mirror of
https://github.com/MODSetter/SurfSense.git
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aabc24f82c
- Introduced slow callback logging in FastAPI to identify blocking calls. - Added performance logging for agent creation and tool loading processes. - Implemented caching for MCP tools to reduce redundant server calls. - Enhanced sandbox management with in-process caching for improved efficiency. - Refactored several functions for better readability and performance tracking. - Updated tests to ensure proper functionality of new features and optimizations.
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
from app.prompts import SUMMARY_PROMPT_TEMPLATE
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from app.utils.document_converters import optimize_content_for_context_window
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async def summarize_document(
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source_markdown: str, llm, metadata: dict | None = None
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) -> str:
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"""Generate a text summary of a document using an LLM, prefixed with metadata when provided."""
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model_name = getattr(llm, "model", "gpt-3.5-turbo")
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optimized_content = optimize_content_for_context_window(
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source_markdown, metadata, model_name
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)
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summary_chain = SUMMARY_PROMPT_TEMPLATE | llm
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content_with_metadata = (
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f"<DOCUMENT><DOCUMENT_METADATA>\n\n{metadata}\n\n</DOCUMENT_METADATA>"
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f"\n\n<DOCUMENT_CONTENT>\n\n{optimized_content}\n\n</DOCUMENT_CONTENT></DOCUMENT>"
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)
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summary_result = await summary_chain.ainvoke({"document": content_with_metadata})
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summary_content = summary_result.content
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if metadata:
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metadata_parts = ["# DOCUMENT METADATA"]
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for key, value in metadata.items():
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if value:
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metadata_parts.append(f"**{key.replace('_', ' ').title()}:** {value}")
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metadata_section = "\n".join(metadata_parts)
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return f"{metadata_section}\n\n# DOCUMENT SUMMARY\n\n{summary_content}"
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return summary_content
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