diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 778170d..630971f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -2,9 +2,9 @@ name: CI
 
 on:
   push:
-    branches: [master]
+    branches: [master, main, feat/*]
   pull_request:
-    branches: [master]
+    branches: [master, main, feat/*]
 
 permissions:
   contents: read
@@ -17,23 +17,34 @@ jobs:
     timeout-minutes: 10
 
     steps:
+      # ── Checkout backend ──
       - uses: actions/checkout@v4
+        with:
+          path: chatbot-plugin
+
+      # ── Checkout SDK (sibling directory so ../chatbot-plugin-sdk resolves) ──
+      - uses: actions/checkout@v4
+        with:
+          repository: Teng91/chatbot-plugin-sdk
+          path: chatbot-plugin-sdk
 
       - uses: astral-sh/setup-uv@v5
         with:
           python-version: "3.11"
 
       - name: Install dependencies
+        working-directory: ./chatbot-plugin
         run: uv sync --all-groups
 
       - name: Run tests
+        working-directory: ./chatbot-plugin
         run: uv run pytest src/tests/ -v --tb=short --cov=chatbot_plugin --cov-report=xml:coverage.xml --junitxml=pytest.xml
 
       - name: Post coverage comment
         if: github.event_name == 'pull_request'
         uses: MishaKav/pytest-coverage-comment@v1.6.0
         with:
-          pytest-xml-coverage-path: ./coverage.xml
-          junitxml-path: ./pytest.xml
+          pytest-xml-coverage-path: ./chatbot-plugin/coverage.xml
+          junitxml-path: ./chatbot-plugin/pytest.xml
           title: Test Coverage
           github-token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.gitignore b/.gitignore
index 91c2602..6228439 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,3 +14,5 @@ htmlcov/
 src/DB_COMPARISON.md
 .pytest_cache/
 providers.toml
+coverage.xml
+pytest.xml
diff --git a/alembic/versions/002_toolbox_schema.py b/alembic/versions/002_toolbox_schema.py
index 8c067f2..fe38e55 100644
--- a/alembic/versions/002_toolbox_schema.py
+++ b/alembic/versions/002_toolbox_schema.py
@@ -41,6 +41,9 @@ def upgrade() -> None:
     op.create_index("idx_articles_url", "articles", ["url"])
     op.create_unique_constraint("articles_url_key", "articles", ["url"])
 
+    # pgvector: enable extension
+    op.execute("CREATE EXTENSION IF NOT EXISTS vector")
+
     # Create article_chunks table
     op.create_table(
         "article_chunks",
@@ -49,24 +52,32 @@ def upgrade() -> None:
         sa.Column("chunk_index", sa.Integer(), nullable=False),
         sa.Column("content", sa.Text(), nullable=False),
         sa.Column("dense_vector", sa.Text(), nullable=True),
-        sa.Column("sparse_vector", JSONB, nullable=True),
+        sa.Column("sparse_vector", sa.Text(), nullable=True),
         sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.func.now(), nullable=False),
     )
 
-    # pgvector: enable extension + cast dense_vector to proper vector type
-    op.execute("CREATE EXTENSION IF NOT EXISTS vector")
+    # Cast dense_vector to proper vector type
     op.execute("ALTER TABLE article_chunks ALTER COLUMN dense_vector TYPE vector(1024) USING dense_vector::vector")
+    # Cast sparse_vector to proper sparsevec type
+    op.execute("ALTER TABLE article_chunks ALTER COLUMN sparse_vector TYPE sparsevec(250002) USING sparse_vector::sparsevec")
 
     op.create_unique_constraint("uq_article_chunk_idx", "article_chunks", ["article_id", "chunk_index"])
     op.create_index("idx_chunks_article_id", "article_chunks", ["article_id"])
 
-    # HNSW index for dense similarity search (required by spec)
+    # HNSW index for dense similarity search
     op.execute(
         "CREATE INDEX hnsw_chunks_dense ON article_chunks USING hnsw (dense_vector vector_cosine_ops)"
     )
+    # Simple index for sparse vector (sparsevec HNSW not available in pgvector 0.8.0)
+    op.execute(
+        "CREATE INDEX idx_chunks_sparse ON article_chunks (sparse_vector)"
+    )
 
 
 def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS idx_chunks_sparse")
+    op.execute("DROP INDEX IF EXISTS hnsw_chunks_dense")
+
     op.drop_table("article_chunks")
     op.drop_table("articles")
 
diff --git a/pyproject.toml b/pyproject.toml
index bd6b261..7d6f340 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,26 +1,24 @@
 [project]
 name = "chatbot-plugin"
-version = "0.1.0"
-description = "Vector storage toolbox for scrape-and-analyze — chunk storage and retrieval"
+version = "0.3.0"
+description = "OpenAI-compatible RAG chat backend"
 requires-python = ">=3.11"
 dependencies = [
     "fastapi",
     "uvicorn[standard]",
     "pydantic>=2.0",
-    "pydantic-settings>=2.0",
-    "structlog",
-    "sqlalchemy[asyncio]>=2.0",
-    "asyncpg",
-    "alembic",
-    "pgvector",
+    "chatbot-plugin-sdk",
 ]
 
+[tool.uv.sources]
+chatbot-plugin-sdk = { path = "../chatbot-plugin-sdk", editable = true }
+
 [dependency-groups]
 dev = [
+    "httpx",
     "pytest",
     "pytest-cov",
     "pytest-asyncio",
-    "httpx",
 ]
 
 [build-system]
diff --git a/scripts/fix_sparsevec.py b/scripts/fix_sparsevec.py
new file mode 100644
index 0000000..b79d134
--- /dev/null
+++ b/scripts/fix_sparsevec.py
@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""Fix existing databases: sparse_vector column type jsonb -> sparsevec.
+
+Migration 002 originally created sparse_vector as JSONB (a bug in the migration).
+This script safely converts existing data to the correct sparsevec(250002) type.
+"""
+
+import asyncio
+
+from sqlalchemy import text
+from sqlalchemy.ext.asyncio import create_async_engine
+from pgvector import SparseVector
+from chatbot_plugin.config import settings
+
+
+async def fix_sparse_vector() -> None:
+    engine = create_async_engine(settings.database_url)
+
+    async with engine.begin() as conn:
+        # Check current type
+        result = await conn.execute(text(
+            "SELECT data_type "
+            "FROM information_schema.columns "
+            "WHERE table_name = 'article_chunks' AND column_name = 'sparse_vector'"
+        ))
+        row = result.fetchone()
+        if row is None:
+            print("Column sparse_vector not found — nothing to fix.")
+            return
+
+        current_type = row[0]
+
+        if current_type == "sparsevec":
+            print(f"sparse_vector is already {current_type} — nothing to fix.")
+            return
+
+        print(f"Current type: {current_type}. Converting to sparsevec(250002)...")
+
+        # Ensure extension is available
+        await conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
+
+        # Drop any existing index on sparse_vector first
+        await conn.execute(text("DROP INDEX IF EXISTS idx_chunks_sparse"))
+
+        # Add a temporary sparsevec column
+        await conn.execute(text(
+            "ALTER TABLE article_chunks "
+            "ADD COLUMN IF NOT EXISTS sparse_vector_new sparsevec(250002)"
+        ))
+
+        # Transfer data: read jsonb dicts and write as SparseVector
+        result = await conn.execute(text(
+            "SELECT id, sparse_vector FROM article_chunks WHERE sparse_vector IS NOT NULL"
+        ))
+        rows = result.fetchall()
+        print(f"Converting {len(rows)} rows...")
+
+        for chunk_id, sparse_data in rows:
+            if sparse_data is None:
+                continue
+            # PostgreSQL sparsevec uses 1-based indices in text format:
+            # '{1:val,2:val}/dims'.
+            items = ",".join(f"{int(k) + 1}:{float(v)}" for k, v in sorted(sparse_data.items()))
+            sparse_text = f"{{{items}}}/{settings.sparse_dimension}"
+            await conn.execute(
+                text("UPDATE article_chunks SET sparse_vector_new = text(:sv)::sparsevec(250002) WHERE id = :id"),
+                {"sv": sparse_text, "id": chunk_id},
+            )
+
+        # Drop old column and rename
+        await conn.execute(text("ALTER TABLE article_chunks DROP COLUMN sparse_vector"))
+        await conn.execute(text("ALTER TABLE article_chunks RENAME COLUMN sparse_vector_new TO sparse_vector"))
+
+        # Note: pgvector 0.8.0 does not provide sparsevec HNSW indexing.
+        # A simple index on the column is sufficient for now.
+        await conn.execute(text(
+            "CREATE INDEX idx_chunks_sparse ON article_chunks (sparse_vector)"
+        ))
+
+        print("Done. sparse_vector is now sparsevec(250002).")
+
+    await engine.dispose()
+
+
+if __name__ == "__main__":
+    asyncio.run(fix_sparse_vector())
diff --git a/specs/integration.md b/specs/integration.md
index 9127a2c..9ae9f1d 100644
--- a/specs/integration.md
+++ b/specs/integration.md
@@ -21,7 +21,7 @@ uvicorn chatbot_plugin.main:app --reload
 CHATBOT_DATABASE_URL=postgresql+asyncpg://... uvicorn chatbot_plugin.main:app --host 0.0.0.0 --port 8000
 ```
 
-## How External Services Send Data
+## How External Services Send Data, Search, and Chat
 
 Scrape-and-analyze (or any service) POSTs to the toolbox:
 
@@ -42,8 +42,32 @@ async def send_chunks():
         assert resp.status_code == 201
 ```
 
