parquet mcp server

npx -y @smithery/cli install @DeepSpringAI/parquet_mcp_server --client claude

git clone ... cd parquet_mcp_server

uv venv .venv\Scripts\activate # On Windows source .venv/bin/activate # On macOS/Linux

uv pip install -e .

EMBEDDING_URL=http://sample-url.com/api/embed # URL for the embedding service OLLAMA_URL=http://sample-url.com/ # URL for Ollama server EMBEDDING_MODEL=sample-model # Model to use for generating embeddings SEARCHAPI_API_KEY=your_searchapi_api_key FIRECRAWL_API_KEY=your_firecrawl_api_key VOYAGE_API_KEY=your_voyage_api_key AZURE_OPENAI_ENDPOINT=http://sample-url.com/azure_openai AZURE_OPENAI_API_KEY=your_azure_openai_api_key

{ "mcpServers": { "parquet-mcp-server": { "command": "uv", "args": [ "--directory", "/home/${USER}/workspace/parquet_mcp_server/src/parquet_mcp_server", "run", "main.py" ] } } }

"Please perform a web search for 'macbook' and 'laptop' and scrape the results from page 1"

"Please extract relevant information from the previous searches for 'macbook'"

python src/tests/run_tests.py

# Test Web Search python src/tests/test_search_web.py # Test Extract Info from Search python src/tests/test_extract_info_from_search.py

from parquet_mcp_server.client import ( perform_search_and_scrape, # New web search function find_similar_chunks # New extract info function ) # Perform a web search perform_search_and_scrape(["macbook", "laptop"], page_number=1) # Extract information from the search results find_similar_chunks(["macbook"])

-- Create the function for vector similarity search CREATE OR REPLACE FUNCTION match_web_search( query_embedding vector(1024), -- Adjusted vector size match_threshold float, match_count int -- User-defined limit for number of results ) RETURNS TABLE ( id bigint, metadata jsonb, text TEXT, -- Added text column to the result date TIMESTAMP, -- Using the date column instead of created_at similarity float ) LANGUAGE plpgsql AS $$ BEGIN RETURN QUERY SELECT web_search.id, web_search.metadata, web_search.text, -- Returning the full text of the chunk web_search.date, -- Returning the date timestamp 1 - (web_search.embedding <=> query_embedding) as similarity FROM web_search WHERE 1 - (web_search.embedding <=> query_embedding) > match_threshold ORDER BY web_search.date DESC, -- Sort by date in descending order (newest first) web_search.embedding <=> query_embedding -- Sort by similarity LIMIT match_count; -- Limit the results to the match_count specified by the user END; $$;

CREATE TABLE web_search ( id SERIAL PRIMARY KEY, text TEXT, metadata JSONB, embedding VECTOR(1024), -- This will be auto-updated date TIMESTAMP DEFAULT NOW() );