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Time chat API sub-components for performance analysis
Time and the search query extraction, search and response generation components
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1 changed files with 9 additions and 6 deletions
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@ -207,16 +207,19 @@ def chat(q: Optional[str] = None):
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return {"status": "ok", "response": []}
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# Infer search queries from user message
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inferred_queries = extract_questions(q, model=model, api_key=api_key, conversation_log=meta_log)
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with timer("Extracting search queries took", logger):
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inferred_queries = extract_questions(q, model=model, api_key=api_key, conversation_log=meta_log)
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# Collate search results as context for GPT
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result_list = []
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for query in inferred_queries:
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result_list.extend(search(query, n=5, r=True, score_threshold=-5.0, dedupe=False))
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collated_result = "\n\n".join({f"# {item.additional['compiled']}" for item in result_list})
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with timer("Searching knowledge base took", logger):
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result_list = []
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for query in inferred_queries:
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result_list.extend(search(query, n=5, r=True, score_threshold=-5.0, dedupe=False))
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collated_result = "\n\n".join({f"# {item.additional['compiled']}" for item in result_list})
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try:
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gpt_response = converse(collated_result, q, meta_log, api_key=api_key)
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with timer("Generating chat response took", logger):
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gpt_response = converse(collated_result, q, meta_log, api_key=api_key)
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status = "ok"
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except Exception as e:
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gpt_response = str(e)
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