MCP Search Server

# Streamlit MCP RAG Agent example This Streamlit example shows a RAG Agent that is able to augment its responses using data from Qdrant vector database. <img width="834" alt="Image" src="https://github.com/user-attachments/assets/14072029-1f37-4ac5-bccf-a76e726ba9b2" /> --- ```plaintext ┌───────────┐ ┌─────────┐ ┌──────────────┐ │ Streamlit │─────▶│ Agent │─────▶│ Qdrant │ │ App │ │ │ │ MCP Server │ └───────────┘ └─────────┘ └──────────────┘ ``` ## `1` App set up First, clone the repo and navigate to the streamlit mcp rag agent example: ```bash git clone https://github.com/lastmile-ai/mcp-agent.git cd mcp-agent/examples/usecase/streamlit_mcp_rag_agent ``` Install `uv` (if you don’t have it): ```bash pip install uv ``` Sync `mcp-agent` project dependencies: ```bash uv sync ``` Install requirements specific to this example: ```bash uv pip install -r requirements.txt ``` ## `1.1` Install Qdrant Download latest Qdrant image from Dockerhub: ```bash docker pull qdrant/qdrant ``` Then, run the Qdrant server locally with docker: ```bash docker run -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrant ``` ## `2` Set up secrets and environment variables Copy and configure your secrets and env variables: ```bash cp mcp_agent.secrets.yaml.example mcp_agent.secrets.yaml ``` Then open `mcp_agent.secrets.yaml` and add your api key for your preferred LLM. ## `3` Run locally Run your MCP Agent app: ```bash uv run streamlit run main.py ```