip install valkey
-pip install strands-valkey-session-manager
+uv pip install strands-agents strands-agents-tools python-dotenv
+uv pip install valkey python-dotenv
+uv pip install strands-valkey-session-manager python-dotenv
 ```
 
 **Package:** `strands-valkey-session-manager` is a community package. Source: [GitHub](https://github.com/jeromevdl/strands-valkey-session-manager) - Docs: [strandsagents.com](https://strandsagents.com/docs/community/session-managers/strands-valkey-session-manager/)
@@ -42,6 +42,11 @@ pip install strands-valkey-session-manager
 ## Step 3: Connect to Valkey
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 from strands_valkey_session_manager import ValkeySessionManager
 
diff --git a/content/strands/02-session-internals.md b/content/strands/02-session-internals.md
index 95a0919..e6a7669 100644
--- a/content/strands/02-session-internals.md
+++ b/content/strands/02-session-internals.md
@@ -62,6 +62,11 @@ Assistant message with tool use:
 Create a new `Agent` with the same `session_id` - Strands reloads the full conversation history from Valkey automatically:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 from strands import Agent
 from strands_valkey_session_manager import ValkeySessionManager
diff --git a/content/strands/03-managing-your-session-store.md b/content/strands/03-managing-your-session-store.md
index 086d569..34bfd89 100644
--- a/content/strands/03-managing-your-session-store.md
+++ b/content/strands/03-managing-your-session-store.md
@@ -3,6 +3,11 @@
 A single session can hold state for multiple agents - each gets its own agent key and message keys under the same session ID. This is the foundation for orchestrator/subagent patterns where agents need to share context:
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from strands import Agent
 from strands_valkey_session_manager import ValkeySessionManager
 import valkey
diff --git a/content/vector-search/01-getting-started.md b/content/vector-search/01-getting-started.md
index 15f2fab..3524474 100644
--- a/content/vector-search/01-getting-started.md
+++ b/content/vector-search/01-getting-started.md
@@ -1,26 +1,25 @@
 ## Prerequisites
 
   * Valkey server with the **valkey-search** module loaded
-  * Python 3.9+ with the `valkey` and `numpy` packages
+  * Python 3.12+ with the `valkey` and `numpy` packages
 
 ## Step 1: Start Valkey with Search Module
 
 ```bash
-# Start Valkey with the search module loaded
-valkey-server --loadmodule /usr/lib/valkey/libsearch.so
+docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 ```
 
 Verify the module is loaded:
 
 ```bash
-valkey-cli MODULE LIST
+docker exec valkey valkey-cli MODULE LIST
 # Should show "search" in the output
 ```
 
 ## Step 2: Install Python Client
 
 ```bash
-pip install valkey numpy
+uv pip install valkey numpy python-dotenv
 ```
 
 We use the standard `valkey` Python client. Module commands like `FT.CREATE` are called via `execute_command()`.
@@ -28,6 +27,11 @@ We use the standard `valkey` Python client. Module commands like `FT.CREATE` are
 ## Step 3: Create a Vector Index
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import numpy as np
 
diff --git a/content/vector-search/02-semantic-search.md b/content/vector-search/02-semantic-search.md
index f23cf18..4ac6489 100644
--- a/content/vector-search/02-semantic-search.md
+++ b/content/vector-search/02-semantic-search.md
@@ -11,7 +11,7 @@
 ## Step 1: Setup
 
 ```bash
-pip install valkey openai numpy
+uv pip install valkey openai numpy python-dotenv
 
 
 import valkey
diff --git a/content/vector-search/03-hybrid-search.md b/content/vector-search/03-hybrid-search.md
index c9af2b5..b5b3a7b 100644
--- a/content/vector-search/03-hybrid-search.md
+++ b/content/vector-search/03-hybrid-search.md
@@ -5,6 +5,11 @@ Pure vector search returns the most similar items globally. But often you need t
 ## Step 1: Create an Index with Multiple Field Types
 
 ```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import numpy as np
 
diff --git a/cookbooks/betterdb-agent-cache/01-getting-started.html b/cookbooks/betterdb-agent-cache/01-getting-started.html
index 235f06e..a92fd85 100644
--- a/cookbooks/betterdb-agent-cache/01-getting-started.html
+++ b/cookbooks/betterdb-agent-cache/01-getting-started.html
@@ -44,7 +44,7 @@ 
Prerequisites

 
 
Step 1: Start Valkey

 
No special image needed - the standard Valkey image works:

-
docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+
docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 

 
docker exec valkey valkey-cli PING
 # PONG
@@ -181,6 +181,7 @@ 
Cleanup

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/betterdb-agent-cache/02-llm-and-tool-cache.html b/cookbooks/betterdb-agent-cache/02-llm-and-tool-cache.html
index 131d1b0..7ffb1b0 100644
--- a/cookbooks/betterdb-agent-cache/02-llm-and-tool-cache.html
+++ b/cookbooks/betterdb-agent-cache/02-llm-and-tool-cache.html
@@ -224,6 +224,7 @@ 
Tool Effectiveness Recommendations

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/betterdb-agent-cache/03-session-store.html b/cookbooks/betterdb-agent-cache/03-session-store.html
index 4771b78..c53f1ba 100644
--- a/cookbooks/betterdb-agent-cache/03-session-store.html
+++ b/cookbooks/betterdb-agent-cache/03-session-store.html
@@ -196,6 +196,7 @@ 
Per-Field TTL Override

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/betterdb-agent-cache/04-langgraph.html b/cookbooks/betterdb-agent-cache/04-langgraph.html
index da73ccf..13b8188 100644
--- a/cookbooks/betterdb-agent-cache/04-langgraph.html
+++ b/cookbooks/betterdb-agent-cache/04-langgraph.html
@@ -221,6 +221,7 @@ 
Combined: LLM + Tool + Session + Checkpointing

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/betterdb-semantic-cache/01-getting-started.html b/cookbooks/betterdb-semantic-cache/01-getting-started.html
index 1f174f1..746f7c1 100644
--- a/cookbooks/betterdb-semantic-cache/01-getting-started.html
+++ b/cookbooks/betterdb-semantic-cache/01-getting-started.html
@@ -39,7 +39,7 @@ 
Prerequisites

 
 
Step 1: Start Valkey

 
The valkey-bundle image includes the valkey-search module required for vector indexing.

-
docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+
docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 

 
Verify the module is loaded:

 
docker exec valkey valkey-cli MODULE LIST
@@ -174,6 +174,7 @@ 
Step 7: Inspect Stats

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/betterdb-semantic-cache/02-adapters.html b/cookbooks/betterdb-semantic-cache/02-adapters.html
index 58ba950..f7048e2 100644
--- a/cookbooks/betterdb-semantic-cache/02-adapters.html
+++ b/cookbooks/betterdb-semantic-cache/02-adapters.html
@@ -180,6 +180,7 @@ 
Direct API vs Adapters

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/betterdb-semantic-cache/03-production.html b/cookbooks/betterdb-semantic-cache/03-production.html
index dd7e1f4..ce2867f 100644
--- a/cookbooks/betterdb-semantic-cache/03-production.html
+++ b/cookbooks/betterdb-semantic-cache/03-production.html
@@ -328,6 +328,7 @@ 
Production Checklist

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/context-engineering/01-fundamentals.html b/cookbooks/context-engineering/01-fundamentals.html
index 2eafa3d..146475d 100644
--- a/cookbooks/context-engineering/01-fundamentals.html
+++ b/cookbooks/context-engineering/01-fundamentals.html
@@ -81,12 +81,21 @@ 
The 5 Context Sources

 
Prerequisites

 
    
 
  • Valkey with the valkey-search module (or ElastiCache for Valkey 8.2+)
    • 
-
  • Python 3.9+ with valkey
    • 
+
  • Python 3.12+ with valkey
    • 
 

-
pip install valkey
+
Step 1: Start Valkey

+
docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 

-
Step 1: Store System Instructions

-
import valkey
+
Step 2: Install Dependencies

+
uv pip install valkey python-dotenv
+

+
Step 3: Store System Instructions

+
import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import json
 
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
@@ -100,7 +109,7 @@ 
Step 1: Store System Instructions

 })
 print("System instructions stored")
 

-
Step 2: Manage Conversation History

+
Step 4: Manage Conversation History

 
import time
 
 def add_message(session_id: str, role: str, content: str):
@@ -126,7 +135,7 @@ 
Step 2: Manage Conversation History

 for msg in history:
     print(f"  {msg['role']}: {msg['content'][:60]}...")
 

-
Step 3: Store Tool Outputs

+
Step 5: Store Tool Outputs

 
def store_tool_output(session_id: str, step: int, tool_name: str, result: dict):
     """Store the output of a tool call for context assembly."""
     key = f"tool:{session_id}:step_{step}"
@@ -156,7 +165,7 @@ 
Step 3: Store Tool Outputs

     "date": "2025-03-15",
 })
 

-
Step 4: Long-term User Memory

+
Step 6: Long-term User Memory

 
def remember(user_id: str, key: str, value: str):
     """Store a long-term memory about a user."""
     client.hset(f"memory:{user_id}", key, value)
@@ -195,6 +204,7 @@ 
The Pyramid Approach

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/context-engineering/02-assembly-pipeline.html b/cookbooks/context-engineering/02-assembly-pipeline.html
index da65236..8a56195 100644
--- a/cookbooks/context-engineering/02-assembly-pipeline.html
+++ b/cookbooks/context-engineering/02-assembly-pipeline.html
@@ -31,7 +31,12 @@ 
Context Assembly Pipeline

 
 
The Context Assembly Function

 
Before every LLM call, an agent needs to assemble context from multiple sources. This function is the core of context engineering - it gathers everything the LLM needs into a structured prompt.

-
import valkey
+
import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import json
 import struct
 import time
@@ -218,6 +223,7 @@ 
Context Assembly Flow

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/context-engineering/03-production.html b/cookbooks/context-engineering/03-production.html
index f98d033..32e27ff 100644
--- a/cookbooks/context-engineering/03-production.html
+++ b/cookbooks/context-engineering/03-production.html
@@ -31,7 +31,12 @@ 
Production Context Management

 
 
Short-term vs Long-term Memory

 
The memory layer is the foundation of context engineering at scale. Valkey supports both memory types through TTL management:

-
import valkey
+
import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import json
 import time
 
@@ -286,6 +291,7 @@ 
Production Checklist

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/conversation-memory/01-getting-started.html b/cookbooks/conversation-memory/01-getting-started.html
index 339e87c..316d06c 100644
--- a/cookbooks/conversation-memory/01-getting-started.html
+++ b/cookbooks/conversation-memory/01-getting-started.html
@@ -40,17 +40,17 @@ 
Why Valkey for Conversation Memory?

 
Prerequisites

 
    
 
  • Docker installed (or a running Valkey instance)
    • 
-
  • Python 3.9+
    • 
+
  • Python 3.12+
    • 
 

 
Step 1: Start Valkey

-
docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+
docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 

 
Verify it's running:

 
docker exec valkey valkey-cli PING
 # PONG
 

 
Step 2: Install GLIDE

-
pip install valkey-glide
+
uv pip install valkey-glide python-dotenv
 

 
GLIDE is the official Valkey client - Rust core with Python bindings. It works with both standalone Valkey and ElastiCache for Valkey clusters.

 
Step 3: Understand the Data Model

@@ -67,7 +67,12 @@ 
Step 3: Understand the Data Model

 

 
How this works: Valkey Lists are doubly-linked lists optimized for push/pop at both ends. RPUSH appends in O(1). LRANGE -N -1 retrieves the last N messages in O(N). This maps perfectly to conversation history - always appending, always reading the tail.

 
Step 4: Connect and Store Messages

-
import asyncio
+
import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import asyncio
 import json
 from glide import GlideClient, GlideClientConfiguration, NodeAddress
 
@@ -216,6 +221,7 @@ 
ElastiCache for Valkey

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/conversation-memory/02-session-management.html b/cookbooks/conversation-memory/02-session-management.html
index 00a16c0..17ea766 100644
--- a/cookbooks/conversation-memory/02-session-management.html
+++ b/cookbooks/conversation-memory/02-session-management.html
@@ -48,7 +48,12 @@ 
Data Model: Two Keys Per Session

 

 
Why this matters: Valkey Hashes are perfect for metadata - each field is independently readable and writable. HINCRBY atomically increments token counts without read-modify-write races. HSET updates individual fields without touching others.

 
Pattern 1: Create a Session with Metadata

-
import asyncio, json, time
+
import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import asyncio, json, time
 from glide import GlideClient, GlideClientConfiguration, NodeAddress
 
 
@@ -179,6 +184,7 @@ 
Valkey Commands Reference

 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/conversation-memory/03-semantic-memory.html b/cookbooks/conversation-memory/03-semantic-memory.html
index b8cb43b..56733d9 100644
--- a/cookbooks/conversation-memory/03-semantic-memory.html
+++ b/cookbooks/conversation-memory/03-semantic-memory.html
@@ -49,11 +49,16 @@ 
Prerequisites

 
  • An embedding model (we use Amazon Bedrock Titan, but any works)
    • 
 
 
    Step 1: Start Valkey with Search Module
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    The valkey-bundle image includes valkey-search for vector similarity search and valkey-json for JSON document storage.
    
 
    Step 2: Create the Vector Index
    
-
    from glide import (
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from glide import (
     GlideClient, GlideClientConfiguration, NodeAddress,
     ft, TagField, NumericField,
     VectorField, VectorAlgorithm, VectorFieldAttributesHnsw,
@@ -239,6 +244,7 @@ 
    Filtering: Scope + Semantic
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/conversation-memory/04-semantic-caching.html b/cookbooks/conversation-memory/04-semantic-caching.html
index f158cb6..dc09c98 100644
--- a/cookbooks/conversation-memory/04-semantic-caching.html
+++ b/cookbooks/conversation-memory/04-semantic-caching.html
@@ -49,7 +49,12 @@ 
    Architecture
    
         └── score < 0.90 → ❌ Cache MISS → call LLM → cache result
 
    
 
    Step 1: Create the Cache Index
    
-
    from glide import (
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from glide import (
     ft, VectorField, VectorAlgorithm, VectorFieldAttributesHnsw,
     VectorType, DistanceMetricType, NumericField,
     FtCreateOptions, DataType,
@@ -247,6 +252,7 @@ 
    Valkey Commands Reference
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/conversation-memory/05-agent-state.html b/cookbooks/conversation-memory/05-agent-state.html
index e5d6e8d..e39a68e 100644
--- a/cookbooks/conversation-memory/05-agent-state.html
+++ b/cookbooks/conversation-memory/05-agent-state.html
@@ -52,7 +52,12 @@ 
    Three Valkey Patterns, One Agent
    
 
    
 
    Pattern 1: Agent Checkpointing with HASH
    
 
    Save the agent's state after each step. If it crashes, resume from the last checkpoint.
    
-
    import json
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import json
 from glide import GlideClient, GlideClientConfiguration, NodeAddress
 
 async def save_checkpoint(client, run_id, step, data):
@@ -273,6 +278,7 @@ 
    Summary: Five Valkey Data Structures, One Conversation System
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/crewai/01-getting-started.html b/cookbooks/crewai/01-getting-started.html
index 52592f7..0c30e2c 100644
--- a/cookbooks/crewai/01-getting-started.html
+++ b/cookbooks/crewai/01-getting-started.html
@@ -40,18 +40,23 @@ 
    What is CrewAI + Valkey?
    
 
 
    Step 1: Start Valkey
    
 
    Docker installed and Python 3.10+ required. AWS credentials needed for Bedrock (later guides).
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    The valkey-bundle image includes JSON and Search modules. Verify:
    
 
    docker exec valkey valkey-cli PING
 # PONG
 
    
 
    Step 2: Install Dependencies
    
-
    pip install crewai valkey-glide boto3
+
    uv pip install crewai valkey-glide boto3 python-dotenv
 
    
 
    Why GLIDE? Valkey GLIDE is the official Valkey client with a Rust core for high performance. It supports both standalone Valkey and ElastiCache for Valkey clusters. Unlike generic Redis clients, GLIDE is purpose-built for Valkey and supports all Valkey-specific features including FT.* search commands.
    
 
    Step 3: Connection Configuration
    
-
    from dataclasses import dataclass
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from dataclasses import dataclass
 import os
 
 @dataclass(frozen=True)
@@ -139,6 +144,7 @@ 
    How It Works Under the Hood
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/crewai/02-memory-storage.html b/cookbooks/crewai/02-memory-storage.html
index d8c0d0d..08804bc 100644
--- a/cookbooks/crewai/02-memory-storage.html
+++ b/cookbooks/crewai/02-memory-storage.html
@@ -31,7 +31,12 @@ 
    Memory Storage
    
 
 
    The MemoryRecord Model
    
 
    Each memory is a structured record with content, scope, categories, importance, and an embedding vector:
    
-
    from pydantic import BaseModel, Field
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from pydantic import BaseModel, Field
 from datetime import datetime
 from uuid import uuid4
 
@@ -184,6 +189,7 @@ 
    Next Steps
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/crewai/03-agent-memory.html b/cookbooks/crewai/03-agent-memory.html
index 226acc0..b7c4444 100644
--- a/cookbooks/crewai/03-agent-memory.html
+++ b/cookbooks/crewai/03-agent-memory.html
@@ -30,7 +30,12 @@ 
    Agent Memory
    
 
 
 
    Step 1: Configure Memory with Bedrock
    
-
    import boto3
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import boto3
 from crewai.memory.unified_memory import Memory
 from valkey_storage import ValkeyStorage
 
@@ -134,6 +139,7 @@ 
    Next Steps
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/feature-store/01-getting-started.html b/cookbooks/feature-store/01-getting-started.html
index 268114e..77b3122 100644
--- a/cookbooks/feature-store/01-getting-started.html
+++ b/cookbooks/feature-store/01-getting-started.html
@@ -40,17 +40,17 @@ 
    Why Valkey for Feature Stores?
    
 
    Prerequisites
    
 
      
 
    • Docker installed (or a running Valkey instance)
      • 
-
    • Python 3.9+
      • 
+
    • Python 3.12+
      • 
 
    
 
    Step 1: Start Valkey
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    Verify it's running:
    
 
    docker exec valkey valkey-cli ping
 # PONG
 
    
 
    Step 2: Install Dependencies
    
-
    pip install valkey
+
    uv pip install valkey python-dotenv
 
    
 
    The valkey package is the official Valkey Python client - no special drivers needed.
    
 
    Step 3: Understand the Data Model
    
@@ -68,7 +68,12 @@ 
    Step 3: Understand the Data Model
    
 
    How it works: Valkey Hashes map perfectly to feature vectors. Each field is a feature, each key is an entity. One HGETALL returns the full feature vector. One HMGET returns selective features. Both complete in ~0.1ms.
    
 
    Step 4: Write Features with Raw Valkey Commands
    
 
    Starting with raw Valkey commands to see what's happening under the hood:
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import time
 
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
@@ -190,6 +195,7 @@ 
    How It Works Under the Hood
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/feature-store/02-online-serving.html b/cookbooks/feature-store/02-online-serving.html
index 91f6492..1c01a16 100644
--- a/cookbooks/feature-store/02-online-serving.html
+++ b/cookbooks/feature-store/02-online-serving.html
@@ -43,7 +43,12 @@ 
    The Inference-Time Challenge
    
 
    Pattern 1: Single Entity Lookup
    
 
    The simplest pattern - fetch all features for one entity:
    
 
    Raw Valkey: HGETALL
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
@@ -204,6 +209,7 @@ 
    TTL-Based Feature Expiry
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/feature-store/03-realtime-aggregations.html b/cookbooks/feature-store/03-realtime-aggregations.html
index ba2fe1c..06642b2 100644
--- a/cookbooks/feature-store/03-realtime-aggregations.html
+++ b/cookbooks/feature-store/03-realtime-aggregations.html
@@ -39,7 +39,12 @@ 
    Why In-Valkey Aggregations?
    
 
    Pattern 1: Sliding Window Count
    
 
    Count events in a sliding time window - e.g., "transactions in the last hour."
    
 
    We use a Sorted Set where each member is a unique event ID and the score is the timestamp:
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import time
 import uuid
 
@@ -253,6 +258,7 @@ 
    Data Structure Comparison
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/feature-store/04-streaming-updates.html b/cookbooks/feature-store/04-streaming-updates.html
index 0d91d2f..cf579ac 100644
--- a/cookbooks/feature-store/04-streaming-updates.html
+++ b/cookbooks/feature-store/04-streaming-updates.html
@@ -48,7 +48,12 @@ 
    The Architecture
    
 
    Step 1: Publish Feature Updates
    
 
    Any service can publish feature updates using XADD:
    
 
    Raw Valkey
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import json
 import time
 
@@ -271,6 +276,7 @@ 
    Valkey Streams Command Reference
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/feature-store/05-ml-integration.html b/cookbooks/feature-store/05-ml-integration.html
index 354b618..243a360 100644
--- a/cookbooks/feature-store/05-ml-integration.html
+++ b/cookbooks/feature-store/05-ml-integration.html
@@ -38,7 +38,12 @@ 
    The Prediction Pipeline
    
 
    The goal: keep feature fetching so fast that it's invisible compared to model inference.
    
 
    Example 1: Fraud Detection with scikit-learn
    
 
    A fraud model trained on 4 features. At inference, we fetch them from Valkey and predict:
    
-
    import numpy as np
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import numpy as np
 from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
 
 # Setup store (same as Cookbook 01)
@@ -314,6 +319,7 @@ 
    Performance Summary
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/feature-store/06-production.html b/cookbooks/feature-store/06-production.html
index 81924ef..6a3ea11 100644
--- a/cookbooks/feature-store/06-production.html
+++ b/cookbooks/feature-store/06-production.html
@@ -31,7 +31,12 @@ 
    Production Patterns
    
 
 
    Pattern 1: Feature Freshness Monitoring
    
 
    Every feature Hash includes an _updated_at timestamp. Use it to detect stale features before they poison your model:
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import time
 
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
@@ -346,6 +351,7 @@ 
    Production Checklist
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/haystack/01-getting-started.html b/cookbooks/haystack/01-getting-started.html
index e72cf95..d67be37 100644
--- a/cookbooks/haystack/01-getting-started.html
+++ b/cookbooks/haystack/01-getting-started.html
@@ -37,7 +37,7 @@ 
    Prerequisites
    
 
 
    Step 1: Start Valkey
    
 
    Vector search requires the valkey-bundle image, which includes the valkey-search module:
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    Verify it's running and the search module is loaded:
    
 
    docker exec valkey valkey-cli MODULE LIST
@@ -47,13 +47,18 @@ 
    Step 1: Start Valkey
    
 
    Note: On first run you'll see Error executing command: Index not found - this is expected. The document store checks for an existing index, doesn't find one, then creates it automatically when you write your first documents.
    
 
 
    Step 2: Install Dependencies
    
-
    pip install haystack-ai
-pip install valkey-haystack
-pip install sentence-transformers
+
    uv pip install haystack-ai python-dotenv
+uv pip install valkey-haystack python-dotenv
+uv pip install sentence-transformers python-dotenv
 
    
 
    The valkey-haystack package provides ValkeyDocumentStore and ValkeyEmbeddingRetriever as first-class Haystack components.
    
 
    Step 3: Connect to ValkeyDocumentStore
    
-
    from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
 
 document_store = ValkeyDocumentStore(
     nodes_list=[("localhost", 6379)],
@@ -146,6 +151,7 @@ 
    How It Works Under the Hood
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/haystack/02-rag-pipeline.html b/cookbooks/haystack/02-rag-pipeline.html
index 51391ee..079c59f 100644
--- a/cookbooks/haystack/02-rag-pipeline.html
+++ b/cookbooks/haystack/02-rag-pipeline.html
@@ -39,7 +39,12 @@ 
    How RAG Works
    
 
    Valkey handles the retrieval step - sub-millisecond KNN over your document embeddings.
    
 
    Step 1: Indexing Pipeline
    
 
    Run this once to embed and store your documents:
    
-
    from haystack import Pipeline, Document
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from haystack import Pipeline, Document
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder
 from haystack.components.writers import DocumentWriter
 from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
@@ -195,6 +200,7 @@ 
    Swap the Embedding Model
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/langchain/01-getting-started.html b/cookbooks/langchain/01-getting-started.html
index b5c696e..f85aa8a 100644
--- a/cookbooks/langchain/01-getting-started.html
+++ b/cookbooks/langchain/01-getting-started.html
@@ -40,14 +40,14 @@ 
    What is LangGraph + Valkey?
    
 
 
    Step 1: Start Valkey
    
 
    Docker installed and Python 3.10+ required.
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    The valkey-bundle image includes JSON and Search modules needed for ValkeyStore. Verify:
    
 
    docker exec valkey valkey-cli PING
 # PONG
 
    
 
    Step 2: Install the Package
    
-
    pip install 'langgraph-checkpoint-aws[valkey]' langchain-aws
+
    uv pip install 'langgraph-checkpoint-aws[valkey]' langchain-aws python-dotenv
 
    
 
    This installs ValkeySaver, ValkeyStore, ValkeyCache, and the Bedrock integrations.
    
 
    Step 3: Understand the Data Model
    
@@ -64,7 +64,12 @@ 
    Step 3: Understand the Data Model
    
 
    
 
    Under the Hood: ValkeySaver uses Valkey JSON (JSON.SET) for structured storage and FT.CREATE/FT.SEARCH for indexing checkpoints by thread ID and namespace. TTL is applied via EXPIRE.
    
 
    Step 4: Persist a LangGraph Agent
    
-
    from langgraph.graph import StateGraph, MessagesState
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from langgraph.graph import StateGraph, MessagesState
 from langgraph_checkpoint_aws import ValkeySaver
 from langchain_aws import ChatBedrockConverse
 from langchain_core.messages import HumanMessage
@@ -160,6 +165,7 @@ 
    How It Works Under the Hood
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/langchain/02-llm-caching.html b/cookbooks/langchain/02-llm-caching.html
index 5715a0a..42006e2 100644
--- a/cookbooks/langchain/02-llm-caching.html
+++ b/cookbooks/langchain/02-llm-caching.html
@@ -39,7 +39,12 @@ 
    The Problem
    
 
    Exact-match caching eliminates redundant calls. (For meaning-based matching, see Guide 03.)
    
 
    Upstream Contribution: The ValkeyCache integration was contributed to langchain-ai/langchain-aws in PR #717 by the Valkey team.
    
 
    Step 1: Initialize ValkeyCache
    
-
    from langgraph_checkpoint_aws import ValkeyCache
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from langgraph_checkpoint_aws import ValkeyCache
 from valkey import Valkey
 
 # Create Valkey client
@@ -161,6 +166,7 @@ 
    Next Steps
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/langchain/03-semantic-search.html b/cookbooks/langchain/03-semantic-search.html
index 442266f..23115a2 100644
--- a/cookbooks/langchain/03-semantic-search.html
+++ b/cookbooks/langchain/03-semantic-search.html
@@ -37,7 +37,12 @@ 
    Why Semantic Search?
    
 
  • Real-time updates - new vectors are searchable immediately, no rebuild needed
    • 
 
 
    Step 1: Configure ValkeyStore
    
-
    from langgraph_checkpoint_aws import ValkeyStore
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from langgraph_checkpoint_aws import ValkeyStore
 from langchain_aws import BedrockEmbeddings
 
 # Amazon Titan embeddings - 1536 dimensions
@@ -171,6 +176,7 @@ 
    Next Steps
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/langchain/04-full-agent.html b/cookbooks/langchain/04-full-agent.html
index 6dfefab..ba6477e 100644
--- a/cookbooks/langchain/04-full-agent.html
+++ b/cookbooks/langchain/04-full-agent.html
@@ -44,7 +44,12 @@ 
    Architecture
    
    └─ ValkeySaver ─── checkpoint conversation state
 
    
 
    Step 1: Shared Valkey Connection
    
-
    from valkey import Valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from valkey import Valkey
 from langgraph_checkpoint_aws import ValkeySaver, ValkeyStore, ValkeyCache
 from langchain_aws import ChatBedrockConverse, BedrockEmbeddings
 
@@ -172,6 +177,7 @@ 
    Next Steps
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/mem0/01-getting-started.html b/cookbooks/mem0/01-getting-started.html
index f854170..a9c10bd 100644
--- a/cookbooks/mem0/01-getting-started.html
+++ b/cookbooks/mem0/01-getting-started.html
@@ -33,15 +33,19 @@ 
    What is Mem0?
    
 
    Mem0 ("mem-zero") is an open-source memory layer for AI applications (YC S24). It has a dedicated Valkey connector (provider: "valkey") that uses the valkey Python client with native FT.CREATE/FT.SEARCH for vector-based memory storage and retrieval.
    
 
    Mem0 has a first-class Valkey integration - not just Redis compatibility. The valkey.py connector in Mem0's source uses the native valkey Python package and supports HNSW/FLAT indexing with configurable parameters.
    
 
    Step 1: Install
    
-
    pip install mem0ai
+
    uv pip install mem0ai python-dotenv
 
    
 
    The valkey Python client is included as a core dependency - no separate install needed.
    
 
    Step 2: Start Valkey with Search Module
    
-
    # Valkey with valkey-search module loaded
-valkey-server --loadmodule /usr/lib/valkey/libsearch.so
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    Step 3: Configure Mem0 with Valkey
    
-
    from mem0 import Memory
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from mem0 import Memory
 
 config = {
     "vector_store": {
@@ -178,6 +182,7 @@ 
    How It Works Under the Hood
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/mem0/02-multi-user-memory.html b/cookbooks/mem0/02-multi-user-memory.html
index 2332c11..73450b9 100644
--- a/cookbooks/mem0/02-multi-user-memory.html
+++ b/cookbooks/mem0/02-multi-user-memory.html
@@ -56,7 +56,12 @@ 
    Memory Isolation
    
 
 
 
    Step 1: Setup
    
-
    from mem0 import Memory
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from mem0 import Memory
 
 config = {
     "vector_store": {
@@ -150,6 +155,7 @@ 
    Valkey Data Model
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/mem0/03-production.html b/cookbooks/mem0/03-production.html
index 41ab77d..daeb5a2 100644
--- a/cookbooks/mem0/03-production.html
+++ b/cookbooks/mem0/03-production.html
@@ -31,7 +31,12 @@ 
    Production with ElastiCache
    
 
 
    Step 1: ElastiCache for Valkey 8.2+
    
 
    ElastiCache for Valkey 8.2 includes built-in vector search at no additional cost. Create a cluster via the AWS Console or CLI, then use the cluster endpoint as your Valkey URL.
    