+```python
+async def search():
+    async with httpx.AsyncClient() as client:
+        resp = await client.post(
+            "http://toolbox:8000/tools/search",
+            json={"query": "What is RAG?", "top_k": 10},
+        )
+        assert resp.status_code == 200
+        print(resp.json()["chunks"])
+```
+
+```python
+async def chat():
+    async with httpx.AsyncClient() as client:
+        resp = await client.post(
+            "http://toolbox:8000/tools/chat",
+            json={"message": "Explain RAG in simple terms"},
+        )
+        assert resp.status_code == 200
+        print(resp.json()["reply"])
+```
+
 This exposes:
 - `POST /tools/chunks` — store article + chunks
+- `POST /tools/search` — hybrid dense + sparse search
+- `POST /tools/chat` — chat with RAG context
 
 ## Environment Variables
 
@@ -56,6 +80,14 @@ CHATBOT_DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/chatb
 # Embedding model config (must match what scrape-and-analyze uses)
 CHATBOT_EMBEDDING_MODEL=BAAI/bge-m3
 CHATBOT_EMBEDDING_DIMENSION=1024
+CHATBOT_SPARSE_DIMENSION=250002
+CHATBOT_RRF_K=60
+CHATBOT_SEARCH_CANDIDATES=50
+CHATBOT_MAX_CONTEXT_CHUNKS=10
+
+# LLM config (optional — only needed for /tools/chat)
+CHATBOT_LLM_API_KEY=sk-ant-api-...
+CHATBOT_LLM_MODEL=claude-sonnet-4-6-20250514
 ```
 
 ## Database
@@ -84,7 +116,7 @@ CREATE TABLE article_chunks (
     chunk_index   INT NOT NULL,
     content       TEXT NOT NULL,
     dense_vector  vector(1024),
-    sparse_vector JSONB,
+    sparse_vector sparsevec(250002),
     created_at    TIMESTAMPTZ DEFAULT NOW(),
     UNIQUE(article_id, chunk_index)
 );
diff --git a/specs/rag-pipeline.md b/specs/rag-pipeline.md
index 0f7d23f..d0083f6 100644
--- a/specs/rag-pipeline.md
+++ b/specs/rag-pipeline.md
@@ -93,8 +93,108 @@ CREATE INDEX hnsw_chunks_dense ON article_chunks
 2. **Vector dimension validation**: Reject chunks where `len(dense_vector)` != `CHATBOT_EMBEDDING_DIMENSION` (default 1024).
 3. **No embedding generation here**: Scrape-and-analyze (or a separate embedding service) generates vectors before calling this API.
 
+## Phase 3: Query Embedding + Hybrid Retrieval + Chat
+
+### Architecture
+
+```
+User Query
+    │
+    ▼
+Embed Query (BGE-M3 in-process CPU)
+    │ → query_dense_vec (1024-dim float)
+    │ → query_sparse_vec (250002-dim sparsevec)
+    │
+    ▼
+Parallel Hybrid Search
+    │
+    ├─ Dense:  cosine similarity via pgvector HNSW
+    │          ORDER BY dense_vector <=> query_vec LIMIT candidates
+    │
+    └─ Sparse: max inner product via pgvector sparsevec
+               ORDER BY sparse_vector <#> query_sparse LIMIT candidates
+    │
+    ▼
+RRF Fusion (k=60)
+    score_d(i) = 1 / (k + rank_d(i))
+    score_s(i) = 1 / (k + rank_s(i))
+    final(i)   = score_d(i) + score_s(i)
+    │
+    ▼
+Top-K Chunks (default 10)
+    │ Deduplicate, assemble into context
+    │
+    ▼
+Chat Response (Claude API via HTTP)
+    System prompt + context chunks + user message
+    Each context segment annotated [source: article_title]
+```
+
+### Embedding Model
+
+- **Model**: `BAAI/bge-m3` (loaded via `FlagEmbedding` library)
+- **Loading**: Singleton, loaded once at startup in FastAPI lifespan
+- **CPU inference**: ~300-500ms per query, ~2GB RAM footprint
+- **Output**: dense (1024-dim) + sparse (250002-dim token weights)
+- Sparse dimension: `250002` (BGE-M3 tokenizer vocab size)
+
+### Hybrid Search Algorithm
+
+1. Embed query text via `embed_query(text)` → `(dense_vec, sparse_vec)`
+2. **Dense candidates**: `SELECT chunk_id, article_id, ... FROM article_chunks ORDER BY dense_vector <=> :dense LIMIT :candidates`
+3. **Sparse candidates**: `SELECT chunk_id, article_id, ... FROM article_chunks ORDER BY sparse_vector <#> :sparse LIMIT :candidates`
+4. **RRF fusion**: Combine the two candidate lists
+   - Rank each chunk in each list (1-indexed)
+   - `score = 1/(rrf_k + rank_dense) + 1/(rrf_k + rank_sparse)`
+   - If a chunk only appears in one list, its missing rank contributes 0
+5. Sort by final score descending
+6. Return top `top_k` chunks
+
+### Chat Service
+
+1. Call search service with `message` as query → top chunks
+2. Deduplicate chunks by `article_id`
+3. Build context string:
+   ```
+   [source: Article Title]
+   Chunk content...
+
+   [source: Another Title]
+   Another chunk...
+   ```
+4. Send to LLM (fallback chain):
+   1. **Anthropic Claude** — if `CHATBOT_LLM_API_KEY` is set. On any error, fall through.
+   2. **Google Gemini** — if `CHATBOT_GEMINI_API_KEY` is set. On any error (rate limit, unreachable, etc.), fall through.
+   3. **Raw context fallback** — if all LLM calls fail or no key is configured, return the assembled context + question directly so the caller can still inspect retrieved sources.
+   - System: "You are a helpful research assistant..."
+   - User: context + "\n\nQuestion: {message}"
+5. Return: `{reply, articles_used, chunks}`
+
+### Sparse Vector Storage
+
+- PostgreSQL type: `sparsevec(250002)` via pgvector
+- SQLAlchemy type: `pgvector.sqlalchemy.SPARSEVEC(250002)`
+- Index: no HNSW for sparsevec (pgvector 0.7+ uses btree or ivfflat)
+  - For now create a standard btree index on `(sparse_vector)`
+- Input translation: API receives `dict[str, float]` → `SparseVector(dict, dim=250002)` → DB
+- Output translation: DB → `SparseVector.from_text()` → consumed by search
+
 ## Configuration
 
 | Variable | Default | Description |
 |----------|---------|-------------|
 | `CHATBOT_EMBEDDING_DIMENSION` | `1024` | Dense vector dimension (matches embedding model) |
+| `CHATBOT_SPARSE_DIMENSION` | `250002` | Sparse vector dimension (BGE-M3 vocab size) |
+| `CHATBOT_RRF_K` | `60` | RRF constant k |
+| `CHATBOT_SEARCH_CANDIDATES` | `50` | Number of candidates from each sub-search before RRF |
+| `CHATBOT_MAX_CONTEXT_CHUNKS` | `10` | Max chunks to include in chat context |
+| `CHATBOT_LLM_API_KEY` | `""` | Anthropic API key (optional) |
+| `CHATBOT_LLM_MODEL` | `"claude-sonnet-4-6-20250514"` | Claude model name |
+| `CHATBOT_GEMINI_API_KEY` | `""` | Google Gemini API key (fallback) |
+| `CHATBOT_GEMINI_MODEL` | `"gemini-2.0-flash"` | Gemini model name |
+
+## New Dependencies
+
+- `FlagEmbedding` — BGE-M3 embedding (dense + sparse)
+- `torch` — PyTorch backend (CPU mode)
+- `anthropic` — Claude API client
diff --git a/specs/toolbox-api.md b/specs/toolbox-api.md
index 560bde8..957344c 100644
--- a/specs/toolbox-api.md
+++ b/specs/toolbox-api.md
@@ -15,6 +15,52 @@ All endpoints are under `/tools`.
 
 ---
 
+## SDK Usage (Python)
+
+Services may also import the toolbox as a Python SDK instead of calling HTTP endpoints.
+
+```python
+from chatbot_plugin.sdk import IngestToolboxSDK, QueryToolboxSDK
+
+# Ingest SDK — handles text ingestion (normalise → chunk → embed → save)
+from chatbot_plugin.sdk import IngestToolboxSDK
+
+ingest_sdk = IngestToolboxSDK()
+ingest_sdk.configure(
+    dbname="chatbot_plugin",
+    user="postgres",
+    password="postgres",
+    embedding_model_api="http://localhost:8080",  # embedding microservice
+)
+
+await ingest_sdk.ingest(
+    full_text="Retrieval augmented generation is a technique ...",
+    metadata={"url": "https://example.com/article", "title": "RAG 101"},
+)
+
+# Query SDK — handles read-only RAG queries
+query_sdk = QueryToolboxSDK()
+query_sdk.configure(
+    dbname="chatbot_plugin",
