Delete ask_candid/tools/question_reformulation.py

ask_candid/tools/question_reformulation.py

@@ -1,68 +0,0 @@
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.language_models.llms import LLM
-
-from ask_candid.agents.schema import AgentState
-
-
-def reformulate_question_using_history(
-    state: AgentState,
-    llm: LLM,
-    focus_on_recommendations: bool = False
-) -> AgentState:
-    """Transform the query to produce a better query with details from previous messages and emphasize aspects important
-    for recommendations if needed.
-
-    Parameters
-    ----------
-    state : AgentState
-        The current state
-    llm : LLM
-    focus_on_recommendations : bool, optional
-        Flag to determine if the reformulation should emphasize recommendation-relevant aspects such as geographies,
-        cause areas, etc., by default False
-
-    Returns
-    -------
-    AgentState
-        The updated state
-    """
-
-    print("---REFORMULATE THE USER INPUT---")
-    messages = state["messages"]
-    question = messages[-1].content
-
-    if len(messages[:-1]) > 1:  # need to skip the system message
-        if focus_on_recommendations:
-            prompt_text = """Given a chat history and the latest user input which might reference context in the chat
-            history, especially geographic locations, cause areas and/or population groups, formulate a standalone input
-            which can be understood without the chat history.
-            Chat history: ```{chat_history}```
-            User input: ```{question}```
-
-            Reformulate the question without adding implications or assumptions about the user's needs or intentions. 
-            Focus solely on clarifying any contextual details present in the original input."""
-        else:
-            prompt_text = """Given a chat history and the latest user input which might reference context in the chat
-            history, formulate a standalone input which can be understood without the chat history. Include hints as to
-            what the user is getting at given the context in the chat history.
-            Chat history: ```{chat_history}```
-            User input: ```{question}```
-
-            Do NOT answer the question, just reformulate it if needed and otherwise return it as is.
-            """
-
-        contextualize_q_prompt = ChatPromptTemplate([
-            ("system", prompt_text),
-            ("human", question),
-        ])
-
-        rag_chain = contextualize_q_prompt | llm | StrOutputParser()
-        # new_question = rag_chain.invoke({"chat_history": messages, "question": question})
-        new_question = rag_chain.invoke({
-            "chat_history": '\n'.join(f"{m.type.upper()}: {m.content}" for m in messages[1:]),
-            "question": question
-        })
-        print(f"user asked: '{question}', agent reformulated the question basing on the chat history: {new_question}")
-        return {"messages": [new_question], "user_input" : question}
-    return {"messages": [question], "user_input" : question}