-
    from mem0 import Memory
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from mem0 import Memory
 
 config = {
     "vector_store": {
@@ -170,6 +175,7 @@ 
    Production Checklist
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/pubsub-streaming/01-getting-started.html b/cookbooks/pubsub-streaming/01-getting-started.html
index 6d9141d..2308344 100644
--- a/cookbooks/pubsub-streaming/01-getting-started.html
+++ b/cookbooks/pubsub-streaming/01-getting-started.html
@@ -40,16 +40,21 @@ 
    What is Pub/Sub?
    
 
    Prerequisites
    
 
      
 
    • Docker installed (or a running Valkey instance)
      • 
-
    • Python 3.9+
      • 
+
    • Python 3.12+
      • 
 
    
 
    Step 1: Start Valkey
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    Step 2: Install Dependencies
    
-
    pip install valkey
+
    uv pip install valkey python-dotenv
 
    
 
    Step 3: Create a Publisher
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import time
 import json
 
@@ -187,6 +192,7 @@ 
    How It Works
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/pubsub-streaming/02-streaming-llm-tokens.html b/cookbooks/pubsub-streaming/02-streaming-llm-tokens.html
index ed87fac..c3cac84 100644
--- a/cookbooks/pubsub-streaming/02-streaming-llm-tokens.html
+++ b/cookbooks/pubsub-streaming/02-streaming-llm-tokens.html
@@ -37,7 +37,12 @@ 
    The Problem
    
 #                                                     ──▶  Client C
 
    
 
    Step 1: Token Publisher (Server Side)
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import json
 import time
 import uuid
@@ -201,6 +206,7 @@ 
    Multi-Client Fan-Out
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/pubsub-streaming/03-valkey-streams.html b/cookbooks/pubsub-streaming/03-valkey-streams.html
index 0bfaae8..72f3487 100644
--- a/cookbooks/pubsub-streaming/03-valkey-streams.html
+++ b/cookbooks/pubsub-streaming/03-valkey-streams.html
@@ -65,7 +65,12 @@ 
    Pub/Sub vs Streams
    
 
 
 
    Step 1: Add Messages
    
-
    import valkey, json, time
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey, json, time
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
 # XADD - append to stream
@@ -153,6 +158,7 @@ 
    Step 4: Consumer Loop
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/pubsub-streaming/04-consumer-groups.html b/cookbooks/pubsub-streaming/04-consumer-groups.html
index dd1b01c..d8755ec 100644
--- a/cookbooks/pubsub-streaming/04-consumer-groups.html
+++ b/cookbooks/pubsub-streaming/04-consumer-groups.html
@@ -32,7 +32,12 @@ 
    Consumer Groups
    
 
    Why Consumer Groups?
    
 
    With plain XREAD, every consumer gets every message. With consumer groups, messages are distributed - enabling parallel processing of AI tasks like embeddings, completions, and tool calls.
    
 
    Step 1: Create a Group
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
 try:
@@ -106,6 +111,7 @@ 
    Step 3: Handle Pending Messages
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/pubsub-streaming/05-fan-out-patterns.html b/cookbooks/pubsub-streaming/05-fan-out-patterns.html
index 8133bc2..05b6563 100644
--- a/cookbooks/pubsub-streaming/05-fan-out-patterns.html
+++ b/cookbooks/pubsub-streaming/05-fan-out-patterns.html
@@ -30,7 +30,12 @@ 
    Fan-Out Patterns
    
 
 
 
    Pattern 1: Pub/Sub Fan-Out (Live)
    
-
    import valkey, json, time
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey, json, time
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
 def broadcast_agent_event(agent_id, event_type, payload):
@@ -106,6 +111,7 @@ 
    Pattern 3: Hybrid - Stream + Pub/Sub
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/pubsub-streaming/06-production.html b/cookbooks/pubsub-streaming/06-production.html
index e4b450a..4b32ae3 100644
--- a/cookbooks/pubsub-streaming/06-production.html
+++ b/cookbooks/pubsub-streaming/06-production.html
@@ -30,7 +30,12 @@ 
    Production Patterns
    
 
 
 
    Pattern 1: Backpressure with MAXLEN
    
-
    import valkey, time
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey, time
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
 
 # Approximate trim (faster, recommended)
@@ -131,6 +136,7 @@ 
    Production Checklist
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rag-pipelines/01-getting-started.html b/cookbooks/rag-pipelines/01-getting-started.html
index 40fff75..442bad5 100644
--- a/cookbooks/rag-pipelines/01-getting-started.html
+++ b/cookbooks/rag-pipelines/01-getting-started.html
@@ -37,7 +37,7 @@ 
    Step 1: Start Valkey with Docker
    
 
    # Pull and run Valkey with search module
 docker run -d --name valkey \
   -p 6379:6379 \
-  valkey/valkey-bundle:latest
+  valkey/valkey-bundle:9-alpine
 
 # Verify it's running
 docker exec -it valkey valkey-cli ping
@@ -58,6 +58,11 @@ 
    Step 2: Create a Vector Search Index
    
 
    Step 3: Store Document Chunks
    
 
    Store your document chunks as hashes with their embeddings.
    
 
    # Python example
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import valkey
 import numpy as np
 
@@ -159,6 +164,7 @@ 
    Step 5: Complete Python Example
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rag-pipelines/02-semantic-caching.html b/cookbooks/rag-pipelines/02-semantic-caching.html
index 3d15e80..9296e78 100644
--- a/cookbooks/rag-pipelines/02-semantic-caching.html
+++ b/cookbooks/rag-pipelines/02-semantic-caching.html
@@ -186,6 +186,7 @@ 
    Complete Caching Class
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rag-pipelines/03-vector-search.html b/cookbooks/rag-pipelines/03-vector-search.html
index 75c7c0b..a94d3e7 100644
--- a/cookbooks/rag-pipelines/03-vector-search.html
+++ b/cookbooks/rag-pipelines/03-vector-search.html
@@ -143,7 +143,12 @@ 
    Metadata Filtering
    
   ...
 
    
 
    Python Implementation
    
-
    from valkey.commands.search.query import Query
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from valkey.commands.search.query import Query
 
 def hybrid_search(query_text, category=None, date_range=None, k=5):
     # Build filter
@@ -176,6 +181,7 @@ 
    Python Implementation
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rag-pipelines/04-cache-invalidation.html b/cookbooks/rag-pipelines/04-cache-invalidation.html
index 1cd2941..8fb19e9 100644
--- a/cookbooks/rag-pipelines/04-cache-invalidation.html
+++ b/cookbooks/rag-pipelines/04-cache-invalidation.html
@@ -151,6 +151,7 @@ 
    Combined Approach
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rag-pipelines/05-scaling-production.html b/cookbooks/rag-pipelines/05-scaling-production.html
index bda9270..530cd8c 100644
--- a/cookbooks/rag-pipelines/05-scaling-production.html
+++ b/cookbooks/rag-pipelines/05-scaling-production.html
@@ -70,19 +70,19 @@ 
    Cluster Mode
    
 version: '3.8'
 services:
   valkey-1:
-    image: valkey/valkey-bundle:latest
+    image: valkey/valkey-bundle:9-alpine
     command: valkey-server --cluster-enabled yes --cluster-node-timeout 5000
     ports:
       - "7001:6379"
 
   valkey-2:
-    image: valkey/valkey-bundle:latest
+    image: valkey/valkey-bundle:9-alpine
     command: valkey-server --cluster-enabled yes --cluster-node-timeout 5000
     ports:
       - "7002:6379"
 
   valkey-3:
-    image: valkey/valkey-bundle:latest
+    image: valkey/valkey-bundle:9-alpine
     command: valkey-server --cluster-enabled yes --cluster-node-timeout 5000
     ports:
       - "7003:6379"
@@ -148,6 +148,11 @@ 
    AWS ElastiCache / MemoryDB
    
 )
 
 # For cluster mode
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from valkey.cluster import ValkeyCluster
 
 cluster = ValkeyCluster(
@@ -167,6 +172,7 @@ 
    AWS ElastiCache / MemoryDB
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rag-pipelines/06-monitoring.html b/cookbooks/rag-pipelines/06-monitoring.html
index b69ec6b..522f2a5 100644
--- a/cookbooks/rag-pipelines/06-monitoring.html
+++ b/cookbooks/rag-pipelines/06-monitoring.html
@@ -54,7 +54,12 @@ 
    Built-in INFO Command
    
 FT.INFO idx:docs
 
    
 
    Application-Level Metrics
    
-
    import time
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import time
 from dataclasses import dataclass, field
 
 @dataclass
@@ -200,6 +205,7 @@ 
    🎉 Cookbook Complete
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rate-limiting/01-getting-started.html b/cookbooks/rate-limiting/01-getting-started.html
index c31820b..15cb4e7 100644
--- a/cookbooks/rate-limiting/01-getting-started.html
+++ b/cookbooks/rate-limiting/01-getting-started.html
@@ -32,21 +32,26 @@ 
    Getting Started with Rate Limiting
    
 
    Prerequisites
    
 
      
 
    • Docker installed (or a running Valkey instance)
      • 
-
    • Python 3.9+
      • 
+
    • Python 3.12+
      • 
 
    
 
    Step 1: Start Valkey
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    Verify it's running:
    
 
    docker exec valkey valkey-cli ping
 # PONG
 
    
 
    Step 2: Install Dependencies
    
-
    pip install valkey
+
    uv pip install valkey python-dotenv
 
    
 
    That's it - no special libraries needed. The valkey package is the official Valkey Python client.
    
 
    Step 3: Your First Rate Limiter
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import time
 
 client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
@@ -127,6 +132,7 @@ 
    How It Works
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rate-limiting/02-token-aware.html b/cookbooks/rate-limiting/02-token-aware.html
index 4c8979c..b3808be 100644
--- a/cookbooks/rate-limiting/02-token-aware.html
+++ b/cookbooks/rate-limiting/02-token-aware.html
@@ -38,7 +38,12 @@ 
    The Problem
    
 A request-based limiter treats all 3 the same. That's wrong.
 
    
 
    Step 1: Count Tokens Before Calling the LLM
    
-
    import tiktoken
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import tiktoken
 
 def count_tokens(text: str, model: str = "gpt-4") -> int:
     """Count tokens using tiktoken (OpenAI's tokenizer)."""
@@ -173,6 +178,7 @@ 
    Key Takeaways
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rate-limiting/03-agent-limiting.html b/cookbooks/rate-limiting/03-agent-limiting.html
index 037f227..af4e480 100644
--- a/cookbooks/rate-limiting/03-agent-limiting.html
+++ b/cookbooks/rate-limiting/03-agent-limiting.html
@@ -123,6 +123,7 @@ 
    Step 4: Agent Budget Tracking
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rate-limiting/04-hierarchical.html b/cookbooks/rate-limiting/04-hierarchical.html
index 9ef2fc0..38b483b 100644
--- a/cookbooks/rate-limiting/04-hierarchical.html
+++ b/cookbooks/rate-limiting/04-hierarchical.html
@@ -45,7 +45,12 @@ 
    The Problem
    
   4. Is agent-1 under 25K? ❌ → DENIED
 
    
 
    Step 1: Define Tiers
    
-
    from dataclasses import dataclass
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from dataclasses import dataclass
 
 @dataclass
 class Tier:
@@ -137,6 +142,7 @@ 
    Performance
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rate-limiting/05-cost-based.html b/cookbooks/rate-limiting/05-cost-based.html
index e38056f..7c34cda 100644
--- a/cookbooks/rate-limiting/05-cost-based.html
+++ b/cookbooks/rate-limiting/05-cost-based.html
@@ -128,6 +128,7 @@ 
    Budget Tiers
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/rate-limiting/06-production.html b/cookbooks/rate-limiting/06-production.html
index ac31ead..19bff5c 100644
--- a/cookbooks/rate-limiting/06-production.html
+++ b/cookbooks/rate-limiting/06-production.html
@@ -31,7 +31,12 @@ 
    Production Patterns
    
 
 
    Pattern 1: Retry-After Headers
    
 
    Always tell clients when to retry:
    
-
    from fastapi import FastAPI, Response, HTTPException
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from fastapi import FastAPI, Response, HTTPException
 
 app = FastAPI()
 
@@ -134,6 +139,7 @@ 
    Valkey Configuration
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/semantic-caching/01-getting-started.html b/cookbooks/semantic-caching/01-getting-started.html
index abf9744..4f19b43 100644
--- a/cookbooks/semantic-caching/01-getting-started.html
+++ b/cookbooks/semantic-caching/01-getting-started.html
@@ -30,28 +30,47 @@ 
    Getting Started with Semantic Caching
    
 
    Build a semantic cache that stores LLM responses and returns cached answers for semantically similar prompts - cutting costs by 60%+ and latency from seconds to milliseconds.
    
 
 
    How Semantic Caching Works
    
-
    # Flow:
-# 1. User sends prompt
-# 2. Embed the prompt → vector
-# 3. FT.SEARCH: find similar cached prompts (KNN)
-# 4. If similarity score < threshold → CACHE HIT → return cached response
-# 5. Else → CACHE MISS → call LLM → cache the prompt+response → return
-#
-#  User Prompt → Embed → FT.SEARCH KNN → Hit? → Return cached
-#                                        → Miss? → Call LLM → HSET → Return
+
    Gist
    
+
    User Prompt → Embed → FT.SEARCH KNN → Hit?  → Return cached response (~1ms)
+                                      Miss? → Call LLM (~1-3s) → HSET in Valkey → Return
 
    
+
    Diagram
    
+
    flowchart TD
+    A["User Prompt"] --> B["Embed → Vector"]
+    B --> C["FT.SEARCH KNN\n(find similar cached prompts)"]
+    C --> D{"score < threshold?"}
+    D -- "✅ Cache Hit" --> E["Return cached response\n⚡ ~1ms"]
+    D -- "❌ Cache Miss" --> F["Call LLM\n🕐 ~1-3s"]
+    F --> G["HSET prompt + response\n+ embedding in Valkey"]
+    G --> H["Return LLM response"]
+
    
 
    
 
    Why semantic, not exact? "What is Valkey?" and "Can you explain what Valkey is?" are different strings but mean the same thing. Exact-match caching misses these. Semantic caching uses vector similarity to match by meaning, dramatically increasing hit rates.
    
 
    
 
    Prerequisites
    
 
      
-
    • Valkey with the valkey-search module loaded (or ElastiCache for Valkey 8.2+)
      • 
-
    • Python 3.9+ with valkey, openai, numpy
      • 
+
    • Valkey with the valkey-search module loaded (or ElastiCache for Valkey 8.2+ or Memorystore for Valkey 8+)
      • 
+
    • Python 3.12+ with valkey, openai, numpy and python-dotenv
      • 
 
    
-
    Step 1: Setup
    
-
    pip install valkey openai numpy
+
    Step 1: Start Valkey
    
+
    1.1 - If you haven't already started a Valkey server with the valkey-search module you can start with the valkey-bundle container.
    
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
-
    import valkey
+
    Step 2: Setup
    
+
    2.1 - Create a .env file with the following environment variables:
    
+
    OPENAI_API_KEY=<your_openai_api_key>
+OPENAI_MODEL=gpt-5.1
+
    
+
    2.2 - Install dependencies
    
+
    uv pip install valkey openai numpy python-dotenv
+
    
+
    2.3 - Connect to Valkey and OpenAI
    
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import numpy as np
 import json
 import hashlib
@@ -66,7 +85,7 @@ 
    Step 1: Setup
    
 SIMILARITY_THRESHOLD = 0.15  # COSINE distance: 0=identical, 2=opposite
 CACHE_TTL = 3600  # 1 hour
 
    
-
    Step 2: Create the Cache Index
    
+
    Step 3: Create the Cache Index
    
 
    def create_cache_index():
     """Create a vector index for the semantic cache."""
     try:
@@ -86,7 +105,7 @@ 
    Step 2: Create the Cache Index
    
 
 create_cache_index()
 
    
-
    Step 3: Embedding Helper
    
+
    Step 4: Embedding Helper
    
 
    def get_embedding(text: str) -> bytes:
     """Embed text using OpenAI and return as FLOAT32 bytes."""
     response = openai_client.embeddings.create(
@@ -96,7 +115,7 @@ 
    Step 3: Embedding Helper
    
     vec = response.data[0].embedding
     return np.array(vec, dtype=np.float32).tobytes()
 
    
-
    Step 4: The Semantic Cache
    
+
    Step 5: The Semantic Cache
    
 
    def semantic_cache_lookup(prompt: str) -> dict:
     """Check if a semantically similar prompt is cached."""
     query_vec = get_embedding(prompt)
@@ -177,7 +196,7 @@ 
    Step 4: The Semantic Cache
    
         "latency_ms": round(elapsed, 1),
     }
 
    
-
    Step 5: Test It
    
+
    Step 6: Test It
    
 
    # First call - cache MISS (calls LLM)
 result1 = ask_with_cache("What is Valkey?")
 print(f"Source: {result1['source']}, Latency: {result1['latency_ms']}ms")
@@ -241,6 +260,7 @@ 
    How It Works Under the Hood
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/semantic-caching/02-multiturn-caching.html b/cookbooks/semantic-caching/02-multiturn-caching.html
index 177338b..0eebb9f 100644
--- a/cookbooks/semantic-caching/02-multiturn-caching.html
+++ b/cookbooks/semantic-caching/02-multiturn-caching.html
@@ -40,7 +40,12 @@ 
    The Challenge
    
 
    Strategy: Context-Aware Cache Keys
    
 
    Instead of embedding only the last user message, we embed a summary of the full conversation context. This way, "Tell me more" in a Valkey conversation matches other Valkey follow-ups, not Python follow-ups.
    
 
    Step 1: Create Index with TAG Filter for User/Session
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import numpy as np
 import hashlib
 import time
@@ -207,6 +212,7 @@ 
    Cache Isolation Strategies
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/semantic-caching/03-production.html b/cookbooks/semantic-caching/03-production.html
index 5b99019..de47772 100644
--- a/cookbooks/semantic-caching/03-production.html
+++ b/cookbooks/semantic-caching/03-production.html
@@ -83,7 +83,12 @@ 
    Pattern 1: Similarity Threshold Tuning
    
     print(f"Threshold {threshold}: hit_rate={hit_rate:.1%}, false_positive_rate={fp_rate:.1%}")
 
    
 
    Pattern 2: Cache Hit Rate Monitoring
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 
 client = valkey.Valkey(host="localhost", port=6379)
 
@@ -231,6 +236,7 @@ 
    Production Checklist
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/strands/01-getting-started.html b/cookbooks/strands/01-getting-started.html
index d428d22..b12f19b 100644
--- a/cookbooks/strands/01-getting-started.html
+++ b/cookbooks/strands/01-getting-started.html
@@ -64,19 +64,24 @@ 
    Prerequisites
    
 
  • An LLM provider configured (Strands supports Amazon Bedrock, Anthropic, OpenAI, and others)
    • 
 
 
    Step 1: Start Valkey
    
-
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:latest
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    docker exec valkey valkey-cli PING
 # PONG
 
    
 
    Step 2: Install Dependencies
    
-
    pip install strands-agents strands-agents-tools
-pip install valkey
-pip install strands-valkey-session-manager
+
    uv pip install strands-agents strands-agents-tools python-dotenv
+uv pip install valkey python-dotenv
+uv pip install strands-valkey-session-manager python-dotenv
 
    
 
    Package: strands-valkey-session-manager is a community package. Source: GitHub - Docs: strandsagents.com
    
 
    Step 3: Connect to Valkey
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 from strands_valkey_session_manager import ValkeySessionManager
 
 # Connect to Valkey
@@ -110,6 +115,7 @@ 
    Step 4: Give Your Agent Memory
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/strands/02-session-internals.html b/cookbooks/strands/02-session-internals.html
index 0441031..7b8d307 100644
--- a/cookbooks/strands/02-session-internals.html
+++ b/cookbooks/strands/02-session-internals.html
@@ -74,7 +74,12 @@ 
    Conversation Messages
    
 
    
 
    Pick Up Where You Left Off
    
 
    Create a new Agent with the same session_id - Strands reloads the full conversation history from Valkey automatically:
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 from strands import Agent
 from strands_valkey_session_manager import ValkeySessionManager
 
@@ -190,6 +195,7 @@ 
    How Keys Are Organized
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/strands/03-managing-your-session-store.html b/cookbooks/strands/03-managing-your-session-store.html
index 099f117..2866a3d 100644
--- a/cookbooks/strands/03-managing-your-session-store.html
+++ b/cookbooks/strands/03-managing-your-session-store.html
@@ -31,7 +31,12 @@ 
    Managing Your ValkeySession Store
    
 
 
    Share a Session Across Multiple Agents
    
 
    A single session can hold state for multiple agents - each gets its own agent key and message keys under the same session ID. This is the foundation for orchestrator/subagent patterns where agents need to share context:
    
-
    from strands import Agent
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from strands import Agent
 from strands_valkey_session_manager import ValkeySessionManager
 import valkey
 
@@ -93,6 +98,7 @@ 
    Query Valkey Directly
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/vector-search/01-getting-started.html b/cookbooks/vector-search/01-getting-started.html
index a3c8396..458a896 100644
--- a/cookbooks/vector-search/01-getting-started.html
+++ b/cookbooks/vector-search/01-getting-started.html
@@ -32,22 +32,26 @@ 
    Getting Started with Vector Search
    
 
    Prerequisites
    
 
      
 
    • Valkey server with the valkey-search module loaded
      • 
-
    • Python 3.9+ with the valkey and numpy packages
      • 
+
    • Python 3.12+ with the valkey and numpy packages
      • 
 
    
 
    Step 1: Start Valkey with Search Module
    
-
    # Start Valkey with the search module loaded
-valkey-server --loadmodule /usr/lib/valkey/libsearch.so
+
    docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
 
    
 
    Verify the module is loaded:
    
-
    valkey-cli MODULE LIST
+
    docker exec valkey valkey-cli MODULE LIST
 # Should show "search" in the output
 
    
 
    Step 2: Install Python Client
    
-
    pip install valkey numpy
+
    uv pip install valkey numpy python-dotenv
 
    
 
    We use the standard valkey Python client. Module commands like FT.CREATE are called via execute_command().
    
 
    Step 3: Create a Vector Index
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import numpy as np
 
 client = valkey.Valkey(host="localhost", port=6379)
@@ -186,6 +190,7 @@ 
    How It Works
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/vector-search/02-semantic-search.html b/cookbooks/vector-search/02-semantic-search.html
index d3cc1e6..1d0cfa2 100644
--- a/cookbooks/vector-search/02-semantic-search.html
+++ b/cookbooks/vector-search/02-semantic-search.html
@@ -37,7 +37,7 @@ 
    The Architecture
    
 #  Query    → OpenAI embed → FT.SEARCH idx "*=>[KNN 5 @embedding $vec]"
 
    
 
    Step 1: Setup
    
-
    pip install valkey openai numpy
+
    uv pip install valkey openai numpy python-dotenv
 
 
 import valkey
@@ -191,6 +191,7 @@ 
    Distance Metrics
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/cookbooks/vector-search/03-hybrid-search.html b/cookbooks/vector-search/03-hybrid-search.html
index 83c6636..36eaf19 100644
--- a/cookbooks/vector-search/03-hybrid-search.html
+++ b/cookbooks/vector-search/03-hybrid-search.html
@@ -32,7 +32,12 @@ 
    Hybrid Search
    
 
    Why Hybrid Search?
    
 
    Pure vector search returns the most similar items globally. But often you need to filter first - "find similar articles, but only in the tech category" or "similar products under $50." Hybrid search lets you combine vector KNN with traditional filters in a single query.
    
 
    Step 1: Create an Index with Multiple Field Types
    
-
    import valkey
+
    import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import valkey
 import numpy as np
 
 client = valkey.Valkey(host="localhost", port=6379)
@@ -189,6 +194,7 @@ 
    Filter Syntax Reference
    
 
 
 
+
 
 
 
