base on A proxy for converting the OpenAI API protocol to the Google Gemini Pro protocol. # Gemini-OpenAI-Proxy Gemini-OpenAI-Proxy is a proxy designed to convert the OpenAI API protocol to the Google Gemini protocol. This enables applications built for the OpenAI API to seamlessly communicate with the Gemini protocol, including support for Chat Completion, Embeddings, and Model(s) endpoints. --- ## Table of Contents - [Gemini-OpenAI-Proxy](#gemini-openai-proxy) - [Table of Contents](#table-of-contents) - [Build](#build) - [Deploy](#deploy) - [Usage](#usage) - [Compatibility](#compatibility) - [License](#license) --- ## Build To build the Gemini-OpenAI-Proxy, follow these steps: ```bash go build -o gemini main.go ``` --- ## Deploy We recommend deploying Gemini-OpenAI-Proxy using Docker for a straightforward setup. Follow these steps to deploy with Docker: You can either do this on the command line: ```bash docker run --restart=unless-stopped -it -d -p 8080:8080 --name gemini zhu327/gemini-openai-proxy:latest ``` Or with the following docker-compose config: ```yaml version: '3' services: gemini: container_name: gemini environment: # Set Environment Variables here. Defaults listed below - GPT_4_VISION_PREVIEW=gemini-1.5-flash-latest - DISABLE_MODEL_MAPPING=0 ports: - "8080:8080" image: zhu327/gemini-openai-proxy:latest restart: unless-stopped ``` Adjust the port mapping (e.g., `-p 8080:8080`) as needed, and ensure that the Docker image version (`zhu327/gemini-openai-proxy:latest`) aligns with your requirements. --- ## Usage Gemini-OpenAI-Proxy offers a straightforward way to integrate OpenAI functionalities into any application that supports custom OpenAI API endpoints. Follow these steps to leverage the capabilities of this proxy: 1. **Set Up OpenAI Endpoint:** Ensure your application is configured to use a custom OpenAI API endpoint. Gemini-OpenAI-Proxy seamlessly works with any OpenAI-compatible endpoint. 2. **Get Google AI Studio API Key:** Before using the proxy, you'll need to obtain an API key from [ai.google.dev](https://ai.google.dev). Treat this API key as your OpenAI API key when interacting with Gemini-OpenAI-Proxy. 3. **Integrate the Proxy into Your Application:** Modify your application's API requests to target the Gemini-OpenAI-Proxy, providing the acquired Google AI Studio API key as if it were your OpenAI API key. Example Chat Completion API Request (Assuming the proxy is hosted at `http://localhost:8080`): ```bash curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $YOUR_GOOGLE_AI_STUDIO_API_KEY" \ -d '{ "model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Say this is a test!"}], "temperature": 0.7 }' ``` Alternatively, use Gemini Pro Vision: ```bash curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $YOUR_GOOGLE_AI_STUDIO_API_KEY" \ -d '{ "model": "gpt-4-vision-preview", "messages": [{"role": "user", "content": [ {"type": "text", "text": "What’s in this image?"}, { "type": "image_url", "image_url": { "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" } } ]}], "temperature": 0.7 }' ``` If you wish to map `gpt-4-vision-preview` to `gemini-1.5-pro-latest`, you can configure the environment variable `GPT_4_VISION_PREVIEW = gemini-1.5-pro-latest`. This is because `gemini-1.5-pro-latest` now also supports multi-modal data. Otherwise, the default uses the `gemini-1.5-flash-latest` model If you already have access to the Gemini 1.5 Pro api, you can use: ```bash curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $YOUR_GOOGLE_AI_STUDIO_API_KEY" \ -d '{ "model": "gpt-4-turbo-preview", "messages": [{"role": "user", "content": "Say this is a test!"}], "temperature": 0.7 }' ``` Example Embeddings API Request: ```bash curl http://localhost:8080/v1/embeddings \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $YOUR_GOOGLE_AI_STUDIO_API_KEY" \ -d '{ "model": "text-embedding-ada-002", "input": "This is a test sentence." }' ``` You can also pass in multiple input strings as a list: ```bash curl http://localhost:8080/v1/embeddings \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $YOUR_GOOGLE_AI_STUDIO_API_KEY" \ -d '{ "model": "text-embedding-ada-002", "input": ["This is a test sentence.", "This is another test sentence"] }' ``` Model Mapping: | GPT Model | Gemini Model | |---|---| | gpt-4 | gemini-1.5-flash-002 | | gpt-4-turbo-preview | gemini-1.5-pro-latest | | gpt-4o | gemini-2.0-flash-exp | | text-embedding-ada-002 | text-embedding-004 | If you want to disable model mapping, configure the environment variable `DISABLE_MODEL_MAPPING=1`. This will allow you to refer to the Gemini models directly. Here is an example API request with model mapping disabled: ```bash curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $YOUR_GOOGLE_AI_STUDIO_API_KEY" \ -d '{ "model": "gemini-1.0-pro-latest", "messages": [{"role": "user", "content": "Say this is a test!"}], "temperature": 0.7 }' ``` 4. **Handle Responses:** Process the responses from the Gemini-OpenAI-Proxy in the same way you would handle responses from OpenAI. Now, your application is equipped to leverage OpenAI functionality through the Gemini-OpenAI-Proxy, bridging the gap between OpenAI and applications using the Google Gemini Pro protocol. ## Compatibility - <https://github.com/zhu327/gemini-openai-proxy/issues/4> ## Important Notice Google AI Studio now provides an OpenAI-compatible API endpoint. Users of this project are recommended to use the officially provided compatibility API. Documentation is available at: <https://ai.google.dev/gemini-api/docs/openai> --- ## License Gemini-OpenAI-Proxy is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ", Assign "at most 3 tags" to the expected json: {"id":"6622","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"