chatbot

Wrapper around LangChain functions, specifying:

• A two-step chunk lookup process: title search followed by content search

• Use of HuggingFace to obtain a local embeddings model

• Use of NVIDIA or Bedrock for a remote LLM (Llama/Claude)

• Use of Qdrant (server, memory or disk) as a vector store

• A LangGraph as follows:

  

And adding:

• Support for a meta-graph of document relationships to aid retrieval

• Support for keywords in addition to titles for chunk lookup

Getting started

Configuration

Within a .env.development file (.env for production), specify values for the following, according to the stated constraints:

Recommended models

Local installation

Add the following to the pyproject.toml of a consuming project in the monorepo:

[tool.uv.sources]
chatbot = { path = "../path/to/lib", editable = true }
[dependency-groups]
dev = [
    "chatbot",
    ...
]

Usage

from chatbot.knowledge_based import KnowledgeBase
from chatbot.chatbot import Chatbot
KnowledgeBase().load_web_documents(documents, classes)
Chatbot().retrieve_and_generate(message)

Testing

1. Install dependencies

   uv sync
   

2. Run tests: uv run pytest tests

License

This project uses the CC BY-NC-ND 4.0 license (see LICENSE).