\ No newline at end of file
diff --git a/dev.js b/dev.js
new file mode 100644
index 0000000..4a3a72a
--- /dev/null
+++ b/dev.js
@@ -0,0 +1,48 @@
+#!/usr/bin/env node
+/**
+ * dev.js - Development server with live reload
+ *
+ * Watches content/*.md and meta.json for changes, rebuilds the affected track,
+ * and triggers a browser refresh via browser-sync.
+ *
+ * Usage: node dev.js
+ */
+
+const { execSync } = require('child_process');
+const path = require('path');
+const chokidar = require('chokidar');
+const browserSync = require('browser-sync').create();
+
+// Initial full build
+console.log('🔨 Running initial build...\n');
+execSync('node build.js', { stdio: 'inherit' });
+
+// Start browser-sync
+browserSync.init({
+  server: '.',
+  files: [
+    'cookbooks/**/*.html',
+    'cookbooks/cookbook.css',
+    'index.html',
+    'styles.css',
+  ],
+  open: false,
+  notify: false,
+  ui: false,
+});
+
+console.log('\n👀 Watching content/ for changes...\n');
+
+// Watch markdown + meta.json, rebuild the affected track on change
+chokidar
+  .watch('content/**/*.{md,json}', { ignoreInitial: true })
+  .on('change', (filePath) => {
+    const track = filePath.split(path.sep)[1]; // content//file.md
+    console.log(`\n📝 Changed: ${filePath}`);
+    console.log(`🔨 Rebuilding: ${track}`);
+    try {
+      execSync(`node build.js ${track}`, { stdio: 'inherit' });
+    } catch (e) {
+      console.error(`❌ Build failed for ${track}`);
+    }
+  });
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..81e968d
--- /dev/null
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from valkey-for-ai!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/package-lock.json b/package-lock.json
index c14a442..5549660 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -10,18 +10,1825 @@
       "license": "ISC",
       "dependencies": {
         "marked": "^17.0.4"
+      },
+      "devDependencies": {
+        "browser-sync": "^3.0.4",
+        "chokidar": "^5.0.0"
+      }
+    },
+    "node_modules/@socket.io/component-emitter": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/@socket.io/component-emitter/-/component-emitter-3.1.2.tgz",
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+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/@types/cors": {
+      "version": "2.8.19",
+      "resolved": "https://registry.npmjs.org/@types/cors/-/cors-2.8.19.tgz",
+      "integrity": "sha512-mFNylyeyqN93lfe/9CSxOGREz8cpzAhH+E93xJ4xWQf62V8sQ/24reV2nyzUWM6H6Xji+GGHpkbLe7pVoUEskg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@types/node": "*"
+      }
+    },
+    "node_modules/@types/node": {
+      "version": "25.6.0",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-25.6.0.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "undici-types": "~7.19.0"
+      }
+    },
+    "node_modules/@types/ws": {
+      "version": "8.18.1",
+      "resolved": "https://registry.npmjs.org/@types/ws/-/ws-8.18.1.tgz",
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+      "license": "MIT",
+      "dependencies": {
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+      "license": "Apache-2.0",
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+        "browser-sync-ui": "^3.0.4",
+        "bs-recipes": "1.3.4",
+        "chalk": "4.1.2",
+        "chokidar": "^3.5.1",
+        "connect": "3.6.6",
+        "connect-history-api-fallback": "^1",
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+        "easy-extender": "^2.3.4",
+        "eazy-logger": "^4.1.0",
+        "etag": "^1.8.1",
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+        "micromatch": "^4.0.8",
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+        "portscanner": "2.2.0",
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+        "resp-modifier": "6.0.2",
+        "rx": "4.1.0",
+        "send": "^0.19.0",
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+        "serve-static": "^1.16.2",
+        "server-destroy": "1.0.1",
+        "socket.io": "^4.4.1",
+        "ua-parser-js": "^1.0.33",
+        "yargs": "^17.3.1"
+      },
+      "bin": {
+        "browser-sync": "dist/bin.js"
+      },
+      "engines": {
+        "node": ">= 8.0.0"
+      }
+    },
+    "node_modules/browser-sync-client": {
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+        "throttleproxy": "bin/throttleproxy.js"
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       "engines": {
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+        "node": ">= 0.10.0"
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+      "funding": [
+        {
+          "type": "opencollective",
+          "url": "https://opencollective.com/ua-parser-js"
+        },
+        {
+          "type": "paypal",
+          "url": "https://paypal.me/faisalman"
+        },
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/faisalman"
+        }
+      ],
+      "license": "MIT",
+      "bin": {
+        "ua-parser-js": "script/cli.js"
+      },
+      "engines": {
+        "node": "*"
+      }
+    },
+    "node_modules/undici-types": {
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+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/universalify": {
+      "version": "0.1.2",
+      "resolved": "https://registry.npmjs.org/universalify/-/universalify-0.1.2.tgz",
+      "integrity": "sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg==",
+      "dev": true,
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+      }
+    },
+    "node_modules/unpipe": {
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+      "resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
+      "integrity": "sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
+    "node_modules/utils-merge": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz",
+      "integrity": "sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
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+      }
+    },
+    "node_modules/vary": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz",
+      "integrity": "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==",
+      "dev": true,
+      "license": "MIT",
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+      },
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+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
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+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz",
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+    "node_modules/y18n": {
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+      "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
+      "dev": true,
+      "license": "ISC",
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/yargs": {
+      "version": "17.7.2",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz",
+      "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "cliui": "^8.0.1",
+        "escalade": "^3.1.1",
+        "get-caller-file": "^2.0.5",
+        "require-directory": "^2.1.1",
+        "string-width": "^4.2.3",
+        "y18n": "^5.0.5",
+        "yargs-parser": "^21.1.1"
+      },
+      "engines": {
+        "node": ">=12"
+      }
+    },
+    "node_modules/yargs-parser": {
+      "version": "21.1.1",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz",
+      "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==",
+      "dev": true,
+      "license": "ISC",
+      "engines": {
+        "node": ">=12"
       }
     }
   }
diff --git a/package.json b/package.json
index 497a8b7..d80d5a8 100644
--- a/package.json
+++ b/package.json
@@ -4,7 +4,10 @@
   "description": "A practical, use-case-driven hub that helps developers understand how to use Valkey in real AI workloads. Instead of making users piece together low-level features, this site offers clear cookbooks, reference architectures, GitHub demos, and runnable projects for patterns like semantic caching, conversation memory, vector search, agent session state, rate limiting, streaming responses, and more.",
   "main": "script.js",
   "scripts": {
-    "build": "node build.js", "test": "echo \"Error: no test specified\" && exit 1"
+    "build": "node build.js",
+    "dev": "node dev.js",
+    "build:notebooks": "node build-notebooks.js",
+    "test": "echo \"Error: no test specified\" && exit 1"
   },
   "repository": {
     "type": "git",
@@ -20,5 +23,9 @@
   "homepage": "https://github.com/meet-bhagdev/valkeyforai#readme",
   "dependencies": {
     "marked": "^17.0.4"
+  },
+  "devDependencies": {
+    "browser-sync": "^3.0.4",
+    "chokidar": "^5.0.0"
   }
 }
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..25fbd93
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,54 @@
+[project]
+name = "valkey-for-ai"
+version = "0.1.0"
+description = "Cookbooks and runnable demos for using Valkey in AI workloads"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "valkey",
+    "numpy",
+    "openai",
+    "python-dotenv",
+    "tiktoken",
+    "ipykernel",
+    "pytest",
+]
+
+[project.optional-dependencies]
+# Framework-specific tracks
+langchain = [
+    "langgraph-checkpoint-aws[valkey]",
+    "langchain-aws",
+]
+crewai = [
+    "crewai",
+    "valkey-glide",
+    "boto3",
+]
+mem0 = [
+    "mem0ai",
+]
+strands = [
+    "strands-agents",
+    "strands-agents-tools",
+    "strands-valkey-session-manager",
+]
+haystack = [
+    "haystack-ai",
+    "valkey-haystack",
+    "sentence-transformers",
+]
+# Production cookbook extras (monitoring, APIs)
+production = [
+    "fastapi",
+    "pydantic",
+    "prometheus-client",
+    "structlog",
+]
+# Everything
+all = [
+    "valkey-for-ai[langchain,crewai,mem0,strands,haystack,production]",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/betterdb_agent_cache/__init__.py b/tests/betterdb_agent_cache/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/betterdb_agent_cache/test_01_getting_started.py b/tests/betterdb_agent_cache/test_01_getting_started.py
new file mode 100644
index 0000000..38233b1
--- /dev/null
+++ b/tests/betterdb_agent_cache/test_01_getting_started.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/agent-cache - Getting Started with @betterdb/agent-cache."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestGettingStartedWithBetterdbagentcache:
+    """Tests for cookbook: Getting Started with @betterdb/agent-cache."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for Getting Started with @betterdb/agent-cache."""
+        assert valkey_client.ping()
diff --git a/tests/betterdb_agent_cache/test_02_llm_and_tool_cache.py b/tests/betterdb_agent_cache/test_02_llm_and_tool_cache.py
new file mode 100644
index 0000000..1fd7626
--- /dev/null
+++ b/tests/betterdb_agent_cache/test_02_llm_and_tool_cache.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/agent-cache - LLM & Tool Cache."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestLLMToolCache:
+    """Tests for cookbook: LLM & Tool Cache."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for LLM & Tool Cache."""
+        assert valkey_client.ping()
diff --git a/tests/betterdb_agent_cache/test_03_session_store.py b/tests/betterdb_agent_cache/test_03_session_store.py
new file mode 100644
index 0000000..73fbde2
--- /dev/null
+++ b/tests/betterdb_agent_cache/test_03_session_store.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/agent-cache - Agent Session Store."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestAgentSessionStore:
+    """Tests for cookbook: Agent Session Store."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for Agent Session Store."""
+        assert valkey_client.ping()
diff --git a/tests/betterdb_agent_cache/test_04_langgraph.py b/tests/betterdb_agent_cache/test_04_langgraph.py
new file mode 100644
index 0000000..46a7d46
--- /dev/null
+++ b/tests/betterdb_agent_cache/test_04_langgraph.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/agent-cache - LangGraph Checkpointing."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestLangGraphCheckpointing:
+    """Tests for cookbook: LangGraph Checkpointing."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for LangGraph Checkpointing."""
+        assert valkey_client.ping()
diff --git a/tests/betterdb_semantic_cache/__init__.py b/tests/betterdb_semantic_cache/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/betterdb_semantic_cache/test_01_getting_started.py b/tests/betterdb_semantic_cache/test_01_getting_started.py
new file mode 100644
index 0000000..77806d2
--- /dev/null
+++ b/tests/betterdb_semantic_cache/test_01_getting_started.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/semantic-cache - Getting Started with @betterdb/semantic-cache."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestGettingStartedWithBetterdbsemanticcache:
+    """Tests for cookbook: Getting Started with @betterdb/semantic-cache."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for Getting Started with @betterdb/semantic-cache."""
+        assert valkey_client.ping()
diff --git a/tests/betterdb_semantic_cache/test_02_adapters.py b/tests/betterdb_semantic_cache/test_02_adapters.py
new file mode 100644
index 0000000..4f30a95
--- /dev/null
+++ b/tests/betterdb_semantic_cache/test_02_adapters.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/semantic-cache - LangChain & Vercel AI Adapters."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestLangChainVercelAIAdapters:
+    """Tests for cookbook: LangChain & Vercel AI Adapters."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for LangChain & Vercel AI Adapters."""
+        assert valkey_client.ping()
diff --git a/tests/betterdb_semantic_cache/test_03_production.py b/tests/betterdb_semantic_cache/test_03_production.py
new file mode 100644
index 0000000..9d761ea
--- /dev/null
+++ b/tests/betterdb_semantic_cache/test_03_production.py
@@ -0,0 +1,21 @@
+"""Tests for @betterdb/semantic-cache - Production Patterns."""
+
+import pytest
+
+
+@pytest.fixture
+def valkey_client():
+    """Create a Valkey client for testing."""
+    import valkey
+
+    client = valkey.Valkey(host="localhost", port=6379, decode_responses=True)
+    yield client
+    client.close()
+
+
+class TestProductionPatterns:
+    """Tests for cookbook: Production Patterns."""
+
+    def test_placeholder(self, valkey_client):
+        """TODO: implement test for Production Patterns."""
+        assert valkey_client.ping()
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..0b86436
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,68 @@
+"""Shared fixtures for all cookbook integration tests.
+
+These tests require:
+- Valkey running with search module: docker run -d --name valkey -p 6379:6379 valkey/valkey-bundle:9-alpine
+- OPENAI_API_KEY set in .env or environment (for cookbooks that use OpenAI)
+"""
+
+import pytest
+import valkey
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+@pytest.fixture(scope="session")
+def valkey_url():
+    """Valkey connection URL."""
+    return os.environ.get("VALKEY_URL", "valkey://localhost:6379")
+
+
+@pytest.fixture
+def client():
+    """Valkey client with decode_responses=True."""
+    c = valkey.Valkey(
+        host=os.environ.get("VALKEY_HOST", "localhost"),
+        port=int(os.environ.get("VALKEY_PORT", 6379)),
+        decode_responses=True,
+    )
+    yield c
+    c.close()
+
+
+@pytest.fixture
+def raw_client():
+    """Valkey client without decode_responses (for binary/vector ops)."""
+    c = valkey.Valkey(
+        host=os.environ.get("VALKEY_HOST", "localhost"),
+        port=int(os.environ.get("VALKEY_PORT", 6379)),
+    )
+    yield c
+    c.close()
+
+
+@pytest.fixture
+def openai_client():
+    """OpenAI client. Requires OPENAI_API_KEY in env."""
+    from openai import OpenAI
+
+    api_key = os.environ.get("OPENAI_API_KEY")
+    if not api_key:
+        pytest.skip("OPENAI_API_KEY not set")
+    return OpenAI(api_key=api_key)
+
+
+@pytest.fixture
+def openai_model():
+    """OpenAI model to use. Defaults to gpt-4o-mini for cost efficiency."""
+    return os.environ.get("OPENAI_MODEL", "gpt-4o-mini")
+
+
+@pytest.fixture(autouse=True)
+def _check_valkey(client):
+    """Skip all tests if Valkey is not reachable."""
+    try:
+        client.ping()
+    except valkey.ConnectionError:
+        pytest.skip("Valkey not available")
diff --git a/tests/context_engineering/__init__.py b/tests/context_engineering/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/context_engineering/test_01_fundamentals.py b/tests/context_engineering/test_01_fundamentals.py
new file mode 100644
index 0000000..9e0877b
--- /dev/null
+++ b/tests/context_engineering/test_01_fundamentals.py
@@ -0,0 +1,100 @@
+"""Integration tests for Context Engineering - Context Engineering Fundamentals.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_fundamentals(client):
+    """Run all code blocks from: Context Engineering Fundamentals."""
+
+    # --- Block 1 ---
+    import json
+
+    client = client
+
+    # Store agent configuration
+    client.hset("agent:config:support_bot", mapping={
+        "role": "You are a helpful customer support agent for Acme Corp.",
+        "constraints": "Always be polite. Never share internal pricing. Escalate billing issues.",
+        "output_format": "Respond in markdown. Keep answers under 200 words.",
+        "tools_available": json.dumps(["search_kb", "check_order", "create_ticket"]),
+    })
+    print("System instructions stored")
+
+    # --- Block 2 ---
+    import time
+
+    def add_message(session_id: str, role: str, content: str):
+        """Append a message to the conversation history."""
+        msg = json.dumps({"role": role, "content": content, "ts": time.time()})
+        client.rpush(f"chat:{session_id}", msg)
+        # Keep only last 50 messages (sliding window)
+        client.ltrim(f"chat:{session_id}", -50, -1)
+        # Set TTL for session cleanup (30 min inactivity)
+        client.expire(f"chat:{session_id}", 1800)
+
+    def get_history(session_id: str, last_n: int = 10) -> list:
+        """Retrieve recent conversation history."""
+        raw = client.lrange(f"chat:{session_id}", -last_n, -1)
+        return [json.loads(m) for m in raw]
+
+    # Example conversation
+    add_message("sess_001", "user", "What's your refund policy?")
+    add_message("sess_001", "assistant", "Our refund policy allows returns within 30 days...")
+    add_message("sess_001", "user", "Can I return an opened item?")
+
+    history = get_history("sess_001")
+    for msg in history:
+        print(f"  {msg['role']}: {msg['content'][:60]}...")
+
+    # --- Block 3 ---
+    def store_tool_output(session_id: str, step: int, tool_name: str, result: dict):
+        """Store the output of a tool call for context assembly."""
+        key = f"tool:{session_id}:step_{step}"
+        client.hset(key, mapping={
+            "tool": tool_name,
+            "result": json.dumps(result),
+            "timestamp": str(time.time()),
+        })
+        client.expire(key, 3600)  # 1 hour TTL
+
+    def get_tool_outputs(session_id: str) -> list:
+        """Retrieve all tool outputs for this session."""
+        keys = client.keys(f"tool:{session_id}:step_*")
+        outputs = []
+        for key in sorted(keys):
+            data = client.hgetall(key)
+            outputs.append({
+                "tool": data["tool"],
+                "result": json.loads(data["result"]),
+            })
+        return outputs
+
+    # Example: agent called the order lookup tool
+    store_tool_output("sess_001", 1, "check_order", {
+        "order_id": "ORD-12345",
+        "status": "delivered",
+        "date": "2025-03-15",
+    })
+
+    # --- Block 4 ---
+    def remember(user_id: str, key: str, value: str):
+        """Store a long-term memory about a user."""
+        client.hset(f"memory:{user_id}", key, value)
+        # No TTL - persists across sessions
+
+    def recall(user_id: str) -> dict:
+        """Retrieve all memories about a user."""
+        return client.hgetall(f"memory:{user_id}")
+
+    # Store user preferences
+    remember("alice", "preferred_language", "English")
+    remember("alice", "tier", "premium")
+    remember("alice", "last_issue", "billing dispute on ORD-12345")
+
+    memories = recall("alice")
+    print(f"Alice's memories: {memories}")
+
diff --git a/tests/context_engineering/test_02_assembly_pipeline.py b/tests/context_engineering/test_02_assembly_pipeline.py
new file mode 100644
index 0000000..41b0c3f
--- /dev/null
+++ b/tests/context_engineering/test_02_assembly_pipeline.py
@@ -0,0 +1,138 @@
+"""Integration tests for Context Engineering - Building the Context Assembly Pipeline.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_assembly_pipeline(raw_client):
+    """Run all code blocks from: Building the Context Assembly Pipeline."""
+
+    # --- Block 1 ---
+    import json
+    import struct
+    import time
+
+    client = raw_client
+    client_bin = raw_client
+
+    def assemble_context(user_id: str, session_id: str, agent_id: str, current_message: str) -> list:
+        """Assemble complete context for an LLM call from all 5 sources in Valkey."""
+        messages = []
+
+        # 1. System instructions (broadest context)
+        config = client.hgetall(f"agent:config:{agent_id}")
+        if config:
+            system_prompt = f"{config.get('role', '')}\n\nConstraints: {config.get('constraints', '')}\nFormat: {config.get('output_format', '')}"
+            messages.append({"role": "system", "content": system_prompt})
+
+        # 2. User memory (cross-session preferences)
+        memories = client.hgetall(f"memory:{user_id}")
+        if memories:
+            mem_str = "\n".join(f"- {k}: {v}" for k, v in memories.items())
+            messages.append({"role": "system", "content": f"User context:\n{mem_str}"})
+
+        # 3. Retrieved knowledge (RAG - would use FT.SEARCH in production)
+        # Simplified here; see the Vector Search cookbook for full implementation
+        kb_results = client.lrange(f"kb:results:{session_id}", 0, -1)
+        if kb_results:
+            docs = [json.loads(d) for d in kb_results]
+            kb_str = "\n\n".join(f"[{d.get('title', 'Doc')}]: {d.get('content', '')}" for d in docs)
+            messages.append({"role": "system", "content": f"Relevant knowledge:\n{kb_str}"})
+
+        # 4. Conversation history (recent turns)
+        history = client.lrange(f"chat:{session_id}", -10, -1)
+        for raw in history:
+            msg = json.loads(raw)
+            messages.append({"role": msg["role"], "content": msg["content"]})
+
+        # 5. Tool outputs from current session
+        tool_keys = client.keys(f"tool:{session_id}:step_*")
+        for key in sorted(tool_keys):
+            data = client.hgetall(key)
+            messages.append({
+                "role": "system",
+                "content": f"Tool '{data.get('tool', 'unknown')}' returned: {data.get('result', '{}')}",
+            })
+
+        # 6. Current user message (most specific)
+        messages.append({"role": "user", "content": current_message})
+
+        return messages
+
+    # --- Block 2 ---
+    def budget_context(messages: list, max_tokens: int = 8000) -> list:
+        """Trim context to fit within the token budget.
+        
+        Simple heuristic: ~4 chars per token for English text.
+        In production, use tiktoken for accurate counting.
+        """
+        CHARS_PER_TOKEN = 4
+        max_chars = max_tokens * CHARS_PER_TOKEN
+
+        total = sum(len(m["content"]) for m in messages)
+        if total <= max_chars:
+            return messages  # Fits already
+
+        # Priority: keep system prompt + current message, trim history from oldest
+        system_msgs = [m for m in messages if m["role"] == "system"]
+        user_msgs = [m for m in messages if m["role"] != "system"]
+
+        # Always keep the last user message
+        current = user_msgs[-1:]
+        history = user_msgs[:-1]
+
+        # Trim history from the oldest until it fits
+        budget_remaining = max_chars - sum(len(m["content"]) for m in system_msgs + current)
+        trimmed_history = []
+        for msg in reversed(history):
+            if budget_remaining - len(msg["content"]) > 0:
+                trimmed_history.insert(0, msg)
+                budget_remaining -= len(msg["content"])
+            else:
+                break
+
+        return system_msgs + trimmed_history + current
+
+    # --- Block 3 ---
+    # Setup: store some context
+    client.hset("agent:config:demo_agent", mapping={
+        "role": "You are a helpful AI assistant.",
+        "constraints": "Be concise and accurate.",
+        "output_format": "Plain text.",
+    })
+
+    client.hset("memory:user_42", mapping={
+        "name": "Bob",
+        "preferred_language": "English",
+        "tier": "free",
+    })
+
+    # Add conversation
+    for msg in [
+        ("user", "Hi, I need help with my account"),
+        ("assistant", "Sure! What do you need help with?"),
+        ("user", "I want to upgrade to premium"),
+    ]:
+        data = json.dumps({"role": msg[0], "content": msg[1], "ts": time.time()})
+        client.rpush("chat:sess_42", data)
+    client.expire("chat:sess_42", 1800)
+
+    # Store a tool result
+    client.hset("tool:sess_42:step_1", mapping={
+        "tool": "check_account",
+        "result": json.dumps({"plan": "free", "eligible_upgrade": True}),
+        "timestamp": str(time.time()),
+    })
+    client.expire("tool:sess_42:step_1", 3600)
+
+    # Assemble context
+    context = assemble_context("user_42", "sess_42", "demo_agent", "How much does premium cost?")
+    budgeted = budget_context(context, max_tokens=4000)
+
+    print(f"Assembled {len(context)} messages, budgeted to {len(budgeted)}")
+    for msg in budgeted:
+        print(f"  [{msg['role']}] {msg['content'][:80]}...")
+
diff --git a/tests/context_engineering/test_03_production.py b/tests/context_engineering/test_03_production.py
new file mode 100644
index 0000000..65c13b6
--- /dev/null
+++ b/tests/context_engineering/test_03_production.py
@@ -0,0 +1,105 @@
+"""Integration tests for Context Engineering - Production Context Management.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_production(client):
+    """Run all code blocks from: Production Context Management."""
+
+    # --- Block 1 ---
+    import json
+    import time
+
+    client = client
+
+    # Short-term memory: active session context (low TTL)
+    def store_short_term(session_id: str, key: str, value: str, ttl: int = 1800):
+        """Store session-scoped context that expires after inactivity."""
+        client.hset(f"session:{session_id}", key, value)
+        client.expire(f"session:{session_id}", ttl)
+
+    # Long-term memory: persists across sessions (no TTL or high TTL)
+    def store_long_term(user_id: str, key: str, value: str):
+        """Store cross-session user knowledge that persists indefinitely."""
+        client.hset(f"memory:{user_id}", key, value)
+        # No EXPIRE - this persists
+
+    # Example
+    store_short_term("sess_100", "current_topic", "billing")
+    store_short_term("sess_100", "escalation_level", "0")
+    store_long_term("alice", "communication_style", "prefers concise answers")
+    store_long_term("alice", "timezone", "America/Los_Angeles")
+
+    # --- Block 2 ---
+    def prune_old_messages(session_id: str, max_messages: int = 20):
+        """Keep only the most recent messages."""
+        client.ltrim(f"chat:{session_id}", -max_messages, -1)
+
+    def summarize_and_store(user_id: str, session_id: str, summary: str):
+        """After a session ends, store a summary for long-term recall."""
+        date = time.strftime("%Y-%m-%d")
+        client.hset(f"summary:{user_id}:{date}", mapping={
+            "session_id": session_id,
+            "summary": summary,
+            "timestamp": str(time.time()),
+        })
+        client.expire(f"summary:{user_id}:{date}", 86400 * 30)  # 30 days
+
+    # Prune chat to last 20 messages
+    prune_old_messages("sess_100")
+
+    # Store session summary for future context
+    summarize_and_store("alice", "sess_100", 
+        "User asked about billing. Resolved a refund request for order ORD-12345.")
+
+    # --- Block 3 ---
+    def get_user_context(user_id: str, session_id: str) -> dict:
+        """Get all context for a specific user, properly isolated."""
+        return {
+            "memory": client.hgetall(f"memory:{user_id}"),
+            "session": client.hgetall(f"session:{session_id}"),
+            "history": [
+                json.loads(m) for m in 
+                client.lrange(f"chat:{session_id}", -10, -1)
+            ],
+        }
+
+    # Alice's context is completely separate from Bob's
+    alice_ctx = get_user_context("alice", "sess_alice_001")
+    bob_ctx = get_user_context("bob", "sess_bob_001")
+
+    # --- Block 4 ---
+    def record_context_metrics(session_id: str, metrics: dict):
+        """Track context assembly metrics."""
+        client.hset(f"metrics:context:{session_id}", mapping={
+            "sources_used": str(metrics.get("sources_used", 0)),
+            "total_tokens": str(metrics.get("total_tokens", 0)),
+            "assembly_time_ms": str(metrics.get("assembly_time_ms", 0)),
+            "pruned_messages": str(metrics.get("pruned_messages", 0)),
+            "timestamp": str(time.time()),
+        })
+        client.expire(f"metrics:context:{session_id}", 86400 * 7)  # 7 days
+
+    # After assembling context
+    record_context_metrics("sess_100", {
+        "sources_used": 4,
+        "total_tokens": 2850,
+        "assembly_time_ms": 3.2,
+        "pruned_messages": 5,
+    })
+
+    # Aggregate metrics
+    def get_avg_assembly_time(pattern: str = "metrics:context:*") -> float:
+        """Calculate average context assembly time."""
+        keys = client.keys(pattern)
+        times = []
+        for key in keys[:100]:  # Sample last 100 sessions
+            t = client.hget(key, "assembly_time_ms")
+            if t:
+                times.append(float(t))
+        return sum(times) / len(times) if times else 0.0
+
diff --git a/tests/conversation_memory/__init__.py b/tests/conversation_memory/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/conversation_memory/test_01_getting_started.py b/tests/conversation_memory/test_01_getting_started.py
new file mode 100644
index 0000000..a1f3873
--- /dev/null
+++ b/tests/conversation_memory/test_01_getting_started.py
@@ -0,0 +1,108 @@
+"""Integration tests for Conversation Memory - Getting Started with Conversation Memory.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Conversation Memory."""
+
+    # --- Block 1 ---
+    import asyncio
+    import json
+    from glide import GlideClient, GlideClientConfiguration, NodeAddress
+
+
+    async def main():
+        # Connect to Valkey with GLIDE
+        config = GlideClientConfiguration([NodeAddress("localhost", 6379)])
+        client = await GlideClient.create(config)
+
+        session_id = "sess_abc123"
+        key = f"chat:{session_id}"
+
+        # Store a conversation
+        messages = [
+            {"role": "user", "content": "What is Valkey?"},
+            {"role": "assistant", "content": "Valkey is an open-source, high-performance key-value store."},
+            {"role": "user", "content": "How fast is it?"},
+            {"role": "assistant", "content": "Sub-millisecond latency for most operations."},
+        ]
+
+        for msg in messages:
+            await client.rpush(key, [json.dumps(msg)])
+
+        # Set TTL - session expires after 1 hour
+        await client.expire(key, 3600)
+
+        print("✅ Conversation stored")
+
+
+    asyncio.run(main())
+
+    # --- Block 2 ---
+    async def get_history(client, session_id, last_n=50):
+        """Retrieve the last N messages from a conversation."""
+        key = f"chat:{session_id}"
+
+        # LRANGE with negative indices = last N messages
+        raw = await client.lrange(key, -last_n, -1)
+
+        return [json.loads(msg) for msg in raw]
+
+
+    # Usage
+    history = await get_history(client, "sess_abc123")
+    for msg in history:
+        print(f"{msg['role']}: {msg['content']}")
+
+    # user: What is Valkey?
+    # assistant: Valkey is an open-source, high-performance key-value store.
+    # user: How fast is it?
+    # assistant: Sub-millisecond latency for most operations.
+
+    # --- Block 3 ---
+    import boto3, json
+
+    async def chat(client, session_id, user_message):
+        # 1. Save the user message
+        key = f"chat:{session_id}"
+        await client.rpush(key, [json.dumps({"role": "user", "content": user_message})])
+
+        # 2. Get conversation history (last 20 messages)
