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Feat: add or logic operations for meta data filters. (#11404)
### What problem does this PR solve? #11376 #11387 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
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@ -159,10 +159,10 @@ async def webhook(tenant_id: str, agent_id: str):
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data=False, message=str(e),
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code=RetCode.EXCEPTION_ERROR)
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def sse():
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async def sse():
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nonlocal canvas
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try:
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for ans in canvas.run(query=req.get("query", ""), files=req.get("files", []), user_id=req.get("user_id", tenant_id), webhook_payload=req):
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async for ans in canvas.run(query=req.get("query", ""), files=req.get("files", []), user_id=req.get("user_id", tenant_id), webhook_payload=req):
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yield "data:" + json.dumps(ans, ensure_ascii=False) + "\n\n"
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cvs.dsl = json.loads(str(canvas))
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@ -120,7 +120,7 @@ async def retrieval(tenant_id):
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retrieval_setting = req.get("retrieval_setting", {})
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similarity_threshold = float(retrieval_setting.get("score_threshold", 0.0))
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top = int(retrieval_setting.get("top_k", 1024))
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metadata_condition = req.get("metadata_condition", {})
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metadata_condition = req.get("metadata_condition", {}) or {}
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metas = DocumentService.get_meta_by_kbs([kb_id])
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doc_ids = []
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@ -132,7 +132,7 @@ async def retrieval(tenant_id):
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embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
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if metadata_condition:
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doc_ids.extend(meta_filter(metas, convert_conditions(metadata_condition)))
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doc_ids.extend(meta_filter(metas, convert_conditions(metadata_condition), metadata_condition.get("logic", "and")))
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if not doc_ids and metadata_condition:
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doc_ids = ["-999"]
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ranks = settings.retriever.retrieval(
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@ -1442,9 +1442,9 @@ async def retrieval_test(tenant_id):
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if doc_id not in doc_ids_list:
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return get_error_data_result(f"The datasets don't own the document {doc_id}")
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if not doc_ids:
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metadata_condition = req.get("metadata_condition", {})
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metadata_condition = req.get("metadata_condition", {}) or {}
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metas = DocumentService.get_meta_by_kbs(kb_ids)
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doc_ids = meta_filter(metas, convert_conditions(metadata_condition))
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doc_ids = meta_filter(metas, convert_conditions(metadata_condition), metadata_condition.get("logic", "and"))
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similarity_threshold = float(req.get("similarity_threshold", 0.2))
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vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
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top = int(req.get("top_k", 1024))
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@ -428,17 +428,15 @@ async def agents_completion_openai_compatibility(tenant_id, agent_id):
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return resp
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else:
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# For non-streaming, just return the response directly
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response = next(
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completion_openai(
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async for response in completion_openai(
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tenant_id,
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agent_id,
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question,
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session_id=req.pop("session_id", req.get("id", "")) or req.get("metadata", {}).get("id", ""),
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stream=False,
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**req,
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)
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)
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return jsonify(response)
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):
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return jsonify(response)
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@manager.route("/agents/<agent_id>/completions", methods=["POST"]) # noqa: F821
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@ -977,12 +975,12 @@ async def retrieval_test_embedded():
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metas = DocumentService.get_meta_by_kbs(kb_ids)
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if meta_data_filter.get("method") == "auto":
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chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", ""))
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filters = gen_meta_filter(chat_mdl, metas, question)
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doc_ids.extend(meta_filter(metas, filters))
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filters: dict = gen_meta_filter(chat_mdl, metas, question)
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doc_ids.extend(meta_filter(metas, filters["conditions"], filters.get("logic", "and")))
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if not doc_ids:
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doc_ids = None
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elif meta_data_filter.get("method") == "manual":
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doc_ids.extend(meta_filter(metas, meta_data_filter["manual"]))
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doc_ids.extend(meta_filter(metas, meta_data_filter["manual"], meta_data_filter.get("logic", "and")))
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if not doc_ids:
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doc_ids = None
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