1807435339
- Introduction of dynamic Processors - Introduction of caching system - Introduction of a better template manager - Adaptation of ModelVariables to support dynamic Processors / Retrievers / Specialists - Start adaptation of chat client
290 lines
12 KiB
Python
290 lines
12 KiB
Python
from datetime import datetime
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from typing import Dict, Any, List
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from flask import current_app
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from langchain_core.exceptions import LangChainException
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from common.langchain.outputs.base import OutputRegistry
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from common.langchain.outputs.rag import RAGOutput
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from common.utils.business_event_context import current_event
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from .specialist_typing import SpecialistArguments, SpecialistResult
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from ..chat_session_cache import CachedInteraction, get_chat_history
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from ..retrievers.registry import RetrieverRegistry
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from ..retrievers.base import BaseRetriever
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from common.models.interaction import SpecialistRetriever, Specialist
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from common.utils.model_utils import get_model_variables, create_language_template, replace_variable_in_template
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from .base import BaseSpecialist
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from .registry import SpecialistRegistry
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from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments, RetrieverResult
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class RAGSpecialist(BaseSpecialist):
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"""
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Standard Q&A RAG Specialist implementation that combines retriever results
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with LLM processing to generate answers.
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"""
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def __init__(self, tenant_id: int, specialist_id: int, session_id: str):
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super().__init__(tenant_id, specialist_id, session_id)
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# Check and load the specialist
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specialist = Specialist.query.get_or_404(specialist_id)
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# Set the specific configuration for the RAG Specialist
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self.specialist_context = specialist.configuration.get('specialist_context', '')
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self.temperature = specialist.configuration.get('temperature', 0.3)
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self.tuning = specialist.tuning
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# Initialize retrievers
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self.retrievers = self._initialize_retrievers()
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# Initialize model variables
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self.model_variables = get_model_variables(tenant_id)
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@property
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def type(self) -> str:
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return "STANDARD_RAG"
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def _initialize_retrievers(self) -> List[BaseRetriever]:
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"""Initialize all retrievers associated with this specialist"""
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retrievers = []
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# Get retriever associations from database
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specialist_retrievers = (
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SpecialistRetriever.query
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.filter_by(specialist_id=self.specialist_id)
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.all()
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)
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self._log_tuning("_initialize_retrievers", {"Nr of retrievers": len(specialist_retrievers)})
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for spec_retriever in specialist_retrievers:
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# Get retriever configuration from database
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retriever = spec_retriever.retriever
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retriever_class = RetrieverRegistry.get_retriever_class(retriever.type)
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self._log_tuning("_initialize_retrievers", {
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"Retriever id": spec_retriever.retriever_id,
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"Retriever Type": retriever.type,
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"Retriever Class": str(retriever_class),
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})
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# Initialize retriever with its configuration
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retrievers.append(
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retriever_class(
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tenant_id=self.tenant_id,
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retriever_id=retriever.id,
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)
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)
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return retrievers
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@property
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def required_templates(self) -> List[str]:
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"""List of required templates for this specialist"""
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return ['rag', 'history']
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# def _detail_question(question, language, model_variables, session_id):
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# retriever = EveAIHistoryRetriever(model_variables=model_variables, session_id=session_id)
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# llm = model_variables['llm']
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# template = model_variables['history_template']
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# language_template = create_language_template(template, language)
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# full_template = replace_variable_in_template(language_template, "{tenant_context}",
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# model_variables['rag_context'])
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# history_prompt = ChatPromptTemplate.from_template(full_template)
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# setup_and_retrieval = RunnableParallel({"history": retriever, "question": RunnablePassthrough()})
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# output_parser = StrOutputParser()
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#
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# chain = setup_and_retrieval | history_prompt | llm | output_parser
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#
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# try:
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# answer = chain.invoke(question)
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# return answer
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# except LangChainException as e:
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# current_app.logger.error(f'Error detailing question: {e}')
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# raise
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def _detail_question(self, language: str, question: str) -> str:
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"""Detail question based on conversation history"""
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try:
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# Get cached session history
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cached_session = get_chat_history(self.session_id)
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# Format history for the prompt
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formatted_history = "\n\n".join([
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f"HUMAN:\n{interaction.specialist_results.get('detailed_query')}\n\n"
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f"AI:\n{interaction.specialist_results.get('answer')}"
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for interaction in cached_session.interactions
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])
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# Get LLM and template
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llm = self.model_variables.get_llm(temperature=0.3)
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template = self.model_variables.get_template('history')
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language_template = create_language_template(template, language)
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# Create prompt
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history_prompt = ChatPromptTemplate.from_template(language_template)
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# Create chain
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chain = (
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history_prompt |
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llm |
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StrOutputParser()
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)
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# Execute chain
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detailed_question = chain.invoke({
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"history": formatted_history,
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"question": question
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})
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if self.tuning:
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self._log_tuning("_detail_question", {
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"cached_session_id": cached_session.session_id,
