https://github.com/vblagoje/openapi-rag-service

A flexible Haystack 2.0 pipeline integrating Retrieval-Augmented Generation (RAG) with any OpenAPI service
https://github.com/vblagoje/openapi-rag-service

nlp openapi rag

Last synced: 30 days ago
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A flexible Haystack 2.0 pipeline integrating Retrieval-Augmented Generation (RAG) with any OpenAPI service

• Host: GitHub
• URL: https://github.com/vblagoje/openapi-rag-service
• Owner: vblagoje
• License: apache-2.0
• Created: 2024-01-23T07:17:14.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-11-25T10:32:15.000Z (about 1 month ago)
• Last Synced: 2024-12-03T12:10:43.066Z (about 1 month ago)
• Topics: nlp, openapi, rag
• Language: Python
• Homepage:
• Size: 104 KB
• Stars: 5
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# vblagoje/openapi-rag-service

`vblagoje/openapi-rag-service` demonstrates the seamless integration of OpenAPI-defined services with Large Language Models (LLMs) in the Haystack RAG (Retriever-Augmented Generation) pipeline. This project offers an easy way to explore the enhanced capabilities of Haystack's 2.x RAG architecture, enriching LLMs with structured data from various OpenAPI services. The vblagoje/openapi-rag-service Docker image facilitates quick experimentation, opening new possibilities for enriching LLM outputs in the RAG framework.

## Key Features

- **Seamless OpenAPI Integration**: Incorporates any OpenAPI-specified service into the RAG pipeline, expanding the potential sources of information beyond unstructured text.

- **Enhanced RAG Functionality**: Enhances traditional RAG capabilities with structured, service-driven data, offering more contextually rich and accurate outputs.

- **Flexible LLM Compatibility**: Supports various Large Language Models (LLMs) that adhere to OpenAI Python client standards, ensuring broad applicability and flexibility.

## Getting Started

### Prerequisites

- Docker installed on your machine.

- Access to LLM providers compatible with the OpenAI Python client.

### Running the Service

To run the `vblagoje/openapi-rag-service`, pull the Docker image and execute it with the necessary parameters:

```sh

docker pull vblagoje/openapi-rag-service:latest

docker run  vblagoje/openapi-rag-service

```

## Configuration

Configure the service using the following environment variables:

- `OPENAI_API_KEY`: Your API key for OpenAI.

- `OPENAI_BASE_URL`: The base URL for the OpenAI API.

- `TEXT_GENERATION_MODEL`: Name of the model used for text generation (e.g., `gpt-4-1106-preview`).

- `FUNCTION_CALLING_MODEL`: Model name for handling function calls (e.g., `gpt-3.5-turbo-0613`).

- `SYSTEM_PROMPT`: System message or prompt URL to assist the model in generating content.

- `FUNCTION_CALLING_PROMPT`: Prompt for the function calling model.

- `USER_PROMPT`: Additional user-defined prompt for guided output text generation.

- `FUNCTION_CALLING_VALIDATION_SCHEMA`: Schema URL or json text for validating OpenAPI function calls (adds correction loop)

- `BOT_NAME`: Bot name used in guiding the generation process.

- `OPENAPI_SERVICE_SPEC`: URL or path to the OpenAPI service specification.

- `OPENAPI_SERVICE_TOKEN`: Token for authenticating with the specified OpenAPI service.

- `SERVICE_RESPONSE_SUBTREE`: Specific subtree to parse in the service response.

- `OUTPUT_KEY`: Json key for the output of the text generation (if not the output is not a json) 

- `OUTPUT_SCHEMA`: Schema URL or json text defining the output format.

- `QUIET`: Set to `true` to disable output to standard output (STDOUT).

## Usage Example

TODO

## License

Licensed under (LICENCE)[LICENCSE]