Migration Phase 6: Autonomous agent core modules

agent/nodes.py

@@ -5,6 +5,7 @@ from langchain_core.prompts import ChatPromptTemplate
 from agent.state import AgentState
 from rag.retriever import retrieve_documents
 from tools.lead_capture import mock_lead_capture
+<<<<<<< HEAD
 from langchain_huggingface import HuggingFacePipeline
 from transformers import pipeline
 import os
@@ -26,6 +27,10 @@ def get_llm():
             )
             _local_llm = HuggingFacePipeline(pipeline=pipe)
         return _local_llm
+=======
+
+def get_llm():
+>>>>>>> 128a106 (Migration Phase 6: Autonomous agent core modules)
     return ChatOpenAI(model="gpt-4o-mini", temperature=0)
 
 class IntentResponse(BaseModel):
@@ -44,6 +49,7 @@ def detect_intent(state: AgentState) -> AgentState:
         ("user", "{message}")
     ])
 
+<<<<<<< HEAD
     if hasattr(llm, "with_structured_output"):
         chain = prompt | llm.with_structured_output(IntentResponse)
     else:
@@ -56,14 +62,21 @@ def detect_intent(state: AgentState) -> AgentState:
         ])
 
         chain = prompt | llm | parser
+=======
+    chain = prompt | llm.with_structured_output(IntentResponse)
+>>>>>>> 128a106 (Migration Phase 6: Autonomous agent core modules)
 
     history_str = "\n".join([f"{msg['role']}: {msg['content']}" for msg in state.get("conversation_history", [])[-3:]])
     context_message = f"Recent history:\n{history_str}\n\nCurrent message:\n{state['current_message']}"
 
+<<<<<<< HEAD
     if hasattr(llm, "with_structured_output"):
         response = chain.invoke({"message": context_message})
     else:
         response = chain.invoke({"message": context_message, "format_instructions": parser.get_format_instructions()})
+=======
+    response = chain.invoke({"message": context_message})
+>>>>>>> 128a106 (Migration Phase 6: Autonomous agent core modules)
 
     return {"detected_intent": response.intent}
 
@@ -92,9 +105,13 @@ def generate_rag_response(state: AgentState) -> AgentState:
         "message": state["current_message"]
     })
 
+<<<<<<< HEAD
     content = response.content if hasattr(response, "content") else str(response)
 
     return {"response": content}
+=======
+    return {"response": response.content}
+>>>>>>> 128a106 (Migration Phase 6: Autonomous agent core modules)
 
 def process_lead(state: AgentState) -> AgentState:
     llm = get_llm()
@@ -103,6 +120,7 @@ def process_lead(state: AgentState) -> AgentState:
         ("system", "Extract the user's name, email, and creator platform (e.g. YouTube, TikTok, Instagram) from the message if present. Return null for fields not found."),
         ("user", "{message}")
     ])
+<<<<<<< HEAD
 
     if hasattr(llm, "with_structured_output"):
         extract_chain = extract_prompt | llm.with_structured_output(LeadExtractionResponse)
@@ -114,14 +132,21 @@ def process_lead(state: AgentState) -> AgentState:
             ("user", "{message}")
         ])
         extract_chain = extract_prompt | llm | parser
+=======
+    extract_chain = extract_prompt | llm.with_structured_output(LeadExtractionResponse)
+>>>>>>> 128a106 (Migration Phase 6: Autonomous agent core modules)
 
     history_str = "\n".join([f"{msg['role']}: {msg['content']}" for msg in state.get("conversation_history", [])[-3:]])
     context_message = f"Recent history:\n{history_str}\n\nCurrent message:\n{state['current_message']}"
 
+<<<<<<< HEAD
     if hasattr(llm, "with_structured_output"):
         extracted = extract_chain.invoke({"message": context_message})
     else:
         extracted = extract_chain.invoke({"message": context_message, "format_instructions": parser.get_format_instructions()})
+=======
+    extracted = extract_chain.invoke({"message": context_message})
+>>>>>>> 128a106 (Migration Phase 6: Autonomous agent core modules)
 
     updates = {}
     if extracted.user_name and not state.get("user_name"):