+        history = await get_history(client, session_id, last_n=20)
+
+        # 3. Call the LLM with full context
+        bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
+        response = bedrock.invoke_model(
+            modelId="anthropic.claude-3-haiku-20240307-v1:0",
+            body=json.dumps({
+                "anthropic_version": "bedrock-2023-05-31",
+                "max_tokens": 500,
+                "messages": history,  # ← directly from Valkey
+            }),
+        )
+        assistant_msg = json.loads(response["body"].read())["content"][0]["text"]
+
+        # 4. Save the assistant response
+        await client.rpush(key, [json.dumps({"role": "assistant", "content": assistant_msg})])
+
+        # 5. Refresh TTL
+        await client.expire(key, 3600)
+
+        return assistant_msg
+
+    # --- Block 4 ---
+    # Local Docker
+    config = GlideClientConfiguration([NodeAddress("localhost", 6379)])
+
+    # ElastiCache for Valkey
+    config = GlideClientConfiguration(
+        [NodeAddress("my-cluster.xxxxx.cache.amazonaws.com", 6379)],
+        use_tls=True,
+    )
+
diff --git a/tests/conversation_memory/test_02_session_management.py b/tests/conversation_memory/test_02_session_management.py
new file mode 100644
index 0000000..f5815fc
--- /dev/null
+++ b/tests/conversation_memory/test_02_session_management.py
@@ -0,0 +1,86 @@
+"""Integration tests for Conversation Memory - Session Management.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_02_session_management(client):
+    """Run all code blocks from: Session Management."""
+
+    # --- Block 1 ---
+    import asyncio, json, time
+    from glide import GlideClient, GlideClientConfiguration, NodeAddress
+
+
+    async def create_session(client, session_id, user_id, model="claude-3-haiku"):
+        meta_key = f"meta:{session_id}"
+        await client.hset(meta_key, {
+            "user_id": user_id,
+            "model": model,
+            "created_at": str(time.time()),
+            "last_active": str(time.time()),
+            "message_count": "0",
+            "token_count": "0",
+        })
+        # Both keys expire together
+        await client.expire(meta_key, 86400)  # 24 hours
+        await client.expire(f"chat:{session_id}", 86400)
+
+    # --- Block 2 ---
+    async def add_message(client, session_id, role, content, tokens_used=0):
+        chat_key = f"chat:{session_id}"
+        meta_key = f"meta:{session_id}"
+
+        # Append message to conversation
+        await client.rpush(chat_key, [json.dumps({"role": role, "content": content})])
+
+        # Update metadata atomically
+        await client.hincrby(meta_key, "message_count", 1)
+        await client.hincrby(meta_key, "token_count", tokens_used)
+        await client.hset(meta_key, {"last_active": str(time.time())})
+
+    # --- Block 3 ---
+    async def add_message_windowed(client, session_id, role, content, max_messages=50):
+        chat_key = f"chat:{session_id}"
+
+        # Append
+        await client.rpush(chat_key, [json.dumps({"role": role, "content": content})])
+
+        # Trim to last N messages - O(1) for small trims
+        await client.ltrim(chat_key, -max_messages, -1)
+
+    # --- Block 4 ---
+    async def list_user_sessions(client, user_id):
+        """Find all active sessions for a user using SCAN."""
+        sessions = []
+        cursor = "0"
+        while True:
+            cursor, keys = await client.scan(cursor, match="meta:*", count=100)
+            for key in keys:
+                uid = await client.hget(key, "user_id")
+                if uid and uid.decode() == user_id:
+                    meta = await client.hgetall(key)
+                    sessions.append({
+                        k.decode(): v.decode() for k, v in meta.items()
+                    })
+            if cursor == 0:
+                break
+        return sessions
+
+    # --- Block 5 ---
+    async def get_session_info(client, session_id):
+        meta = await client.hgetall(f"meta:{session_id}")
+        msg_count = await client.llen(f"chat:{session_id}")
+        ttl = await client.ttl(f"meta:{session_id}")
+
+        return {
+            "session_id": session_id,
+            "metadata": {k.decode(): v.decode() for k, v in meta.items()},
+            "messages_stored": msg_count,
+            "expires_in_seconds": ttl,
+        }
+
diff --git a/tests/conversation_memory/test_03_semantic_memory.py b/tests/conversation_memory/test_03_semantic_memory.py
new file mode 100644
index 0000000..511138a
--- /dev/null
+++ b/tests/conversation_memory/test_03_semantic_memory.py
@@ -0,0 +1,140 @@
+"""Integration tests for Conversation Memory - Semantic Memory.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_03_semantic_memory(raw_client):
+    """Run all code blocks from: Semantic Memory."""
+
+    # --- Block 1 ---
+    from glide import (
+        GlideClient, GlideClientConfiguration, NodeAddress,
+        ft, TagField, NumericField,
+        VectorField, VectorAlgorithm, VectorFieldAttributesHnsw,
+        VectorType, DistanceMetricType,
+        FtCreateOptions, DataType,
+    )
+
+    async def create_memory_index(client):
+        # Check if index already exists
+        existing = await ft.list(client)
+        names = [n.decode() for n in existing]
+        if "memory_idx" in names:
+            return
+
+        hnsw = VectorFieldAttributesHnsw(
+            dimensions=1536,  # Titan embedding size
+            distance_metric=DistanceMetricType.COSINE,
+            type=VectorType.FLOAT32,
+        )
+
+        await ft.create(
+            client, "memory_idx",
+            schema=[
+                TagField("$.session_id", alias="session_id"),
+                TagField("$.role", alias="role"),
+                TagField("$.user_id", alias="user_id"),
+                NumericField("$.timestamp", alias="timestamp"),
+                VectorField("$.embedding", VectorAlgorithm.HNSW,
+                            alias="embedding", attributes=hnsw),
+            ],
+            options=FtCreateOptions(data_type=DataType.JSON, prefixes=["mem:"]),
+        )
+        print("✅ Index created")
+
+    # --- Block 2 ---
+    import json, struct, time, uuid, boto3
+    from glide import glide_json
+
+    def get_embedding(text):
+        """Get embedding from Bedrock Titan."""
+        bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
+        response = bedrock.invoke_model(
+            modelId="amazon.titan-embed-text-v1",
+            body=json.dumps({"inputText": text}),
+        )
+        return json.loads(response["body"].read())["embedding"]
+
+
+    async def store_memory(client, session_id, user_id, role, content):
+        """Store a message with its embedding for semantic search."""
+        embedding = get_embedding(content)
+        doc_id = f"mem:{uuid.uuid4().hex[:12]}"
+
+        doc = {
+            "session_id": session_id,
+            "user_id": user_id,
+            "role": role,
+            "content": content,
+            "timestamp": time.time(),
+            "embedding": embedding,
+        }
+
+        await glide_json.set(client, doc_id, "$", json.dumps(doc))
+        return doc_id
+
+    # --- Block 3 ---
+    async def search_memory(client, query, limit=5, user_id=None):
+        """Find semantically similar messages across all sessions."""
+        from glide import FtSearchOptions
+
+        query_embedding = get_embedding(query)
+        vec_bytes = struct.pack(f"<{len(query_embedding)}f", *query_embedding)
+
+        # Build filter - optionally scope to a user
+        filter_expr = "*"
+        if user_id:
+            filter_expr = f"@user_id:{{{user_id}}}"
+
+        query_str = f"({filter_expr})==>[KNN {limit} @embedding $vec AS score]"
+
+        result = await ft.search(
+            client, "memory_idx", query_str,
+            options=FtSearchOptions(params={"vec": vec_bytes}),
+        )
+
+        # Parse results
+        memories = []
+        if result and len(result) >= 2:
+            for key, fields in result[1].items():
+                doc = json.loads(fields[b"$"])
+                score = 1.0 - float(fields.get(b"score", 1))
+                memories.append({
+                    "content": doc["content"],
+                    "session_id": doc["session_id"],
+                    "role": doc["role"],
+                    "score": round(score, 3),
+                })
+        return memories
+
+    # --- Block 4 ---
+    async def demo():
+        config = GlideClientConfiguration([NodeAddress("localhost", 6379)])
+        client = await GlideClient.create(config)
+
+        await create_memory_index(client)
+
+        # Store memories from different sessions
+        await store_memory(client, "sess_1", "alice", "user",
+            "We deployed to ECS using a blue-green strategy")
+        await store_memory(client, "sess_2", "alice", "assistant",
+            "Valkey HNSW index provides sub-millisecond vector search")
+        await store_memory(client, "sess_3", "alice", "user",
+            "Our CI/CD pipeline runs on CodePipeline with canary deploys")
+
+        # Search across all sessions
+        results = await search_memory(client, "How do I deploy my service?")
+        print("🔍 Results for 'How do I deploy my service?':")
+        for r in results:
+            print(f"   [{r['score']:.3f}] ({r['session_id']}) {r['content']}")
+
+        # Output:
+        # [0.847] (sess_1) We deployed to ECS using a blue-green strategy
+        # [0.812] (sess_3) Our CI/CD pipeline runs on CodePipeline with canary deploys
+        # [0.234] (sess_2) Valkey HNSW index provides sub-millisecond vector search
+
diff --git a/tests/conversation_memory/test_04_semantic_caching.py b/tests/conversation_memory/test_04_semantic_caching.py
new file mode 100644
index 0000000..75d615f
--- /dev/null
+++ b/tests/conversation_memory/test_04_semantic_caching.py
@@ -0,0 +1,109 @@
+"""Integration tests for Conversation Memory - Semantic Caching.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_04_semantic_caching(raw_client):
+    """Run all code blocks from: Semantic Caching."""
+
+    # --- Block 1 ---
+    from glide import (
+        ft, VectorField, VectorAlgorithm, VectorFieldAttributesHnsw,
+        VectorType, DistanceMetricType, NumericField,
+        FtCreateOptions, DataType,
+    )
+
+    async def create_cache_index(client):
+        existing = await ft.list(client)
+        if b"cache_idx" in existing:
+            return
+
+        hnsw = VectorFieldAttributesHnsw(
+            dimensions=1536,
+            distance_metric=DistanceMetricType.COSINE,
+            type=VectorType.FLOAT32,
+        )
+        await ft.create(
+            client, "cache_idx",
+            schema=[
+                NumericField("$.created_at", alias="created_at"),
+                VectorField("$.embedding", VectorAlgorithm.HNSW,
+                            alias="embedding", attributes=hnsw),
+            ],
+            options=FtCreateOptions(data_type=DataType.JSON, prefixes=["llmcache:"]),
+        )
+
+    # --- Block 2 ---
+    import json, struct, hashlib, time
+    from glide import glide_json, FtSearchOptions
+
+    SIMILARITY_THRESHOLD = 0.90  # Tune this: higher = stricter matching
+
+    async def cache_lookup(client, query_embedding):
+        """Check if a semantically similar query was already answered."""
+        vec_bytes = struct.pack(f"<{len(query_embedding)}f", *query_embedding)
+
+        result = await ft.search(
+            client, "cache_idx",
+            "(*)==>[KNN 1 @embedding $vec AS score]",
+            options=FtSearchOptions(params={"vec": vec_bytes}),
+        )
+
+        if result and len(result) >= 2 and result[1]:
+            for key, fields in result[1].items():
+                score = 1.0 - float(fields[b"score"])
+                if score >= SIMILARITY_THRESHOLD:
+                    doc = json.loads(fields[b"$"])
+                    return {"hit": True, "response": doc["response"], "score": score}
+
+        return {"hit": False}
+
+    # --- Block 3 ---
+    async def cache_store(client, query, response, embedding, ttl=3600):
+        """Cache an LLM response with its query embedding."""
+        cache_id = hashlib.md5(query.encode()).hexdigest()[:12]
+        key = f"llmcache:{cache_id}"
+
+        doc = {
+            "query": query,
+            "response": response,
+            "embedding": embedding,
+            "created_at": time.time(),
+        }
+        await glide_json.set(client, key, "$", json.dumps(doc))
+        await client.expire(key, ttl)  # Auto-expire stale cache
+
+    # --- Block 4 ---
+    async def chat_with_cache(client, user_message):
+        # 1. Embed the query
+        embedding = get_embedding(user_message)
+
+        # 2. Check semantic cache
+        cached = await cache_lookup(client, embedding)
+        if cached["hit"]:
+            print(f"⚡ Cache HIT (similarity: {cached['score']:.3f})")
+            return cached["response"]
+
+        # 3. Cache miss - call the LLM
+        print("🔄 Cache MISS - calling LLM...")
+        response = call_llm(user_message)  # your LLM call here
+
+        # 4. Store in cache for next time
+        await cache_store(client, user_message, response, embedding)
+
+        return response
+
+
+    # First call: cache miss, calls LLM (~2 seconds)
+    await chat_with_cache(client, "What is Valkey?")
+    # 🔄 Cache MISS - calling LLM...
+
+    # Second call: cache hit, instant (~3ms)
+    await chat_with_cache(client, "Can you explain Valkey?")
+    # ⚡ Cache HIT (similarity: 0.953)
+
diff --git a/tests/conversation_memory/test_05_agent_state.py b/tests/conversation_memory/test_05_agent_state.py
new file mode 100644
index 0000000..e85d4d2
--- /dev/null
+++ b/tests/conversation_memory/test_05_agent_state.py
@@ -0,0 +1,145 @@
+"""Integration tests for Conversation Memory - Agent State.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_05_agent_state(client):
+    """Run all code blocks from: Agent State."""
+
+    # --- Block 1 ---
+    import json
+    from glide import GlideClient, GlideClientConfiguration, NodeAddress
+
+    async def save_checkpoint(client, run_id, step, data):
+        """Save agent state after completing a step."""
+        key = f"agent:state:{run_id}"
+        await client.hset(key, {
+            "current_step": str(step),
+            "status": "in_progress",
+            f"result_step_{step}": json.dumps(data),
+            "updated_at": str(time.time()),
+        })
+        await client.expire(key, 86400)  # 24h TTL
+
+
+    async def load_checkpoint(client, run_id):
+        """Load the last checkpoint to resume."""
+        key = f"agent:state:{run_id}"
+        state = await client.hgetall(key)
+        if not state:
+            return None
+        return {k.decode(): v.decode() for k, v in state.items()}
+
+
+    async def mark_complete(client, run_id):
+        await client.hset(f"agent:state:{run_id}", {"status": "complete"})
+
+    # --- Block 2 ---
+    import hashlib
+
+    async def cached_tool_call(client, tool_name, args, tool_fn, ttl=300):
+        """Call a tool, caching the result in Valkey."""
+        # Create a cache key from tool name + arguments
+        args_hash = hashlib.md5(json.dumps(args, sort_keys=True).encode()).hexdigest()[:12]
+        cache_key = f"tool:cache:{tool_name}:{args_hash}"
+
+        # Check cache first
+        cached = await client.get(cache_key)
+        if cached:
+            print(f"⚡ Tool cache HIT: {tool_name}")
+            return json.loads(cached)
+
+        # Cache miss - call the tool
+        print(f"🔄 Tool cache MISS: {tool_name} - calling...")
+        result = tool_fn(**args)
+
+        # Store with TTL
+        await client.set(cache_key, json.dumps(result))
+        await client.expire(cache_key, ttl)
+
+        return result
+
+
+    # Usage
+    result = await cached_tool_call(
+        client, "web_search",
+        {"query": "Valkey vector search benchmarks"},
+        tool_fn=web_search,
+        ttl=600,  # cache for 10 minutes
+    )
+
+    # --- Block 3 ---
+    async def log_action(client, run_id, action, details):
+        """Append an action to the agent's activity log."""
+        stream_key = f"agent:log:{run_id}"
+        await client.xadd(stream_key, [
+            ("action", action),
+            ("details", json.dumps(details)),
+            ("ts", str(time.time())),
+        ])
+
+
+    async def get_action_log(client, run_id):
+        """Read the full activity log for a run."""
+        from glide import StreamReadOptions
+        stream_key = f"agent:log:{run_id}"
+        result = await client.xread({stream_key: "0"}, StreamReadOptions(count=1000))
+        if not result:
+            return []
+
+        entries = []
+        for key, events in result.items():
+            for entry_id, fields in events.items():
+                entry = {k.decode(): v.decode() for k, v in fields}
+                entry["id"] = entry_id.decode()
+                entries.append(entry)
+        return entries
+
+    # --- Block 4 ---
+    async def run_agent(client, run_id, query):
+        # Check for existing checkpoint
+        checkpoint = await load_checkpoint(client, run_id)
+        start_step = int(checkpoint["current_step"]) + 1 if checkpoint else 1
+
+        if start_step > 1:
+            print(f"♻️  Resuming from step {start_step}")
+
+        # Step 1: Search
+        if start_step <= 1:
+            await log_action(client, run_id, "search", {"query": query})
+            results = await cached_tool_call(
+                client, "web_search", {"query": query}, web_search)
+            await save_checkpoint(client, run_id, 1, {"search_results": results})
+
+        # Step 2: Analyze
+        if start_step <= 2:
+            await log_action(client, run_id, "analyze", {"input": "search results"})
+            analysis = call_llm(f"Analyze: {results}")
+            await save_checkpoint(client, run_id, 2, {"analysis": analysis})
+
+        # Step 3: Respond
+        if start_step <= 3:
+            await log_action(client, run_id, "respond", {"status": "generating"})
+            response = call_llm(f"Respond based on: {analysis}")
+            await save_checkpoint(client, run_id, 3, {"response": response})
+
+        await mark_complete(client, run_id)
+        await log_action(client, run_id, "complete", {"status": "done"})
+        return response
+
+    # --- Block 5 ---
+    log = await get_action_log(client, "run_001")
+    for entry in log:
+        print(f"  [{entry['id']}] {entry['action']}: {entry['details']}")
+
+    # Output:
+    # [1710000000001-0] search: {"query": "Valkey benchmarks"}
+    # [1710000000002-0] analyze: {"input": "search results"}
+    # [1710000000003-0] respond: {"status": "generating"}
+    # [1710000000004-0] complete: {"status": "done"}
+
diff --git a/tests/crewai/__init__.py b/tests/crewai/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/crewai/test_01_getting_started.py b/tests/crewai/test_01_getting_started.py
new file mode 100644
index 0000000..1d40f0d
--- /dev/null
+++ b/tests/crewai/test_01_getting_started.py
@@ -0,0 +1,54 @@
+"""Integration tests for CrewAI - Getting Started with CrewAI + Valkey.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with CrewAI + Valkey."""
+
+    # --- Block 1 ---
+    from dataclasses import dataclass
+
+    @dataclass(frozen=True)
+    class ValkeyConfig:
+        host: str
+        port: int
+        use_tls: bool
+        password: str | None
+
+    def get_valkey_config() -> ValkeyConfig:
+        return ValkeyConfig(
+            host=os.environ.get("VALKEY_HOST", "localhost"),
+            port=int(os.environ.get("VALKEY_PORT", "6379")),
+            use_tls=os.environ.get("VALKEY_TLS", "false").lower() in ("true", "1"),
+            password=os.environ.get("VALKEY_PASSWORD") or None,
+        )
+
+    # --- Block 2 ---
+    import asyncio
+    from glide import GlideClient, GlideClientConfiguration, NodeAddress
+
+    async def test_connection():
+        config = get_valkey_config()
+        glide_config = GlideClientConfiguration(
+            [NodeAddress(config.host, config.port)],
+            use_tls=config.use_tls,
+        )
+        client = await GlideClient.create(glide_config)
+
+        # Test basic operations
+        await client.set("test:hello", "world")
+        val = await client.get("test:hello")
+        print(f"✅ Connected! Got: {val.decode()}")
+
+        # Clean up
+        await client.delete(["test:hello"])
+
+    asyncio.run(test_connection())
+    # ✅ Connected! Got: world
+
diff --git a/tests/crewai/test_02_memory_storage.py b/tests/crewai/test_02_memory_storage.py
new file mode 100644
index 0000000..27bebcc
--- /dev/null
+++ b/tests/crewai/test_02_memory_storage.py
@@ -0,0 +1,102 @@
+"""Integration tests for CrewAI - Memory Storage.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_02_memory_storage(raw_client):
+    """Run all code blocks from: Memory Storage."""
+
+    # --- Block 1 ---
+    from glide import (
+        ft, VectorField, VectorAlgorithm, VectorFieldAttributesHnsw,
+        VectorType, DistanceMetricType, NumericField, TextField,
+        FtCreateOptions, DataType,
+    )
+
+    INDEX_NAME = "memory_idx"
+    KEY_PREFIX = "memory:"
+    VECTOR_DIM = 1536  # Amazon Titan embeddings
+
+    async def ensure_index(client):
+        # Check if index already exists
+        existing = await ft.list(client)
+        if INDEX_NAME.encode() in existing:
+            return
+
+        hnsw = VectorFieldAttributesHnsw(
+            dimensions=VECTOR_DIM,
+            distance_metric=DistanceMetricType.COSINE,
+            type=VectorType.FLOAT32,
+        )
+        await ft.create(
+            client, INDEX_NAME,
+            schema=[
+                TextField("$.scope", alias="scope"),
+                TextField("$.categories_str", alias="categories_str"),
+                NumericField("$.importance", alias="importance"),
+                NumericField("$.created_at", alias="created_at"),
+                VectorField("$.embedding", VectorAlgorithm.HNSW,
+                            alias="embedding", attributes=hnsw),
+            ],
+            options=FtCreateOptions(data_type=DataType.JSON, prefixes=[KEY_PREFIX]),
+        )
+
+    # --- Block 2 ---
+    from glide import glide_json
+
+    async def store(client, record: MemoryRecord, ttl: int = 3600):
+        key = f"{KEY_PREFIX}{record.id}"
+        doc = serialize_record(record)
+        await glide_json.set(client, key, "$", json.dumps(doc))
+        await client.expire(key, ttl)
+
+    # --- Block 3 ---
+    JSON.SET memory:abc-123-def $ '{"id":"abc-123-def","content":"Always check for null...","embedding":[0.12,-0.45,...],...}'
+    EXPIRE memory:abc-123-def 3600
+
+    # --- Block 4 ---
+    import struct
+
+    async def recall(client, query_embedding: list[float], limit: int = 5):
+        # Pack embedding to bytes for FT.SEARCH
+        vec_bytes = struct.pack(f"<{len(query_embedding)}f", *query_embedding)
+
+        result = await ft.search(
+            client, INDEX_NAME,
+            f"(*)=>[KNN {limit} @embedding $vec AS score]",
+            options=FtSearchOptions(params={"vec": vec_bytes}),
+        )
+
+        # Parse results - score is cosine distance, convert to similarity
+        memories = []
+        if result and len(result) >= 2 and result[1]:
+            for key, fields in result[1].items():
+                doc = json.loads(fields[b"$"])
+                score = 1.0 - float(fields[b"score"])
+                memories.append((doc, score))
+
+        return sorted(memories, key=lambda x: x[1], reverse=True)
+
+    # --- Block 5 ---
+    async def search(client, query_embedding, scope: str = None, categories: list = None, limit: int = 5):
+        # Build filter expression
+        filters = []
+        if scope:
+            escaped = scope.replace("/", "\\/")
+            filters.append(f"@scope:{{{escaped}*}}")
+        if categories:
+            joined = "|".join(categories)
+            filters.append(f"@categories_str:{{{joined}}}")
+
+        filter_str = " ".join(filters) if filters else "*"
+        query = f"({filter_str})=>[KNN {limit} @embedding $vec AS score]"
+
+        vec_bytes = struct.pack(f"<{len(query_embedding)}f", *query_embedding)
+        result = await ft.search(client, INDEX_NAME, query, options=FtSearchOptions(params={"vec": vec_bytes}))
+        # ... parse results same as recall()
+
diff --git a/tests/crewai/test_03_agent_memory.py b/tests/crewai/test_03_agent_memory.py
new file mode 100644
index 0000000..992e495
--- /dev/null
+++ b/tests/crewai/test_03_agent_memory.py
@@ -0,0 +1,97 @@
+"""Integration tests for CrewAI - Agent Memory.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_agent_memory(client):
+    """Run all code blocks from: Agent Memory."""
+
+    # --- Block 1 ---
+    import boto3
+    from crewai.memory.unified_memory import Memory
+    from valkey_storage import ValkeyStorage
+
+    def build_memory() -> Memory:
+        session = boto3.Session(region_name="us-west-2")
+        return Memory(
+            storage=ValkeyStorage(),
+            llm="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+            embedder={
+                "provider": "amazon-bedrock",
+                "config": {
+                    "model_name": "amazon.titan-embed-text-v1",
+                    "session": session,
+                },
+            },
+        )
+
+    # --- Block 2 ---
+    from crewai import Agent, Crew, Task
+
+    memory = build_memory()
+
+    reviewer = Agent(
+        role="Senior Code Reviewer",
+        goal="Review code for quality, security, and best practices",
+        backstory="You are an experienced engineer who has reviewed thousands of PRs.",
+        memory=True,
+        verbose=True,
+    )
+
+    # --- Block 3 ---
+    # First crew run - agent learns patterns
+    learn_task = Task(
+        description="""Review this code and remember the patterns you find:
+        
+        def get_user(id):
+            user = db.query(f"SELECT * FROM users WHERE id = {id}")
+            return user
+        """,
+        expected_output="Code review with identified patterns",
+        agent=reviewer,
+    )
+
+    crew = Crew(
+        agents=[reviewer],
+        tasks=[learn_task],
+        memory=memory,
+        verbose=True,
+    )
+
+    result = crew.kickoff()
+    print(result)
+    # Agent identifies: SQL injection risk, no input validation, no error handling
+
+    # --- Block 4 ---
+    JSON.SET memory:7f3a-b2c1 $ '{"content":"SQL injection risk in get_user...","scope":"/code-review","embedding":[0.12,-0.45,...],...}'
+    EXPIRE memory:7f3a-b2c1 3600
+
+    # --- Block 5 ---
+    # Later crew run - agent recalls what it learned
+    recall_task = Task(
+        description="""Review this new code. Use your memory of past reviews:
+        
+        def delete_account(user_id):
+            db.execute(f"DELETE FROM accounts WHERE user_id = {user_id}")
+            return {"status": "deleted"}
+        """,
+        expected_output="Code review informed by past patterns",
+        agent=reviewer,
+    )
+
+    crew2 = Crew(
+        agents=[reviewer],
+        tasks=[recall_task],
+        memory=memory,  # Same memory - recalls from Valkey
+        verbose=True,
+    )
+
+    result2 = crew2.kickoff()
+    print(result2)
+    # Agent recalls the SQL injection pattern from the first review!
+    # "I've seen this pattern before - f-string SQL is vulnerable to injection..."
+
diff --git a/tests/feature_store/__init__.py b/tests/feature_store/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/feature_store/test_01_getting_started.py b/tests/feature_store/test_01_getting_started.py
new file mode 100644
index 0000000..db41677
--- /dev/null
+++ b/tests/feature_store/test_01_getting_started.py
@@ -0,0 +1,83 @@
+"""Integration tests for Feature Store - Getting Started with Feature Store.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Feature Store."""
+
+    # --- Block 1 ---
+    import time
+
+    client = client
+
+    # Write features for a user
+    key = "fs:v1:user_profile:user_001"
+    features = {
+        "age": "28",
+        "lifetime_value": "1250.50",
+        "segment": "premium",
+        "_updated_at": str(time.time()),
+        "_feature_view": "user_profile",
+    }
+
+    # HSET - write all features atomically
+    client.hset(key, mapping=features)
+
+    # Set TTL - features expire after 1 hour
+    client.expire(key, 3600)
+
+    print("✅ Features written")
+
+    # --- Block 2 ---
+    # HGETALL - read all features for an entity
+    result = client.hgetall("fs:v1:user_profile:user_001")
+    print(result)
+    # {'age': '28', 'lifetime_value': '1250.50', 'segment': 'premium', ...}
+
+    # HMGET - read only specific features
+    age, ltv = client.hmget("fs:v1:user_profile:user_001", ["age", "lifetime_value"])
+    print(f"age={age}, ltv={ltv}")
+    # age=28, ltv=1250.50
+
+    # --- Block 3 ---
+    from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
+
+    # Connect
+    store = ValkeyFeatureStore(host="localhost", port=6379)
+
+    # Define an entity
+    user = Entity(name="user", join_keys=["user_id"])
+
+    # Define a feature view (schema for this entity's features)
+    user_features = FeatureView(
+        name="user_profile",
+        entity=user,
+        features=[
+            Feature("age", FeatureType.INT),
+            Feature("lifetime_value", FeatureType.FLOAT),
+            Feature("segment", FeatureType.STRING),
+        ],
+        ttl=3600,  # 1 hour
+    )
+
+    # Register it with the store
+    store.register(user_features)
+
+    # Write - automatically serializes, sets TTL, adds metadata
+    store.write("user_profile", "user_001", {
+        "age": 28,
+        "lifetime_value": 1250.50,
+        "segment": "premium",
+    })
+
+    # Read - automatically deserializes to correct Python types
+    features = store.read("user_profile", "user_001")
+    print(features)
+    # {'age': 28, 'lifetime_value': 1250.5, 'segment': 'premium'}
+    # Note: age is int, ltv is float - types are preserved!
+
diff --git a/tests/feature_store/test_02_online_serving.py b/tests/feature_store/test_02_online_serving.py
new file mode 100644
index 0000000..73c2fb2
--- /dev/null
+++ b/tests/feature_store/test_02_online_serving.py
@@ -0,0 +1,98 @@
+"""Integration tests for Feature Store - Online Feature Serving.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_online_serving(client):
+    """Run all code blocks from: Online Feature Serving."""
+
+    # --- Block 1 ---
+    # Prediction request arrives
+    user_id = "user_123"
+
+    # 1. Fetch features from online store  ← This must be FAST
+    # 2. Assemble feature vector
+    # 3. Run model.predict(features)
+    # 4. Return prediction
+
+    # --- Block 2 ---
+
+    client = client
+
+    # Fetch all features for user_123
+    raw = client.hgetall("fs:v1:user_profile:user_123")
+    # {'age': '28', 'lifetime_value': '1250.50', 'segment': 'premium', ...}
+
+    # Filter out internal metadata
+    features = {k: v for k, v in raw.items() if not k.startswith("_")}
+    # {'age': '28', 'lifetime_value': '1250.50', 'segment': 'premium'}
+
+    # --- Block 3 ---
+    from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
+
+    store = ValkeyFeatureStore(host="localhost", port=6379)
+    # ... register feature views ...
+
+    # All features (uses HGETALL internally)
+    features = store.read("user_profile", "user_123")
+    # {'age': 28, 'lifetime_value': 1250.5, 'segment': 'premium'}
+
+    # Selective features (uses HMGET internally)
+    features = store.read("user_profile", "user_123", ["age", "lifetime_value"])
+    # {'age': 28, 'lifetime_value': 1250.5}
+
+    # --- Block 4 ---
+    entity_ids = [f"user_{i:03d}" for i in range(100)]
+
+    # Pipeline sends all commands in ONE round-trip
+    pipe = client.pipeline(transaction=False)
+    for eid in entity_ids:
+        pipe.hgetall(f"fs:v1:user_profile:{eid}")
+
+    # Execute all 100 HGETALL commands at once
+    results = pipe.execute()
+
+    # Map results back to entity IDs
+    batch = {}
+    for eid, raw in zip(entity_ids, results):
+        if raw:
+            batch[eid] = {k: v for k, v in raw.items() if not k.startswith("_")}
+
+    print(f"Fetched features for {len(batch)} entities")
+
+    # --- Block 5 ---
+    # Batch read - uses pipeline internally
+    entity_ids = [f"user_{i:03d}" for i in range(100)]
+    batch = store.read_batch("user_profile", entity_ids)
+
+    # Returns: {'user_000': {'age': 28, ...}, 'user_001': {'age': 35, ...}, ...}