+    user="postgres",
+    password="postgres",
+    embedding_model_api="http://localhost:8080",
+)
+
+resp = await query_sdk.query("What is RAG?")
+print(resp.reply)
+```
+
+### Class Hierarchy
+
+| Class | DB | Embedding | Write | Query | LLM |
+|-------|----|-----------|-------|-------|-----|
+| `BaseRagProcessor` (not exported) | ✓ (internal) | — | — | `search()`, `chat()` | ✓ (fallback) |
+| `RagArticleProcessor` | ✓ | ✓ (HTTP) | `ingest()` | inherits base | inherits base |
+| `RagQueryProcessor` | ✓ | ✓ (HTTP) | — | `query()` → `chat()` | inherits base |
+
+---
+
 ## `POST /tools/chunks`
 
 Store or update an article and its pre-chunked, pre-embedded data.
@@ -89,3 +135,129 @@ Store or update an article and its pre-chunked, pre-embedded data.
 - `title` omitted → stored as `NULL`
 - Empty `metadata` → stored as `NULL`/`{}`
 - Duplicate `chunk_index` values in the same request → last one wins (databases with `ON CONFLICT` will upsert)
+
+---
+
+## `POST /tools/search`
+
+Hybrid dense + sparse search. The toolbox embeds the query text locally with BGE-M3, then queries pgvector for the most similar chunks.
+
+### Request
+
+```json
+{
+  "query": "What is retrieval augmented generation?",
+  "top_k": 10
+}
+```
+
+| Field   | Type     | Required | Description                                      |
+|---------|----------|----------|--------------------------------------------------|
+| `query` | `string` | yes      | Raw query text (will be embedded by toolbox)     |
+| `top_k` | `integer`| no       | Number of top chunks to return (default: 10, max: 100) |
+
+### Response `200`
+
+```json
+{
+  "chunks": [
+    {
+      "chunk_id": "uuid-string",
+      "article_id": "uuid-string",
+      "article_title": "Article Title",
+      "article_url": "https://example.com",
+      "chunk_index": 0,
+      "content": "RAG is a technique...",
+      "score": 0.876
+    }
+  ]
+}
+```
+
+| Field              | Type     | Description                                          |
+|--------------------|----------|------------------------------------------------------|
+| `chunks`           | `array`  | Ordered list of matching chunks (best first)         |
+| `chunks[].chunk_id`| `string (uuid)` | Chunk UUID                                    |
+| `chunks[].article_id`| `string (uuid)` | Parent article UUID                          |
+| `chunks[].article_title`| `string` | Article title or `null`                      |
+| `chunks[].article_url` | `string` | Source URL                                   |
+| `chunks[].chunk_index` | `integer`| Position within article                            |
+| `chunks[].content` | `string` | Chunk text content                                   |
+| `chunks[].score`   | `float`  | RRF fusion score (higher is better)                  |
+
+### Error Responses
+
+| Code | Condition |
+|------|-----------|
+| `400` | Query text is empty or whitespace-only |
+| `500` | Embedding model is not loaded or LLM call failed |
+
+---
+
+## `POST /tools/chat`
+
+Chat with RAG context. Performs hybrid search, assembles top chunks into context, calls LLM, and returns the reply with citations.
+
+### Request
+
+```json
+{
+  "message": "Explain RAG in simple terms"
+}
+```
+
+| Field     | Type     | Required | Description                                      |
+|-----------|----------|----------|--------------------------------------------------|
+| `message` | `string` | yes      | User message (will be embedded by toolbox)       |
+
+### Response `200`
+
+```json
+{
+  "reply": "RAG is a technique where...",
+  "articles_used": [
+    {
+      "id": "uuid-string",
+      "title": "Article Title",
+      "url": "https://example.com"
+    }
+  ],
+  "chunks": [
+    {
+      "chunk_id": "uuid-string",
+      "article_id": "uuid-string",
+      "chunk_index": 0,
+      "content": "RAG is a technique...",
+      "score": 0.876
+    }
+  ]
+}
+```
+
+| Field           | Type     | Description                                          |
+|-----------------|----------|------------------------------------------------------|
+| `reply`         | `string` | LLM-generated answer                                 |
+| `articles_used` | `array`  | Unique articles that contributed to the context      |
+| `chunks`        | `array`  | All chunks used in the context (ordered by score)    |
+| `chunks[].chunk_id`| `string (uuid)` | Chunk UUID                                    |
+| `chunks[].article_id`| `string (uuid)` | Parent article UUID                          |
+| `chunks[].chunk_index`| `integer`| Position within article                            |
+| `chunks[].content` | `string` | Chunk text content                                   |
+| `chunks[].score` | `float`  | RRF fusion score                                     |
+
+### Error Responses
+
+| Code | Condition |
+|------|-----------|
+| `400` | Message is empty or whitespace-only |
+| `500` | Embedding model is not loaded or LLM call failed |
+| `503` | LLM API key not configured |
+
+---
+
+## Sparse Vector Format
+
+- Stored as `sparsevec` in PostgreSQL / pgvector
+- Dimension: `250002` (BGE-M3 tokenizer vocab size)
+- API layer still accepts `dict[str, float]` (token_index → weight)
+- The toolbox translates the dict to sparsevec at storage time
diff --git a/src/chatbot_plugin/config.py b/src/chatbot_plugin/config.py
deleted file mode 100644
index 2dec95e..0000000
--- a/src/chatbot_plugin/config.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Configuration for the toolbox.
-
-All settings are read from environment variables with the `CHATBOT_` prefix.
-"""
-
-from pydantic_settings import BaseSettings
-
-
-class ChatbotSettings(BaseSettings):
-    """Toolbox settings."""
-
-    # Database
-    database_url: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/chatbot_plugin"
-
-    # Embedding model config (must match what scrape-and-analyze uses)
-    embedding_model: str = "BAAI/bge-m3"
-    embedding_dimension: int = 1024
-
-    model_config = {"env_prefix": "CHATBOT_"}
-
-
-settings = ChatbotSettings()
diff --git a/src/chatbot_plugin/contracts/__init__.py b/src/chatbot_plugin/contracts/__init__.py
index 1a3495d..7c25773 100644
--- a/src/chatbot_plugin/contracts/__init__.py
+++ b/src/chatbot_plugin/contracts/__init__.py
@@ -1,16 +1,19 @@
-"""Pydantic contracts — structured versions of specs/toolbox-api.md.
+"""OpenAI-compatible chat completion contracts."""
 
-These models are the source of truth for request/response validation.
-They must stay in sync with specs/toolbox-api.md. If the spec changes,
-update the corresponding model here first.
-"""
-
-from chatbot_plugin.contracts.requests import ArticleInfo, ChunkData, StoreChunksRequest
-from chatbot_plugin.contracts.responses import StoreChunksResponse
+from chatbot_plugin.contracts.chat_completion import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionMessage,
+    ChatCompletionChoice,
+    ChatCompletionChoiceMessage,
+    ChatCompletionUsage,
+)
 
 __all__ = [
-    "ArticleInfo",
-    "ChunkData",
-    "StoreChunksRequest",
-    "StoreChunksResponse",
+    "ChatCompletionRequest",
+    "ChatCompletionResponse",
+    "ChatCompletionMessage",
+    "ChatCompletionChoice",
+    "ChatCompletionChoiceMessage",
+    "ChatCompletionUsage",
 ]
