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e77665f | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import os
from unittest import result
from dotenv import load_dotenv
from openai import OpenAI
from backend.app.agents.crewai_agent import CrewAIAgent
from backend.app.rag.embedding_manager import EmbeddingManager
from backend.app.tools.tool_registry import ToolRegistry
from backend.app.core.memory import ConversationMemory
from backend.app.agents.planner import Planner
from configs.logging_config import setup_logger
from backend.app.core.feedback_store import FeedbackStore
from configs.settings import config
from backend.app.agents.langgraph_agent import langgraph_app
from backend.app.agents.langchain_agent import LangChainAgent
load_dotenv()
logger = setup_logger("agent_v2")
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
MODEL_NAME = config["llm"]["model"]
EMBD_MODEL_NAME = config["embedding"]["model"]
embedding_manager = EmbeddingManager(EMBD_MODEL_NAME)
class AgentV2:
def __init__(self):
self.tools = ToolRegistry()
self.memory = ConversationMemory()
self.planner = Planner()
self.feedback_store = FeedbackStore()
self.embedding_manager = embedding_manager
self.crewai_agent = CrewAIAgent( self.tools, self.memory, self.planner, self.embedding_manager, self.feedback_store )
self.langchain_agent = LangChainAgent( self.tools, self.memory, self.planner, self.embedding_manager, self.feedback_store )
def run(self, query, llm_model, framework="LangGraph", api_key=None, auth_mode=None):
logger.info(f"[Framework Selected]: {framework}")
if auth_mode == "external" and api_key:
logger.info("Using external API key for OpenAI client")
os.environ["OPENAI_API_KEY"] = api_key
if framework == "LangGraph":
logger.info("Running with LangGraph Agent")
return self.run_langgraph(query, llm_model)
if framework == "CrewAI":
logger.info("Running with CrewAI Agent")
return self.crewai_agent.run(query, llm_model)
elif framework == "LangChain":
logger.info("Running with LangChain Agent")
return self.run_langchain(query, llm_model)
else:
logger.warning(f"Unknown framework: {framework}, defaulting to LangGraph")
return self.run_langgraph(query, llm_model)
def run_langgraph(self, query, llm_model=MODEL_NAME):
result = langgraph_app.invoke({
"query": query,
"llm_model": llm_model
},
config={"recursion_limit": 10})
return {
"final_answer": result.get("final_answer"),
"trace": result.get("trace", [])
}
def run_langchain(self, query, llm_model=MODEL_NAME):
return self.langchain_agent.run(query, llm_model)
def run_cli():
agent = AgentV2()
print("=== Agent V2 (Memory + Planning) ===")
print("Type 'exit' to quit\n")
while True:
query = input("Enter your query: ")
if query.lower() == "exit":
break
result = agent.run(query)
print(f"\nAgent Response:\n{result}\n")
feedback = input("Was this helpful? (yes/no): ")
if feedback.lower() == "yes":
agent.feedback_store.save_feedback(query, result, "positive")
else:
agent.feedback_store.save_feedback(query, result, "negative")
if __name__ == "__main__":
run_cli() |