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"cached_session.interactions": str(cached_session.interactions),
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"original_question": question,
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"history_used": formatted_history,
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"detailed_question": detailed_question,
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})
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return detailed_question
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except Exception as e:
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current_app.logger.error(f"Error detailing question: {e}")
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return question # Fallback to original question
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def execute(self, arguments: SpecialistArguments) -> SpecialistResult:
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"""
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Execute the RAG specialist to generate an answer
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"""
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start_time = datetime.now()
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try:
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with current_event.create_span("Specialist Detail Question"):
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# Get required arguments
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language = arguments.language
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query = arguments.query
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detailed_question = self._detail_question(language, query)
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# Log the start of retrieval process if tuning is enabled
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with current_event.create_span("Specialist Retrieval"):
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self._log_tuning("Starting context retrieval", {
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"num_retrievers": len(self.retrievers),
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"all arguments": arguments.model_dump(),
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})
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# Get retriever-specific arguments
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retriever_arguments = arguments.retriever_arguments
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# Collect context from all retrievers
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all_context = []
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for retriever in self.retrievers:
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# Get arguments for this specific retriever
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retriever_id = str(retriever.retriever_id)
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if retriever_id not in retriever_arguments:
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current_app.logger.error(f"Missing arguments for retriever {retriever_id}")
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continue
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# Get the retriever's arguments and update the query
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current_retriever_args = retriever_arguments[retriever_id]
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if isinstance(retriever_arguments[retriever_id], RetrieverArguments):
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updated_args = current_retriever_args.model_dump()
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updated_args['query'] = detailed_question
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retriever_args = RetrieverArguments(**updated_args)
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else:
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# Create a new RetrieverArguments instance from the dictionary
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current_retriever_args['query'] = detailed_question
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retriever_args = RetrieverArguments(**current_retriever_args)
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# Each retriever gets its own specific arguments
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retriever_result = retriever.retrieve(retriever_args)
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all_context.extend(retriever_result)
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# Sort by similarity if available and get unique contexts
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all_context.sort(key=lambda x: x.similarity, reverse=True)
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unique_contexts = []
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seen_chunks = set()
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for ctx in all_context:
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if ctx.chunk not in seen_chunks:
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unique_contexts.append(ctx)
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seen_chunks.add(ctx.chunk)
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self._log_tuning("Context retrieval completed", {
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"total_contexts": len(all_context),
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"unique_contexts": len(unique_contexts),
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"average_similarity": sum(ctx.similarity for ctx in unique_contexts) / len(
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unique_contexts) if unique_contexts else 0
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})
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# Prepare context for LLM
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formatted_context = "\n\n".join([
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f"SOURCE: {ctx.metadata.document_id}\n\n{ctx.chunk}"
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for ctx in unique_contexts
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])
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with current_event.create_span("Specialist RAG invocation"):
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try:
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# Get LLM with specified temperature
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llm = self.model_variables.get_llm(temperature=self.temperature)
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# Get template
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template = self.model_variables.get_template('rag')
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language_template = create_language_template(template, language)
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full_template = replace_variable_in_template(
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language_template,
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"{tenant_context}",
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self.specialist_context
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)
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if self.tuning:
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self._log_tuning("Template preparation completed", {
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"template": full_template,
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"context": formatted_context,
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"tenant_context": self.specialist_context,
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})
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# Create prompt
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rag_prompt = ChatPromptTemplate.from_template(full_template)
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# Setup chain components
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setup_and_retrieval = RunnableParallel({
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"context": lambda x: formatted_context,
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"question": lambda x: x
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})
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# Get output schema for structured output
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output_schema = OutputRegistry.get_schema(self.type)
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structured_llm = llm.with_structured_output(output_schema)
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chain = setup_and_retrieval | rag_prompt | structured_llm
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raw_result = chain.invoke(query)
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result = SpecialistResult.create_for_type(
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"STANDARD_RAG",
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detailed_query=detailed_question,
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answer=raw_result.answer,
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citations=[ctx.metadata.document_id for ctx in unique_contexts
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if ctx.id in raw_result.citations],
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insufficient_info=raw_result.insufficient_info
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)
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if self.tuning:
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self._log_tuning("LLM chain execution completed", {
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"Result": result.model_dump()
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})
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except Exception as e:
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current_app.logger.error(f"Error in LLM processing: {e}")
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if self.tuning:
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self._log_tuning("LLM processing error", {"error": str(e)})
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raise
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return result
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except Exception as e:
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current_app.logger.error(f'Error in RAG specialist execution: {str(e)}')
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raise
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# Register the specialist type
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SpecialistRegistry.register("STANDARD_RAG", RAGSpecialist)
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OutputRegistry.register("STANDARD_RAG", RAGOutput)
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