diff --git a/src/chatbot_plugin/contracts/chat_completion.py b/src/chatbot_plugin/contracts/chat_completion.py
new file mode 100644
index 0000000..daaee78
--- /dev/null
+++ b/src/chatbot_plugin/contracts/chat_completion.py
@@ -0,0 +1,61 @@
+"""OpenAI-compatible chat completion contracts."""
+
+from pydantic import BaseModel, Field
+
+
+class ChatCompletionMessage(BaseModel):
+    """A message in the messages array."""
+
+    role: str = Field(..., description="one of system, user, assistant")
+    content: str = Field(..., description="message text")
+
+
+class ChatCompletionRequest(BaseModel):
+    """POST /v1/chat/completions request body."""
+
+    model: str = Field(default="rag-default", description="model identifier (unused, forwarded for compatibility)")
+    messages: list[ChatCompletionMessage] = Field(..., min_length=1)
+    temperature: float = Field(default=0.7, ge=0.0, le=2.0)
+    max_tokens: int = Field(default=2048, ge=1, le=8192)
+    stream: bool = Field(default=False)
+
+    def get_last_user_message(self) -> str:
+        """Return the last user message, or raise if missing."""
+        for msg in reversed(self.messages):
+            if msg.role == "user":
+                return msg.content
+        return ""
+
+
+class ChatCompletionChoiceMessage(BaseModel):
+    """The message object inside a choice."""
+
+    role: str = "assistant"
+    content: str = Field(...)
+
+
+class ChatCompletionChoice(BaseModel):
+    """A single choice in the response."""
+
+    index: int = 0
+    message: ChatCompletionChoiceMessage = Field(...)
+    finish_reason: str = "stop"
+
+
+class ChatCompletionUsage(BaseModel):
+    """Token usage — always zero since we don't count tokens."""
+
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+
+
+class ChatCompletionResponse(BaseModel):
+    """POST /v1/chat/completions response."""
+
+    id: str = Field(default_factory=lambda: f"chatcmpl-{__import__('secrets').token_hex(12)}")
+    object: str = "chat.completion"
+    created: int = Field(default_factory=lambda: int(__import__('time').time()))
+    model: str = "rag-default"
+    choices: list[ChatCompletionChoice]
+    usage: ChatCompletionUsage = Field(default_factory=ChatCompletionUsage)
diff --git a/src/chatbot_plugin/contracts/requests.py b/src/chatbot_plugin/contracts/requests.py
deleted file mode 100644
index 4ce8c30..0000000
--- a/src/chatbot_plugin/contracts/requests.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Request contracts — mirrors specs/toolbox-api.md request bodies."""
-
-from pydantic import BaseModel, Field
-
-
-class ArticleInfo(BaseModel):
-    """Article metadata included in a store-chunks request."""
-
-    id: str = Field(..., description="Article UUID (upsert key)")
-    url: str = Field(..., description="Source URL")
-    title: str | None = Field(default=None, description="Article title")
-    source: str | None = Field(default=None, description="Source domain / feed name")
-    metadata: dict | None = Field(default=None, description="Arbitrary JSON metadata")
-
-
-class ChunkData(BaseModel):
-    """Single pre-chunked, pre-embedded data fragment."""
-
-    chunk_index: int = Field(..., ge=0, description="Position within article (0-based)")
-    content: str = Field(..., description="Chunk text content")
-    dense_vector: list[float] = Field(..., description="Dense embedding vector")
-    sparse_vector: dict[str, float] | None = Field(
-        default=None, description="Lexical weights as {token_index: weight}"
-    )
-
-
-class StoreChunksRequest(BaseModel):
-    """POST /tools/chunks request.
-
-    Spec: specs/toolbox-api.md — POST /tools/chunks
-    """
-
-    article: ArticleInfo = Field(..., description="Article metadata")
-    chunks: list[ChunkData] = Field(..., min_length=1, description="Pre-chunked pre-embedded data fragments")
diff --git a/src/chatbot_plugin/contracts/responses.py b/src/chatbot_plugin/contracts/responses.py
deleted file mode 100644
index 3b20262..0000000
--- a/src/chatbot_plugin/contracts/responses.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Response contracts — mirrors specs/toolbox-api.md response bodies."""
-
-from pydantic import BaseModel, Field
-
-
-class StoreChunksResponse(BaseModel):
-    """POST /tools/chunks response.
-
-    Spec: specs/toolbox-api.md — POST /tools/chunks — 201
-    """
-
-    stored: int = Field(..., ge=0, description="Number of chunks stored")
-    article_id: str = Field(..., description="The article UUID (echoed back)")
diff --git a/src/chatbot_plugin/db.py b/src/chatbot_plugin/db.py
deleted file mode 100644
index 9094e06..0000000
--- a/src/chatbot_plugin/db.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""Async database session for the toolbox's independent PostgreSQL."""
-
-from collections.abc import AsyncGenerator
-
-from sqlalchemy import text
-from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
-
-from chatbot_plugin.config import settings
-from chatbot_plugin.models import Base
-
-engine = create_async_engine(settings.database_url, echo=False)
-async_session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
-
-
-async def get_db() -> AsyncGenerator[AsyncSession, None]:
-    """FastAPI dependency that yields an async DB session."""
-    async with async_session() as session:
-        yield session
-
-
-async def init_db() -> None:
-    """Initialize database: create extensions and tables if they don't exist.
-
-    For production, use Alembic migrations instead.
-    """
-    async with engine.begin() as conn:
-        await conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
-        await conn.run_sync(Base.metadata.create_all)
diff --git a/src/chatbot_plugin/main.py b/src/chatbot_plugin/main.py
index 8ff2810..cb93cd6 100644
--- a/src/chatbot_plugin/main.py
+++ b/src/chatbot_plugin/main.py
@@ -1,33 +1,59 @@
-"""Toolbox standalone server.
+"""Toolbox standalone server — OpenAI-compatible backend.
 
 Run with::
 
     uvicorn chatbot_plugin.main:app --reload
-
-Or via the entry point::
-
-    chatbox-toolbox
 """
 
 from contextlib import asynccontextmanager
+from os import getenv
+from urllib.parse import urlparse
 
 from fastapi import FastAPI
 
-from chatbot_plugin.db import init_db
-from chatbot_plugin.routers import toolbox_router
+from chatbot_plugin.routers import api_router
+from chatbot_plugin_sdk import RagQueryProcessor
+
+
+def _get_db_url() -> str:
+    """Read database URL from environment, fallback to local default."""
+    return getenv(
+        "CHATBOT_DATABASE_URL",
+        "postgresql+asyncpg://postgres:postgres@localhost:5432/chatbot_plugin",
+    )
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    await init_db()
+    db_url = _get_db_url()
+    embedding_api = getenv("CHATBOT_EMBEDDING_MODEL_API", "")
+
+    parsed = urlparse(db_url)
+    dbname = parsed.path.lstrip("/")
+    user = parsed.username or "postgres"
+    password = parsed.password or ""
+    host = parsed.hostname or "localhost"
+    port = parsed.port or 5432
+
+    processor = RagQueryProcessor()
+    processor.configure(
+        dbname=dbname,
+        user=user,
+        password=password,
+        embedding_model_api=embedding_api or None,
+        host=host,
+        port=port,
+    )
+    app.state.processor = processor
+
     yield
 
 
 app = FastAPI(
     title="Toolbox",
