This is a Rest-Backend for a Conversational Agent, that allows to embedd Documentes, search for them using Semantic Search, to QA based on Documents and do document processing with Large Language Models.

• Conversational Agent
  • Recent Updates
  • Quickstart
  • Project Description
  • Semantic Search
  • Architecture
  • Components
  • Available LLM Backends
  • Secret Management
  • Installation & Development Backend
  • Development Frontend
  • Vector Database
  • Qdrant API Key
  • Bulk Ingestion
  • Star History

• GPT4ALL Uses Mistral now! If you want to enable it go to config/ai/gpt4all.yaml and change the string. Then you need to restart.
• Switching for Aleph Alpha to Embeddings with luminous base control.

If you want to use an Aleph Alpha only backend i would recommend my other backend: https://github.com/mfmezger/aleph-alpha-rag.

To run the complete system with docker use this command:

git clone https://github.com/mfmezger/conversational-agent-langchain.git
cd conversational-agent-langchain

Create a .env file from the .env-template and set the qdrant api key. For tests just set it to test. QDRANT_API_KEY="test"

Then start the system with

  docker compose up -d

Then go to http://127.0.0.1:8001/docs or http://127.0.0.1:8001/redoc to see the API documentation.

Frontend: localhost:8501 Qdrant Dashboard: localhost:6333/dashboard

This project is a conversational agent that uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries. The agent also includes a vector database and a REST API built with FastAPI.

Features

• Uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries.
• Includes a vector database to store and retrieve information.
• Provides a REST API built with FastAPI for easy integration with other applications.
• Has a basic gui.

Semantic search is an advanced search technique that aims to understand the meaning and context of a user's query, rather than matching keywords. It involves natural language processing (NLP) and machine learning algorithms to analyze and interpret user intent, synonyms, relationships between words, and the structure of content. By considering these factors, semantic search improves the accuracy and relevance of search results, providing a more intuitive and personalized user experience.

Langchain is a library for natural language processing and machine learning. FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. A Vectordatabase is a database that stores vectors, which can be used for similarity searches and other machine learning tasks.

• Aleph Alpha Luminous
• GPT4All
• (Azure) OpenAI

Two ways to manage your api keys are available, the easiest approach is to sent the api token in the request as the token. Another possiblity is to create a .env file and add the api token there. If you use OpenAI from Azure or OpenAI directly you need to set the correct parameters in the .env file.

First install Python Dependencies:

pip install poetry
poetry install

Start the complete system with:

docker compose up -d

To run the Qdrant Database local just run:

docker compose up qdrant

To run the Backend use this command in the root directory:

poetry run uvicorn agent.api:app --reload

To run the tests you can use this command:

poetry run coverage run -m pytest -o log_cli=true -vvv tests

To run the Frontend use this command in the root directory:

poetry run streamlit run gui.py --theme.base="dark"

Qdrant Dashboard is available at http://127.0.0.1:6333/dashboard. There you need to enter the api key.

To use the Qdrant API you need to set the correct parameters in the .env file. QDRANT_API_KEY is the API key for the Qdrant API. And you need to change it in the qdrant.yaml file in the config folder.

If you want to ingest large amount of data i would recommend you use the scripts located in agent/ingestion.