English | 简体中文 | 日本語

Open Assistant API is an open-source, self-hosted AI intelligent assistant API, compatible with the official OpenAI interface. It can be used directly with the official OpenAI Client to build LLM applications.

It supports One API for integration with more commercial and private models.

It supports R2R RAG engine。

Below is an example of using the official OpenAI Python openai library:

import openai

client = openai.OpenAI(
    base_url="http://127.0.0.1:8086/api/v1",
    api_key="xxx"
)

assistant = client.beta.assistants.create(
    name="demo",
    instructions="You are a helpful assistant.",
    model="gpt-4-1106-preview"
)

• LLM Support: Compared to the official OpenAI version, more models can be supported by integrating with One API.
• Tool: Currently supports online search; can easily expand more tools.
• RAG Engine: The currently supported file types are txt, html, markdown, pdf, docx, pptx, xlsx, png, mp3, mp4, etc. We provide a preliminary implementation.
• Message Streaming Output: Support message streaming output for a smoother user experience.
• Ecosystem Strategy: Open source, you can deploy the service locally and expand the existing features.

The easiest way to start the Open Assistant API is to run the docker-compose.yml file. Make sure Docker and Docker Compose are installed on your machine before running.

Go to the project root directory, open docker-compose.yml, fill in the openai api_key and bing search key (optional).

# openai api_key (supports OneAPI api_key)
OPENAI_API_KEY=<openai_api_key>

# bing search key (optional)
BING_SUBSCRIPTION_KEY=<bing_subscription_key>

It is recommended to configure the R2R RAG engine to replace the default RAG implementation to provide better RAG capabilities. You can learn about and use R2R through the R2R Github repository.

# RAG config
# FILE_SERVICE_MODULE=app.services.file.impl.oss_file.OSSFileService
FILE_SERVICE_MODULE=app.services.file.impl.r2r_file.R2RFileService
R2R_BASE_URL=http://<r2r_api_address>
R2R_USERNAME=<r2r_username>
R2R_PASSWORD=<r2r_password>

docker compose up -d

Api Base URL: http://127.0.0.1:8086/api/v1

Interface documentation address: http://127.0.0.1:8086/docs

In this example, an AI assistant is created and run using the official OpenAI client library. If you need to explore other usage methods, such as streaming output, tools (web_search, retrieval, function), etc., you can find the corresponding code under the examples directory. Before running, you need to run pip install openai to install the Python openai library.

# !pip install openai
export PYTHONPATH=$(pwd)
python examples/run_assistant.py

Simple user isolation is provided based on tokens to meet SaaS deployment requirements. It can be enabled by configuring APP_AUTH_ENABLE.

1. The authentication method is Bearer token. You can include Authorization: Bearer *** in the header for authentication.
2. Token management is described in the token section of the API documentation. Relevant APIs need to be authenticated with an admin token, which is configured as APP_AUTH_ADMIN_TOKEN and defaults to "admin".
3. When creating a token, you need to provide the base URL and API key of the large model. The created assistant will use the corresponding configuration to access the large model.

According to the OpenAPI/Swagger specification, it allows the integration of various tools into the assistant, empowering and enhancing its capability to connect with the external world.

1. Facilitates connecting your application with other systems or services, enabling interaction with the external environment, such as code execution or accessing proprietary information sources.
2. During usage, you need to create tools first, and then you can integrate them with the assistant. Refer to the test cases for more details.Assistant With Action
3. If you need to use tools with authentication information, simply add the authentication information at runtime. The specific parameter format can be found in the API documentation. Refer to the test cases for more details. Run With Auth Action

• Join the Slack channel to see new releases, discuss issues, and participate in community interactions.

• Join the Discord channel to interact with other community members.

• Join the WeChat group:

  

We mainly referred to and relied on the following projects:

• OpenOpenAI: Assistant API implemented in Node
• One API: Multi-model management tool
• R2R: RAG engine
• OpenAI-Python: OpenAI Python Client
• OpenAI API: OpenAI interface definition
• LangChain: LLM application development library
• OpenGPTs: LangChain GPTs
• TaskingAI: TaskingAI Client SDK

Please read our contribution document to learn how to contribute.

This repository follows the MIT open source license. For more information, please see the LICENSE file.