-    description="Vector storage toolbox — receives pre-chunked, pre-embedded data and serves retrieval APIs.",
-    version="0.1.0",
+    description="Vector storage toolbox — OpenAI-compatible RAG chat API.",
+    version="0.3.0",
     lifespan=lifespan,
 )
 
-app.include_router(toolbox_router, prefix="/tools", tags=["tools"])
+app.include_router(api_router)
diff --git a/src/chatbot_plugin/models/__init__.py b/src/chatbot_plugin/models/__init__.py
deleted file mode 100644
index bf8bb69..0000000
--- a/src/chatbot_plugin/models/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""SQLAlchemy models for the toolbox."""
-
-from chatbot_plugin.models.article import Article, Base
-from chatbot_plugin.models.chunk import ArticleChunk
-
-__all__ = ["Article", "ArticleChunk", "Base"]
diff --git a/src/chatbot_plugin/models/article.py b/src/chatbot_plugin/models/article.py
deleted file mode 100644
index b3aa229..0000000
--- a/src/chatbot_plugin/models/article.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Article model — minimal schema for storing article metadata.
-
-The actual chunking and embedding are done by scrape-and-analyze.
-This model only stores article-level metadata.
-"""
-
-from datetime import datetime, timezone
-
-from sqlalchemy import Column, DateTime, Index, String, func
-from sqlalchemy.dialects.postgresql import JSONB, UUID
-from sqlalchemy.orm import DeclarativeBase, relationship
-
-
-class Base(DeclarativeBase):
-    pass
-
-
-class Article(Base):
-    __tablename__ = "articles"
-
-    id = Column(UUID(as_uuid=True), primary_key=True)
-    url = Column(String, nullable=False, unique=True)
-    title = Column(String, nullable=True)
-    source = Column(String, nullable=True)
-    metadata_ = Column("metadata", JSONB, nullable=True)
-    created_at = Column(
-        DateTime(timezone=True),
-        nullable=False,
-        server_default=func.now(),
-        default=lambda: datetime.now(timezone.utc),
-    )
-    updated_at = Column(
-        DateTime(timezone=True),
-        nullable=False,
-        server_default=func.now(),
-        server_onupdate=func.now(),
-        default=lambda: datetime.now(timezone.utc),
-    )
-
-    chunks = relationship("ArticleChunk", back_populates="article", cascade="all, delete-orphan")
-
-    __table_args__ = (
-        Index("idx_articles_source", "source"),
-        Index("idx_articles_url", "url"),
-    )
-
-    def __repr__(self) -> str:
-        return f"<Article(id={self.id}, title={self.title!r})>"
diff --git a/src/chatbot_plugin/models/chunk.py b/src/chatbot_plugin/models/chunk.py
deleted file mode 100644
index 4a9f8f6..0000000
--- a/src/chatbot_plugin/models/chunk.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""Chunk model — stores pre-chunked, pre-embedded article data.
-
-Each chunk belongs to an Article and holds a dense vector (pgvector)
-and optional sparse vector (JSONB lexical weights).
-"""
-
-from sqlalchemy import Column, ForeignKey, Integer, Text, DateTime, UniqueConstraint, func
-from sqlalchemy.dialects.postgresql import JSONB, UUID
-from sqlalchemy.orm import relationship
-
-from pgvector.sqlalchemy import Vector
-
-from chatbot_plugin.config import settings
-from chatbot_plugin.models.article import Base
-
-
-class ArticleChunk(Base):
-    __tablename__ = "article_chunks"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, server_default=func.gen_random_uuid())
-    article_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("articles.id", ondelete="CASCADE"),
-        nullable=False,
-    )
-    chunk_index = Column(Integer, nullable=False)
-    content = Column(Text, nullable=False)
-    dense_vector = Column(
-        Vector(settings.embedding_dimension),
-        nullable=True,
-    )
-    sparse_vector = Column(JSONB, nullable=True)
-    created_at = Column(
-        DateTime(timezone=True),
-        nullable=False,
-        server_default=func.now(),
-    )
-
-    # String reference avoids any import-order issues
-    article = relationship("Article", back_populates="chunks")
-
-    __table_args__ = (
-        UniqueConstraint("article_id", "chunk_index", name="uq_article_chunk_idx"),
-    )
-
-    def __repr__(self) -> str:
-        return f"<ArticleChunk(id={self.id}, article_id={self.article_id}, index={self.chunk_index})>"
diff --git a/src/chatbot_plugin/routers.py b/src/chatbot_plugin/routers.py
index a6f879f..2e12b65 100644
--- a/src/chatbot_plugin/routers.py
+++ b/src/chatbot_plugin/routers.py
@@ -1,27 +1,50 @@
-"""FastAPI router for the toolbox.
+"""FastAPI router — single OpenAI-compatible /v1/chat/completions endpoint."""
 
-Mounted under /tools in the standalone server (main.py).
-External services POST chunk + embedding data here.
-"""
+from __future__ import annotations
 
-from fastapi import APIRouter, Depends
-from sqlalchemy.ext.asyncio import AsyncSession
+from fastapi import APIRouter, HTTPException, Request
 
-from chatbot_plugin.contracts import (
-    StoreChunksRequest,
-    StoreChunksResponse,
+from chatbot_plugin.contracts.chat_completion import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionChoice,
+    ChatCompletionChoiceMessage,
 )
-from chatbot_plugin.db import get_db
-from chatbot_plugin.service import ToolboxService
+from chatbot_plugin_sdk import RagQueryProcessor
 
-toolbox_router = APIRouter()
+api_router = APIRouter(prefix="/v1")
 
 
-@toolbox_router.post("/chunks", status_code=201, response_model=StoreChunksResponse)
-async def store_chunks(
-    req: StoreChunksRequest,
-    db: AsyncSession = Depends(get_db),
-) -> StoreChunksResponse:
-    """Store or update an article and its pre-chunked pre-embedded data."""
-    service = ToolboxService(db)
-    return await service.store_chunks(req)
+def _get_processor(request: Request) -> RagQueryProcessor:
+    processor: RagQueryProcessor | None = \
+        getattr(request.app.state, "processor", None)
+    if processor is None:
+        raise HTTPException(status_code=500, detail="RagQueryProcessor not initialised")
+    return processor
+
+
+@api_router.post("/chat/completions", response_model=ChatCompletionResponse)
+async def chat_completions(
+    req: ChatCompletionRequest,
+    request: Request,
+) -> ChatCompletionResponse:
+    """OpenAI-compatible chat completions with RAG context."""
+    processor = _get_processor(request)
+
+    last_message = req.get_last_user_message()
+    if not last_message.strip():
+        raise HTTPException(
+            status_code=400,
+            detail="messages must contain at least one user message with non-empty content",
+        )
+
+    result = await processor.chat(last_message)
+
+    return ChatCompletionResponse(
+        model="rag-default",
+        choices=[
+            ChatCompletionChoice(
+                message=ChatCompletionChoiceMessage(content=result.reply),
+            )
+        ],
+    )
diff --git a/src/chatbot_plugin/service.py b/src/chatbot_plugin/service.py
deleted file mode 100644
index 70084ec..0000000
--- a/src/chatbot_plugin/service.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""Toolbox service — handles chunk storage.
-
-Orchestrates article upserts and chunk persistence.
-Spec reference: specs/toolbox-api.md
-"""
-
-from uuid import UUID
-
-from fastapi import HTTPException
