chore: restore the feedback enrichment cot retriever functionality
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lxobr
parent
46e6d87c1f
commit
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1 changed files with 178 additions and 72 deletions
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@ -1,5 +1,7 @@
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import asyncio
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import json
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from typing import Optional, List, Type, Any
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from pydantic import BaseModel
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from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
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from cognee.shared.logging_utils import get_logger
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@ -17,6 +19,20 @@ from cognee.infrastructure.databases.cache.config import CacheConfig
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logger = get_logger()
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def _as_answer_text(completion: Any) -> str:
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"""Convert completion to human-readable text for validation and follow-up prompts."""
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if isinstance(completion, str):
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return completion
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if isinstance(completion, BaseModel):
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# Add notice that this is a structured response
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json_str = completion.model_dump_json(indent=2)
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return f"[Structured Response]\n{json_str}"
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try:
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return json.dumps(completion, indent=2)
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except TypeError:
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return str(completion)
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class GraphCompletionCotRetriever(GraphCompletionRetriever):
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"""
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Handles graph completion by generating responses based on a series of interactions with
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@ -25,6 +41,7 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
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questions based on reasoning. The public methods are:
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- get_completion
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- get_structured_completion
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Instance variables include:
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- validation_system_prompt_path
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@ -61,6 +78,160 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
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self.followup_system_prompt_path = followup_system_prompt_path
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self.followup_user_prompt_path = followup_user_prompt_path
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async def _run_cot_completion(
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self,
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query: str,
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context: Optional[List[Edge]] = None,
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session_id: Optional[str] = None,
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max_iter: int = 4,
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response_model: Type = str,
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) -> tuple[Any, str, List[Edge]]:
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"""
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Run chain-of-thought completion with optional structured output and session caching.
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Returns:
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--------
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- completion_result: The generated completion (string or structured model)
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- context_text: The resolved context text
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- triplets: The list of triplets used
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"""
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followup_question = ""
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triplets = []
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completion = ""
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# Retrieve conversation history if session saving is enabled
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cache_config = CacheConfig()
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user = session_user.get()
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user_id = getattr(user, "id", None)
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session_save = user_id and cache_config.caching
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conversation_history = ""
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if session_save:
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conversation_history = await get_conversation_history(session_id=session_id)
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for round_idx in range(max_iter + 1):
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if round_idx == 0:
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if context is None:
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triplets = await self.get_context(query)
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context_text = await self.resolve_edges_to_text(triplets)
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else:
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context_text = await self.resolve_edges_to_text(context)
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else:
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triplets += await self.get_context(followup_question)
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context_text = await self.resolve_edges_to_text(list(set(triplets)))
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if response_model is str:
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completion = await generate_completion(
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query=query,
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context=context_text,
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user_prompt_path=self.user_prompt_path,
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system_prompt_path=self.system_prompt_path,
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system_prompt=self.system_prompt,
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conversation_history=conversation_history if session_save else None,
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)
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else:
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args = {"question": query, "context": context_text}
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user_prompt = render_prompt(self.user_prompt_path, args)
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system_prompt = (
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self.system_prompt
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if self.system_prompt
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else read_query_prompt(self.system_prompt_path)
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)
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completion = await LLMGateway.acreate_structured_output(
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text_input=user_prompt,
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system_prompt=system_prompt,
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response_model=response_model,
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)
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logger.info(f"Chain-of-thought: round {round_idx} - answer: {completion}")
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if round_idx < max_iter:
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answer_text = _as_answer_text(completion)
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valid_args = {"query": query, "answer": answer_text, "context": context_text}
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valid_user_prompt = render_prompt(
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filename=self.validation_user_prompt_path, context=valid_args
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)
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valid_system_prompt = read_query_prompt(
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prompt_file_name=self.validation_system_prompt_path
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)
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reasoning = await LLMGateway.acreate_structured_output(
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text_input=valid_user_prompt,
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system_prompt=valid_system_prompt,
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response_model=str,
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)
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followup_args = {"query": query, "answer": answer_text, "reasoning": reasoning}
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followup_prompt = render_prompt(
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filename=self.followup_user_prompt_path, context=followup_args
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)
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followup_system = read_query_prompt(
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prompt_file_name=self.followup_system_prompt_path
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)
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followup_question = await LLMGateway.acreate_structured_output(
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text_input=followup_prompt, system_prompt=followup_system, response_model=str
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)
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logger.info(
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f"Chain-of-thought: round {round_idx} - follow-up question: {followup_question}"
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)
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# Save to session cache
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if session_save:
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context_summary = await summarize_text(context_text)
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await save_conversation_history(
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query=query,
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context_summary=context_summary,
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answer=str(completion),
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session_id=session_id,
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)
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return completion, context_text, triplets
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async def get_structured_completion(
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self,
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query: str,
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context: Optional[List[Edge]] = None,
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session_id: Optional[str] = None,
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max_iter: int = 4,
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response_model: Type = str,
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) -> Any:
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"""
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Generate structured completion responses based on a user query and contextual information.