+    print(f"Fetched {len(batch)} entities")
+
+    # --- Block 6 ---
+    user_id = "user_123"
+
+    pipe = client.pipeline(transaction=False)
+    # Fetch from user_profile view
+    pipe.hmget(f"fs:v1:user_profile:{user_id}", ["age", "lifetime_value"])
+    # Fetch from user_risk view
+    pipe.hmget(f"fs:v1:user_risk_profile:{user_id}", ["fraud_score", "txn_count_24h"])
+
+    results = pipe.execute()
+    # results[0] = ['28', '1250.50']       ← from user_profile
+    # results[1] = ['0.05', '12']           ← from user_risk_profile
+
+    # --- Block 7 ---
+    # Set during write (the library does this automatically)
+    pipe = client.pipeline(transaction=True)
+    pipe.hset(key, mapping=features)
+    pipe.expire(key, 3600)  # 1 hour TTL
+    pipe.execute()
+
+    # Check remaining TTL
+    ttl = client.ttl("fs:v1:user_profile:user_123")
+    print(f"Expires in {ttl} seconds")
+
diff --git a/tests/feature_store/test_03_realtime_aggregations.py b/tests/feature_store/test_03_realtime_aggregations.py
new file mode 100644
index 0000000..2453e8a
--- /dev/null
+++ b/tests/feature_store/test_03_realtime_aggregations.py
@@ -0,0 +1,139 @@
+"""Integration tests for Feature Store - Real-time Aggregations.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_realtime_aggregations(client):
+    """Run all code blocks from: Real-time Aggregations."""
+
+    # --- Block 1 ---
+    import time
+    import uuid
+
+    client = client
+
+    def record_event(user_id: str, window_seconds: int = 3600):
+        """Record an event and return the count in the sliding window."""
+        key = f"fs:agg:txn_count:{user_id}"
+        now = time.time()
+        cutoff = now - window_seconds
+
+        pipe = client.pipeline(transaction=True)
+
+        # 1. Add this event with timestamp as score
+        pipe.zadd(key, {str(uuid.uuid4()): now})
+
+        # 2. Remove events outside the window
+        pipe.zremrangebyscore(key, "-inf", cutoff)
+
+        # 3. Count remaining events in window
+        pipe.zcard(key)
+
+        # 4. Set TTL so the key self-cleans if user goes inactive
+        pipe.expire(key, window_seconds * 2)
+
+        results = pipe.execute()
+        count = results[2]  # zcard result
+
+        return count
+
+    # Usage
+    for i in range(5):
+        count = record_event("user_001", window_seconds=60)
+        print(f"Transactions in last 60s: {count}")
+    # Transactions in last 60s: 1
+    # Transactions in last 60s: 2
+    # Transactions in last 60s: 3
+    # ...
+
+    # --- Block 2 ---
+    def update_running_average(user_id: str, amount: float):
+        """Update running average and return new value."""
+        sum_key = f"fs:agg:txn_sum:{user_id}"
+        count_key = f"fs:agg:txn_count_total:{user_id}"
+
+        pipe = client.pipeline(transaction=True)
+        pipe.incrbyfloat(sum_key, amount)
+        pipe.incr(count_key)
+        pipe.expire(sum_key, 86400)   # 24h TTL
+        pipe.expire(count_key, 86400)
+        results = pipe.execute()
+
+        total_sum = float(results[0])
+        total_count = int(results[1])
+        avg = total_sum / total_count if total_count > 0 else 0
+
+        return {"sum": total_sum, "count": total_count, "average": round(avg, 2)}
+
+    # Usage
+    amounts = [25.00, 150.00, 12.50, 89.99]
+    for amt in amounts:
+        result = update_running_average("user_001", amt)
+        print(f"${amt:>7.2f} → avg=${result['average']}")
+    # $ 25.00 → avg=$25.0
+    # $150.00 → avg=$87.5
+    # $ 12.50 → avg=$62.5
+    # $ 89.99 → avg=$69.37
+
+    # --- Block 3 ---
+    def record_unique_merchant(user_id: str, merchant_id: str):
+        """Track unique merchants and return approximate count."""
+        key = f"fs:agg:unique_merchants:{user_id}"
+
+        pipe = client.pipeline(transaction=True)
+        pipe.pfadd(key, merchant_id)     # Add to HyperLogLog
+        pipe.pfcount(key)                # Get approximate count
+        pipe.expire(key, 86400)          # 24h TTL
+        results = pipe.execute()
+
+        return results[1]  # pfcount result
+
+    # Usage
+    merchants = ["amazon", "walmart", "amazon", "target", "starbucks", "amazon"]
+    for m in merchants:
+        count = record_unique_merchant("user_001", m)
+        print(f"After {m}: {count} unique merchants")
+    # After amazon: 1 unique merchants
+    # After walmart: 2 unique merchants
+    # After amazon: 2 unique merchants   ← duplicate, no change
+    # After target: 3 unique merchants
+    # After starbucks: 4 unique merchants
+    # After amazon: 4 unique merchants   ← duplicate, no change
+
+    # --- Block 4 ---
+    def materialize_user_features(user_id: str):
+        """Compute aggregations and write to the feature store Hash."""
+        pipe = client.pipeline(transaction=False)
+
+        # Read aggregation state
+        now = time.time()
+        pipe.zcount(f"fs:agg:txn_count:{user_id}", now - 3600, now)
+        pipe.get(f"fs:agg:txn_sum:{user_id}")
+        pipe.get(f"fs:agg:txn_count_total:{user_id}")
+        pipe.pfcount(f"fs:agg:unique_merchants:{user_id}")
+        results = pipe.execute()
+
+        txn_count_1h = results[0] or 0
+        txn_sum = float(results[1] or 0)
+        txn_total = int(results[2] or 0)
+        unique_merchants = results[3] or 0
+
+        avg_amount = txn_sum / txn_total if txn_total > 0 else 0
+
+        # Write to feature store Hash
+        feature_key = f"fs:v1:user_risk_profile:{user_id}"
+        features = {
+            "txn_count_1h": str(txn_count_1h),
+            "avg_txn_amount": str(round(avg_amount, 2)),
+            "unique_merchants_24h": str(unique_merchants),
+            "_updated_at": str(now),
+        }
+        client.hset(feature_key, mapping=features)
+        client.expire(feature_key, 86400)
+
+        return features
+
diff --git a/tests/feature_store/test_04_streaming_updates.py b/tests/feature_store/test_04_streaming_updates.py
new file mode 100644
index 0000000..2a5b597
--- /dev/null
+++ b/tests/feature_store/test_04_streaming_updates.py
@@ -0,0 +1,159 @@
+"""Integration tests for Feature Store - Streaming Updates.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_04_streaming_updates(client):
+    """Run all code blocks from: Streaming Updates."""
+
+    # --- Block 1 ---
+    import json
+    import time
+
+    client = client
+
+    # Publish a feature update to the stream
+    stream_key = "fs:stream:user_profile"
+    message = {
+        "entity_id": "user_001",
+        "features": json.dumps({"age": 28, "segment": "premium"}),
+        "timestamp": str(time.time()),
+        "feature_view": "user_profile",
+    }
+
+    # XADD with MAXLEN to cap stream size
+    entry_id = client.xadd(stream_key, message, maxlen=10000)
+    print(f"Published: {entry_id}")
+    # Published: 1710000000000-0
+
+    # --- Block 2 ---
+    from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
+
+    store = ValkeyFeatureStore(host="localhost", port=6379)
+
+    user = Entity(name="user", join_keys=["user_id"])
+    user_features = FeatureView(
+        name="user_profile",
+        entity=user,
+        features=[
+            Feature("age", FeatureType.INT),
+            Feature("segment", FeatureType.STRING),
+        ],
+        ttl=3600,
+    )
+    store.register(user_features)
+
+    # Publish via streaming (uses XADD internally)
+    entry_id = store.streaming.publish(
+        user_features,
+        "user_001",
+        {"age": 28, "segment": "premium"},
+        maxlen=10000,
+    )
+    print(f"Published: {entry_id}")
+
+    # --- Block 3 ---
+    def consume_features(stream_key: str, last_id: str = "0-0"):
+        """Read stream entries and write to online store."""
+        entries = client.xread({stream_key: last_id}, count=100, block=1000)
+
+        if not entries:
+            return last_id
+
+        new_last_id = last_id
+        for stream, messages in entries:
+            for msg_id, data in messages:
+                entity_id = data["entity_id"]
+                features = json.loads(data["features"])
+                timestamp = float(data["timestamp"])
+                view_name = data["feature_view"]
+
+                # Write to online store Hash
+                feature_key = f"fs:v1:{view_name}:{entity_id}"
+                serialized = {k: str(v) for k, v in features.items()}
+                serialized["_updated_at"] = str(timestamp)
+                serialized["_feature_view"] = view_name
+                client.hset(feature_key, mapping=serialized)
+
+                new_last_id = msg_id
+                print(f"Materialized {entity_id}: {features}")
+
+        return new_last_id
+
+    # Run consumer loop
+    last_id = "0-0"
+    while True:
+        last_id = consume_features("fs:stream:user_profile", last_id)
+
+    # --- Block 4 ---
+    # One-shot consume: read pending entries and write to Hashes
+    last_id = store.streaming.consume_once(
+        user_features,
+        last_id="0-0",   # Start from beginning
+        count=100,        # Process up to 100 messages
+        block_ms=1000,    # Block for 1 second if no messages
+    )
+
+    # Or start a background consumer thread
+    store.streaming.start_consumer(user_features)
+    # Consumer runs in background, materializing features as they arrive
+
+    # Later, stop it
+    store.streaming.stop_consumer()
+
+    # --- Block 5 ---
+    # Publish 1000 entities in one pipeline
+    entity_features = {
+        f"user_{i:03d}": {"age": 20 + i % 50, "segment": "premium"}
+        for i in range(1000)
+    }
+
+    count = store.streaming.publish_batch(
+        user_features,
+        entity_features,
+        maxlen=10000,
+    )
+    print(f"Published {count} entities to stream")
+    # Published 1000 entities to stream
+
+    # --- Block 6 ---
+    # Create a consumer group
+    stream_key = "fs:stream:user_profile"
+    try:
+        client.xgroup_create(stream_key, "feature_writers", id="0", mkstream=True)
+    except valkey.ResponseError:
+        pass  # Group already exists
+
+    # Consumer reads from the group
+    def consume_from_group(consumer_name: str):
+        entries = client.xreadgroup(
+            "feature_writers",  # group name
+            consumer_name,            # consumer name
+            {stream_key: ">"},        # ">" means new messages only
+            count=50,
+            block=2000,
+        )
+
+        if not entries:
+            return
+
+        for stream, messages in entries:
+            for msg_id, data in messages:
+                entity_id = data["entity_id"]
+                features = json.loads(data["features"])
+
+                # Write to online store
+                feature_key = f"fs:v1:user_profile:{entity_id}"
+                client.hset(feature_key, mapping={k: str(v) for k, v in features.items()})
+
+                # Acknowledge processing
+                client.xack(stream_key, "feature_writers", msg_id)
+
+    # Run workers (each on a different process/machine)
+    consume_from_group("worker-1")
+    consume_from_group("worker-2")
+
diff --git a/tests/feature_store/test_05_ml_integration.py b/tests/feature_store/test_05_ml_integration.py
new file mode 100644
index 0000000..d7ef90d
--- /dev/null
+++ b/tests/feature_store/test_05_ml_integration.py
@@ -0,0 +1,239 @@
+"""Integration tests for Feature Store - ML Integration.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_05_ml_integration(raw_client):
+    """Run all code blocks from: ML Integration."""
+
+    # --- Block 1 ---
+    import numpy as np
+    from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
+
+    # Setup store (same as Cookbook 01)
+    store = ValkeyFeatureStore(host="localhost", port=6379)
+
+    user = Entity(name="user", join_keys=["user_id"])
+    risk_features = FeatureView(
+        name="user_risk_profile",
+        entity=user,
+        features=[
+            Feature("txn_count_1h", FeatureType.INT),
+            Feature("avg_txn_amount", FeatureType.FLOAT),
+            Feature("unique_merchants_24h", FeatureType.INT),
+            Feature("fraud_score", FeatureType.FLOAT),
+        ],
+        ttl=86400,
+    )
+    store.register(risk_features)
+
+    # Seed some features
+    store.write("user_risk_profile", "user_001", {
+        "txn_count_1h": 12,
+        "avg_txn_amount": 85.50,
+        "unique_merchants_24h": 4,
+        "fraud_score": 0.15,
+    })
+
+    # ── Inference Time ─────────────────────────────────────────────────
+
+    def predict_fraud(user_id: str) -> dict:
+        """Fetch features from Valkey and run fraud prediction."""
+
+        # 1. Fetch features (~0.1ms)
+        features = store.read(
+            "user_risk_profile",
+            user_id,
+            ["txn_count_1h", "avg_txn_amount", "unique_merchants_24h"],
+        )
+
+        if not features:
+            return {"error": "No features found"}
+
+        # 2. Build NumPy feature vector
+        vector = np.array([[
+            features["txn_count_1h"],
+            features["avg_txn_amount"],
+            features["unique_merchants_24h"],
+        ]])
+
+        # 3. Run model (replace with your trained model)
+        # prediction = model.predict_proba(vector)[0][1]
+        # For demo, use a simple heuristic:
+        score = 0.1
+        if features["txn_count_1h"] > 10: score += 0.3
+        if features["avg_txn_amount"] > 200: score += 0.2
+        if features["unique_merchants_24h"] > 8: score += 0.2
+
+        return {
+            "user_id": user_id,
+            "fraud_probability": round(score, 3),
+            "is_fraud": score > 0.5,
+            "features_used": features,
+        }
+
+    result = predict_fraud("user_001")
+    print(result)
+    # {'user_id': 'user_001', 'fraud_probability': 0.4, 'is_fraud': False,
+    #  'features_used': {'txn_count_1h': 12, 'avg_txn_amount': 85.5, ...}}
+
+    # --- Block 2 ---
+    # Register item features
+    item = Entity(name="item", join_keys=["item_id"])
+    item_features = FeatureView(
+        name="item_catalog",
+        entity=item,
+        features=[
+            Feature("price", FeatureType.FLOAT),
+            Feature("category", FeatureType.STRING),
+            Feature("popularity", FeatureType.FLOAT),
+        ],
+        ttl=86400,
+    )
+    store.register(item_features)
+
+    # Write item features
+    store.write("item_catalog", "item_042", {
+        "price": 29.99,
+        "category": "electronics",
+        "popularity": 0.85,
+    })
+
+    # ── Recommendation inference ──────────────────────────────────────
+
+    def score_item_for_user(user_id: str, item_id: str) -> float:
+        """Score how likely a user is to click on an item."""
+
+        # Fetch user features
+        user_feats = store.read("user_risk_profile", user_id,
+                                ["avg_txn_amount"])
+
+        # Fetch item features
+        item_feats = store.read("item_catalog", item_id,
+                                ["price", "popularity"])
+
+        if not user_feats or not item_feats:
+            return 0.0
+
+        # Simple scoring: users who spend more prefer pricier items
+        price_affinity = min(1.0, user_feats["avg_txn_amount"] / (item_feats["price"] * 10))
+        score = price_affinity * 0.4 + item_feats["popularity"] * 0.6
+
+        return round(score, 3)
+
+    # Score candidates
+    items = ["item_042", "item_043", "item_044"]
+    scores = [(item_id, score_item_for_user("user_001", item_id)) for item_id in items]
+    ranked = sorted(scores, key=lambda x: x[1], reverse=True)
+    print(ranked)
+
+    # --- Block 3 ---
+    from fastapi import FastAPI, HTTPException
+    from pydantic import BaseModel
+    import time
+
+    app = FastAPI()
+
+    # Initialize store once at startup
+    store = ValkeyFeatureStore(host="localhost", port=6379)
+    # ... register feature views ...
+
+    class PredictionResponse(BaseModel):
+        user_id: str
+        fraud_probability: float
+        is_fraud: bool
+        latency_ms: float
+
+    @app.get("/predict/fraud/{user_id}")
+    async def predict_fraud_endpoint(user_id: str):
+        start = time.time()
+
+        # Fetch features from Valkey
+        features = store.read(
+            "user_risk_profile", user_id,
+            ["txn_count_1h", "avg_txn_amount", "unique_merchants_24h"],
+        )
+
+        if not features:
+            raise HTTPException(status_code=404, detail="No features found")
+
+        # Run model prediction
+        score = 0.1
+        if features.get("txn_count_1h", 0) > 10: score += 0.3
+        if features.get("avg_txn_amount", 0) > 200: score += 0.2
+        if features.get("unique_merchants_24h", 0) > 8: score += 0.2
+
+        elapsed = (time.time() - start) * 1000
+
+        return PredictionResponse(
+            user_id=user_id,
+            fraud_probability=round(score, 3),
+            is_fraud=score > 0.5,
+            latency_ms=round(elapsed, 2),
+        )
+
+    # Run: uvicorn app:app --reload
+    # Test: curl localhost:8000/predict/fraud/user_001
+
+    # --- Block 4 ---
+    def build_personalized_prompt(user_id: str, question: str) -> str:
+        """Fetch user features and build a personalized LLM prompt."""
+
+        # Fetch user context from Valkey
+        features = store.read("user_risk_profile", user_id)
+
+        if features:
+            context = f"""User context:
+    - Average transaction: ${features.get('avg_txn_amount', 'unknown')}
+    - Transaction frequency: {features.get('txn_count_1h', 'unknown')}/hour
+    - Merchant diversity: {features.get('unique_merchants_24h', 'unknown')} unique merchants"""
+        else:
+            context = "No user context available."
+
+        prompt = f"""{context}
+
+    User question: {question}
+
+    Please provide a personalized response based on the user's profile."""
+
+        return prompt
+
+    # Usage
+    prompt = build_personalized_prompt("user_001", "Should I upgrade my account?")
+    print(prompt)
+    # Pass to your LLM: response = openai.chat(prompt)
+
+    # --- Block 5 ---
+    # Define a view with vector features
+    embedding_view = FeatureView(
+        name="user_embeddings",
+        entity=user,
+        features=[
+            Feature("embedding", FeatureType.VECTOR),
+            Feature("model_version", FeatureType.STRING),
+        ],
+        ttl=604800,  # 7 days
+    )
+    store.register(embedding_view)
+
+    # Write an embedding vector
+    store.write("user_embeddings", "user_001", {
+        "embedding": [0.1, 0.23, -0.05, 0.87, 0.42],  # list → comma-separated
+        "model_version": "v2.1",
+    })
+
+    # Read embedding back (auto-deserializes to list of floats)
+    result = store.read("user_embeddings", "user_001")
+    print(result["embedding"])
+    # [0.1, 0.23, -0.05, 0.87, 0.42]
+
+    # Convert to NumPy for model input
+    embedding = np.array(result["embedding"])
+    print(embedding.shape)
+    # (5,)
+
diff --git a/tests/feature_store/test_06_production.py b/tests/feature_store/test_06_production.py
new file mode 100644
index 0000000..d6a9114
--- /dev/null
+++ b/tests/feature_store/test_06_production.py
@@ -0,0 +1,249 @@
+"""Integration tests for Feature Store - Production Patterns.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_06_production(client):
+    """Run all code blocks from: Production Patterns."""
+
+    # --- Block 1 ---
+    import time
+
+    client = client
+
+    def check_freshness(feature_view: str, entity_id: str, threshold_seconds: float = 300):
+        """Check if features are fresh enough for inference."""
+        key = f"fs:v1:{feature_view}:{entity_id}"
+        updated_at = client.hget(key, "_updated_at")
+
+        if updated_at is None:
+            return {"status": "missing", "age_seconds": None}
+
+        age = time.time() - float(updated_at)
+
+        return {
+            "status": "fresh" if age < threshold_seconds else "stale",
+            "age_seconds": round(age, 1),
+            "threshold": threshold_seconds,
+        }
+
+    # Check a single entity
+    result = check_freshness("user_risk_profile", "user_001", threshold_seconds=60)
+    print(result)
+    # {'status': 'fresh', 'age_seconds': 12.3, 'threshold': 60}
+
+    # --- Block 2 ---
+    def batch_freshness_check(feature_view: str, entity_ids: list, threshold: float = 300):
+        """Check freshness for multiple entities in one pipeline."""
+        pipe = client.pipeline(transaction=False)
+        for eid in entity_ids:
+            pipe.hget(f"fs:v1:{feature_view}:{eid}", "_updated_at")
+
+        results = pipe.execute()
+        now = time.time()
+        report = {"fresh": 0, "stale": 0, "missing": 0, "stale_entities": []}
+
+        for eid, updated_at in zip(entity_ids, results):
+            if updated_at is None:
+                report["missing"] += 1
+            elif now - float(updated_at) > threshold:
+                report["stale"] += 1
+                report["stale_entities"].append(eid)
+            else:
+                report["fresh"] += 1
+
+        return report
+
+    # Check 100 entities
+    ids = [f"user_{i:03d}" for i in range(100)]
+    report = batch_freshness_check("user_risk_profile", ids, threshold=60)
+    print(report)
+    # {'fresh': 85, 'stale': 10, 'missing': 5, 'stale_entities': [...]}
+
+    # --- Block 3 ---
+    from src import ValkeyFeatureStore, Entity, FeatureView, Feature, FeatureType
+
+    store = ValkeyFeatureStore(host="localhost", port=6379)
+    # ... register feature views ...
+
+    # The monitor module provides freshness checking
+    fv = store.get_feature_view("user_risk_profile")
+    report = store.monitor.check_freshness(
+        fv,
+        entity_ids=["user_001", "user_002"],
+        threshold_seconds=300,
+    )
+    print(report)
+
+    # --- Block 4 ---
+    def health_check() -> dict:
+        """Full health check for the feature store."""
+        health = {"status": "healthy", "checks": {}}
+
+        # 1. Valkey connectivity
+        try:
+            start = time.time()
+            client.ping()
+            latency = (time.time() - start) * 1000
+            health["checks"]["valkey_ping"] = {
+                "status": "ok",
+                "latency_ms": round(latency, 2),
+            }
+        except Exception as e:
+            health["status"] = "unhealthy"
+            health["checks"]["valkey_ping"] = {"status": "failed", "error": str(e)}
+
+        # 2. Write/read test
+        try:
+            test_key = "fs:health_check"
+            client.set(test_key, "ok", ex=10)
+            val = client.get(test_key)
+            health["checks"]["write_read"] = {"status": "ok" if val == "ok" else "failed"}
+        except Exception as e:
+            health["status"] = "unhealthy"
+            health["checks"]["write_read"] = {"status": "failed", "error": str(e)}
+
+        # 3. Memory usage
+        try:
+            info = client.info("memory")
+            used_mb = info["used_memory"] / (1024 * 1024)
+            max_mb = info.get("maxmemory", 0) / (1024 * 1024)
+            health["checks"]["memory"] = {
+                "used_mb": round(used_mb, 1),
+                "max_mb": round(max_mb, 1) if max_mb > 0 else "unlimited",
+                "status": "ok",
+            }
+        except Exception as e:
+            health["checks"]["memory"] = {"status": "failed"}
+
+        return health
+
+    print(health_check())
+    # {'status': 'healthy', 'checks': {'valkey_ping': {'status': 'ok', 'latency_ms': 0.12}, ...}}
+
+    # --- Block 5 ---
+    # Built-in health check
+    health = store.health()
+    print(health)
+
+    # Full info including registered views
+    info = store.info()
+    print(info)
+    # {'registered_views': ['user_profile', 'user_risk_profile'], 'health': {...}}
+
+    # --- Block 6 ---
+    # Current: fs:v1:user_profile:user_001
+    # New:     fs:v2:user_profile:user_001
+
+    # Step 1: Create v2 feature view with new schema
+    user_features_v2 = FeatureView(
+        name="user_profile",
+        entity=user,
+        features=[
+            Feature("age", FeatureType.INT),
+            Feature("lifetime_value", FeatureType.FLOAT),
+            Feature("segment", FeatureType.STRING),
+            Feature("churn_risk", FeatureType.FLOAT),  # ← New feature
+        ],
+        version="v2",  # ← Version bump
+        ttl=3600,
+    )
+
+    # Step 2: Dual-write during migration
+    def dual_write(entity_id, features, features_v2):
+        """Write to both v1 and v2 during migration."""
+        pipe = client.pipeline(transaction=False)
+
+        # Write v1
+        key_v1 = f"fs:v1:user_profile:{entity_id}"
+        pipe.hset(key_v1, mapping={k: str(v) for k, v in features.items()})
+        pipe.expire(key_v1, 3600)
+
+        # Write v2 (includes new fields)
+        key_v2 = f"fs:v2:user_profile:{entity_id}"
+        pipe.hset(key_v2, mapping={k: str(v) for k, v in features_v2.items()})
+        pipe.expire(key_v2, 3600)
+
+        pipe.execute()
+
+    # Step 3: Migrate readers to v2
+    # Step 4: Stop writing to v1
+    # Step 5: Let v1 keys expire naturally (TTL)
+
+    # --- Block 7 ---
+
+    # Create a connection pool (once at app startup)
+    pool = valkey.ConnectionPool(
+        host="localhost",
+        port=6379,
+        max_connections=50,    # Max concurrent connections
+        decode_responses=True,
+        socket_connect_timeout=5,
+        socket_timeout=2,
+        retry_on_timeout=True,
+    )
+
+    # Create client from pool (cheap - reuses connections)
+    client = client
+
+    # Use with the feature store library
+    store = ValkeyFeatureStore(client=client)
+
+    # Check pool stats
+    print(f"Pool connections: {len(pool._available_connections)}")
+
+    # --- Block 8 ---
+    def safe_read_features(store, view_name: str, entity_id: str, defaults: dict = None):
+        """Read features with error handling and fallback defaults."""
+        try:
+            features = store.read(view_name, entity_id)
+            if features:
+                return features
+        except valkey.ConnectionError:
+            # Valkey is down - use defaults
+            print(f"⚠️ Valkey connection error, using defaults")
+        except valkey.TimeoutError:
+            # Valkey is slow - use defaults
+            print(f"⚠️ Valkey timeout, using defaults")
+        except Exception as e:
+            print(f"⚠️ Unexpected error: {e}")
+
+        # Return defaults so the model can still make a prediction
+        return defaults or {}
+
+    # Usage
+    features = safe_read_features(store, "user_risk_profile", "user_001",
+        defaults={"txn_count_1h": 0, "avg_txn_amount": 50.0, "fraud_score": 0.5})
+
+    # --- Block 9 ---
+    def get_store_metrics() -> dict:
+        """Collect feature store metrics for monitoring dashboards."""
+        info = client.info()
+
+        return {
+            # Throughput
+            "ops_per_second": info.get("instantaneous_ops_per_sec", 0),
+
+            # Memory
+            "memory_used_mb": round(info["used_memory"] / 1048576, 1),
+            "memory_peak_mb": round(info["used_memory_peak"] / 1048576, 1),
+
+            # Connections
+            "connected_clients": info.get("connected_clients", 0),
+
+            # Keys
+            "total_keys": info.get("db0", {}).get("keys", 0),
+
+            # Hit rate (if using Valkey as cache too)
+            "keyspace_hits": info.get("keyspace_hits", 0),
+            "keyspace_misses": info.get("keyspace_misses", 0),
+        }
+
+    metrics = get_store_metrics()
+    print(metrics)
+    # {'ops_per_second': 1250, 'memory_used_mb': 45.2, ...}
+
diff --git a/tests/haystack/__init__.py b/tests/haystack/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/haystack/test_01_getting_started.py b/tests/haystack/test_01_getting_started.py
new file mode 100644
index 0000000..fd56b02
--- /dev/null
+++ b/tests/haystack/test_01_getting_started.py
@@ -0,0 +1,65 @@
+"""Integration tests for Haystack - Getting Started with Haystack + Valkey.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Haystack + Valkey."""
+
+    # --- Block 1 ---
+    from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
+
+    document_store = ValkeyDocumentStore(
+        nodes_list=[("localhost", 6379)],
+        index_name="my_documents",
+        embedding_dim=768,        # must match your embedding model
+        distance_metric="cosine", # cosine | l2 | ip
+    )
+
+    print(document_store.count_documents())  # 0
+
+    # --- Block 2 ---
+    from haystack import Document
+
+    docs = [
+        Document(content="Valkey is a high-performance in-memory data store."),
+        Document(content="Haystack is an open-source LLM framework by deepset."),
+        Document(content="Vector search finds semantically similar documents."),
+    ]
+
+    document_store.write_documents(docs)
+    print(document_store.count_documents())  # 3
+
+    # --- Block 3 ---
+    from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+    doc_embedder = SentenceTransformersDocumentEmbedder(
+        model="sentence-transformers/all-mpnet-base-v2"  # 768-dim, matches embedding_dim above
+    )
+    doc_embedder.warm_up()
+
+    docs_with_embeddings = doc_embedder.run(docs)["documents"]
+    document_store.write_documents(docs_with_embeddings, policy="overwrite")
+
+    # --- Block 4 ---
+    from haystack.components.embedders import SentenceTransformersTextEmbedder
+    from haystack_integrations.components.retrievers.valkey import ValkeyEmbeddingRetriever
+
+    text_embedder = SentenceTransformersTextEmbedder(
+        model="sentence-transformers/all-mpnet-base-v2"
+    )
+    text_embedder.warm_up()
+
+    retriever = ValkeyEmbeddingRetriever(document_store=document_store, top_k=2)
+
+    query = "What is Valkey used for?"
+    query_embedding = text_embedder.run(query)["embedding"]
+    results = retriever.run(query_embedding=query_embedding)
+
+    for doc in results["documents"]:
+        print(f"Score: {doc.score:.3f} | {doc.content}")
+
diff --git a/tests/haystack/test_02_rag_pipeline.py b/tests/haystack/test_02_rag_pipeline.py
new file mode 100644
index 0000000..bd266e3
--- /dev/null
+++ b/tests/haystack/test_02_rag_pipeline.py
@@ -0,0 +1,111 @@
+"""Integration tests for Haystack - RAG Pipeline with Haystack + Valkey.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_rag_pipeline(client):
+    """Run all code blocks from: RAG Pipeline with Haystack + Valkey."""
+
+    # --- Block 1 ---
+    from haystack import Pipeline, Document
+    from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+    from haystack.components.writers import DocumentWriter
+    from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
+
+    document_store = ValkeyDocumentStore(
+        nodes_list=[("localhost", 6379)],
+        index_name="rag_docs",
+        embedding_dim=768,
+        distance_metric="cosine",
+    )
+
+    indexing_pipeline = Pipeline()
+    indexing_pipeline.add_component(
+        "embedder",
+        SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-mpnet-base-v2")
+    )
+    indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
+    indexing_pipeline.connect("embedder.documents", "writer.documents")
+
+    # Your documents - swap this for file loaders, web crawlers, etc.
+    docs = [
+        Document(content="Valkey supports vector search natively via its module system."),
+        Document(content="The ValkeyDocumentStore integrates directly with Haystack pipelines."),
+        Document(content="Cosine similarity measures the angle between two embedding vectors."),
+        Document(content="RAG grounds LLM responses in retrieved facts, reducing hallucinations."),
+        Document(content="Haystack pipelines are composable - swap any component without rewriting the rest."),
+    ]
+
+    indexing_pipeline.run({"embedder": {"documents": docs}})
+    print(f"Indexed {document_store.count_documents()} documents")
+
+    # --- Block 2 ---
+    from haystack import Pipeline
+    from haystack.utils import Secret
+    from haystack.dataclasses import ChatMessage
+    from haystack.components.embedders import SentenceTransformersTextEmbedder
+    from haystack.components.builders import ChatPromptBuilder
+    from haystack.components.generators.chat import OpenAIChatGenerator
+    from haystack_integrations.components.retrievers.valkey import ValkeyEmbeddingRetriever
+
+    prompt_template = [
+        ChatMessage.from_system(
+            "Answer the question using only the provided context. "
+            "If the context doesn't contain the answer, say 'I don't know'."
+        ),
+        ChatMessage.from_user(
