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.
At the moment i am reworking to langgraph, therefore not all versions on main will work with all of the providers. I will update the providers in the next weeks. Please use the releases to get a working version.
- Complete Rework to Langgraph
- Adding Cohere
- Adding Ollama
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-langchainCreate 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 -dThen 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.
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.
On Linux or Mac you need to adjust your /etc/hosts file to include the following line:
127.0.0.1 qdrantFirst install Python Dependencies:
You need to instal rye if you want to use it for syncing the requirements.lock file. Rye Installation.
rye sync
# or if you do not want to use rye
pip install -r requirements.lockStart the complete system with:
docker compose up -dTo run the Qdrant Database local just run:
docker compose up qdrantTo run the Backend use this command in the root directory:
poetry run uvicorn agent.api:app --reloadTo run the tests you can use this command:
poetry run coverage run -m pytest -o log_cli=true -vvv testsTo run the Frontend use this command in the root directory:
poetry run streamlit run gui.py --theme.base="dark"mypy rag --explicit-package-bases
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.
To Test the API i would recommend Bruno. The API Requests are store in ConvAgentBruno folder.