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RAG Document Search

Document search pipeline with advanced search capabilities and summarization features.

Overview​

The RAG document search system provides comprehensive document search capabilities including indexing, search, and summarization.

Core Features​

  • Document Indexing: Efficient document indexing and storage
  • Advanced Search: Full-text, semantic, and hybrid search
  • Summarization: Automatic document summarization
  • Caching: Smart caching for improved performance
  • Analytics: Search analytics and insights

Usage Examples​

from recoagent.rag.document_search import DocumentSearchEngine

# Create document search engine
search_engine = DocumentSearchEngine()

# Index documents
search_engine.index_documents([
    {"id": "doc1", "content": "Document content...", "metadata": {"title": "Doc 1"}},
    {"id": "doc2", "content": "Another document...", "metadata": {"title": "Doc 2"}}
])

# Search documents
results = search_engine.search("query text", limit=10)

Advanced Search with Summarization​

# Search with summarization
search_results = search_engine.search_with_summarization(
    query="machine learning algorithms",
    summarize_results=True,
    summary_length=200
)

# Get summarized results
for result in search_results:
    print(f"Title: {result.title}")
    print(f"Summary: {result.summary}")
    print(f"Relevance: {result.relevance_score}")

API Reference​

DocumentSearchEngine Methods​

index_documents(documents: List[Dict]) -> None​

Index documents for search

Parameters:

  • documents (List[Dict]): List of documents to index

search(query: str, limit: int = 10) -> List[SearchResult]​

Search documents

Parameters:

  • query (str): Search query
  • limit (int): Maximum results

Returns: List of search results

See Also​