+            "Context:\n{% for doc in documents %}{{ doc.content }}\n{% endfor %}\n"
+            "Question: {{query}}"
+        ),
+    ]
+
+    query_pipeline = Pipeline()
+    query_pipeline.add_component(
+        "text_embedder",
+        SentenceTransformersTextEmbedder(model="sentence-transformers/all-mpnet-base-v2")
+    )
+    query_pipeline.add_component(
+        "retriever",
+        ValkeyEmbeddingRetriever(document_store=document_store, top_k=3)
+    )
+    query_pipeline.add_component(
+        "prompt_builder",
+        ChatPromptBuilder(template=prompt_template, required_variables=["query", "documents"])
+    )
+    query_pipeline.add_component(
+        "generator",
+        OpenAIChatGenerator(
+            api_key=Secret.from_env_var("OPENAI_API_KEY"),
+            model="gpt-4o"
+        )
+    )
+
+    query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+    query_pipeline.connect("retriever.documents", "prompt_builder.documents")
+    query_pipeline.connect("prompt_builder.messages", "generator.messages")
+
+    # --- Block 3 ---
+    query = "How does Valkey integrate with Haystack?"
+
+    result = query_pipeline.run({
+        "text_embedder": {"text": query},
+        "prompt_builder": {"query": query},
+    })
+
+    print(result["generator"]["replies"][0].content)
+
+    # --- Block 4 ---
+    # OpenAI embeddings (1536-dim)
+    from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder
+
+    document_store = ValkeyDocumentStore(
+        nodes_list=[("localhost", 6379)],
+        index_name="rag_docs_openai",
+        embedding_dim=1536,  # text-embedding-3-small
+        distance_metric="cosine",
+    )
+
diff --git a/tests/langchain/__init__.py b/tests/langchain/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/langchain/test_01_getting_started.py b/tests/langchain/test_01_getting_started.py
new file mode 100644
index 0000000..2c055b6
--- /dev/null
+++ b/tests/langchain/test_01_getting_started.py
@@ -0,0 +1,66 @@
+"""Integration tests for LangChain - Getting Started with LangChain + Valkey.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with LangChain + Valkey."""
+
+    # --- Block 1 ---
+    from langgraph.graph import StateGraph, MessagesState
+    from langgraph_checkpoint_aws import ValkeySaver
+    from langchain_aws import ChatBedrockConverse
+    from langchain_core.messages import HumanMessage
+
+
+    # 1. Create a simple chatbot graph
+    model = ChatBedrockConverse(
+        model="us.anthropic.claude-sonnet-4-20250514-v1:0",
+        region_name="us-west-2",
+    )
+
+    def chatbot(state: MessagesState):
+        return {"messages": [model.invoke(state["messages"])]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("chatbot", chatbot)
+    builder.set_entry_point("chatbot")
+    builder.set_finish_point("chatbot")
+
+    # 2. Compile with ValkeySaver - this is the key line
+    with ValkeySaver.from_conn_string(
+        "valkey://localhost:6379",
+        ttl_seconds=3600,
+    ) as checkpointer:
+        graph = builder.compile(checkpointer=checkpointer)
+
+        # 3. Invoke with a thread ID
+        config = {"configurable": {"thread_id": "session-1"}}
+        result = graph.invoke(
+            {"messages": [HumanMessage(content="What is Valkey?")]},
+            config,
+        )
+        print(result["messages"][-1].content)
+
+        # 4. Continue the conversation - state is persisted!
+        result = graph.invoke(
+            {"messages": [HumanMessage(content="How fast is it?")]},
+            config,
+        )
+        # The agent remembers the previous message about Valkey
+        print(result["messages"][-1].content)
+
+    # --- Block 2 ---
+    # In a NEW Python process:
+    with ValkeySaver.from_conn_string("valkey://localhost:6379") as checkpointer:
+        graph = builder.compile(checkpointer=checkpointer)
+        config = {"configurable": {"thread_id": "session-1"}}
+
+        # List checkpoints for this thread
+        for cp in checkpointer.list(config):
+            print(cp.metadata)  # Shows previous conversation
+
diff --git a/tests/langchain/test_02_llm_caching.py b/tests/langchain/test_02_llm_caching.py
new file mode 100644
index 0000000..75515c9
--- /dev/null
+++ b/tests/langchain/test_02_llm_caching.py
@@ -0,0 +1,111 @@
+"""Integration tests for LangChain - LLM Caching.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_02_llm_caching(client):
+    """Run all code blocks from: LLM Caching."""
+
+    # --- Block 1 ---
+    from langgraph_checkpoint_aws import ValkeyCache
+    from valkey import Valkey
+
+    # Create Valkey client
+    valkey_client = Valkey.from_url(
+        "valkey://localhost:6379",
+        decode_responses=False,
+    )
+
+    # Initialize cache with 1-hour TTL
+    cache = ValkeyCache(
+        client=valkey_client,
+        prefix="llm_cache:",
+        ttl=3600,
+    )
+
+    # --- Block 2 ---
+    import hashlib
+
+    def cache_key(prompt: str, model: str = "claude-sonnet", temp: float = 0.7) -> tuple:
+        content = f"{model}|temp={temp}|{prompt.strip()}"
+        key = hashlib.sha256(content.encode()).hexdigest()[:16]
+        return (("llm_responses",), key)
+
+    # --- Block 3 ---
+    import time
+    from langchain_aws import ChatBedrockConverse
+    from langchain_core.messages import HumanMessage
+
+    model = ChatBedrockConverse(
+        model="us.anthropic.claude-sonnet-4-20250514-v1:0",
+        region_name="us-west-2",
+    )
+
+    async def cached_llm_call(prompt: str) -> dict:
+        key = cache_key(prompt)
+
+        # 1. Check cache
+        start = time.time()
+        cached = await cache.aget([key])
+        cache_time = time.time() - start
+
+        if key in cached:
+            return {
+                "response": cached[key]["response"],
+                "cached": True,
+                "latency_ms": cache_time * 1000,
+            }
+
+        # 2. Cache miss - call LLM
+        start = time.time()
+        response = await model.ainvoke([HumanMessage(content=prompt)])
+        llm_time = time.time() - start
+
+        # 3. Store in cache
+        await cache.aset({key: ({"response": response.content}, 3600)})
+
+        return {
+            "response": response.content,
+            "cached": False,
+            "latency_ms": llm_time * 1000,
+        }
+
+    # --- Block 4 ---
+    import asyncio
+
+    async def benchmark():
+        prompt = "What is Amazon ElastiCache for Valkey?"
+
+        # First call - cache miss
+        r1 = await cached_llm_call(prompt)
+        print(f"❌ Cache MISS: {r1['latency_ms']:.0f}ms")
+
+        # Second call - cache hit
+        r2 = await cached_llm_call(prompt)
+        print(f"✅ Cache HIT:  {r2['latency_ms']:.1f}ms")
+
+        speedup = r1["latency_ms"] / r2["latency_ms"]
+        print(f"🚀 Speedup:   {speedup:.0f}x faster")
+
+    asyncio.run(benchmark())
+
+    # Output:
+    # ❌ Cache MISS: 4200ms
+    # ✅ Cache HIT:  1.2ms
+    # 🚀 Speedup:   3500x faster
+
+    # --- Block 5 ---
+    # Default TTL (set at cache creation)
+    cache = ValkeyCache(client=valkey_client, ttl=3600)  # 1 hour
+
+    # Custom TTL per entry
+    await cache.aset({key: (data, 300)})  # 5 minutes for volatile data
+
+    # Clear all cached entries
+    await cache.aclear()
+
diff --git a/tests/langchain/test_03_semantic_search.py b/tests/langchain/test_03_semantic_search.py
new file mode 100644
index 0000000..8b620f1
--- /dev/null
+++ b/tests/langchain/test_03_semantic_search.py
@@ -0,0 +1,69 @@
+"""Integration tests for LangChain - Semantic Search.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_semantic_search(raw_client):
+    """Run all code blocks from: Semantic Search."""
+
+    # --- Block 1 ---
+    from langgraph_checkpoint_aws import ValkeyStore
+    from langchain_aws import BedrockEmbeddings
+
+    # Amazon Titan embeddings - 1536 dimensions
+    embeddings = BedrockEmbeddings(
+        model_id="amazon.titan-embed-text-v2:0",
+        region_name="us-west-2",
+    )
+
+    # ValkeyStore with HNSW vector index
+    store = ValkeyStore.from_conn_string(
+        "valkey://localhost:6379",
+        index={
+            "collection_name": "semantic_cache",
+            "dims": 1536,
+            "embed": embeddings,
+            "fields": ["text"],
+            "index_type": "hnsw",
+            "distance_metric": "COSINE",
+        },
+        ttl={"default_ttl": 60.0},  # 60 minutes
+    )
+    store.setup()  # Creates the FT index
+
+    # --- Block 2 ---
+    # Search with a paraphrased query
+    results = store.search(
+        ("help-desk",),  # namespace prefix
+        query="I forgot my password, help!",
+        limit=3,
+    )
+
+    for r in results:
+        print(f"Score: {r.score:.3f} - {r.value['text']}")
+
+    # Output:
+    # Score: 0.943 - How do I reset my password?
+    # Score: 0.412 - How do I connect to the VPN?
+
+    # --- Block 3 ---
+    # Index creation (once)
+    FT.CREATE semantic_cache_idx ON JSON PREFIX 1 "store:semantic_cache:"
+      SCHEMA $.text AS text TAG
+             $.embedding AS embedding VECTOR HNSW 6
+               TYPE FLOAT32 DIM 1536 DISTANCE_METRIC COSINE
+
+    # Store document
+    JSON.SET store:semantic_cache:help-desk:passwords:q1 $ '{...}'
+    EXPIRE store:semantic_cache:help-desk:passwords:q1 3600
+
+    # Vector search
+    FT.SEARCH semantic_cache_idx
+      "(*)==>[KNN 3 @embedding $vec AS score]"
+      PARAMS 2 vec 
+      LIMIT 0 3
+
diff --git a/tests/langchain/test_04_full_agent.py b/tests/langchain/test_04_full_agent.py
new file mode 100644
index 0000000..31c552c
--- /dev/null
+++ b/tests/langchain/test_04_full_agent.py
@@ -0,0 +1,128 @@
+"""Integration tests for LangChain - Full Agent.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_04_full_agent(raw_client):
+    """Run all code blocks from: Full Agent."""
+
+    # --- Block 1 ---
+    from valkey import Valkey
+    from langgraph_checkpoint_aws import ValkeySaver, ValkeyStore, ValkeyCache
+    from langchain_aws import ChatBedrockConverse, BedrockEmbeddings
+
+    # Single Valkey connection shared across all components
+    valkey_client = Valkey.from_url("valkey://localhost:6379", decode_responses=False)
+
+    # Embeddings for semantic search
+    embeddings = BedrockEmbeddings(
+        model_id="amazon.titan-embed-text-v2:0",
+        region_name="us-west-2",
+    )
+
+    # LLM
+    model = ChatBedrockConverse(
+        model="us.anthropic.claude-sonnet-4-20250514-v1:0",
+        region_name="us-west-2",
+    )
+
+    # --- Block 2 ---
+    # 1. Checkpointer - persists conversation state
+    saver = ValkeySaver(client=valkey_client, ttl=3600)
+
+    # 2. Semantic store - vector search for cache lookups
+    store = ValkeyStore(
+        client=valkey_client,
+        index={
+            "collection_name": "helpdesk_cache",
+            "dims": 1536,
+            "embed": embeddings,
+            "fields": ["query"],
+            "index_type": "hnsw",
+        },
+        ttl={"default_ttl": 60.0},
+    )
+    store.setup()
+
+    # 3. Exact cache - fast key-value cache for repeated prompts
+    cache = ValkeyCache(client=valkey_client, prefix="llm_cache:", ttl=3600)
+
+    # --- Block 3 ---
+    from langgraph.graph import StateGraph, MessagesState
+    from langchain_core.messages import HumanMessage
+    import hashlib
+
+    SIMILARITY_THRESHOLD = 0.90
+
+    def helpdesk_agent(state: MessagesState):
+        user_msg = state["messages"][-1].content
+
+        # 1. Check semantic cache
+        hits = store.search(("helpdesk",), query=user_msg, limit=1)
+        if hits and hits[0].score >= SIMILARITY_THRESHOLD:
+            cached_answer = hits[0].value["answer"]
+            return {"messages": [AIMessage(content=cached_answer)]}
+
+        # 2. Cache miss - call LLM
+        response = model.invoke(state["messages"])
+
+        # 3. Store in semantic cache for future hits
+        key = hashlib.md5(user_msg.encode()).hexdigest()[:12]
+        store.put(
+            ("helpdesk",), key,
+            {"query": user_msg, "answer": response.content},
+        )
+
+        return {"messages": [response]}
+
+    # Build and compile with ValkeySaver
+    builder = StateGraph(MessagesState)
+    builder.add_node("agent", helpdesk_agent)
+    builder.set_entry_point("agent")
+    builder.set_finish_point("agent")
+
+    graph = builder.compile(checkpointer=saver)
+
+    # --- Block 4 ---
+    import time
+
+    config = {"configurable": {"thread_id": "user-42"}}
+
+    # First question - cache miss, calls LLM
+    t0 = time.time()
+    r1 = graph.invoke(
+        {"messages": [HumanMessage(content="How do I reset my password?")]},
+        config,
+    )
+    print(f"❌ MISS: {(time.time()-t0)*1000:.0f}ms")
+
+    # Paraphrased question - cache hit!
+    t0 = time.time()
+    r2 = graph.invoke(
+        {"messages": [HumanMessage(content="I forgot my password, help!")]},
+        config,
+    )
+    print(f"✅ HIT:  {(time.time()-t0)*1000:.1f}ms")
+
+    # Output:
+    # ❌ MISS: 4200ms
+    # ✅ HIT:  3.1ms
+
+    # --- Block 5 ---
+    # 1. Semantic cache lookup
+    FT.SEARCH helpdesk_cache_idx "(*)==>[KNN 1 @embedding $vec]" ...
+
+    # 2. (No match - call LLM via Bedrock)
+
+    # 3. Store response in semantic cache
+    JSON.SET store:helpdesk_cache:helpdesk:a1b2c3d4e5f6 $ '{...}'
+    EXPIRE store:helpdesk_cache:helpdesk:a1b2c3d4e5f6 3600
+
+    # 4. Checkpoint conversation state
+    JSON.SET checkpoint:user-42:__empty__:cp_001 $ '{...}'
+    EXPIRE checkpoint:user-42:__empty__:cp_001 3600
+
diff --git a/tests/mem0/__init__.py b/tests/mem0/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/mem0/test_01_getting_started.py b/tests/mem0/test_01_getting_started.py
new file mode 100644
index 0000000..0875bf1
--- /dev/null
+++ b/tests/mem0/test_01_getting_started.py
@@ -0,0 +1,90 @@
+"""Integration tests for Mem0 - Getting Started with Mem0 + Valkey.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Mem0 + Valkey."""
+
+    # --- Block 1 ---
+    # Add memories from a conversation
+    messages = [
+        {"role": "user", "content": "I love Italian food, especially pasta carbonara."},
+        {"role": "assistant", "content": "Great choice! Carbonara is a classic Roman dish."},
+    ]
+    result = memory.add(messages, user_id="user_001")
+    print(f"Added: {result}")
+
+    # Add more context
+    messages2 = [
+        {"role": "user", "content": "I'm allergic to shellfish and prefer spicy food."},
+        {"role": "assistant", "content": "Noted! I'll keep that in mind for recommendations."},
+    ]
+    memory.add(messages2, user_id="user_001")
+
+    # --- Block 2 ---
+    # Search for relevant memories
+    results = memory.search(
+        query="What food does this user like?",
+        user_id="user_001",
+        limit=3,
+    )
+
+    for entry in results["results"]:
+        print(f"Memory: {entry['memory']}")
+        print(f"Score: {entry.get('score', 'N/A')}\n")
+
+    # Output:
+    # Memory: Loves Italian food, especially pasta carbonara
+    # Score: 0.87
+    # Memory: Allergic to shellfish, prefers spicy food
+    # Score: 0.62
+
+    # --- Block 3 ---
+    # Retrieve all memories for a user
+    all_memories = memory.get_all(user_id="user_001")
+    for m in all_memories["results"]:
+        print(f"  - {m['memory']}")
+
+    # --- Block 4 ---
+    from openai import OpenAI
+
+    openai_client = OpenAI()
+
+    def chat_with_memory(message: str, user_id: str) -> str:
+        # 1. Retrieve relevant memories
+        relevant = memory.search(query=message, user_id=user_id, limit=3)
+        memories_str = "\n".join(
+            f"- {entry['memory']}" for entry in relevant["results"]
+        )
+
+        # 2. Build prompt with memories
+        system = f"You are a helpful AI. Use these memories about the user:\n{memories_str}"
+        messages = [
+            {"role": "system", "content": system},
+            {"role": "user", "content": message},
+        ]
+
+        # 3. Generate response
+        response = openai_client.chat.completions.create(
+            model="gpt-4", messages=messages,
+        )
+        answer = response.choices[0].message.content
+
+        # 4. Save new memories from this conversation
+        memory.add(
+            [{"role": "user", "content": message},
+             {"role": "assistant", "content": answer}],
+            user_id=user_id,
+        )
+        return answer
+
+    # Usage
+    response = chat_with_memory("Recommend me a restaurant", "user_001")
+    print(response)
+    # Will recommend Italian restaurants, avoid shellfish, suggest spicy options!
+
diff --git a/tests/mem0/test_02_multi_user_memory.py b/tests/mem0/test_02_multi_user_memory.py
new file mode 100644
index 0000000..bca0ef6
--- /dev/null
+++ b/tests/mem0/test_02_multi_user_memory.py
@@ -0,0 +1,67 @@
+"""Integration tests for Mem0 - Multi-User Memory.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_multi_user_memory(client):
+    """Run all code blocks from: Multi-User Memory."""
+
+    # --- Block 1 ---
+    # User Alice
+    memory.add(
+        [{"role": "user", "content": "I prefer dark mode and Python."}],
+        user_id="alice",
+    )
+
+    # User Bob
+    memory.add(
+        [{"role": "user", "content": "I use TypeScript and like light mode."}],
+        user_id="bob",
+    )
+
+    # Search for Alice - only gets Alice's memories
+    alice_results = memory.search("What are the user preferences?", user_id="alice")
+    print("Alice:", [r["memory"] for r in alice_results["results"]])
+    # Alice: ['Prefers dark mode and Python']
+
+    # Search for Bob - only gets Bob's memories
+    bob_results = memory.search("What are the user preferences?", user_id="bob")
+    print("Bob:", [r["memory"] for r in bob_results["results"]])
+    # Bob: ['Uses TypeScript and likes light mode']
+
+    # --- Block 2 ---
+    # Support agent knowledge
+    memory.add(
+        [{"role": "user", "content": "Our refund policy is 30 days for unused items."}],
+        agent_id="support_bot",
+    )
+
+    # Sales agent knowledge
+    memory.add(
+        [{"role": "user", "content": "Current promotion: 20% off all premium plans."}],
+        agent_id="sales_bot",
+    )
+
+    # Each agent only sees its own knowledge
+    support_results = memory.search("What is the refund policy?", agent_id="support_bot")
+    sales_results = memory.search("Any promotions?", agent_id="sales_bot")
+
+    # --- Block 3 ---
+    # Add a memory scoped to both user AND agent
+    memory.add(
+        [{"role": "user", "content": "I had an issue with order #12345."}],
+        user_id="alice",
+        agent_id="support_bot",
+    )
+
+    # Search: finds Alice's support interactions only
+    results = memory.search(
+        "Previous issues",
+        user_id="alice",
+        agent_id="support_bot",
+    )
+
diff --git a/tests/mem0/test_03_production.py b/tests/mem0/test_03_production.py
new file mode 100644
index 0000000..1b69c77
--- /dev/null
+++ b/tests/mem0/test_03_production.py
@@ -0,0 +1,33 @@
+"""Integration tests for Mem0 - Production with ElastiCache.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_production(raw_client):
+    """Run all code blocks from: Production with ElastiCache."""
+
+    # --- Block 1 ---
+
+    # Connect directly to check index health
+    client = valkey.from_url("valkey://your-cluster:6379")
+
+    # Check index info
+    info = client.execute_command("FT.INFO", "prod_memories")
+    print(info)
+
+    # Check memory usage
+    mem_info = client.info("memory")
+    print(f"Used: {mem_info['used_memory_human']}")
+
+    # Count memories per user
+    results = client.execute_command(
+        "FT.SEARCH", "prod_memories",
+        "@user_id:{alice}",
+        "LIMIT", "0", "0",  # count only
+    )
+    print(f"Alice has {results[0]} memories")
+
diff --git a/tests/pubsub_streaming/__init__.py b/tests/pubsub_streaming/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/pubsub_streaming/test_01_getting_started.py b/tests/pubsub_streaming/test_01_getting_started.py
new file mode 100644
index 0000000..e7bd72d
--- /dev/null
+++ b/tests/pubsub_streaming/test_01_getting_started.py
@@ -0,0 +1,94 @@
+"""Integration tests for Pub/Sub & Streaming - Getting Started with Pub/Sub.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Pub/Sub."""
+
+    # --- Block 1 ---
+    import time
+    import json
+
+    client = client
+
+    def publish_message(channel: str, message: dict):
+        """Publish a JSON message to a channel."""
+        payload = json.dumps(message)
+        num_subscribers = client.publish(channel, payload)
+        print(f"Published to {channel} → {num_subscribers} subscriber(s)")
+        return num_subscribers
+
+    # Publish some messages
+    publish_message("ai:events", {
+        "type": "prediction",
+        "model": "gpt-4",
+        "result": "positive",
+        "confidence": 0.95,
+        "timestamp": time.time(),
+    })
+    # Published to ai:events → 0 subscriber(s)  (no one listening yet)
+
+    # --- Block 2 ---
+    import json
+
+    client = client
+
+    # Create a Pub/Sub object
+    pubsub = client.pubsub()
+
+    # Subscribe to the channel
+    pubsub.subscribe("ai:events")
+    print("Subscribed to ai:events - waiting for messages...")
+
+    # Listen for messages (blocks)
+    for message in pubsub.listen():
+        if message["type"] == "message":
+            data = json.loads(message["data"])
+            print(f"Received: {data}")
+
+    # Output when publisher sends a message:
+    # Received: {'type': 'prediction', 'model': 'gpt-4', 'result': 'positive', ...}
+
+    # --- Block 3 ---
+    # Subscribe to ALL ai:* channels at once
+    pubsub.psubscribe("ai:*")
+
+    # This matches:
+    #   ai:events
+    #   ai:predictions
+    #   ai:agent:tool_calls
+    #   ai:llm:tokens
+
+    for message in pubsub.listen():
+        if message["type"] == "pmessage":
+            channel = message["channel"]
+            data = json.loads(message["data"])
+            print(f"[{channel}] {data}")
+
+    # --- Block 4 ---
+    import threading
+
+    def message_handler(message):
+        """Called for each message received."""
+        if message["type"] == "message":
+            data = json.loads(message["data"])
+            print(f"Handler: {data}")
+
+    # Subscribe with a callback (non-blocking)
+    pubsub.subscribe(**{"ai:events": message_handler})
+
+    # Run in background thread
+    thread = pubsub.run_in_thread(sleep_time=0.01)
+    print("Subscriber running in background")
+
+    # Do other work...
+    time.sleep(10)
+
+    # Stop when done
+    thread.stop()
+
diff --git a/tests/pubsub_streaming/test_02_streaming_llm_tokens.py b/tests/pubsub_streaming/test_02_streaming_llm_tokens.py
new file mode 100644
index 0000000..ac31c09
--- /dev/null
+++ b/tests/pubsub_streaming/test_02_streaming_llm_tokens.py
@@ -0,0 +1,134 @@
+"""Integration tests for Pub/Sub & Streaming - Streaming LLM Tokens.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_streaming_llm_tokens(client):
+    """Run all code blocks from: Streaming LLM Tokens."""
+
+    # --- Block 1 ---
+    import json
+    import time
+    import uuid
+
+    client = client
+
+    def stream_llm_response(prompt: str, request_id: str = None):
+        """Simulate an LLM streaming response, publishing each token."""
+        request_id = request_id or str(uuid.uuid4())[:8]
+        channel = f"llm:stream:{request_id}"
+
+        # Notify clients that streaming has started
+        client.publish(channel, json.dumps({
+            "type": "start",
+            "request_id": request_id,
+            "model": "gpt-4",
+            "timestamp": time.time(),
+        }))
+
+        # Simulate token-by-token streaming
+        tokens = ["Valkey", " is", " an", " open", "-source",
+                  " in", "-memory", " data", " store", "."]
+
+        for i, token in enumerate(tokens):
+            # In production, this would come from the LLM API:
+            # for chunk in openai.chat.completions.create(stream=True):
+            #     token = chunk.choices[0].delta.content
+
+            client.publish(channel, json.dumps({
+                "type": "token",
+                "content": token,
+                "index": i,
+                "timestamp": time.time(),
+            }))
+            time.sleep(0.05)  # Simulate token delay
+
+        # Notify clients that streaming is complete
+        client.publish(channel, json.dumps({
+            "type": "end",
+            "request_id": request_id,
+            "total_tokens": len(tokens),
+            "timestamp": time.time(),
+        }))
+
+        return request_id
+
+    # Usage
+    rid = stream_llm_response("What is Valkey?")
+    print(f"Streamed response: {rid}")
+
+    # --- Block 2 ---
+    import json
+
+    client = client
+
+    def watch_stream(request_id: str):
+        """Subscribe to an LLM response stream and reassemble tokens."""
+        pubsub = client.pubsub()
+        channel = f"llm:stream:{request_id}"
+        pubsub.subscribe(channel)
+
+        full_response = []
+
+        for message in pubsub.listen():
+            if message["type"] != "message":
+                continue
+
+            data = json.loads(message["data"])
+
+            if data["type"] == "start":
+                print(f"Stream started (model: {data['model']})")
+
+            elif data["type"] == "token":
+                token = data["content"]
+                full_response.append(token)
+                print(token, end="", flush=True)  # Print token-by-token
+
+            elif data["type"] == "end":
+                print(f"\n--- Complete ({data['total_tokens']} tokens) ---")
+                break
+
+        pubsub.unsubscribe(channel)
+        return "".join(full_response)
+
+    # Run in another terminal:
+    # result = watch_stream("abc123")
+    # Output: Valkey is an open-source in-memory data store.
+
+    # --- Block 3 ---
+    from openai import OpenAI
+
+    openai_client = OpenAI()
+
+    def stream_openai_to_valkey(prompt: str, request_id: str):
+        """Stream from OpenAI API → Valkey Pub/Sub → all subscribers."""
+        channel = f"llm:stream:{request_id}"
+
+        client.publish(channel, json.dumps({"type": "start"}))
+
+        stream = openai_client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": prompt}],
+            stream=True,
+        )
+
+        token_count = 0
+        for chunk in stream:
+            content = chunk.choices[0].delta.content
+            if content:
+                client.publish(channel, json.dumps({
+                    "type": "token",
+                    "content": content,
+                    "index": token_count,
+                }))
+                token_count += 1
+
+        client.publish(channel, json.dumps({
+            "type": "end",
+            "total_tokens": token_count,
+        }))
+
diff --git a/tests/pubsub_streaming/test_03_valkey_streams.py b/tests/pubsub_streaming/test_03_valkey_streams.py
new file mode 100644
index 0000000..8f712b3
--- /dev/null
+++ b/tests/pubsub_streaming/test_03_valkey_streams.py
@@ -0,0 +1,56 @@
+"""Integration tests for Pub/Sub & Streaming - Valkey Streams.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_valkey_streams(client):
+    """Run all code blocks from: Valkey Streams."""
+
+    # --- Block 1 ---
+    import valkey, json, time
+    client = client
+
+    # XADD - append to stream
+    entry_id = client.xadd("ai:tasks", {
+        "type": "embedding",
+        "text": "Hello world",
+        "model": "text-embedding-3-small",
+    })
+    print(f"Added: {entry_id}")
+
+    # With MAXLEN cap
+    client.xadd("ai:tasks", {"type": "completion"}, maxlen=10000)
+
+    # --- Block 2 ---
+    # XREAD - blocking read for new messages
+    entries = client.xread({"ai:tasks": "0-0"}, count=10, block=5000)
+    for stream, messages in entries:
+        for msg_id, data in messages:
+            print(f"[{msg_id}] {data}")
+
+    # XRANGE - replay from beginning
+    messages = client.xrange("ai:tasks", min="-", max="+", count=5)
+
+    # XREVRANGE - newest first
+    latest = client.xrevrange("ai:tasks", max="+", min="-", count=1)
+
+    # --- Block 3 ---
+    length = client.xlen("ai:tasks")
+    info = client.xinfo_stream("ai:tasks")
+    client.xtrim("ai:tasks", maxlen=1000)
+
+    # --- Block 4 ---
+    def consume(stream_key):
+        last_id = "$"  # Only new messages
+        while True:
+            entries = client.xread({stream_key: last_id}, count=10, block=2000)
+            if not entries: continue
+            for stream, messages in entries:
+                for msg_id, data in messages:
+                    print(f"Processing: {data}")
+                    last_id = msg_id
+
diff --git a/tests/pubsub_streaming/test_04_consumer_groups.py b/tests/pubsub_streaming/test_04_consumer_groups.py
new file mode 100644
index 0000000..a94a72a
--- /dev/null
+++ b/tests/pubsub_streaming/test_04_consumer_groups.py
@@ -0,0 +1,35 @@
+"""Integration tests for Pub/Sub & Streaming - Consumer Groups.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_04_consumer_groups(client):
+    """Run all code blocks from: Consumer Groups."""
+
+    # --- Block 1 ---
+    client = client
+
+    try:
+        client.xgroup_create("ai:tasks", "workers", id="0", mkstream=True)
+    except valkey.ResponseError:
+        pass  # Group already exists
+
+    # --- Block 2 ---
+    def worker(worker_name: str):
+        while True:
+            entries = client.xreadgroup(
+                "workers", worker_name,
+                {"ai:tasks": ">"},
+                count=5, block=2000,
+            )
+            if not entries: continue
+            for stream, messages in entries:
+                for msg_id, data in messages:
+                    print(f"[{worker_name}] Processing: {data}")
+                    # Process the task...
+                    client.xack("ai:tasks", "workers", msg_id)
+
diff --git a/tests/pubsub_streaming/test_05_fan_out_patterns.py b/tests/pubsub_streaming/test_05_fan_out_patterns.py
new file mode 100644
index 0000000..3807e6f
--- /dev/null
+++ b/tests/pubsub_streaming/test_05_fan_out_patterns.py
@@ -0,0 +1,44 @@
+"""Integration tests for Pub/Sub & Streaming - Fan-Out Patterns.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_05_fan_out_patterns(client):
+    """Run all code blocks from: Fan-Out Patterns."""
+
+    # --- Block 1 ---
+    import valkey, json, time
+    client = client
+
+    def broadcast_agent_event(agent_id, event_type, payload):
+        """Broadcast to all subscribers watching this agent."""
+        channel = f"agent:{agent_id}:events"
+        message = {"type": event_type, "data": payload, "ts": time.time()}
+        n = client.publish(channel, json.dumps(message))
+        return n  # Number of receivers
+
+    # Dashboard, logger, metrics all receive this:
+    broadcast_agent_event("agent-1", "tool_call", {"tool": "search", "query": "valkey"})
+
+    # --- Block 2 ---
+    # Multiple consumer groups on the SAME stream
+    # Each group gets ALL messages independently
+    client.xgroup_create("ai:events", "loggers", id="0", mkstream=True)
+    client.xgroup_create("ai:events", "metrics", id="0", mkstream=True)
+    client.xgroup_create("ai:events", "alerts", id="0", mkstream=True)
+
+    # Producer writes once
+    client.xadd("ai:events", {"event": "model_prediction", "latency_ms": "45"})
+    # All 3 groups get the message independently
+
+    # --- Block 3 ---
+    def publish_with_durability(event):
+        """Write to Stream (durable) AND Pub/Sub (live)."""
+        payload = json.dumps(event)
+        client.xadd("ai:events", {"data": payload}, maxlen=50000)
+        client.publish("ai:events:live", payload)
+
diff --git a/tests/pubsub_streaming/test_06_production.py b/tests/pubsub_streaming/test_06_production.py
new file mode 100644
index 0000000..7969708
--- /dev/null
+++ b/tests/pubsub_streaming/test_06_production.py
@@ -0,0 +1,66 @@
+"""Integration tests for Pub/Sub & Streaming - Production Patterns.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_06_production(client):
+    """Run all code blocks from: Production Patterns."""
+
+    # --- Block 1 ---
+    import valkey, time
+    client = client
+
+    # Approximate trim (faster, recommended)
+    client.xadd("ai:tasks", {"data": "..."}, maxlen=10000)
+
+    # Time-based trim (remove entries older than 1 hour)
+    cutoff_ms = int((time.time() - 3600) * 1000)
+    client.xtrim("ai:tasks", minid=cutoff_ms)
+
+    # --- Block 2 ---
+    def stream_health(stream_key):
+        info = client.xinfo_stream(stream_key)
+        groups = client.xinfo_groups(stream_key)
+        return {
