londonaicentre-chatbot 1.0.1

Wrapper around LangChain functions

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:

Variable	Description	Required	Default
MAX_RETRIES	Number of times to retry the chat API if it is not responsive	Yes	3
QDRANT_PATH	On-disk or remote location to persist vector store collections	No	-
EMBEDDINGS_MODEL_NAME	Model used to vectorise documents in the vector store	Yes	sentence-transformers/all-mpnet-base-v2
LLM_MODEL_NAME	Name of the model. Models starting with <provider>. are assumed to be hosted on Bedrock (AWS). Others are assumed to be hosted by NVIDIA.	Yes	eu.anthropic.claude-haiku-4-5-20251001-v1:0 (AWS Bedrock)
NVIDIA_API_KEY	Key for NVIDIA-hosted models	Yes (if using an NVIDIA model)	-
AWS_BEARER_TOKEN_BEDROCK	Key for Bedrock-hosted models	Yes (if using a Bedrock model)	-
AWS_REGION	Region for Bedrock-hosted models	Yes (if using a Bedrock model)	eu-west-2
LLM__EXPLANATION_OF_TERMS	Optional explanatory text to help the LLM interpret retrieved information (e.g., term definitions, context notes). Injected into the system prompt for retrieval tasks.	No	-

Recommended models

Model	Provider	ID
Llama 3.1 8B instruct (default)	NVIDIA	meta/llama-3.1-8b-instruct
Claude 4.5 Haiku	AWS Bedrock	eu.anthropic.claude-haiku-4-5-20251001-v1:0

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).