/llm-embed-qa

Question answering system built with vector dbs and LLMs.

Primary LanguagePython

llm-embed-qa

Question answering system built with vector dbs and LLMs.

Part of the content is implemented with reference to michaelliao/llm-embedding-sample

Requirements (Default Component)

  • Python 3.10
  • Docker

Install

  1. Clone this repo

  2. Install requirements with pip install -r requirements.txt

  3. Startup PostgreSQL with Docker

    docker run -d \
   --rm \
   --name pgvector \
   -p 5432:5432 \
   -e POSTGRES_PASSWORD=password \
   -e POSTGRES_USER=postgres \
   -e POSTGRES_DB=postgres \
   -e PGDATA=/var/lib/postgresql/data/pgdata \
   -v /path/to/llm-embedding-qa/pg-data:/var/lib/postgresql/data \
   -v /path/to/llm-embedding-qa/pg-init-script:/docker-entrypoint-initdb.d \
   ankane/pgvector:latest

    NOTE: replace /path/to/... with real path.

  4. Run python main.py, edit config.yaml to set your api_key of OpenAI.

  5. Put your markdown format documents in docs folder.

    • There are the wiki files of QChatGPT in docs_examples folder.

  6. Run python main.py again, it will automatically build the vector database and start the server.

Usage

  • GET /ask
    • content: the content of the question
    • strict: (Optional) skip LLM request if strict=true and no related answer found in vector db