# rag_api

**Repository Path**: flashpig8014/rag_api

## Basic Information

- **Project Name**: rag_api
- **Description**: 数据向量化API
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: Berry-13-custom-host-port
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-12-29
- **Last Updated**: 2024-12-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ID-based RAG FastAPI

## Overview
This project integrates Langchain with FastAPI in an Asynchronous, Scalable manner, providing a framework for document indexing and retrieval, using PostgreSQL/pgvector.

Files are organized into embeddings by `file_id`. The primary use case is for integration with [LibreChat](https://librechat.ai), but this simple API can be used for any ID-based use case.

The main reason to use the ID approach is to work with embeddings on a file-level. This makes for targeted queries when combined with file metadata stored in a database, such as is done by LibreChat.

The API will evolve over time to employ different querying/re-ranking methods, embedding models, and vector stores.

## Features
- **Document Management**: Methods for adding, retrieving, and deleting documents.
- **Vector Store**: Utilizes Langchain's vector store for efficient document retrieval.
- **Asynchronous Support**: Offers async operations for enhanced performance.

## Setup

### Getting Started

- **Configure `.env` file based on [section below](#environment-variables)**
- **Setup pgvector database:**
  - Run an existing PSQL/PGVector setup, or,
  - Docker: `docker compose up` (also starts RAG API)
    - or, use docker just for DB: `docker compose -f ./db-compose.yaml up`
- **Run API**:
  - Docker: `docker compose up` (also starts PSQL/pgvector)
    - or, use docker just for RAG API: `docker compose -f ./api-compose.yaml up`
  - Local:
    - Make sure to setup `DB_HOST` to the correct database hostname
    - Run the following commands (preferably in a [virtual environment](https://realpython.com/python-virtual-environments-a-primer/))
```bash
pip install -r requirements.txt
uvicorn main:app
```

### Environment Variables

The following environment variables are required to run the application:

- `OPENAI_API_KEY`: The API key for OpenAI API Embeddings (if using default settings).
- `POSTGRES_DB`: (Optional) The name of the PostgreSQL database.
- `POSTGRES_USER`: (Optional) The username for connecting to the PostgreSQL database.
- `POSTGRES_PASSWORD`: (Optional) The password for connecting to the PostgreSQL database.
- `DB_HOST`: (Optional) The hostname or IP address of the PostgreSQL database server.
- `DB_PORT`: (Optional) The port number of the PostgreSQL database server.
- `RAG_HOST`: (Optional) The hostname or IP address where the API server will run. Defaults to "0.0.0.0"
- `RAG_PORT`: (Optional) The port number where the API server will run. Defaults to port 8000.
- `JWT_SECRET`: (Optional) The secret key used for verifying JWT tokens for requests.
  - The secret is only used for verification. This basic approach assumes a signed JWT from elsewhere.
  - Omit to run API without requiring authentication

- `COLLECTION_NAME`: (Optional) The name of the collection in the vector store. Default value is "testcollection".
- `CHUNK_SIZE`: (Optional) The size of the chunks for text processing. Default value is "1500".
- `CHUNK_OVERLAP`: (Optional) The overlap between chunks during text processing. Default value is "100".
- `RAG_UPLOAD_DIR`: (Optional) The directory where uploaded files are stored. Default value is "./uploads/".
- `PDF_EXTRACT_IMAGES`: (Optional) A boolean value indicating whether to extract images from PDF files. Default value is "False".
- `DEBUG_RAG_API`: (Optional) Set to "True" to show more verbose logging output in the server console, and to enable postgresql database routes
- `CONSOLE_JSON`: (Optional) Set to "True" to log as json for Cloud Logging aggregations
- `EMBEDDINGS_PROVIDER`: (Optional) either "openai", "azure", "huggingface", "huggingfacetei" or "ollama", where "huggingface" uses sentence_transformers; defaults to "openai"
- `EMBEDDINGS_MODEL`: (Optional) Set a valid embeddings model to use from the configured provider.
    - **Defaults**
    - openai: "text-embedding-3-small"
    - azure: "text-embedding-3-small"
    - huggingface: "sentence-transformers/all-MiniLM-L6-v2"
    - huggingfacetei: "http://huggingfacetei:3000". Hugging Face TEI uses model defined on TEI service launch.
    - ollama: "nomic-embed-text"
- `AZURE_OPENAI_API_KEY`: (Optional) The API key for Azure OpenAI service.
- `AZURE_OPENAI_ENDPOINT`: (Optional) The endpoint URL for Azure OpenAI service, including the resource. Example: `https://example-resource.azure.openai.com/`.
- `HF_TOKEN`: (Optional) if needed for `huggingface` option.
- `OLLAMA_BASE_URL`: (Optional) defaults to `http://ollama:11434`.

Make sure to set these environment variables before running the application. You can set them in a `.env` file or as system environment variables.