wehub-resource-sync
75 lines
2.0 KiB
Markdown
75 lines
2.0 KiB
Markdown
# MCP-powered Agentic RAG using Firecrawl and Qdrant
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This project implements Agentic RAG using Firecrawl and Qdrant.
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- [Firecrawl](https://www.firecrawl.dev/i/api) is used to scrape data from the web
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- Qdrant as the local vector database.
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- Cursor IDE as the MCP client.
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---
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## Setup and installations
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**Get Firecrawl API Key**:
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- Go to [Firecrawl](https://www.firecrawl.dev/i/api) and sign up for an account.
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- You will find your API key there.
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- Store it in the .env file.
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```
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FIRECRAWL_API_KEY="..."
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```
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**Install Dependencies**:
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Ensure you have Python 3.11 or later installed.
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```bash
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pip install firecrawl-py mcp qdrant-client
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```
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---
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## Run the project
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First, start a Qdrant docker container as follows (make sure you have downloaded Docker):
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```bash
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docker run -p 6333:6333 -p 6334:6334 \
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-v $(pwd)/qdrant_storage:/qdrant/storage:z \
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qdrant/qdrant
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```
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Next, go to the notebook.ipynb file, run the code to create a collection in your vector database.
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Finally, set up your local MCP server as follows:
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- Go to Cursor settings
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- Select MCP
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- Add new global MCP server.
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In the JSON file, add this:
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```json
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{
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"mcpServers": {
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"mcp-rag-app": {
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"command": "python",
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"args": ["/absolute/path/to/server.py"],
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"host": "127.0.0.1",
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"port": 8080,
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"timeout": 30000
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}
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}
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}
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```
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Done! You can now interact with your vector database and fallback to web search if needed.
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---
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## 📬 Stay Updated with Our Newsletter!
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**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
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[](https://join.dailydoseofds.com)
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---
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## Contribution
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Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
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