+            "length": info["length"],
+            "groups": len(groups),
+            "consumers": sum(g["consumers"] for g in groups),
+            "pending": sum(g["pending"] for g in groups),
+        }
+    print(stream_health("ai:tasks"))
+
+    # --- Block 3 ---
+    def resilient_subscriber(channel):
+        while True:
+            try:
+                c = client
+                ps = c.pubsub()
+                ps.subscribe(channel)
+                print(f"Connected to {channel}")
+                for msg in ps.listen():
+                    if msg["type"] == "message":
+                        print(msg["data"])
+            except valkey.ConnectionError:
+                print("Disconnected, reconnecting in 2s...")
+                time.sleep(2)
+
+    # --- Block 4 ---
+    def process_with_dlq(stream, group, consumer, max_retries=3):
+        entries = client.xreadgroup(group, consumer, {stream: ">"}, count=10, block=2000)
+        if not entries: return
+        for s, messages in entries:
+            for msg_id, data in messages:
+                try:
+                    process(data)
+                    client.xack(stream, group, msg_id)
+                except Exception:
+                    # After max retries, move to DLQ
+                    pending = client.xpending_range(stream, group, msg_id, msg_id, 1)
+                    if pending and pending[0]["times_delivered"] >= max_retries:
+                        client.xadd(f"{stream}:dlq", data)
+                        client.xack(stream, group, msg_id)
+
diff --git a/tests/rag_pipelines/__init__.py b/tests/rag_pipelines/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/rag_pipelines/test_01_getting_started.py b/tests/rag_pipelines/test_01_getting_started.py
new file mode 100644
index 0000000..5021f03
--- /dev/null
+++ b/tests/rag_pipelines/test_01_getting_started.py
@@ -0,0 +1,95 @@
+"""Integration tests for RAG Pipelines - Getting Started with RAG.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(raw_client):
+    """Run all code blocks from: Getting Started with RAG."""
+
+    # --- Block 1 ---
+    # Python example
+
+
+    import numpy as np
+
+    client = raw_client
+
+    # Generate embedding (use your embedding provider)
+    embedding = get_embedding("Your document text here...")
+    embedding_bytes = np.array(embedding, dtype=np.float32).tobytes()
+
+    # Store the document
+    client.hset("doc:chunk_001", mapping={
+        "content": "Your document text here...",
+        "section": "Introduction",
+        "embedding": embedding_bytes,
+    })
+
+    # --- Block 2 ---
+    # Generate query embedding
+    query = "How do I create an index?"
+    query_embedding = get_embedding(query)
+    query_bytes = np.array(query_embedding, dtype=np.float32).tobytes()
+
+    # KNN search for top 5 matches
+    results = client.execute_command(
+        'FT.SEARCH', 'idx:docs',
+        '*=>[KNN 5 @embedding $vec AS score]',
+        'PARAMS', '2', 'vec', query_bytes,
+        'RETURN', '3', 'content', 'section', 'score',
+        'DIALECT', '2'
+    )
+    print(f"Found {results[0]} results")
+
+    # --- Block 3 ---
+    import numpy as np
+    from openai import OpenAI
+
+    # Initialize clients
+    openai = OpenAI()
+    vk = raw_client
+
+    def get_embedding(text):
+        response = openai.embeddings.create(
+            input=text,
+            model="text-embedding-3-small"
+        )
+        return response.data[0].embedding
+
+    # Create index (run once)
+    try:
+        vk.execute_command(
+            'FT.CREATE', 'idx:docs',
+            'ON', 'HASH', 'PREFIX', '1', 'doc:',
+            'SCHEMA',
+            'content', 'TAG',
+            'embedding', 'VECTOR', 'HNSW', '6',
+            'TYPE', 'FLOAT32', 'DIM', '1536',
+            'DISTANCE_METRIC', 'COSINE'
+        )
+    except:
+        pass  # Index exists
+
+    # Store a document
+    doc_text = "Valkey supports HNSW indexes for fast vector search."
+    emb = get_embedding(doc_text)
+    vk.hset("doc:1", mapping={
+        "content": doc_text,
+        "embedding": np.array(emb, dtype=np.float32).tobytes()
+    })
+
+    # Search
+    query_emb = get_embedding("How does vector search work?")
+    results = vk.execute_command(
+        'FT.SEARCH', 'idx:docs',
+        '*=>[KNN 3 @embedding $vec AS score]',
+        'PARAMS', '2', 'vec',
+        np.array(query_emb, dtype=np.float32).tobytes(),
+        'DIALECT', '2'
+    )
+    print(results)
+
diff --git a/tests/rag_pipelines/test_02_semantic_caching.py b/tests/rag_pipelines/test_02_semantic_caching.py
new file mode 100644
index 0000000..70e6156
--- /dev/null
+++ b/tests/rag_pipelines/test_02_semantic_caching.py
@@ -0,0 +1,104 @@
+"""Integration tests for RAG Pipelines - Semantic Caching for RAG.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_02_semantic_caching(raw_client):
+    """Run all code blocks from: Semantic Caching for RAG."""
+
+    # --- Block 1 ---
+    async def semantic_cache_lookup(query: str, threshold: float = 0.92):
+        # Embed the query
+        query_embedding = await get_embedding(query)
+        query_bytes = np.array(query_embedding, dtype=np.float32).tobytes()
+
+        # Search for similar cached queries
+        results = client.execute_command(
+            'FT.SEARCH', 'idx:llm_cache',
+            '*=>[KNN 1 @embedding $vec AS score]',
+            'PARAMS', '2', 'vec', query_bytes,
+            'RETURN', '2', 'response', 'score',
+            'DIALECT', '2'
+        )
+
+        if results[0] > 0:
+            similarity = 1 - float(results[2][3])  # Convert distance to similarity
+            if similarity >= threshold:
+                return results[2][1]  # Return cached response
+
+        return None  # Cache miss
+
+    # --- Block 2 ---
+    # Store with TTL
+    def cache_response(query, response, ttl_seconds=3600):
+        key = f"cache:{hash(query)[:16]}"
+        embedding = get_embedding(query)
+        client.hset(key, mapping={
+            "query": query,
+            "response": response,
+            "embedding": np.array(embedding, dtype=np.float32).tobytes()
+        })
+        client.expire(key, ttl_seconds)
+
+    # Different TTLs by content type
+    TTL_CONFIG = {
+        "factual": 86400,   # 24 hours - stable facts
+        "news": 3600,        # 1 hour - current events
+        "weather": 1800,     # 30 min - frequently changes
+        "stock": 60,         # 1 min - real-time data
+    }
+
+    # --- Block 3 ---
+    class SemanticCache:
+        def __init__(self, threshold=0.92, default_ttl=3600):
+            self.client = raw_client
+            self.threshold = threshold
+            self.default_ttl = default_ttl
+            self._create_index()
+
+        def _create_index(self):
+            try:
+                self.client.execute_command(
+                    'FT.CREATE', 'idx:cache',
+                    'ON', 'HASH', 'PREFIX', '1', 'cache:',
+                    'SCHEMA',
+                    'query', 'TAG',
+                    'response', 'TAG',
+                    'embedding', 'VECTOR', 'HNSW', '6',
+                    'TYPE', 'FLOAT32',
+                    'DIM', '1536',
+                    'DISTANCE_METRIC', 'COSINE'
+                )
+            except:
+                pass
+
+        async def get(self, query: str) -> str | None:
+            emb = await get_embedding(query)
+            vec = np.array(emb, dtype=np.float32).tobytes()
+
+            results = self.client.execute_command(
+                'FT.SEARCH', 'idx:cache',
+                '*=>[KNN 1 @embedding $v AS s]',
+                'PARAMS', '2', 'v', vec,
+                'DIALECT', '2'
+            )
+
+            if results[0] > 0 and (1 - float(results[2][3])) >= self.threshold:
+                return results[2][1].decode()
+            return None
+
+        async def set(self, query: str, response: str, ttl: int = None):
+            key = f"cache:{hashlib.sha256(query.encode()).hexdigest()[:16]}"
+            emb = await get_embedding(query)
+            self.client.hset(key, mapping={
+                "query": query,
+                "response": response,
+                "embedding": np.array(emb, dtype=np.float32).tobytes()
+            })
+            self.client.expire(key, ttl or self.default_ttl)
+
diff --git a/tests/rag_pipelines/test_03_vector_search.py b/tests/rag_pipelines/test_03_vector_search.py
new file mode 100644
index 0000000..082a5bf
--- /dev/null
+++ b/tests/rag_pipelines/test_03_vector_search.py
@@ -0,0 +1,35 @@
+"""Integration tests for RAG Pipelines - Vector Search for RAG.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_vector_search(raw_client):
+    """Run all code blocks from: Vector Search for RAG."""
+
+    # --- Block 1 ---
+    from valkey.commands.search.query import Query
+
+    def hybrid_search(query_text, category=None, date_range=None, k=5):
+        # Build filter
+        filters = []
+        if category:
+            filters.append(f"@category:{{{category}}}")
+        if date_range:
+            filters.append(f"@date:[{date_range[0]} {date_range[1]}]")
+
+        filter_str = " ".join(filters) if filters else "*"
+
+        # Build query
+        query_embedding = get_embedding(query_text)
+        query_bytes = np.array(query_embedding, dtype=np.float32).tobytes()
+
+        q = Query(f"({filter_str})=>[KNN {k} @embedding $vec AS score]")
+        q.return_fields("content", "category", "score")
+        q.dialect(2)
+
+        return client.ft("idx:docs").search(q, {"vec": query_bytes})
+
diff --git a/tests/rag_pipelines/test_04_cache_invalidation.py b/tests/rag_pipelines/test_04_cache_invalidation.py
new file mode 100644
index 0000000..7f16c9c
--- /dev/null
+++ b/tests/rag_pipelines/test_04_cache_invalidation.py
@@ -0,0 +1,101 @@
+"""Integration tests for RAG Pipelines - Cache Invalidation.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_04_cache_invalidation(client):
+    """Run all code blocks from: Cache Invalidation."""
+
+    # --- Block 1 ---
+    # Or use Python helper
+    def cache_with_ttl(key, data, ttl=3600):
+        client.hset(key, mapping=data)
+        client.expire(key, ttl)
+
+    # --- Block 2 ---
+    # Store current version
+    SET cache:version 42
+
+    # Include version in cache keys
+    def get_cache_key(query):
+        version = client.get("cache:version")
+        query_hash = hashlib.sha256(query.encode()).hexdigest()[:16]
+        return f"cache:v{version}:{query_hash}"
+
+    # Invalidate ALL cache by bumping version
+    def invalidate_all():
+        client.incr("cache:version")
+        # Old keys will miss and eventually expire
+
+    # --- Block 3 ---
+    # Track which cache entries depend on which documents
+    def cache_response(query, response, source_docs):
+        cache_key = get_cache_key(query)
+        client.hset(cache_key, mapping={
+            "query": query,
+            "response": response,
+            "sources": json.dumps(source_docs)
+        })
+        # Track dependency: doc -> cache entries
+        for doc_id in source_docs:
+            client.sadd(f"doc_deps:{doc_id}", cache_key)
+
+    # When a document is updated
+    def on_document_update(doc_id):
+        # Get all cache entries that depend on this doc
+        cache_keys = client.smembers(f"doc_deps:{doc_id}")
+        # Delete them
+        if cache_keys:
+            client.delete(*cache_keys)
+        # Clean up dependency tracking
+        client.delete(f"doc_deps:{doc_id}")
+
+    # --- Block 4 ---
+    # Use namespaced prefixes
+    # cache:docs:technical:...
+    # cache:docs:support:...
+    # cache:faqs:billing:...
+
+    # Invalidate an entire namespace
+    def invalidate_namespace(namespace):
+        pattern = f"cache:{namespace}:*"
+        cursor = 0
+        while True:
+            cursor, keys = client.scan(cursor, match=pattern, count=100)
+            if keys:
+                client.delete(*keys)
+            if cursor == 0:
+                break
+
+    # --- Block 5 ---
+    class SmartCache:
+        def __init__(self, default_ttl=3600):
+            self.client = client
+            self.default_ttl = default_ttl
+
+        def set(self, query, response, sources=None, ttl=None):
+            version = self.client.get("cache:version") or "1"
+            key = f"cache:v{version}:{hash(query)}"
+            self.client.hset(key, mapping={
+                "response": response,
+                "sources": json.dumps(sources or [])
+            })
+            self.client.expire(key, ttl or self.default_ttl)
+
+            # Track dependencies
+            for src in (sources or []):
+                self.client.sadd(f"deps:{src}", key)
+
+        def invalidate_source(self, source_id):
+            keys = self.client.smembers(f"deps:{source_id}")
+            if keys:
+                self.client.delete(*keys)
+            self.client.delete(f"deps:{source_id}")
+
+        def invalidate_all(self):
+            self.client.incr("cache:version")
+
diff --git a/tests/rag_pipelines/test_05_scaling_production.py b/tests/rag_pipelines/test_05_scaling_production.py
new file mode 100644
index 0000000..7ef24cc
--- /dev/null
+++ b/tests/rag_pipelines/test_05_scaling_production.py
@@ -0,0 +1,52 @@
+"""Integration tests for RAG Pipelines - Scaling for Production.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_05_scaling_production(client):
+    """Run all code blocks from: Scaling for Production."""
+
+    # --- Block 1 ---
+    # Connect to replicas for read scaling
+    primary = client
+    replica = client
+
+    # Write to primary, read from replicas
+    primary.hset(key, mapping=data)
+    results = replica.ft('idx').search(query)
+
+    # --- Block 2 ---
+    # Use connection pools in production
+    pool = valkey.ConnectionPool(
+        host='localhost',
+        port=6379,
+        max_connections=50,
+        decode_responses=False
+    )
+    client = client
+
+    # Or use async with connection pool
+    pool = valkey.asyncio.ConnectionPool.from_url(
+        "valkey://localhost:6379",
+        max_connections=50
+    )
+
+    # --- Block 3 ---
+    # Connect to ElastiCache cluster
+    client = client
+
+    # For cluster mode
+
+
+    from valkey.cluster import ValkeyCluster
+
+    cluster = ValkeyCluster(
+        host='my-cluster.cache.amazonaws.com',
+        port=6379,
+        ssl=True
+    )
+
diff --git a/tests/rag_pipelines/test_06_monitoring.py b/tests/rag_pipelines/test_06_monitoring.py
new file mode 100644
index 0000000..063c044
--- /dev/null
+++ b/tests/rag_pipelines/test_06_monitoring.py
@@ -0,0 +1,116 @@
+"""Integration tests for RAG Pipelines - Monitoring.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_06_monitoring(client):
+    """Run all code blocks from: Monitoring."""
+
+    # --- Block 1 ---
+    import time
+    from dataclasses import dataclass, field
+
+    @dataclass
+    class CacheMetrics:
+        hits: int = 0
+        misses: int = 0
+        hit_latencies: list = field(default_factory=list)
+        miss_latencies: list = field(default_factory=list)
+
+        @property
+        def hit_rate(self):
+            total = self.hits + self.misses
+            return self.hits / total if total > 0 else 0
+
+        @property
+        def avg_hit_latency(self):
+            return sum(self.hit_latencies) / len(self.hit_latencies) if self.hit_latencies else 0
+
+    metrics = CacheMetrics()
+
+    async def cached_query(query):
+        start = time.perf_counter()
+        result = await cache.lookup(query)
+        latency = (time.perf_counter() - start) * 1000
+
+        if result:
+            metrics.hits += 1
+            metrics.hit_latencies.append(latency)
+        else:
+            metrics.misses += 1
+            metrics.miss_latencies.append(latency)
+
+        return result
+
+    # --- Block 2 ---
+    from prometheus_client import Counter, Histogram, Gauge
+
+    # Define metrics
+    cache_hits = Counter('rag_cache_hits_total', 'Cache hits')
+    cache_misses = Counter('rag_cache_misses_total', 'Cache misses')
+    search_latency = Histogram(
+        'rag_search_seconds', 'Search latency',
+        buckets=[.001, .005, .01, .05, .1, .5, 1]
+    )
+    vector_count = Gauge('rag_vectors_total', 'Total vectors')
+
+    # Usage
+    with search_latency.time():
+        results = await vector_store.search(query)
+
+    if cache_hit:
+        cache_hits.inc()
+    else:
+        cache_misses.inc()
+
+    # --- Block 3 ---
+    import structlog
+
+    log = structlog.get_logger()
+
+    async def search_with_logging(query, user_id):
+        start = time.perf_counter()
+
+        try:
+            results = await cache.lookup(query)
+            latency_ms = (time.perf_counter() - start) * 1000
+
+            log.info(
+                "cache_lookup",
+                user_id=user_id,
+                query_length=len(query),
+                cache_hit=results is not None,
+                latency_ms=round(latency_ms, 2),
+                result_count=len(results) if results else 0
+            )
+            return results
+
+        except Exception as e:
+            log.error("cache_error", error=str(e), query=query[:50])
+            raise
+
+    # --- Block 4 ---
+    @app.get("/health")
+    async def health_check():
+        try:
+            # Check Valkey connection
+            client.ping()
+
+            # Check index exists
+            info = client.ft("idx:docs").info()
+
+            return {
+                "status": "healthy",
+                "valkey": "connected",
+                "index_docs": info["num_docs"],
+                "cache_hit_rate": f"{metrics.hit_rate:.1%}",
+                "avg_latency_ms": round(metrics.avg_hit_latency, 2)
+            }
+        except Exception as e:
+            return {"status": "unhealthy", "error": str(e)}
+
diff --git a/tests/rate_limiting/__init__.py b/tests/rate_limiting/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/rate_limiting/test_01_getting_started.py b/tests/rate_limiting/test_01_getting_started.py
new file mode 100644
index 0000000..e8cb536
--- /dev/null
+++ b/tests/rate_limiting/test_01_getting_started.py
@@ -0,0 +1,44 @@
+"""Integration tests for Rate Limiting - Getting Started with Rate Limiting.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Rate Limiting."""
+
+    # --- Block 1 ---
+    import time
+
+    client = client
+
+    def check_rate_limit(user_id: str, max_requests: int = 10, window: int = 60) -> dict:
+        """Fixed-window rate limiter."""
+        window_num = int(time.time() // window)
+        key = f"rl:{user_id}:{window_num}"
+
+        pipe = client.pipeline(transaction=True)
+        pipe.incr(key)
+        pipe.expire(key, window)
+        results = pipe.execute()
+
+        current = results[0]
+        allowed = current <= max_requests
+
+        return {
+            "allowed": allowed,
+            "current": current,
+            "limit": max_requests,
+            "remaining": max(0, max_requests - current),
+        }
+
+    # --- Block 2 ---
+    # Send 12 requests - last 2 should be denied
+    for i in range(12):
+        result = check_rate_limit("user-123", max_requests=10)
+        status = "✅" if result["allowed"] else "❌"
+        print(f"{status} Request {i+1}: {result['current']}/{result['limit']}")
+
diff --git a/tests/rate_limiting/test_02_token_aware.py b/tests/rate_limiting/test_02_token_aware.py
new file mode 100644
index 0000000..abc4eb3
--- /dev/null
+++ b/tests/rate_limiting/test_02_token_aware.py
@@ -0,0 +1,113 @@
+"""Integration tests for Rate Limiting - Token-Aware Rate Limiting.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_token_aware(client):
+    """Run all code blocks from: Token-Aware Rate Limiting."""
+
+    # --- Block 1 ---
+    import tiktoken
+
+    def count_tokens(text: str, model: str = "gpt-4") -> int:
+        """Count tokens using tiktoken (OpenAI's tokenizer)."""
+        try:
+            enc = tiktoken.encoding_for_model(model)
+        except KeyError:
+            enc = tiktoken.get_encoding("cl100k_base")
+        return len(enc.encode(text))
+
+    # Examples
+    print(count_tokens("Hello world"))           # ~2
+    print(count_tokens("Explain quantum physics in detail"))  # ~6
+
+    # --- Block 2 ---
+    import time
+
+    client = client
+
+    def check_token_limit(
+        identifier: str,
+        input_tokens: int,
+        max_tokens: int = 100_000,
+        window: int = 60,
+    ) -> dict:
+        """Rate limit by token consumption per window."""
+        window_num = int(time.time() // window)
+        key = f"tl:{identifier}:tok:{window_num}"
+
+        # Atomic: get current count, then increment
+        pipe = client.pipeline(transaction=True)
+        pipe.get(key)
+        pipe.incrby(key, input_tokens)
+        pipe.expire(key, window)
+        results = pipe.execute()
+
+        current_before = int(results[0] or 0)
+        allowed = current_before + input_tokens <= max_tokens
+
+        if not allowed:
+            client.decrby(key, input_tokens)  # Rollback
+
+        return {
+            "allowed": allowed,
+            "tokens_used": current_before,
+            "tokens_remaining": max(0, max_tokens - current_before),
+            "max_tokens": max_tokens,
+        }
+
+    # --- Block 3 ---
+    def dual_limit_check(
+        identifier: str,
+        tokens: int,
+        max_requests: int = 60,
+        max_tokens: int = 100_000,
+        window: int = 60,
+    ) -> dict:
+        """Limit by both request count AND token count."""
+        window_num = int(time.time() // window)
+        req_key = f"dl:{identifier}:req:{window_num}"
+        tok_key = f"dl:{identifier}:tok:{window_num}"
+
+        pipe = client.pipeline(transaction=True)
+        pipe.incr(req_key)
+        pipe.expire(req_key, window)
+        pipe.incrby(tok_key, tokens)
+        pipe.expire(tok_key, window)
+        results = pipe.execute()
+
+        req_count = results[0]
+        tok_count = results[2]
+
+        req_ok = req_count <= max_requests
+        tok_ok = tok_count <= max_tokens
+        allowed = req_ok and tok_ok
+
+        blocked_by = None
+        if not req_ok: blocked_by = "requests"
+        elif not tok_ok: blocked_by = "tokens"
+
+        return {
+            "allowed": allowed,
+            "blocked_by": blocked_by,
+            "requests": f"{req_count}/{max_requests}",
+            "tokens": f"{tok_count}/{max_tokens}",
+        }
+
+    # --- Block 4 ---
+    OUTPUT_MULTIPLIERS = {
+        "gpt-4": 1.5,
+        "gpt-4o": 1.2,
+        "gpt-3.5-turbo": 1.0,
+        "claude-3-sonnet": 1.3,
+    }
+
+    def estimate_total_tokens(input_tokens: int, model: str) -> int:
+        multiplier = OUTPUT_MULTIPLIERS.get(model, 1.2)
+        estimated_output = int(input_tokens * multiplier)
+        return input_tokens + estimated_output
+
diff --git a/tests/rate_limiting/test_03_agent_limiting.py b/tests/rate_limiting/test_03_agent_limiting.py
new file mode 100644
index 0000000..f119364
--- /dev/null
+++ b/tests/rate_limiting/test_03_agent_limiting.py
@@ -0,0 +1,83 @@
+"""Integration tests for Rate Limiting - Agent Rate Limiting.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_agent_limiting(client):
+    """Run all code blocks from: Agent Rate Limiting."""
+
+    # --- Block 1 ---
+    TOKEN_BUCKET_SCRIPT = """
+    local key = KEYS[1]
+    local capacity = tonumber(ARGV[1])
+    local refill_rate = tonumber(ARGV[2])
+    local requested = tonumber(ARGV[3])
+    local now = tonumber(ARGV[4])
+
+    local bucket = redis.call('HMGET', key, 'tokens', 'last_refill')
+    local tokens = tonumber(bucket[1])
+    local last_refill = tonumber(bucket[2])
+
+    if tokens == nil then
+        tokens = capacity
+        last_refill = now
+    end
+
+    local elapsed = now - last_refill
+    tokens = math.min(capacity, tokens + elapsed * refill_rate)
+
+    if tokens >= requested then
+        tokens = tokens - requested
+        redis.call('HMSET', key, 'tokens', tokens, 'last_refill', now)
+        redis.call('EXPIRE', key, 3600)
+        return {1, tokens}
+    else
+        redis.call('HMSET', key, 'tokens', tokens, 'last_refill', now)
+        redis.call('EXPIRE', key, 3600)
+        local wait = (requested - tokens) / refill_rate
+        return {0, tokens, math.ceil(wait * 1000)}
+    end
+    """
+
+    # --- Block 2 ---
+    TOOL_COSTS = {
+        "web_search": 1,
+        "read_page": 1,
+        "llm_call": 3,       # LLM calls cost 3x
+        "code_execute": 5,   # Code execution costs 5x
+        "image_generate": 10, # Image gen costs 10x
+    }
+
+    def agent_tool_check(agent_id: str, tool_name: str) -> dict:
+        cost = TOOL_COSTS.get(tool_name, 1)
+        result = agent_check(agent_id, capacity=50, refill_rate=5.0, cost=cost)
+        result["tool"] = tool_name
+        result["cost"] = cost
+        return result
+
+    # --- Block 3 ---
+    def acquire_agent_slot(user_id: str, agent_id: str, max_concurrent: int = 3, ttl: int = 300) -> bool:
+        key = f"concurrent:{user_id}"
+        now = time.time()
+        client.zremrangebyscore(key, "-inf", now)  # Clean expired
+        current = client.zcard(key)
+        if current >= max_concurrent:
+            return False
+        client.zadd(key, {agent_id: now + ttl})
+        client.expire(key, ttl)
+        return True
+
+    # --- Block 4 ---
+    def track_agent_spend(agent_id: str, tokens: int, model: str = "gpt-4"):
+        cost_per_1k = {"gpt-4": 0.03, "gpt-4o": 0.005}
+        cost = (tokens / 1000) * cost_per_1k.get(model, 0.01)
+        pipe = client.pipeline()
+        pipe.incrbyfloat(f"agent:spend:{agent_id}:total", cost)
+        pipe.incrbyfloat(f"agent:spend:{agent_id}:today", cost)
+        pipe.expire(f"agent:spend:{agent_id}:today", 86400)
+        pipe.execute()
+
diff --git a/tests/rate_limiting/test_04_hierarchical.py b/tests/rate_limiting/test_04_hierarchical.py
new file mode 100644
index 0000000..cbd5219
--- /dev/null
+++ b/tests/rate_limiting/test_04_hierarchical.py
@@ -0,0 +1,64 @@
+"""Integration tests for Rate Limiting - Hierarchical Rate Limiting.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_04_hierarchical(client):
+    """Run all code blocks from: Hierarchical Rate Limiting."""
+
+    # --- Block 1 ---
+    from dataclasses import dataclass
+
+    @dataclass
+    class Tier:
+        name: str           # "org", "team", "user", "agent", "model"
+        identifier: str     # "acme-corp", "engineering", "alice"
+        max_requests: int
+        max_tokens: int
+        window_seconds: int
+
+    tiers = [
+        Tier("org", "acme-corp", 10000, 1_000_000, 3600),
+        Tier("team", "engineering", 5000, 500_000, 3600),
+        Tier("user", "alice", 1000, 100_000, 3600),
+        Tier("agent", "agent-research-1", 200, 25_000, 3600),
+    ]
+
+    # --- Block 2 ---
+    def hierarchical_check(tiers: list, tokens: int) -> dict:
+        now = time.time()
+
+        # Phase 1: Read all counters in one pipeline
+        pipe = client.pipeline(transaction=False)
+        for tier in tiers:
+            window_num = int(now // tier.window_seconds)
+            pipe.get(f"hier:{tier.name}:{tier.identifier}:req:{window_num}")
+            pipe.get(f"hier:{tier.name}:{tier.identifier}:tok:{window_num}")
+        results = pipe.execute()
+
+        # Phase 2: Check each tier
+        blocked_by = None
+        for i, tier in enumerate(tiers):
+            current_req = int(results[i * 2] or 0)
+            current_tok = int(results[i * 2 + 1] or 0)
+            if current_req + 1 > tier.max_requests or current_tok + tokens > tier.max_tokens:
+                blocked_by = tier.name
+                break
+
+        # Phase 3: If allowed, increment all tiers atomically
+        if not blocked_by:
+            pipe = client.pipeline(transaction=True)
+            for tier in tiers:
+                window_num = int(now // tier.window_seconds)
+                req_key = f"hier:{tier.name}:{tier.identifier}:req:{window_num}"
+                tok_key = f"hier:{tier.name}:{tier.identifier}:tok:{window_num}"
+                pipe.incr(req_key); pipe.expire(req_key, tier.window_seconds)
+                pipe.incrby(tok_key, tokens); pipe.expire(tok_key, tier.window_seconds)
+            pipe.execute()
+
+        return {"allowed": blocked_by is None, "blocked_by": blocked_by}
+
diff --git a/tests/rate_limiting/test_05_cost_based.py b/tests/rate_limiting/test_05_cost_based.py
new file mode 100644
index 0000000..8826d75
--- /dev/null
+++ b/tests/rate_limiting/test_05_cost_based.py
@@ -0,0 +1,71 @@
+"""Integration tests for Rate Limiting - Cost-Based Rate Limiting.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_05_cost_based(client):
+    """Run all code blocks from: Cost-Based Rate Limiting."""
+
+    # --- Block 1 ---
+    MODEL_COSTS = {
+        "gpt-4":          {"input": 0.030, "output": 0.060},
+        "gpt-4o":         {"input": 0.005, "output": 0.015},
+        "gpt-3.5-turbo":  {"input": 0.001, "output": 0.002},
+        "claude-3-opus":  {"input": 0.015, "output": 0.075},
+        "claude-3-sonnet":{"input": 0.003, "output": 0.015},
+    }
+
+    def calculate_cost(input_tokens, output_tokens, model):
+        pricing = MODEL_COSTS.get(model, {"input": 0.01, "output": 0.02})
+        return round(
+            (input_tokens / 1000) * pricing["input"] +
+            (output_tokens / 1000) * pricing["output"], 6
+        )
+
+    # --- Block 2 ---
+    def check_budget(identifier, estimated_cost, budget=10.00, window=3600):
+        window_num = int(time.time() // window)
+        key = f"budget:{identifier}:{window_num}"
+
+        current = float(client.get(key) or 0)
+
+        if current + estimated_cost <= budget:
+            client.incrbyfloat(key, estimated_cost)
+            client.expire(key, window)
+            return {"allowed": True, "remaining": f"${budget - current - estimated_cost:.4f}"}
+        else:
+            return {"allowed": False, "remaining": f"${max(0, budget - current):.4f}"}
+
+    # --- Block 3 ---
+    def smart_model_select(identifier, preferred_model="gpt-4"):
+        window_num = int(time.time() // 3600)
+        current_spend = float(client.get(f"budget:{identifier}:{window_num}") or 0)
+        budget = 10.00
+        remaining_pct = (budget - current_spend) / budget
+
+        if remaining_pct > 0.5:
+            return preferred_model              # Plenty of budget
+        elif remaining_pct > 0.2:
+            return {"gpt-4": "gpt-4o"}.get(preferred_model, preferred_model)
+        elif remaining_pct > 0.05:
+            return "gpt-3.5-turbo"             # Emergency mode
+        else:
+            raise Exception("Budget exhausted")
+
+    # --- Block 4 ---
+    def track_spend(identifier, cost, model):
+        now = datetime.utcnow()
+        pipe = client.pipeline()
+        # Hourly, daily, monthly buckets
+        pipe.incrbyfloat(f"spend:{identifier}:hour:{now:%Y%m%d%H}", cost)
+        pipe.expire(f"spend:{identifier}:hour:{now:%Y%m%d%H}", 7200)
+        pipe.incrbyfloat(f"spend:{identifier}:day:{now:%Y%m%d}", cost)
+        pipe.expire(f"spend:{identifier}:day:{now:%Y%m%d}", 172800)
+        pipe.incrbyfloat(f"spend:{identifier}:month:{now:%Y%m}", cost)
+        pipe.expire(f"spend:{identifier}:month:{now:%Y%m}", 2764800)
+        pipe.execute()
+
diff --git a/tests/rate_limiting/test_06_production.py b/tests/rate_limiting/test_06_production.py
new file mode 100644
index 0000000..bb4137c
--- /dev/null
+++ b/tests/rate_limiting/test_06_production.py
@@ -0,0 +1,75 @@
+"""Integration tests for Rate Limiting - Production Patterns.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_06_production(client):
+    """Run all code blocks from: Production Patterns."""
+
+    # --- Block 1 ---
+    from fastapi import FastAPI, Response, HTTPException
+
+    app = FastAPI()
+
+    @app.post("/api/chat")
+    async def chat(prompt: str, response: Response):
+        result = limiter.check(tokens=count_tokens(prompt))
+
+        response.headers["X-RateLimit-Limit"] = str(result.max_requests)
+        response.headers["X-RateLimit-Remaining"] = str(result.remaining)
+        response.headers["X-RateLimit-Reset"] = str(result.reset_at)
+
+        if not result.allowed:
+            response.headers["Retry-After"] = str(int(result.retry_after_seconds))
+            raise HTTPException(status_code=429, detail={
+                "error": "rate_limit_exceeded",
+                "retry_after_seconds": result.retry_after_seconds,
+            })
+        return await call_llm(prompt)
+
+    # --- Block 2 ---
