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from asgi_correlation_id import correlation_id |
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from fastapi import APIRouter |
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from fastapi.responses import StreamingResponse, JSONResponse |
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from utils.bio_logger import bio_logger as logger |
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from utils.i18n_util import ( |
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get_language, |
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create_error_response, |
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) |
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from utils.i18n_context import with_language |
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from bio_requests.chat_request import ChatRequest |
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from service.chat import ChatService |
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router = APIRouter(prefix="/mcp", tags=["MCP"]) |
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@router.post("/bio_qa", response_model=None, operation_id="bio_qa_stream_chat") |
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async def bio_qa(query: str, lang: str = "en"): |
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""" |
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Biomedical Q&A with Evidence-Based RAG System |
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This MCP tool provides comprehensive, research-backed answers to biological and medical questions |
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using a sophisticated Retrieval-Augmented Generation (RAG) system. The tool performs multi-source |
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research and evidence-based synthesis to deliver accurate, well-cited responses. |
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**Process Overview:** |
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1. **Query Analysis & Rewriting** (30-45 seconds) |
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- Analyzes the user's question and identifies key biomedical concepts |
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- Performs intelligent query rewriting to improve search effectiveness |
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- Generates multiple search variations to capture different aspects of the question |
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- Optimizes search terms for both PubMed and web search engines |
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2. **Multi-Source Literature Search** (60-90 seconds) |
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- **PubMed Database Search**: Searches scientific literature database for peer-reviewed papers |
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- **Web Search**: Conducts web searches for recent developments, clinical guidelines, and additional context |
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- **Concurrent Processing**: Performs both searches simultaneously for efficiency |
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- **Content Extraction**: Extracts and processes relevant content from search results |
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3. **Intelligent Reranking** (30-45 seconds) |
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- Ranks search results by relevance to the specific question |
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- Filters out low-quality or irrelevant content |
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- Prioritizes recent, authoritative, and highly relevant sources |
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- Ensures diversity in source types (papers, guidelines, reviews, etc.) |
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4. **Evidence-Based Answer Generation** (60-90 seconds) |
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- Synthesizes information from multiple high-quality sources |
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- Generates comprehensive, well-structured answers |
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- Includes proper citations and references |
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- Provides evidence-based explanations with source attribution |
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**Input:** |
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- query (string): A biological or medical question |
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Examples: "What causes Alzheimer's disease?", "How do mRNA vaccines work?", |
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"What are the latest treatments for diabetes?", "Explain CRISPR gene editing" |
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- lang (string, optional): Language preference ("en" for English, "zh" for Chinese) |
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- is_pubmed (boolean, optional): Enable PubMed scientific literature search (default: True) |
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- When True: Searches peer-reviewed scientific papers for authoritative evidence |
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- When False: Skips PubMed search to reduce processing time |
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- is_web (boolean, optional): Enable web search for additional context (default: True) |
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- When True: Searches web for recent developments, clinical guidelines, and additional context |
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- When False: Skips web search to reduce processing time |
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**Output:** |
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- A comprehensive answer with the following components: |
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* **Main Answer**: Evidence-based response to the question |
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* **Citations**: Properly formatted references to source materials |
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* **Source Links**: Direct links to PubMed papers and web sources |
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* **Evidence Summary**: Overview of the evidence supporting the answer |
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**Key Features:** |
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- **Real-time Streaming**: Provides progress updates via Server-Sent Events (SSE) |
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- **Multi-Source Research**: Combines PubMed scientific literature with web-based information |
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- **Intelligent Query Processing**: Uses advanced query rewriting for better search results |
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- **Quality Control**: Reranks results to ensure relevance and authority |
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- **Evidence-Based Answers**: All claims are supported by cited sources |
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- **Comprehensive Coverage**: Covers genetics, molecular biology, diseases, treatments, and more |
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**Expected Duration:** 3 minutes (may vary based on query complexity and search configuration) |
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**Performance Notes:** |
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- Full search (is_pubmed=True, is_web=True): ~3 minutes with comprehensive coverage |
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- PubMed only (is_pubmed=True, is_web=False): ~2 minutes, focused on scientific literature |
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- Web only (is_pubmed=False, is_web=True): ~2 minutes, focused on recent developments |
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- Minimal search (is_pubmed=False, is_web=False): ~1 minute, basic query processing only |
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**Use Cases:** |
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- Medical education and learning |
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- Clinical decision support |
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- Research background information |
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- Patient education content |
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- Healthcare professional training |
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- Scientific literature exploration |
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**Evidence Quality:** |
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- Primary sources from peer-reviewed scientific journals |
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- Recent clinical guidelines and recommendations |
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- Authoritative medical websites and databases |
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- Multiple source verification for key claims |
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**Note:** This tool is specifically optimized for biomedical and healthcare questions. |
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For best results, provide specific, well-defined questions about biological or medical topics. |
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""" |
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logger.info(f"{correlation_id.get()} Bio QA for {query}") |
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chat_request = ChatRequest(query=query, language=lang, is_pubmed=True, is_web=True) |
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language = get_language(chat_request.language) |
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with with_language(language): |
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try: |
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chat_service = ChatService() |
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return StreamingResponse( |
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chat_service.generate_stream(chat_request), |
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media_type="text/event-stream", |
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headers={ |
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"Connection": "keep-alive", |
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"Cache-Control": "no-cache", |
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}, |
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) |
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except Exception as e: |
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logger.error(f"{correlation_id.get()} Stream chat error: {e}") |
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error_response = create_error_response( |
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error_key="service_unavailable", |
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details=str(e), |
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error_code=500, |
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) |
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return JSONResponse(content=error_response, status_code=500) |
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