chatbot-plugin-sdk

Python SDK for the vector storage toolbox — RAG article ingestion and querying.

Installation

pip install chatbot-plugin-sdk

Usage

ArticleProcessor — write articles to vector DB

from chatbot_plugin_sdk import RagArticleProcessor

processor = RagArticleProcessor()
processor.configure(
    dbname="chatbot_plugin",
    user="postgres",
    password="postgres",
    embedding_model_api="http://localhost:8080",
)

await processor.ingest(
    full_text="Retrieval augmented generation is ...",
    metadata={"url": "https://example.com/article", "title": "RAG 101"},
)

QueryProcessor — RAG query

from chatbot_plugin_sdk import RagQueryProcessor

processor = RagQueryProcessor()
processor.configure(
    dbname="chatbot_plugin",
    user="postgres",
    password="postgres",
    embedding_model_api="http://localhost:8080",
)

response = await processor.query("What is RAG?")
print(response.reply)

Class Hierarchy

Class	Description
BaseRagProcessor	Internal base class — DB, search, chat, LLM fallback
RagArticleProcessor	Write — configure + ingest pipeline
RagQueryProcessor	Read — configure + query (wraps chat)