-from sqlalchemy import delete, select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from chatbot_plugin.config import settings
-from chatbot_plugin.contracts import (
-    StoreChunksRequest,
-    StoreChunksResponse,
-)
-from chatbot_plugin.models import Article, ArticleChunk
-
-
-class ToolboxService:
-    """Handles article metadata storage and chunk persistence."""
-
-    def __init__(self, db: AsyncSession) -> None:
-        self.db = db
-
-    async def store_chunks(self, request: StoreChunksRequest) -> StoreChunksResponse:
-        """Upsert an article and store its chunks.
-
-        If the article already exists, its metadata is updated and all
-        existing chunks are replaced.
-
-        Args:
-            request: StoreChunksRequest with article info and chunk data.
-
-        Returns:
-            StoreChunksResponse with count of stored chunks.
-
-        Raises:
-            HTTPException: 400 if dense_vector dimension does not match settings.
-        """
-        expected_dim = settings.embedding_dimension
-        for chunk in request.chunks:
-            if len(chunk.dense_vector) != expected_dim:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Dense vector dimension mismatch at chunk_index={chunk.chunk_index}: "
-                        f"expected {expected_dim}, got {len(chunk.dense_vector)}"
-                    ),
-                )
-
-        article_id = UUID(request.article.id)
-
-        # Check for existing article
-        result = await self.db.execute(select(Article).where(Article.id == article_id))
-        existing = result.scalar_one_or_none()
-
-        if existing is not None:
-            # Update metadata
-            existing.url = request.article.url
-            existing.title = request.article.title
-            existing.source = request.article.source
-            existing.metadata_ = request.article.metadata
-
-            # Delete old chunks
-            await self.db.execute(
-                delete(ArticleChunk).where(ArticleChunk.article_id == article_id)
-            )
-        else:
-            # Create new article
-            new_article = Article(
-                id=article_id,
-                url=request.article.url,
-                title=request.article.title,
-                source=request.article.source,
-                metadata_=request.article.metadata,
-            )
-            self.db.add(new_article)
-            await self.db.flush()
-
-        # Insert new chunks
-        for chunk_data in request.chunks:
-            chunk = ArticleChunk(
-                article_id=article_id,
-                chunk_index=chunk_data.chunk_index,
-                content=chunk_data.content,
-                dense_vector=chunk_data.dense_vector,
-                sparse_vector=chunk_data.sparse_vector,
-            )
-            self.db.add(chunk)
-
-        await self.db.commit()
-
-        return StoreChunksResponse(
-            stored=len(request.chunks),
-            article_id=request.article.id,
-        )
diff --git a/src/tests/conftest.py b/src/tests/conftest.py
index 2558859..c0085d5 100644
--- a/src/tests/conftest.py
+++ b/src/tests/conftest.py
@@ -5,11 +5,15 @@
 from fastapi import FastAPI
 
 from chatbot_plugin.main import app as toolbox_app
+from chatbot_plugin_sdk import RagQueryProcessor
 
 
 @pytest.fixture
 def app() -> FastAPI:
-    """Return the toolbox FastAPI app."""
+    """Return the toolbox FastAPI app with a mock processor attached."""
+    processor = RagQueryProcessor()
+    processor.configure(dbname="test_db", user="test", password="test")
+    toolbox_app.state.processor = processor
     return toolbox_app
 
 
diff --git a/src/tests/contracts/test_contracts.py b/src/tests/contracts/test_contracts.py
deleted file mode 100644
index a44779a..0000000
--- a/src/tests/contracts/test_contracts.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""Contract tests — validate Pydantic models match specs/toolbox-api.md.
-
-These tests ensure:
-1. Request models accept valid input and reject invalid input per spec
-2. Response models produce JSON that matches the spec shape
-3. Field constraints (min_length, ge, etc.) align with spec
-"""
-
-import pytest
-from pydantic import ValidationError
-
-from chatbot_plugin.contracts.requests import (
-    ArticleInfo,
-    ChunkData,
-    StoreChunksRequest,
-)
-from chatbot_plugin.contracts.responses import (
-    StoreChunksResponse,
-)
-
-
-# ── ArticleInfo ──
-
-
-class TestArticleInfo:
-    def test_valid(self):
-        info = ArticleInfo(
-            id="a1b2c3d4-5678-90ab-cdef-1234567890ab",
-            url="https://example.com",
-            title="Title",
-            source="example.com",
-            metadata={"author": "Alice"},
-        )
-        assert info.id == "a1b2c3d4-5678-90ab-cdef-1234567890ab"
-        assert info.url == "https://example.com"
-
-    def test_optional_fields_default_none(self):
-        info = ArticleInfo(
-            id="a1b2c3d4-5678-90ab-cdef-1234567890ab",
-            url="https://example.com",
-        )
-        assert info.title is None
-        assert info.source is None
-        assert info.metadata is None
-
-    def test_id_required(self):
-        with pytest.raises(ValidationError):
-            ArticleInfo(url="https://example.com")
-
-    def test_url_required(self):
-        with pytest.raises(ValidationError):
-            ArticleInfo(id="a1b2c3d4-5678-90ab-cdef-1234567890ab")
-
-
-# ── ChunkData ──
-
-
-class TestChunkData:
-    def test_valid(self):
-        chunk = ChunkData(
-            chunk_index=0,
-            content="Hello world",
-            dense_vector=[0.1] * 1024,
-            sparse_vector={"0": 0.5, "1": 0.3},
-        )
-        assert chunk.chunk_index == 0
-        assert chunk.content == "Hello world"
-        assert len(chunk.dense_vector) == 1024
-
-    def test_sparse_vector_optional(self):
-        chunk = ChunkData(
-            chunk_index=0,
-            content="Hello world",
-            dense_vector=[0.1] * 1024,
-        )
-        assert chunk.sparse_vector is None
-
-    def test_chunk_index_negative_rejected(self):
-        with pytest.raises(ValidationError):
-            ChunkData(
-                chunk_index=-1,
-                content="text",
-                dense_vector=[0.1] * 1024,
-            )
-
-    def test_chunk_index_zero_accepted(self):
-        chunk = ChunkData(
-            chunk_index=0,
-            content="text",
-            dense_vector=[0.1] * 1024,
-        )
-        assert chunk.chunk_index == 0
-
-
-# ── StoreChunksRequest ──
-
-
-class TestStoreChunksRequest:
-    def test_valid(self):
-        req = StoreChunksRequest(
-            article=ArticleInfo(id="uuid-1", url="https://example.com"),
-            chunks=[
-                ChunkData(chunk_index=0, content="c1", dense_vector=[0.1] * 1024)
-            ],
-        )
-        assert req.article.id == "uuid-1"
-        assert len(req.chunks) == 1
-
-    def test_multiple_chunks(self):
-        req = StoreChunksRequest(
-            article=ArticleInfo(id="uuid-1", url="https://example.com"),
-            chunks=[
-                ChunkData(chunk_index=0, content="c1", dense_vector=[0.1] * 1024),
-                ChunkData(chunk_index=1, content="c2", dense_vector=[0.2] * 1024),
-            ],
-        )
-        assert len(req.chunks) == 2
