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# This file is now primarily for running the agent directly if needed.
# The core logic (state, prompts, nodes, graph builder) is in the RAG_BOT.agent package.
import json
import os
import sys
import re
from langchain_chroma import Chroma
from langchain_core.messages import HumanMessage, AIMessage
from langchain_huggingface import HuggingFaceEmbeddings
# Add the project root to the Python path
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, project_root)
from RAG_BOT.config import Config
from RAG_BOT.logger import logger
# Import from the new agent package
from RAG_BOT.agent.state import AgentState
from RAG_BOT.agent.graph_builder import build_agent
from RAG_BOT.utils import parse_json_answer # Import the utility function
# --- Example Invocation ---
if __name__ == '__main__':
try:
persist_directory = Config.VECTOR_STORE_PATH
embeddings = HuggingFaceEmbeddings(model_name=Config.EMBEDDING_MODEL_NAME)
vectordb_instance = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
logger.info(f"Chroma DB loaded from: {persist_directory}")
app = build_agent(vectordb_instance)
# Example run - User query no longer needs JSON instruction
# user_question = "Can you summarize the murli from 1950-01-18?"
# user_question = "Can you summarize the murli of 1969-02-06"
user_question = "1969-01-18 की मुरली का सार क्या है? कृपया पुरुषार्थ के दृष्टिकोण से हिंदी भाषा में बताएं"
# Initialize state correctly
initial_state = AgentState(
messages=[HumanMessage(content=user_question)],
original_query=None,
current_query=None,
context=None,
retry_attempted=False,
evaluation_result=None
)
print(f"\n--- Invoking Agent for query: '{user_question}' ---")
print("\n--- Invoking Agent (using invoke) ---")
# Use invoke to get the final state directly
final_state_result = app.invoke(initial_state, {"recursion_limit": 15})
print("\n--- Agent Invocation Complete ---")
# Process final state
if isinstance(final_state_result, dict) and 'messages' in final_state_result:
print("\n--- Final State ---")
print(f"Original Query: {final_state_result.get('original_query')}")
print(f"Current Query: {final_state_result.get('current_query')}")
print(f"Retry Attempted: {final_state_result.get('retry_attempted')}")
print(f"Evaluation Result: {final_state_result.get('evaluation_result')}")
print(f"Context Present: {bool(final_state_result.get('context'))}")
print("\n--- Final State Messages ---")
for m in final_state_result['messages']:
m.pretty_print()
final_answer_message = final_state_result['messages'][-1]
if isinstance(final_answer_message, AIMessage):
print("\nFinal Answer Content:")
print(final_answer_message.content)
# Use the robust parsing function from utils.py
parsed_json = parse_json_answer(final_answer_message.content)
if parsed_json and "answer" in parsed_json:
print("\nParsed JSON Answer:", parsed_json.get("answer"))
else:
print("\nWarning: Final answer content is not valid JSON.")
else:
print("\nFinal message was not an AIMessage:", final_answer_message)
else:
print("\nError: Could not extract final messages from result:", final_state_result)
except Exception as e:
logger.error(f"Error during example run: {e}", exc_info=True)
print(f"\nError during example run: {e}")
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