- Added `applySearchReplace` function to handle search and replace operations with fuzzy matching capabilities. - Introduced tests for various scenarios including fuzzy matching with typos, exact matches, and handling whitespace differences. - Created a parser for search/replace blocks to facilitate the new functionality. - Updated prompts for search-replace operations to clarify usage and examples. - Added utility functions for text normalization and language detection based on file extensions. - Implemented a minimal stdio MCP server for local testing with tools for adding numbers and printing environment variables.
2.4 KiB
Fake LLM Server
A simple server that mimics the OpenAI streaming chat completions API for testing purposes.
Features
- Implements a basic version of the OpenAI chat completions API
- Supports both streaming and non-streaming responses
- Always responds with "hello world" message
- Simulates a 429 rate limit error when the last message is "[429]"
- Configurable through environment variables
Installation
npm install
Usage
Start the server:
# Development mode
npm run dev
# Production mode
npm run build
npm start
Example usage
curl -X POST http://localhost:3500/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"messages":[{"role":"user","content":"Say something"}],"model":"any-model","stream":true}'
The server will be available at http://localhost:3500 by default.
API Endpoints
POST /v1/chat/completions
This endpoint mimics OpenAI's chat completions API.
Request Format
{
"messages": [{ "role": "user", "content": "Your prompt here" }],
"model": "any-model",
"stream": true
}
- Set
stream: trueto receive a streaming response - Set
stream: falseor omit it for a regular JSON response
Response
For non-streaming requests, you'll get a standard JSON response:
{
"id": "chatcmpl-123456789",
"object": "chat.completion",
"created": 1699000000,
"model": "fake-model",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "hello world"
},
"finish_reason": "stop"
}
]
}
For streaming requests, you'll receive a series of server-sent events (SSE), each containing a chunk of the response.
Simulating Rate Limit Errors
To test how your application handles rate limiting, send a message with content exactly equal to [429]:
{
"messages": [{ "role": "user", "content": "[429]" }],
"model": "any-model"
}
This will return a 429 status code with the following response:
{
"error": {
"message": "Too many requests. Please try again later.",
"type": "rate_limit_error",
"param": null,
"code": "rate_limit_exceeded"
}
}
Configuration
You can configure the server by modifying the PORT variable in the code.
Use Case
This server is primarily intended for testing applications that integrate with OpenAI's API, allowing you to develop and test without making actual API calls to OpenAI.