-        assert req.chunks[1].chunk_index == 1
-
-
-# ── StoreChunksResponse ──
-
-
-class TestStoreChunksResponse:
-    def test_valid(self):
-        resp = StoreChunksResponse(stored=5, article_id="uuid-1")
-        assert resp.stored == 5
-        assert resp.article_id == "uuid-1"
-
-    def test_json_matches_spec(self):
-        resp = StoreChunksResponse(stored=3, article_id="uuid-1")
-        data = resp.model_dump()
-        assert data == {"stored": 3, "article_id": "uuid-1"}
diff --git a/src/tests/routers/test_toolbox.py b/src/tests/routers/test_toolbox.py
index b9e872b..3206509 100644
--- a/src/tests/routers/test_toolbox.py
+++ b/src/tests/routers/test_toolbox.py
@@ -1,139 +1,102 @@
-"""Router tests — validate API endpoints match specs/toolbox-api.md.
-
-These tests verify the API contract: endpoint paths, status codes,
-and response shapes. Business logic is tested in service tests.
-"""
+"""Router tests — validate /v1/chat/completions OpenAI-compatible endpoint."""
 
 import pytest
 from unittest.mock import AsyncMock, patch
 from httpx import AsyncClient
 
-from chatbot_plugin.contracts import StoreChunksResponse
-from chatbot_plugin.service import ToolboxService
-
-
-# ── POST /tools/chunks ──
+from chatbot_plugin_sdk import RagQueryProcessor
 
 
-class TestStoreChunks:
+class TestChatCompletions:
     @pytest.mark.asyncio
-    async def test_accepts_valid_payload(self, client: AsyncClient):
+    async def test_accepts_valid_messages(self, client: AsyncClient):
         with patch.object(
-            ToolboxService,
-            "store_chunks",
+            RagQueryProcessor,
+            "chat",
             new_callable=AsyncMock,
         ) as mock:
-            mock.return_value = StoreChunksResponse(stored=1, article_id="uuid-1")
+            mock.return_value = type(
+                "ChatResponse",
+                (),
+                {
+                    "reply": "RAG is a retrieval technique...",
+                    "articles_used": [],
+                    "chunks": [],
+                },
+            )()
             resp = await client.post(
-                "/tools/chunks",
+                "/v1/chat/completions",
                 json={
-                    "article": {
-                        "id": "uuid-1",
-                        "url": "https://example.com",
-                    },
-                    "chunks": [
-                        {
-                            "chunk_index": 0,
-                            "content": "Text chunk",
-                            "dense_vector": [0.1] * 1024,
-                        }
+                    "model": "gpt-4",
+                    "messages": [
+                        {"role": "system", "content": "You are helpful."},
+                        {"role": "user", "content": "What is RAG?"},
                     ],
                 },
             )
-            assert resp.status_code == 201
+            assert resp.status_code == 200
             data = resp.json()
-            assert data["stored"] == 1
-            assert data["article_id"] == "uuid-1"
+            assert data["object"] == "chat.completion"
+            assert len(data["choices"]) == 1
+            assert data["choices"][0]["message"]["role"] == "assistant"
+            assert data["choices"][0]["message"]["content"] == "RAG is a retrieval technique..."
+            assert "usage" in data
+            mock.assert_called_once_with("What is RAG?")
 
     @pytest.mark.asyncio
-    async def test_rejects_empty_chunks(self, client: AsyncClient):
+    async def test_rejects_empty_messages(self, client: AsyncClient):
         resp = await client.post(
-            "/tools/chunks",
-            json={
-                "article": {
-                    "id": "uuid-1",
-                    "url": "https://example.com",
-                },
-                "chunks": [],
-            },
+            "/v1/chat/completions",
+            json={"messages": []},
         )
         assert resp.status_code == 422
 
     @pytest.mark.asyncio
-    async def test_rejects_missing_article_id(self, client: AsyncClient):
+    async def test_rejects_no_messages_field(self, client: AsyncClient):
         resp = await client.post(
-            "/tools/chunks",
-            json={
-                "article": {
-                    "url": "https://example.com",
-                },
-                "chunks": [
-                    {
-                        "chunk_index": 0,
-                        "content": "Text",
-                        "dense_vector": [0.1] * 1024,
-                    }
-                ],
-            },
+            "/v1/chat/completions",
+            json={"model": "gpt-4"},
         )
         assert resp.status_code == 422
 
     @pytest.mark.asyncio
-    async def test_rejects_missing_chunks(self, client: AsyncClient):
-        resp = await client.post(
-            "/tools/chunks",
-            json={
-                "article": {
-                    "id": "uuid-1",
-                    "url": "https://example.com",
+    async def test_default_model(self, client: AsyncClient):
+        with patch.object(
+            RagQueryProcessor,
+            "chat",
+            new_callable=AsyncMock,
+        ) as mock:
+            mock.return_value = type(
+                "ChatResponse",
+                (),
+                {
+                    "reply": "Hello",
+                    "articles_used": [],
+                    "chunks": [],
                 },
-            },
-        )
-        assert resp.status_code == 422
+            )()
+            resp = await client.post(
+                "/v1/chat/completions",
+                json={
+                    "messages": [
+                        {"role": "user", "content": "Hi"},
+                    ],
+                },
+            )
+            assert resp.status_code == 200
+            data = resp.json()
+            assert data["model"] == "rag-default"
+            mock.assert_called_once_with("Hi")
 
     @pytest.mark.asyncio
-    async def test_rejects_wrong_dense_vector_dim(self, client: AsyncClient):
+    async def test_only_system_message_returns_400(self, client: AsyncClient):
+        """No user message → 400 because there's nothing to query."""
         resp = await client.post(
-            "/tools/chunks",
+            "/v1/chat/completions",
             json={
-                "article": {
-                    "id": "uuid-1",
-                    "url": "https://example.com",
-                },
-                "chunks": [
-                    {
-                        "chunk_index": 0,
-                        "content": "Text",
-                        "dense_vector": [0.1] * 512,  # wrong dimension
-                    }
+                "messages": [
+                    {"role": "system", "content": "You are helpful."},
                 ],
             },
         )
         assert resp.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_accepts_sparse_vector(self, client: AsyncClient):
-        with patch.object(
-            ToolboxService,
-            "store_chunks",
-            new_callable=AsyncMock,
-        ) as mock:
-            mock.return_value = StoreChunksResponse(stored=1, article_id="uuid-1")
-            resp = await client.post(
