LanceDB storage location with read/write permissions. - How do I install the project? > Clone the repository and run `pnpm install` to install the dependencies. - Can I contribute to this project? > Yes! Contributions are welcome, and you can submit a Pull Request.26:T2c1d,

# Cursor Chat History Vectorizer & Dockerized Search MCP Vectorize your Cursor chat history and serve it via a simple search API. This project provides tools to: 1. Extract chat history from local Cursor IDE data (`state.vscdb` files within workspace storage). 2. Generate text embeddings for user prompts using a local Ollama instance (`nomic-embed-text`). 3. Store the extracted prompts and their embeddings in a LanceDB vector database. 4. Include a Dockerized **FastAPI application (referred to as an "MCP server" in this context)** to search this LanceDB database via a simple API endpoint. ## ✨ Project Goal The primary goal is to make your Cursor chat history searchable and usable for Retrieval Augmented Generation (RAG) or other LLM-based analysis by: * Converting user prompts into vector embeddings stored efficiently in LanceDB. * Providing a simple and accessible API server to perform vector similarity searches against your vectorized history. ## 🚀 Features * **Data Extraction:** Scans specified Cursor workspace storage paths for `state.vscdb` SQLite files. * **Prompt Extraction:** Extracts user prompts from the `aiService.prompts` key within the database files. * **Embedding Generation:** Uses a locally running Ollama instance to generate embeddings for extracted prompts. * **Embedding Model:** `nomic-embed-embed-text:latest` (default dimension 768). * **Vector Database Storage:** Stores original text, source file, role, and vector embeddings in a LanceDB database. * **LanceDB URI:** `./cursor_chat_history.lancedb` (for the extractor) / `/data/cursor_chat_history.lancedb` (inside Docker container) * **Table Name:** `chat_history` * **Dockerized Search :** Includes a `Dockerfile` to build a container for the Fast search server. * **Fast Server (`main.py`):** Acts as the "MCP server" for handling search requests. * ** Endpoints:** * `/search_chat_history` (POST): Performs vector similarity search. * `/health` (GET): Checks server status and connections (Ollama, LanceDB). ## 📋 Requirements **For Running the Extraction Script (`cursor_history_extractor.py`):** * **Python 3.7+** * **Ollama:** Ensure Ollama is installed and running on your local machine. Pull the `nomic-embed-text` model: ```bash ollama pull nomic-embed-text:latest ``` * **Python Packages:** Install required packages: ```bash pip install ollama lancedb pyarrow pandas python-dotenv ``` * **File Access:** Read access to your Cursor workspace storage directory (default: `C:\Users\\AppData\Roaming\Cursor\User\workspaceStorage`). **For Running the Search API (`main.py`) via Docker:** * **Docker Desktop** (Windows/Mac) or **Docker Engine** (Linux). * An **accessible Ollama instance** from the Docker container's network. * The **LanceDB database directory** (`./cursor_chat_history.lancedb`) already created by the extraction script. ## ⚙️ Setup & Configuration The process involves two main steps: 1. **Run the extraction script** to create or update the LanceDB database on your host machine. 2. **Build and run the Docker container** for the search API, mounting the database created in Step 1

#lancedb

LanceDB Node.js Vector Search

Cursor Chat History Vectorizer & Dockerized Search MCP

Cursor History MCP 📜