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rm recall experience op
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jinli.yl
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4 changed files with 17 additions and 108 deletions
4
TODO.md
4
TODO.md
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@ -25,4 +25,6 @@
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4. 周四下午和兆洋对一下
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# 锦鲤
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1. LOGO更加简洁 agent <-> experience
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1. LOGO更加简洁 agent <-> experience
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2. default等名字的说明
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3. 查看所有的op代码
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@ -20,8 +20,17 @@ class BuildQueryOp(BaseOp):
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if enable_llm_build and enable_llm_build.lower() == "true":
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execution_process = merge_messages_content(request.messages)
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query = self.prompt_format(prompt_name="query_build", execution_process=execution_process)
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else:
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query = request.messages[-1].content
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context_parts = []
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message_summaries = []
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for message in request.messages[-3:]: # Last 3 messages
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content = message.content[:200] + "..." if len(message.content) > 200 else message.content
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message_summaries.append(f"- {message.role.value}: {content}")
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if message_summaries:
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context_parts.append("Recent messages:\n" + "\n".join(message_summaries))
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query = "\n\n".join(context_parts)
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else:
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raise RuntimeError("query or messages is required!")
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@ -30,3 +39,4 @@ class BuildQueryOp(BaseOp):
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from experiencemaker.op.vector_store.recall_vector_store_op import RecallVectorStoreOp
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self.context.set_context(RecallVectorStoreOp.SEARCH_QUERY, query)
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self.context.set_context(RecallVectorStoreOp.SEARCH_MESSAGE, request.messages)
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@ -1,104 +0,0 @@
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from typing import List
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from loguru import logger
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from pydantic import Field
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from experiencemaker.op import OP_REGISTRY
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from experiencemaker.op.base_op import BaseOp
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from experiencemaker.schema.message import Message
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from experiencemaker.schema.vector_node import VectorNode
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from experiencemaker.schema.request import RetrieverRequest
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@OP_REGISTRY.register()
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class RecallExperienceOp(BaseOp):
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"""
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Recall relevant experiences from vector store based on query
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"""
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current_path: str = __file__
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# Configuration parameters
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def execute(self):
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"""Execute recall operation"""
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request: RetrieverRequest = self.context.request
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retrieve_top_k = self.op_params.get("retrieve_top_k",15)
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query_enhancement = self.op_params.get("query_enhancement",False)
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logger.info(request)
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# Extract query and messages from request
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query = getattr(request, 'query', None)
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messages = getattr(request, 'messages', None)
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# Store in context for downstream ops
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self.context.set_context("query", request.query)
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self.context.set_context("messages", request.messages)
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try:
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# Build retrieval query
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retrieval_query = self._build_retrieve_query(query, messages, query_enhancement)
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logger.info(f"Built retrieval query: {retrieval_query}")
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# Retrieve experiences from vector store
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recalled_experiences = self._retrieve_experiences(request.workspace_id, retrieval_query, retrieve_top_k)
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logger.info(f"Recalled {len(recalled_experiences)} experiences")
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# Store results in context for downstream ops
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self.context.set_context("recalled_experiences", recalled_experiences)
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self.context.set_context("retrieval_query", retrieval_query)
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except Exception as e:
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logger.error(f"Error in recall operation: {e}")
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self.context.set_context("recalled_experiences", [])
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def _build_retrieve_query(self, query: str, messages: List[Message] = None, query_enhancement = False) -> str:
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"""Build retrieval query from query and messages"""
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# Use the original query as base
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base_query = query
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# Optionally enhance with current step context if enabled
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if query_enhancement and messages:
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current_context = self._extract_context(messages)
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if current_context:
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base_query = f"{base_query} {current_context}"
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return base_query
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def _retrieve_experiences(self, workspace_id: str, query: str, retrieve_top_k: int) -> List[VectorNode]:
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"""Retrieve experiences from vector store"""
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if not query:
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logger.warning("Empty query provided for vector retrieval")
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return []
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try:
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retrieved_nodes = self.vector_store.search(
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workspace_id=workspace_id,
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query=query,
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top_k=retrieve_top_k
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)
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logger.info(f"Vector retrieval found {len(retrieved_nodes)} candidates")
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return retrieved_nodes
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except Exception as e:
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logger.exception(f"Error in vector retrieval: {e}")
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return []
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def _extract_context(self, messages: List[Message]) -> str:
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"""Extract relevant context from messages for query enhancement"""
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if not messages:
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return ""
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context_parts = []
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# Add recent steps if available
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recent_messages = messages[-3:] # Last 3 messages
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message_summaries = []
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for message in recent_messages:
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content = message.content[:200] + "..." if len(message.content) > 200 else message.content
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message_summaries.append(f"- {message.role.value}: {content}")
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if message_summaries:
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context_parts.append("Recent messages:\n" + "\n".join(message_summaries))
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return "\n\n".join(context_parts)
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@ -13,6 +13,7 @@ from experiencemaker.schema.vector_node import VectorNode
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@OP_REGISTRY.register()
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class RecallVectorStoreOp(BaseOp):
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SEARCH_QUERY = "search_query"
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SEARCH_MESSAGE = "search_message"
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def execute(self):
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# get query
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@ -43,5 +44,5 @@ class RecallVectorStoreOp(BaseOp):
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logger.info(f"after filter by threshold_score size={len(experience_list)}")
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# set response
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request: RetrieverResponse = self.context.response
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request.experience_list = experience_list
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response: RetrieverResponse = self.context.response
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response.experience_list = experience_list
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