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This method applies the same chain-of-thought logic as get_completion but returns
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structured output using the provided response model.
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Parameters:
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-----------
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- query (str): The user's query to be processed and answered.
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- context (Optional[List[Edge]]): Optional context that may assist in answering the query.
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If not provided, it will be fetched based on the query. (default None)
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- session_id (Optional[str]): Optional session identifier for caching. If None,
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defaults to 'default_session'. (default None)
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- max_iter: The maximum number of iterations to refine the answer and generate
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follow-up questions. (default 4)
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- response_model (Type): The Pydantic model type for structured output. (default str)
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Returns:
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--------
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- Any: The generated structured completion based on the response model.
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"""
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completion, context_text, triplets = await self._run_cot_completion(
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query=query,
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context=context,
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session_id=session_id,
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max_iter=max_iter,
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response_model=response_model,
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)
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if self.save_interaction and context and triplets and completion:
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await self.save_qa(
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question=query, answer=str(completion), context=context_text, triplets=triplets
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)
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return completion
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async def get_completion(
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self,
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query: str,
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@ -92,82 +263,17 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
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- List[str]: A list containing the generated answer to the user's query.
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"""
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followup_question = ""
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triplets = []
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completion = ""
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# Retrieve conversation history if session saving is enabled
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cache_config = CacheConfig()
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user = session_user.get()
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user_id = getattr(user, "id", None)
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session_save = user_id and cache_config.caching
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conversation_history = ""
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if session_save:
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conversation_history = await get_conversation_history(session_id=session_id)
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for round_idx in range(max_iter + 1):
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if round_idx == 0:
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if context is None:
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triplets = await self.get_context(query)
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context_text = await self.resolve_edges_to_text(triplets)
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else:
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context_text = await self.resolve_edges_to_text(context)
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else:
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triplets += await self.get_context(followup_question)
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context_text = await self.resolve_edges_to_text(list(set(triplets)))
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completion = await generate_completion(
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query=query,
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context=context_text,
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user_prompt_path=self.user_prompt_path,
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system_prompt_path=self.system_prompt_path,
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system_prompt=self.system_prompt,
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conversation_history=conversation_history if session_save else None,
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)
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logger.info(f"Chain-of-thought: round {round_idx} - answer: {completion}")
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if round_idx < max_iter:
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valid_args = {"query": query, "answer": completion, "context": context_text}
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valid_user_prompt = render_prompt(
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filename=self.validation_user_prompt_path, context=valid_args
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)
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valid_system_prompt = read_query_prompt(
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prompt_file_name=self.validation_system_prompt_path
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)
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reasoning = await LLMGateway.acreate_structured_output(
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text_input=valid_user_prompt,
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system_prompt=valid_system_prompt,
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response_model=str,
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)
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followup_args = {"query": query, "answer": completion, "reasoning": reasoning}
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followup_prompt = render_prompt(
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filename=self.followup_user_prompt_path, context=followup_args
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)
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followup_system = read_query_prompt(
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prompt_file_name=self.followup_system_prompt_path
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)
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followup_question = await LLMGateway.acreate_structured_output(
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text_input=followup_prompt, system_prompt=followup_system, response_model=str
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)
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logger.info(
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f"Chain-of-thought: round {round_idx} - follow-up question: {followup_question}"
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)
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completion, context_text, triplets = await self._run_cot_completion(
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query=query,
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context=context,
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session_id=session_id,
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max_iter=max_iter,
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response_model=str,
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)
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if self.save_interaction and context and triplets and completion:
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await self.save_qa(
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question=query, answer=completion, context=context_text, triplets=triplets
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)
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# Save to session cache
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if session_save:
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context_summary = await summarize_text(context_text)
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await save_conversation_history(
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query=query,
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context_summary=context_summary,
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answer=completion,
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session_id=session_id,
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)
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return [completion]
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