Remove legacy RAG v1 file

agentic_rag_graph.py

@@ -1,160 +0,0 @@
-from typing import TypedDict, List, Optional
-import google.generativeai as genai
-from langgraph.graph import StateGraph, END
-
-from rag_store import search_knowledge
-from eval_logger import log_eval
-
-MODEL_NAME = "gemini-2.5-flash"
-
-
-# ===============================
-# STATE
-# ===============================
-class AgentState(TypedDict):
-    query: str
-    decision: str
-    retrieved_chunks: List[dict]
-    answer: Optional[str]
-    confidence: float
-    answer_known: bool
-
-
-# ===============================
-# DECISION NODE
-# ===============================
-def agent_decision_node(state: AgentState) -> AgentState:
-    q = state["query"].lower()
-
-    rag_keywords = [
-        "summarize", "summary", "fee", "fees", "refund",
-        "tuition", "document", "policy", "offer", "scholarship"
-    ]
-
-    decision = "use_rag" if any(k in q for k in rag_keywords) else "no_rag"
-
-    return {**state, "decision": decision}
-
-
-# ===============================
-# RETRIEVAL NODE (TOOL)
-# ===============================
-def retrieve_node(state: AgentState) -> AgentState:
-    chunks = search_knowledge(state["query"])
-    return {**state, "retrieved_chunks": chunks}
-
-
-# ===============================
-# ANSWER WITH RAG
-# ===============================
-def answer_with_rag_node(state: AgentState) -> AgentState:
-    if not state["retrieved_chunks"]:
-        return no_answer_node(state)
-
-    context = "\n\n".join(c["text"] for c in state["retrieved_chunks"])
-
-    prompt = f"""
-Answer using ONLY the context below.
-If the answer is not present, say "I don't know".
-
-Context:
-{context}
-
-Question:
-{state["query"]}
-"""
-
-    model = genai.GenerativeModel(MODEL_NAME)
-    resp = model.generate_content(prompt)
-    answer_text = resp.text
-
-    confidence = min(1.0, len(state["retrieved_chunks"]) / 5)
-    answer_known = "i don't know" not in answer_text.lower()
-
-    log_eval(
-        query=state["query"],
-        retrieved_count=len(state["retrieved_chunks"]),
-        confidence=confidence,
-        answer_known=answer_known
-    )
-
-    return {
-        **state,
-        "answer": answer_text,
-        "confidence": confidence,
-        "answer_known": answer_known
-    }
-
-
-# ===============================
-# ANSWER WITHOUT RAG
-# ===============================
-def answer_direct_node(state: AgentState) -> AgentState:
-    prompt = f"Answer the following question concisely:\n\n{state['query']}"
-
-    model = genai.GenerativeModel(MODEL_NAME)
-    resp = model.generate_content(prompt)
-
-    log_eval(
-        query=state["query"],
-        retrieved_count=0,
-        confidence=0.3,
-        answer_known=True
-    )
-
-    return {
-        **state,
-        "answer": resp.text,
-        "confidence": 0.3,
-        "answer_known": True
-    }
-
-
-# ===============================
-# NO ANSWER
-# ===============================
-def no_answer_node(state: AgentState) -> AgentState:
-    log_eval(
-        query=state["query"],
-        retrieved_count=0,
-        confidence=0.0,
-        answer_known=False
-    )
-
-    return {
-        **state,
-        "answer": "I don't know based on the provided documents.",
-        "confidence": 0.0,
-        "answer_known": False
-    }
-
-
-# ===============================
-# GRAPH BUILDER
-# ===============================
-def build_agentic_rag_graph():
-    graph = StateGraph(AgentState)
-
-    graph.add_node("decide", agent_decision_node)
-    graph.add_node("retrieve", retrieve_node)
-    graph.add_node("answer_rag", answer_with_rag_node)
-    graph.add_node("answer_direct", answer_direct_node)
-    graph.add_node("no_answer", no_answer_node)
-
-    graph.set_entry_point("decide")
-
-    graph.add_conditional_edges(
-        "decide",
-        lambda s: s["decision"],
-        {
-            "use_rag": "retrieve",
-            "no_rag": "answer_direct"
-        }
-    )
-
-    graph.add_edge("retrieve", "answer_rag")
-    graph.add_edge("answer_rag", END)
-    graph.add_edge("answer_direct", END)
-    graph.add_edge("no_answer", END)
-
-    return graph.compile()