Coool2 commited on
Commit
449486e
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1 Parent(s): f3bb80d

Update agent.py

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  1. agent.py +7 -6
agent.py CHANGED
@@ -319,14 +319,15 @@ forced_rag_pipeline = create_forced_rag_pipeline()
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  # Remplacer les tools individuels par le pipeline
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  information_retrieval_tool = FunctionTool.from_defaults(
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  fn=lambda input_path: forced_rag_pipeline.run(input_path),
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- name="process_docs_urls_tool",
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  description=(
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- "This tool is the PRIMARY and MOST EFFECTIVE method for extracting and retrieving information from URLs or documents. "
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- "It AUTOMATICALLY processes any given web pages, PDFs, or document files by first using read_and_parse to fully extract and parse content. "
 
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  "Then, it creates a powerful Retrieval Augmented Generation (RAG) query engine optimized for semantic search and precise information retrieval. "
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- "Finally, it applies the RAG engine to answer queries directly, providing efficient and accurate results. "
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- "This tool is specifically designed to handle diverse document types and web content, ensuring superior extraction and querying capabilities. "
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- "Avoid manual page access or ad-hoc parsing; always use this tool for best performance and reliability in information extraction and question answering."
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  )
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  )
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  # Remplacer les tools individuels par le pipeline
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  information_retrieval_tool = FunctionTool.from_defaults(
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  fn=lambda input_path: forced_rag_pipeline.run(input_path),
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+ name="information_retrieval_tool",
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  description=(
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+ "This tool is the PRIMARY and MOST EFFECTIVE method for answering user queries by extracting and retrieving information from URLs or documents. "
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+ "When given a document or URL, it AUTOMATICALLY processes the content and DIRECTLY ANSWERS your specific question or information need. "
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+ "The tool first uses read_and_parse to fully extract and parse content from web pages, PDFs, or document files. "
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  "Then, it creates a powerful Retrieval Augmented Generation (RAG) query engine optimized for semantic search and precise information retrieval. "
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+ "Most importantly, it IMMEDIATELY applies this RAG engine to provide direct, accurate answers to your query, eliminating the need for manual searching. "
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+ "This tool is specifically designed to transform user questions into precise answers by leveraging advanced document understanding and query processing capabilities. "
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+ "Instead of manual page access or ad-hoc parsing, use this tool to get immediate, reliable answers to your information retrieval needs."
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  )
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  )
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