-                "/tools/chunks",
-                json={
-                    "article": {
-                        "id": "uuid-1",
-                        "url": "https://example.com",
-                    },
-                    "chunks": [
-                        {
-                            "chunk_index": 0,
-                            "content": "Text",
-                            "dense_vector": [0.1] * 1024,
-                            "sparse_vector": {"0": 0.5},
-                        }
-                    ],
-                },
-            )
-            assert resp.status_code == 201
diff --git a/src/tests/test_service.py b/src/tests/test_service.py
deleted file mode 100644
index 5607fce..0000000
--- a/src/tests/test_service.py
+++ /dev/null
@@ -1,145 +0,0 @@
-"""Tests for ToolboxService."""
-
-import uuid
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from fastapi import HTTPException
-from pydantic import ValidationError
-
-from chatbot_plugin.config import settings
-from chatbot_plugin.contracts import (
-    ArticleInfo,
-    ChunkData,
-    StoreChunksRequest,
-    StoreChunksResponse,
-)
-from chatbot_plugin.models import Article, ArticleChunk
-from chatbot_plugin.service import ToolboxService
-
-
-@pytest.fixture
-def mock_db() -> AsyncMock:
-    """Mock async DB session."""
-    db = AsyncMock()
-    db.add = MagicMock()  # not async in SQLAlchemy
-    return db
-
-
-@pytest.fixture
-def service(mock_db: AsyncMock) -> ToolboxService:
-    """ToolboxService with mocked DB."""
-    return ToolboxService(mock_db)
-
-
-# ── store_chunks ──
-
-
-@pytest.mark.asyncio
-async def test_store_chunks_creates_new_article(service: ToolboxService, mock_db: AsyncMock):
-    """When article doesn't exist, a new one is created."""
-    # simulate no existing article
-    result = MagicMock()
-    result.scalar_one_or_none.return_value = None
-    mock_db.execute.return_value = result
-
-    req = StoreChunksRequest(
-        article=ArticleInfo(id=str(uuid.uuid4()), url="https://example.com", title="T"),
-        chunks=[ChunkData(chunk_index=0, content="hello", dense_vector=[0.1] * 1024)],
-    )
-
-    resp = await service.store_chunks(req)
-
-    assert isinstance(resp, StoreChunksResponse)
-    assert resp.stored == 1
-    assert resp.article_id == req.article.id
-    mock_db.add.assert_called()
-    mock_db.commit.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_store_chunks_updates_existing_article(service: ToolboxService, mock_db: AsyncMock):
-    """When article exists, metadata is updated and old chunks deleted."""
-    existing = MagicMock(spec=Article)
-    result = MagicMock()
-    result.scalar_one_or_none.return_value = existing
-    mock_db.execute.return_value = result
-
-    req = StoreChunksRequest(
-        article=ArticleInfo(id=str(uuid.uuid4()), url="https://example.com", title="New Title"),
-        chunks=[ChunkData(chunk_index=0, content="hello", dense_vector=[0.1] * 1024)],
-    )
-
-    resp = await service.store_chunks(req)
-
-    assert isinstance(resp, StoreChunksResponse)
-    assert resp.stored == 1
-    assert existing.url == "https://example.com"
-    assert existing.title == "New Title"
-    # Should have executed delete statement plus the select
-    assert mock_db.execute.call_count >= 1
-    mock_db.commit.assert_called_once()
-
-
-def test_store_chunks_rejects_empty_chunks():
-    """Empty chunks list is rejected at the contract layer by Pydantic min_length=1."""
-    with pytest.raises(ValidationError):
-        StoreChunksRequest(
-            article=ArticleInfo(id=str(uuid.uuid4()), url="https://example.com"),
-            chunks=[],
-        )
-
-
-@pytest.mark.asyncio
-async def test_store_chunks_rejects_wrong_vector_dimension(service: ToolboxService):
-    req = StoreChunksRequest(
-        article=ArticleInfo(id=str(uuid.uuid4()), url="https://example.com"),
-        chunks=[ChunkData(chunk_index=0, content="hello", dense_vector=[0.1] * 512)],
-    )
-
-    with pytest.raises(HTTPException) as exc_info:
-        await service.store_chunks(req)
-    assert exc_info.value.status_code == 400
-    assert "mismatch" in exc_info.value.detail.lower()
-    assert str(settings.embedding_dimension) in exc_info.value.detail
-
-
-@pytest.mark.asyncio
-async def test_store_chunks_accepts_sparse_vector(service: ToolboxService, mock_db: AsyncMock):
-    result = MagicMock()
-    result.scalar_one_or_none.return_value = None
-    mock_db.execute.return_value = result
-
-    req = StoreChunksRequest(
-        article=ArticleInfo(id=str(uuid.uuid4()), url="https://example.com"),
-        chunks=[
-            ChunkData(
-                chunk_index=0,
-                content="hello",
-                dense_vector=[0.1] * 1024,
-                sparse_vector={"0": 0.5, "1": 0.3},
-            )
-        ],
-    )
-
-    resp = await service.store_chunks(req)
-    assert resp.stored == 1
-
-
-@pytest.mark.asyncio
-async def test_store_chunks_multiple_chunks(service: ToolboxService, mock_db: AsyncMock):
-    result = MagicMock()
-    result.scalar_one_or_none.return_value = None
-    mock_db.execute.return_value = result
-
-    req = StoreChunksRequest(
-        article=ArticleInfo(id=str(uuid.uuid4()), url="https://example.com"),
-        chunks=[
-            ChunkData(chunk_index=0, content="chunk 0", dense_vector=[0.1] * 1024),
-            ChunkData(chunk_index=1, content="chunk 1", dense_vector=[0.2] * 1024),
-        ],
-    )
-
-    resp = await service.store_chunks(req)
-    assert resp.stored == 2
-    assert mock_db.add.call_count == 3  # 1 article + 2 chunks
diff --git a/uv.lock b/uv.lock
index 8052d07..dab4f70 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,172 @@
 version = 1
 revision = 3
 requires-python = ">=3.11"
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+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "accelerate"
+version = "1.13.0"
+source = { registry = "https://pypi.org/simple" }
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+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
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+    { name = "torch" },
+]
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+
 [[package]]
 name = "chatbot-plugin"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "alembic" },
+    { name = "anthropic" },
     { name = "asyncpg" },
     { name = "fastapi" },
+    { name = "flagembedding" },
     { name = "pgvector" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
     { name = "sqlalchemy", extra = ["asyncio"] },
     { name = "structlog" },
+    { name = "torch" },
     { name = "uvicorn", extra = ["standard"] },
 ]
 
@@ -131,13 +436,16 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "alembic" },
+    { name = "anthropic", specifier = ">=0.50" },
     { name = "asyncpg" },
     { name = "fastapi" },
+    { name = "flagembedding", specifier = ">=1.3" },
     { name = "pgvector" },
     { name = "pydantic", specifier = ">=2.0" },
     { name = "pydantic-settings", specifier = ">=2.0" },
     { name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0" },
     { name = "structlog" },
+    { name = "torch", specifier = ">=2.0" },
     { name = "uvicorn", extras = ["standard"] },
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