Spaces:
Runtime error
Runtime error
File size: 3,131 Bytes
cd6f412 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | import logging
import json
from typing import List, Dict, Any
from langchain_core.documents import Document
from insucompass.services import llm_provider
from insucompass.config import settings
from insucompass.prompts.prompt_loader import load_prompt
# Configure logging
logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
llm = llm_provider.get_gemini_pro_llm()
class AdvisorAgent:
"""
The final agent in the Q&A pipeline. It uses a Chain-of-Thought process
to synthesize the user's profile and retrieved context into a comprehensive,
personalized, and conversational response that includes a follow-up question.
"""
def __init__(self):
"""Initializes the AdvisorAgent."""
try:
self.agent_prompt = load_prompt("advisor_agent")
logger.info("Conversational AdvisorAgent initialized successfully.")
except FileNotFoundError:
logger.critical("AdvisorAgent prompt file not found. The agent cannot function.")
raise
def generate_response(
self,
question: str,
user_profile: Dict[str, Any],
documents: List[Document]
) -> str:
"""
Generates the final, synthesized, and conversational response.
Args:
question: The user's original question.
user_profile: The user's complete profile.
documents: The relevant documents retrieved from the knowledge base.
Returns:
A string containing the final, formatted answer and a follow-up question.
"""
if not documents:
logger.warning("AdvisorAgent received no documents. Cannot generate a grounded response.")
return "I'm sorry, but I couldn't find any relevant information to answer your question, even after searching the web. Is there another way I can help you look into this?"
# Prepare the context for the prompt
profile_str = json.dumps(user_profile, indent=2)
context_str = "\n\n---\n\n".join(
[f"[METADATA: source_name='{d.metadata.get('source_name', 'N/A')}', source_url='{d.metadata.get('source_url', 'N/A')}']\n\n{d.page_content}" for d in documents]
)
full_prompt = (
f"{self.agent_prompt}\n\n"
f"### CONTEXT FOR YOUR RESPONSE\n"
f"user_profile: {profile_str}\n\n"
f"user_question: \"{question}\"\n\n"
f"retrieved_context:\n{context_str}"
)
logger.info("Generating final conversational response with AdvisorAgent...")
try:
response = llm.invoke(full_prompt)
generation = response.content.strip()
logger.info("Successfully generated final conversational answer.")
return generation
except Exception as e:
logger.error(f"Error during final answer generation: {e}")
return "I apologize, I encountered an error while trying to formulate the final answer. Please try again."
advisor = AdvisorAgent() |