Fix streaming and mermaid parsing

+ Update docs

Author: bearlike

.env.sample

@@ -0,0 +1,2 @@
+OPENAI_API_BASE_URL=https://api.openai.com/v1
+OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

.gitignore

@@ -159,3 +159,4 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 .python-version
+output.md

README.md

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-# MCTS OpenAI
-Every incoming request is wrapped with a Monte Carlo Tree Search (MCTS) pipeline
+# MCTS OpenAI API Wrapper
+
+![Comparison of Response](docs/screenshot_1.png)
+
+Monte Carlo Tree Search (MCTS) is a method that uses extra compute to explore different candidate responses before selecting a final answer. It works by building a tree of options and running multiple iterations. This is similar in concept to inference scaling, but here a model generates several output candidates, reitereates and picks the best one. Every incoming request is wrapped with a MCTS pipeline to iteratively refine language model outputs.
+
+## Overview
+
+This FastAPI server exposes two endpoints:
+
+| Method | Endpoint               | Description                                                                   |
+|--------|------------------------|-------------------------------------------------------------------------------|
+| POST   | `/v1/chat/completions` | Accepts chat completion requests. The call is wrapped with an MCTS refinement |
+| GET    | `/v1/models`           | Proxies a request to the underlying LLM provider’s models endpoint             |
+
+During a chat completion call, the server executes an MCTS pipeline that generates intermediate updates (including a Mermaid diagram and iteration details). All these intermediate responses are aggregated into a single `<details>` block, and the final answer is appended at the end, following a consistent and structured markdown template.
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.8+
+- [Poetry](https://python-poetry.org) for dependency management
+
+### Setup
+
+1. **Clone the repository:**
+
+   ```bash
+   git clone https://github.com/bearlike/mcts-openai-api.git
+   cd mcts-openai-api
+   ```
+
+2. **Copy the Environment File:**
+
+   Copy the example environment file to `.env` and set your `OPENAI_API_KEY`:
+
+   ```bash
+   cp .env.example .env
+   ```
+
+   Open the `.env` file and update the `OPENAI_API_KEY` (and other settings if needed).
+
+3. **Install Dependencies:**
+
+   Use Poetry to install the required packages:
+
+   ```bash
+   poetry install
+   ```
+
+4. **Run the Server:**
+
+   Start the FastAPI server with Uvicorn:
+
+   ```bash
+   # Visit http://server-ip:8000/docs to view the Swagger API documentation
+   uvicorn main:app --reload
+   ```
+
+## Testing the Server
+
+You can test the server using `curl` or any HTTP client.
+
+### Example Request
+
+```bash
+curl -X 'POST' \
+  'http://192.168.1.198:8000/v1/chat/completions' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "model": "gpt-4o-mini",
+  "messages": [
+    {
+      "role": "user",
+      "content": "How many R in STRAWBERRY?"
+    }
+  ],
+  "max_tokens": 1024,
+  "temperature": 0.5
+}' | jq -r '.choices[0].message.content'
+```
+
+This request will return a JSON response with the aggregated intermediate responses wrapped inside a single `<details>` block, followed by the final answer.
+
+---
+
+## Endpoints
+
+### POST /v1/chat/completions
+
+- **Description:**
+  Wraps a chat completion request in an MCTS pipeline that refines the answer by generating intermediate updates and a final response.
+
+- **Request Body Parameters:**
+
+    - `model`: string (e.g., `"gpt-4o-mini"`)
+    - `messages`: an array of chat messages (with `role` and `content` properties)
+    - `max_tokens`: (optional) number
+    - `temperature`: (optional) number
+    - `stream`: (optional) boolean (if enabled, aggregates intermediate responses with the final answer in one JSON response)
+
+### GET /v1/models
+
+- **Description:**
+  Proxies requests to list available models from the underlying LLM provider using the `OPENAI_API_BASE_URL`.
+
+## License
+
+This project is licensed under the MIT License.