+    async def smart_llm_call(prompt, user_id):
+        # Tier 1: Preferred model
+        result = limiter.check(user_id, model="gpt-4")
+        if result.allowed:
+            return await call_llm(prompt, model="gpt-4")
+
+        # Tier 2: Cheaper model
+        result = limiter.check(user_id, model="gpt-3.5-turbo")
+        if result.allowed:
+            return await call_llm(prompt, model="gpt-3.5-turbo")
+
+        # Tier 3: Semantic cache
+        cached = await semantic_cache_lookup(prompt)
+        if cached:
+            return {"response": cached, "source": "cache"}
+
+        # Tier 4: Queue for later
+        await enqueue_request(user_id, prompt)
+        return {"response": "Queued for processing", "source": "queued"}
+
+    # --- Block 3 ---
+    class RateLimiterWithCircuitBreaker:
+        def __init__(self):
+            self.failure_count = 0
+            self.failure_threshold = 3
+            self.circuit_open_until = 0
+
+        def check(self, tokens):
+            if time.time() < self.circuit_open_until:
+                return {"allowed": True, "source": "circuit_breaker"}
+            try:
+                result = self._do_check(tokens)
+                self.failure_count = 0
+                return result
+            except Exception:
+                self.failure_count += 1
+                if self.failure_count >= self.failure_threshold:
+                    self.circuit_open_until = time.time() + 30
+                return {"allowed": True, "source": "fallback"}
+
diff --git a/tests/semantic_caching/__init__.py b/tests/semantic_caching/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/semantic_caching/test_01_getting_started.py b/tests/semantic_caching/test_01_getting_started.py
new file mode 100644
index 0000000..a0639d5
--- /dev/null
+++ b/tests/semantic_caching/test_01_getting_started.py
@@ -0,0 +1,153 @@
+"""Integration tests for Semantic Caching - Getting Started with Semantic Caching.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+import valkey
+
+
+def test_01_getting_started(raw_client, openai_client, openai_model):
+    """Run all code blocks from: Getting Started with Semantic Caching."""
+
+    # --- Block 1 ---
+    import numpy as np
+    import json
+    import hashlib
+    import time
+
+    client = raw_client
+
+    EMBEDDING_MODEL = "text-embedding-3-small"
+    EMBEDDING_DIM = 1536
+    SIMILARITY_THRESHOLD = 0.15  # COSINE distance: 0=identical, 2=opposite
+    CACHE_TTL = 3600  # 1 hour
+
+    # --- Block 2 ---
+    def create_cache_index():
+        """Create a vector index for the semantic cache."""
+        try:
+            client.execute_command(
+                "FT.CREATE", "cache_idx",
+                "SCHEMA",
+                "prompt", "TAG",
+                "response", "TAG",
+                "embedding", "VECTOR", "HNSW", "6",
+                "TYPE", "FLOAT32",
+                "DIM", str(EMBEDDING_DIM),
+                "DISTANCE_METRIC", "COSINE",
+            )
+            print("Cache index created")
+        except valkey.ResponseError:
+            print("Cache index already exists")
+
+    create_cache_index()
+
+    # --- Block 3 ---
+    def get_embedding(text: str) -> bytes:
+        """Embed text using OpenAI and return as FLOAT32 bytes."""
+        response = openai_client.embeddings.create(
+            model=EMBEDDING_MODEL,
+            input=text,
+        )
+        vec = response.data[0].embedding
+        return np.array(vec, dtype=np.float32).tobytes()
+
+    # --- Block 4 ---
+    def semantic_cache_lookup(prompt: str) -> dict:
+        """Check if a semantically similar prompt is cached."""
+        query_vec = get_embedding(prompt)
+
+        # KNN search: find the 1 nearest cached prompt
+        results = client.execute_command(
+            "FT.SEARCH", "cache_idx",
+            "*=>[KNN 1 @embedding $query_vec]",
+            "PARAMS", "2", "query_vec", query_vec,
+            "DIALECT", "2",
+        )
+
+        if results[0] > 0:
+            fields = results[2]
+            # Decode bytes from FT.SEARCH results (skip binary embedding field)
+            field_dict = {}
+            for j in range(0, len(fields), 2):
+                k = fields[j].decode() if isinstance(fields[j], bytes) else fields[j]
+                try:
+                    v = fields[j+1].decode() if isinstance(fields[j+1], bytes) else fields[j+1]
+                except UnicodeDecodeError:
+                    v = fields[j+1]  # binary field (embedding)
+                field_dict[k] = v
+            score = float(field_dict.get("__embedding_score", "999"))
+
+            if score < SIMILARITY_THRESHOLD:
+                return {
+                    "hit": True,
+                    "response": field_dict.get("response", ""),
+                    "cached_prompt": field_dict.get("prompt", ""),
+                    "score": score,
+                }
+
+        return {"hit": False}
+
+    def cache_response(prompt: str, response: str, embedding_bytes: bytes):
+        """Store a prompt+response in the cache."""
+        cache_key = f"cache:{hashlib.md5(prompt.encode()).hexdigest()}"
+        client.hset(cache_key, mapping={
+            "prompt": prompt,
+            "response": response,
+            "embedding": embedding_bytes,
+            "created_at": str(time.time()),
+        })
+        client.expire(cache_key, CACHE_TTL)
+
+    def ask_with_cache(prompt: str) -> dict:
+        """Main function: check cache first, then call LLM if needed."""
+        start = time.time()
+
+        # 1. Check cache
+        cache_result = semantic_cache_lookup(prompt)
+
+        if cache_result["hit"]:
+            elapsed = (time.time() - start) * 1000
+            return {
+                "response": cache_result["response"],
+                "source": "cache",
+                "similarity_score": cache_result["score"],
+                "latency_ms": round(elapsed, 1),
+            }
+
+        # 2. Cache miss - call LLM
+        llm_response = openai_client.chat.completions.create(
+            model=openai_model,
+            messages=[{"role": "user", "content": prompt}],
+        )
+        answer = llm_response.choices[0].message.content
+
+        # 3. Cache the response
+        embedding_bytes = get_embedding(prompt)
+        cache_response(prompt, answer, embedding_bytes)
+
+        elapsed = (time.time() - start) * 1000
+        return {
+            "response": answer,
+            "source": "llm",
+            "latency_ms": round(elapsed, 1),
+        }
+
+    # --- Block 5 ---
+    # First call - cache MISS (calls LLM)
+    result1 = ask_with_cache("What is Valkey?")
+    print(f"Source: {result1['source']}, Latency: {result1['latency_ms']}ms")
+    # Source: llm, Latency: 1250.3ms
+
+    # Second call - semantically similar - cache HIT!
+    result2 = ask_with_cache("Can you explain what Valkey is?")
+    print(f"Source: {result2['source']}, Latency: {result2['latency_ms']}ms")
+    # Source: cache, Latency: 12.5ms  ← 100x faster!
+
+    # Third call - different topic - cache MISS
+    result3 = ask_with_cache("How do I cook pasta?")
+    print(f"Source: {result3['source']}, Latency: {result3['latency_ms']}ms")
+    # Source: llm, Latency: 980.7ms
+
diff --git a/tests/semantic_caching/test_02_multiturn_caching.py b/tests/semantic_caching/test_02_multiturn_caching.py
new file mode 100644
index 0000000..2fed92b
--- /dev/null
+++ b/tests/semantic_caching/test_02_multiturn_caching.py
@@ -0,0 +1,136 @@
+"""Integration tests for Semantic Caching - Multi-Turn Conversation Caching.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_multiturn_caching(raw_client):
+    """Run all code blocks from: Multi-Turn Conversation Caching."""
+
+    # --- Block 1 ---
+    import numpy as np
+    import hashlib
+    import time
+    from openai import OpenAI
+
+    client = raw_client
+    openai_client = OpenAI()
+    EMBEDDING_DIM = 1536
+
+    # Index with TAG field for per-user cache isolation
+    try:
+        client.execute_command(
+            "FT.CREATE", "conv_cache_idx",
+            "SCHEMA",
+            "context_summary", "TAG",
+            "response", "TAG",
+            "user_id", "TAG",
+            "turn_count", "NUMERIC",
+            "embedding", "VECTOR", "HNSW", "6",
+            "TYPE", "FLOAT32",
+            "DIM", str(EMBEDDING_DIM),
+            "DISTANCE_METRIC", "COSINE",
+        )
+    except valkey.ResponseError:
+        pass
+
+    # --- Block 2 ---
+    def build_context_string(messages: list) -> str:
+        """Build a cacheable context string from conversation messages."""
+        # Use last 3 turns (6 messages: user+assistant pairs)
+        recent = messages[-6:]
+        parts = []
+        for msg in recent:
+            role = msg["role"]
+            content = msg["content"][:200]  # Truncate long messages
+            parts.append(f"{role}: {content}")
+        return " | ".join(parts)
+
+    def get_embedding(text: str) -> bytes:
+        response = openai_client.embeddings.create(
+            model="text-embedding-3-small", input=text,
+        )
+        return np.array(response.data[0].embedding, dtype=np.float32).tobytes()
+
+    # --- Block 3 ---
+    def lookup_conversation_cache(messages: list, user_id: str, threshold: float = 0.12):
+        """Search cache for similar conversation contexts, scoped to user."""
+        context = build_context_string(messages)
+        query_vec = get_embedding(context)
+
+        # Hybrid search: filter by user_id TAG + KNN on context embedding
+        results = client.execute_command(
+            "FT.SEARCH", "conv_cache_idx",
+            f"@user_id:{{{user_id}}}=>[KNN 1 @embedding $query_vec]",
+            "PARAMS", "2", "query_vec", query_vec,
+            "DIALECT", "2",
+        )
+
+        if results[0] > 0:
+            fields = results[2]
+            fd = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
+            score = float(fd.get("__embedding_score", "999"))
+            if score < threshold:
+                return {"hit": True, "response": fd.get("response", ""), "score": score}
+
+        return {"hit": False}
+
+    def store_conversation_cache(messages: list, response: str, user_id: str):
+        """Cache a conversation context + response."""
+        context = build_context_string(messages)
+        embedding_bytes = get_embedding(context)
+        key_hash = hashlib.md5(context.encode()).hexdigest()
+        cache_key = f"conv_cache:{user_id}:{key_hash}"
+
+        client.hset(cache_key, mapping={
+            "context_summary": context,
+            "response": response,
+            "user_id": user_id,
+            "turn_count": str(len(messages)),
+            "embedding": embedding_bytes,
+        })
+        client.expire(cache_key, 1800)  # 30 min TTL for conversations
+
+    # --- Block 4 ---
+    def chat_with_cache(messages: list, user_id: str) -> dict:
+        """Chat with LLM, using conversation-aware semantic cache."""
+        start = time.time()
+
+        # Check cache
+        cache = lookup_conversation_cache(messages, user_id)
+        if cache["hit"]:
+            return {
+                "response": cache["response"],
+                "source": "cache",
+                "score": cache["score"],
+                "latency_ms": round((time.time() - start) * 1000, 1),
+            }
+
+        # Cache miss - call LLM
+        llm = openai_client.chat.completions.create(
+            model="gpt-4", messages=messages,
+        )
+        answer = llm.choices[0].message.content
+
+        # Store in cache
+        store_conversation_cache(messages, answer, user_id)
+
+        return {
+            "response": answer,
+            "source": "llm",
+            "latency_ms": round((time.time() - start) * 1000, 1),
+        }
+
+    # Example multi-turn conversation
+    convo = [
+        {"role": "user", "content": "What is Valkey?"},
+        {"role": "assistant", "content": "Valkey is an open-source in-memory data store..."},
+        {"role": "user", "content": "How does it handle vector search?"},
+    ]
+
+    result = chat_with_cache(convo, user_id="user_123")
+    print(f"Source: {result['source']}, Latency: {result['latency_ms']}ms")
+
diff --git a/tests/semantic_caching/test_03_production.py b/tests/semantic_caching/test_03_production.py
new file mode 100644
index 0000000..b241da3
--- /dev/null
+++ b/tests/semantic_caching/test_03_production.py
@@ -0,0 +1,124 @@
+"""Integration tests for Semantic Caching - Production Patterns.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_production(raw_client):
+    """Run all code blocks from: Production Patterns."""
+
+    # --- Block 1 ---
+    # Test different thresholds to find the right balance
+    def evaluate_threshold(test_pairs: list, threshold: float):
+        """Evaluate cache quality at a given threshold."""
+        hits = 0
+        false_positives = 0
+
+        for query, expected_similar in test_pairs:
+            result = semantic_cache_lookup(query)
+            if result["hit"] and result["score"] < threshold:
+                hits += 1
+                if not expected_similar:
+                    false_positives += 1
+
+        hit_rate = hits / len(test_pairs)
+        fp_rate = false_positives / max(1, hits)
+        print(f"Threshold {threshold}: hit_rate={hit_rate:.1%}, false_positive_rate={fp_rate:.1%}")
+
+    # --- Block 2 ---
+
+    client = raw_client
+
+    def record_cache_event(event_type: str):
+        """Track cache hits and misses using atomic counters."""
+        client.incr(f"cache:metrics:{event_type}")
+
+        # Also track hourly for time-series analysis
+        from datetime import datetime
+        hour_key = datetime.now().strftime("%Y%m%d%H")
+        counter_key = f"cache:metrics:{event_type}:{hour_key}"
+        client.incr(counter_key)
+        client.expire(counter_key, 86400 * 7)  # Keep 7 days
+
+    def get_cache_stats() -> dict:
+        """Get current cache performance metrics."""
+        hits = int(client.get("cache:metrics:hit") or 0)
+        misses = int(client.get("cache:metrics:miss") or 0)
+        total = hits + misses
+        hit_rate = hits / total if total > 0 else 0
+
+        # Estimate cost savings (GPT-4: ~$0.03/1K tokens, avg 500 tokens/request)
+        avg_cost_per_call = 0.015
+        savings = hits * avg_cost_per_call
+
+        return {
+            "total_requests": total,
+            "hits": hits,
+            "misses": misses,
+            "hit_rate": round(hit_rate, 3),
+            "estimated_savings_usd": round(savings, 2),
+        }
+
+    # Usage in ask_with_cache:
+    # if cache_hit: record_cache_event("hit")
+    # else: record_cache_event("miss")
+
+    # --- Block 3 ---
+    # Strategy 1: Fixed TTL - simple, predictable
+    client.expire(cache_key, 3600)  # 1 hour
+
+    # Strategy 2: Category-based TTL
+    TTL_MAP = {
+        "factual": 86400,      # 24h - facts don't change fast
+        "opinion": 3600,       # 1h - opinions evolve
+        "real-time": 300,      # 5 min - stock prices, weather
+        "conversation": 1800,  # 30 min - chat context
+    }
+
+    # Strategy 3: Sliding TTL - reset on each hit
+    def cache_hit_with_refresh(cache_key: str, ttl: int = 3600):
+        """On cache hit, refresh the TTL to keep popular entries alive."""
+        response = client.hget(cache_key, "response")
+        client.expire(cache_key, ttl)  # Reset TTL
+        return response
+
+    # --- Block 4 ---
+    # Set maxmemory policy for cache eviction
+    # In valkey.conf or via CONFIG SET:
+    # maxmemory 1gb
+    # maxmemory-policy allkeys-lru
+    #
+    # LRU = Least Recently Used - evicts least-accessed cache entries first
+    # This is ideal for semantic caching where popular queries should stay
+
+    # Check memory usage
+    info = client.info("memory")
+    used_mb = info["used_memory"] / (1024 * 1024)
+    print(f"Memory: {used_mb:.1f} MB")
+
+    # Estimate cache capacity
+    # Each entry: ~6KB (1536 dims * 4 bytes + prompt + response text)
+    # 1 GB ≈ ~170,000 cached entries
+
+    # --- Block 5 ---
+    # Invalidate specific cached entries
+    def invalidate_by_topic(topic_keyword: str):
+        """Remove cached entries matching a topic (e.g., after a data update)."""
+        results = client.execute_command(
+            "FT.SEARCH", "cache_idx",
+            f"@prompt:{topic_keyword}",
+            "NOCONTENT",  # Only return keys, not fields
+        )
+
+        if results[0] > 0:
+            keys = results[1:]
+            for key in keys:
+                client.delete(key)
+            print(f"Invalidated {len(keys)} cached entries for '{topic_keyword}'")
+
+    # Example: product info changed, invalidate related cache
+    invalidate_by_topic("pricing")
+
diff --git a/tests/strands/__init__.py b/tests/strands/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/strands/test_01_getting_started.py b/tests/strands/test_01_getting_started.py
new file mode 100644
index 0000000..77dc3bd
--- /dev/null
+++ b/tests/strands/test_01_getting_started.py
@@ -0,0 +1,36 @@
+"""Integration tests for Strands - Getting Started with Strands + Valkey.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(client):
+    """Run all code blocks from: Getting Started with Strands + Valkey."""
+
+    # --- Block 1 ---
+    from strands_valkey_session_manager import ValkeySessionManager
+
+    # Connect to Valkey
+    client = client
+
+    # Create the session manager - one per session
+    session_manager = ValkeySessionManager(
+        session_id="user-42",
+        client=client,
+    )
+
+    # --- Block 2 ---
+    from strands import Agent
+
+    agent = Agent(
+        system_prompt="You are a helpful assistant.",
+        session_manager=session_manager,
+    )
+
+    # Strands automatically persists every turn to Valkey
+    response = agent("My name is Alex and I'm building a RAG pipeline.")
+    print(response)
+
diff --git a/tests/strands/test_02_session_internals.py b/tests/strands/test_02_session_internals.py
new file mode 100644
index 0000000..574e589
--- /dev/null
+++ b/tests/strands/test_02_session_internals.py
@@ -0,0 +1,61 @@
+"""Integration tests for Strands - Managing Session Data.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_session_internals(client):
+    """Run all code blocks from: Managing Session Data."""
+
+    # --- Block 1 ---
+    from strands import Agent
+    from strands_valkey_session_manager import ValkeySessionManager
+
+    # In a new Python process - history is restored from Valkey
+    client = client
+    session_manager = ValkeySessionManager(
+        session_id="user-42",  # same session ID
+        client=client,
+    )
+    agent = Agent(session_manager=session_manager)
+
+    response = agent("What was I just telling you about?")
+    print(response)
+    # Agent correctly recalls: "You mentioned you're building a RAG pipeline."
+
+    # --- Block 2 ---
+    # List all messages for this session
+    messages = session_manager.list_messages()
+    for msg in messages:
+        print(f"[{msg['role']}] {str(msg['content'])[:80]}")
+
+    # Or inspect raw keys directly
+    keys = client.keys("session:user-42*")
+    for k in sorted(keys):
+        print(k)
+
+    # --- Block 3 ---
+    from strands_valkey_session_manager import ValkeySessionManager
+
+    client = client
+    sm = ValkeySessionManager(session_id="user-42", client=client)
+
+    # Read session metadata
+    session = sm.read_session("user-42")
+    print(f"Created: {session.created_at}")
+
+    # Read agent state
+    agent_state = sm.read_agent("user-42", "default")
+    print(f"Message count: {agent_state.conversation_manager_state}")
+
+    # List all messages in order
+    messages = sm.list_messages()
+    print(f"{len(messages)} messages in session")
+    for msg in messages:
+        text_blocks = [b["text"] for b in msg.content if "text" in b]
+        preview = text_blocks[0][:80] if text_blocks else "[tool use/result]"
+        print(f"  [{msg.role:9}] {preview}")
+
diff --git a/tests/strands/test_03_managing_your_session_store.py b/tests/strands/test_03_managing_your_session_store.py
new file mode 100644
index 0000000..d499f9a
--- /dev/null
+++ b/tests/strands/test_03_managing_your_session_store.py
@@ -0,0 +1,61 @@
+"""Integration tests for Strands - Managing Your ValkeySession Store.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_managing_your_session_store(client):
+    """Run all code blocks from: Managing Your ValkeySession Store."""
+
+    # --- Block 1 ---
+    from strands import Agent
+    from strands_valkey_session_manager import ValkeySessionManager
+
+    client = client
+
+    # Two agents sharing the same session_id
+    researcher = Agent(
+        system_prompt="You are a research agent.",
+        session_manager=ValkeySessionManager(
+            session_id="workflow-001", client=client
+        ),
+    )
+    writer = Agent(
+        system_prompt="You are a writing agent.",
+        session_manager=ValkeySessionManager(
+            session_id="workflow-001", client=client
+        ),
+    )
+
+    researcher("Research the key benefits of Valkey for AI workloads.")
+    writer("Write a short blog intro about Valkey for AI.")
+
+    # Each agent has its own message keys under the same session
+    # session:workflow-001:agent:researcher:message:
+    # session:workflow-001:agent:writer:message:
+
+    # --- Block 2 ---
+    from strands_valkey_session_manager import ValkeySessionManager
+
+    client = client
+    sm = ValkeySessionManager(session_id="user-42", client=client)
+
+    # Removes session, agent state, and all messages for this session
+    sm.delete_session("user-42")
+    print("Session deleted")
+
+    # --- Block 3 ---
+    # List all keys for a session with their TTLs
+    keys = client.keys("session:user-42*")
+    for k in sorted(keys):
+        ttl = client.ttl(k)
+        print(f"{k}  (TTL: {ttl}s)")
+
+    # session:user-42                                              (TTL: 3598s)
+    # session:user-42:agent:default                                (TTL: 3597s)
+    # session:user-42:agent:default:message:                 (TTL: 3596s)
+    # session:user-42:agent:default:message:                 (TTL: 3596s)
+
diff --git a/tests/vector_search/__init__.py b/tests/vector_search/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/vector_search/test_01_getting_started.py b/tests/vector_search/test_01_getting_started.py
new file mode 100644
index 0000000..ad15597
--- /dev/null
+++ b/tests/vector_search/test_01_getting_started.py
@@ -0,0 +1,90 @@
+"""Integration tests for Vector Search - Getting Started with Vector Search.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_01_getting_started(raw_client):
+    """Run all code blocks from: Getting Started with Vector Search."""
+
+    # --- Block 1 ---
+    import numpy as np
+
+    client = raw_client
+
+    # Create an index with a 3-dimensional FLOAT32 vector field using HNSW
+    # Syntax: FT.CREATE index SCHEMA field_name VECTOR HNSW num_params TYPE DIM DISTANCE_METRIC
+    try:
+        client.execute_command(
+            "FT.CREATE", "doc_index",
+            "SCHEMA",
+            "embedding", "VECTOR", "HNSW", "6",
+            "TYPE", "FLOAT32",
+            "DIM", "3",
+            "DISTANCE_METRIC", "COSINE",
+        )
+        print("Index created.")
+    except valkey.ResponseError as e:
+        print(f"Index may already exist: {e}")
+
+    # --- Block 2 ---
+    # Helper: convert a list of floats to binary FLOAT32
+    def vec_to_bytes(vec):
+        return np.array(vec, dtype=np.float32).tobytes()
+
+    # Store documents with embeddings using HSET
+    documents = {
+        "doc:1": {"content": "Valkey is a fast in-memory data store",
+                  "embedding": vec_to_bytes([0.1, 0.2, 0.9])},
+        "doc:2": {"content": "Python is great for machine learning",
+                  "embedding": vec_to_bytes([0.8, 0.1, 0.3])},
+        "doc:3": {"content": "Vector search finds similar items fast",
+                  "embedding": vec_to_bytes([0.15, 0.25, 0.85])},
+        "doc:4": {"content": "Neural networks power modern AI",
+                  "embedding": vec_to_bytes([0.7, 0.2, 0.4])},
+    }
+
+    for key, fields in documents.items():
+        client.hset(key, mapping=fields)
+        print(f"Stored: {key}")
+
+    print(f"Stored {len(documents)} documents")
+
+    # --- Block 3 ---
+    # Query vector - find documents similar to this
+    query_vec = vec_to_bytes([0.12, 0.22, 0.88])
+
+    # FT.SEARCH with KNN query
+    # "*=>[KNN k @field $param]" finds the k nearest neighbors
+    results = client.execute_command(
+        "FT.SEARCH", "doc_index",
+        "*=>[KNN 3 @embedding $query_vec]",
+        "PARAMS", "2", "query_vec", query_vec,
+        "DIALECT", "2",
+    )
+
+    # Parse results
+    # results[0] = total count
+    # results[1] = first doc key, results[2] = first doc fields
+    # results[3] = second doc key, results[4] = second doc fields, etc.
+    num_results = results[0]
+    print(f"Found {num_results} results:\n")
+
+    for i in range(1, len(results), 2):
+        doc_key = results[i]
+        fields = results[i + 1]
+        # fields is a list of [field_name, value, field_name, value, ...]
+        field_dict = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
+        score = field_dict.get("__embedding_score", "N/A")
+        content = field_dict.get("content", "")
+        print(f"  {doc_key} (score: {score})")
+        print(f"    {content}\n")
+
+    # --- Block 4 ---
+    # Get index metadata
+    info = client.execute_command("FT.INFO", "doc_index")
+    print(info)
+
diff --git a/tests/vector_search/test_02_semantic_search.py b/tests/vector_search/test_02_semantic_search.py
new file mode 100644
index 0000000..00db609
--- /dev/null
+++ b/tests/vector_search/test_02_semantic_search.py
@@ -0,0 +1,100 @@
+"""Integration tests for Vector Search - Semantic Search with Embeddings.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_02_semantic_search(raw_client):
+    """Run all code blocks from: Semantic Search with Embeddings."""
+
+    # --- Block 1 ---
+    try:
+        client.execute_command(
+            "FT.CREATE", "semantic_idx",
+            "SCHEMA",
+            "content", "TAG",
+            "embedding", "VECTOR", "HNSW", "6",
+            "TYPE", "FLOAT32",
+            "DIM", str(EMBEDDING_DIM),
+            "DISTANCE_METRIC", "COSINE",
+        )
+        print("Index created")
+    except valkey.ResponseError:
+        print("Index already exists")
+
+    # --- Block 2 ---
+    def get_embedding(text: str) -> bytes:
+        """Get embedding from OpenAI and return as binary FLOAT32."""
+        response = openai_client.embeddings.create(
+            model=EMBEDDING_MODEL,
+            input=text,
+        )
+        vec = response.data[0].embedding  # list of 1536 floats
+        return np.array(vec, dtype=np.float32).tobytes()
+
+    # Sample documents
+    docs = [
+        "Valkey is an open-source in-memory data store forked from Redis",
+        "Vector similarity search finds documents by semantic meaning",
+        "Machine learning models convert text into numerical embeddings",
+        "Python is the most popular language for AI development",
+        "HNSW algorithm provides fast approximate nearest neighbor search",
+        "Kubernetes orchestrates containerized applications at scale",
+        "LLMs like GPT-4 generate human-like text responses",
+        "Rate limiting protects APIs from excessive usage",
+    ]
+
+    # Embed and store each document
+    for i, text in enumerate(docs):
+        embedding_bytes = get_embedding(text)
+        client.hset(f"doc:{i}", mapping={
+            "content": text,
+            "embedding": embedding_bytes,
+        })
+        print(f"Stored doc:{i}: {text[:50]}...")
+
+    print(f"\nStored {len(docs)} documents with embeddings")
+
+    # --- Block 3 ---
+    def semantic_search(query: str, k: int = 3):
+        """Search for documents semantically similar to the query."""
+
+        # Embed the query
+        query_vec = get_embedding(query)
+
+        # KNN search
+        results = client.execute_command(
+            "FT.SEARCH", "semantic_idx",
+            f"*=>[KNN {k} @embedding $query_vec]",
+            "PARAMS", "2", "query_vec", query_vec,
+            "DIALECT", "2",
+        )
+
+        # Parse results
+        matches = []
+        for i in range(1, len(results), 2):
+            doc_key = results[i]
+            fields = results[i + 1]
+            field_dict = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
+            matches.append({
+                "key": doc_key,
+                "content": field_dict.get("content", ""),
+                "score": field_dict.get("__embedding_score", "N/A"),
+            })
+        return matches
+
+    # Try different queries
+    queries = [
+        "What is Valkey?",
+        "How do I find similar documents?",
+        "Tell me about large language models",
+    ]
+
+    for q in queries:
+        print(f"\nQuery: '{q}'")
+        for m in semantic_search(q, k=2):
+            print(f"  → {m['content']} (score: {m['score']})")
+
diff --git a/tests/vector_search/test_03_hybrid_search.py b/tests/vector_search/test_03_hybrid_search.py
new file mode 100644
index 0000000..bed91a5
--- /dev/null
+++ b/tests/vector_search/test_03_hybrid_search.py
@@ -0,0 +1,87 @@
+"""Integration tests for Vector Search - Hybrid Search.
+
+Auto-generated from content markdown. Runs the cookbook code blocks
+sequentially against real Valkey and external services.
+"""
+
+import pytest
+
+
+def test_03_hybrid_search(raw_client):
+    """Run all code blocks from: Hybrid Search."""
+
+    # --- Block 1 ---
+    import numpy as np
+
+    client = raw_client
+
+    def vec_to_bytes(vec):
+        return np.array(vec, dtype=np.float32).tobytes()
+
+    # Create index with VECTOR + TAG + NUMERIC + TEXT fields
+    try:
+        client.execute_command(
+            "FT.CREATE", "articles_idx",
+            "SCHEMA",
+            "title", "TAG",
+            "category", "TAG",
+            "year", "NUMERIC",
+            "embedding", "VECTOR", "HNSW", "6",
+            "TYPE", "FLOAT32",
+            "DIM", "4",
+            "DISTANCE_METRIC", "COSINE",
+        )
+        print("Index created with TAG + NUMERIC + VECTOR")
+    except valkey.ResponseError as e:
+        print(f"Index exists: {e}")
+
+    # --- Block 2 ---
+    query_vec = vec_to_bytes([0.88, 0.1, 0.2, 0.35])
+
+    # TAG filter: @category:{tech}
+    # Combined with KNN: @category:{tech}=>[KNN 3 @embedding $query_vec]
+    results = client.execute_command(
+        "FT.SEARCH", "articles_idx",
+        "@category:{tech}=>[KNN 3 @embedding $query_vec]",
+        "PARAMS", "2", "query_vec", query_vec,
+        "DIALECT", "2",
+    )
+
+    print(f"Tech articles (KNN): {results[0]} results")
+    for i in range(1, len(results), 2):
+        fields = results[i + 1]
+        fd = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
+        print(f"  {results[i]}: {fd.get('title')} [{fd.get('category')}]")
+
+    # Only returns tech articles - food and finance are excluded!
+
+    # --- Block 3 ---
+    # NUMERIC filter: @year:[2024 +inf]
+    results = client.execute_command(
+        "FT.SEARCH", "articles_idx",
+        "@year:[2024 +inf]=>[KNN 3 @embedding $query_vec]",
+        "PARAMS", "2", "query_vec", query_vec,
+        "DIALECT", "2",
+    )
+
+    print(f"\nArticles from 2024+: {results[0]} results")
+    for i in range(1, len(results), 2):
+        fields = results[i + 1]
+        fd = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
+        print(f"  {results[i]}: {fd.get('title')} ({fd.get('year')})")
+
+    # --- Block 4 ---
+    # Combine multiple filters
+    results = client.execute_command(
+        "FT.SEARCH", "articles_idx",
+        "(@category:{tech} @year:[2024 +inf])=>[KNN 3 @embedding $query_vec]",
+        "PARAMS", "2", "query_vec", query_vec,
+        "DIALECT", "2",
+    )
+
+    print(f"\nTech articles from 2024+: {results[0]} results")
+    for i in range(1, len(results), 2):
+        fields = results[i + 1]
+        fd = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
+        print(f"  {results[i]}: {fd.get('title')} [{fd.get('category')}, {fd.get('year')}]")
+
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..0364747
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,5106 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version < '3.13'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
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