Update base.py to remove ValueError

[epinoia-au]

• Description: This update removes the ValueError raised in the self.memory and _chain_type methods. These errors were causing issues when users attempted to use chains without intending to save. The methods now instead raise warnings, allowing chains to function as intended when not being saved.
• Issue: Not applicable. This is an improvement rather than a bug fix.
• Dependencies: No new dependencies introduced.
• Tag maintainer: @hwchase17, as this change affects the Memory functionality of LangChain.

All checks for linting and testing are passing. No new integration is added so there are no additional tests or example notebooks included.

Maintainer responsibilities:

• General / Misc / if you don't know who to tag: @baskaryan
• DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
• Models / Prompts: @hwchase17, @baskaryan
• Memory: @hwchase17
• Agents / Tools / Toolkits: @hinthornw
• Tracing / Callbacks: @agola11
• Async: @agola11

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1 Ignored Deployment

Name	Status	Preview	Comments	Updated (UTC)
langchain	⬜️ Ignored (Inspect)			Jul 21, 2023 7:11am

[hwchase17]

this raised an error for a reason - these chains are not json serializable and not ready to be saved. are you changing this because you want to save them? if so we should prioritize making them savable

[epinoia-au]

this raised an error for a reason - these chains are not json serializable and not ready to be saved. are you changing this because you want to save them? if so we should prioritize making them savable

I apologise as a I made a mistake based on the type hinting and have pushed a commit since then that should now pass all checks. Agree that making them savable is the solution but I am encountering issues when then Chain class is called which breaks the code. Not trying to save them, just creating my chains in a local utils.py file as a class.

[baskaryan]

could you provide some more info about steps to reproduce the error?

[epinoia-au]

Sure thing so I have a utils.py file as per below:

import re
import os

import gradio as gr
from langchain.chains import (
    ConversationalRetrievalChain,
    RetrievalQAWithSourcesChain,
)
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import HuggingFaceTextGenInference
from langchain.memory import ConversationBufferMemory
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.vectorstores import Chroma

os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"


EMBEDDINGS = OpenAIEmbeddings()
# EMBEDDINGS = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

VECTOR_DB = Chroma(
    persist_directory="./chroma_db", embedding_function=EMBEDDINGS
)


class ChainManager:
    def __init__(self, llm_name=None, chain_type=None):
        if llm_name:
            self.llm = self.select_model(llm_name)
        else:
            self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

        # TODO: ValueError: Saving of memory is not yet supported.
        self.chain = None  # Initialize chain as None

        if chain_type:
            self.select_chain(
                chain_type
            )  # Update chain if chain_type is provided

    def select_model(self, evt: gr.SelectData):
        if evt.value == "OpenAI GPT-4":
            self.llm = ChatOpenAI(model="gpt-4", temperature=0)
        elif evt.value == "OpenAI GPT-3.5-turbo":
            self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
        elif evt.value == "OpenAI GPT-3.5-turbo-16k":
            self.llm = ChatOpenAI(model="gpt-3.5-turbo-16k", temperature=0)
        elif evt.value == "Falcon40B-Instruct":
            inference_server_url = (
                ""  # f'https://{pod["id"]}-80.proxy.runpod.net'
            )
            self.llm = HuggingFaceTextGenInference(
                inference_server_url=inference_server_url,
                max_new_tokens=1000,
                top_k=10,
                top_p=0.95,
                typical_p=0.95,
                temperature=0,
                repetition_penalty=1.03,
            )

        return self.llm

    def select_chain(self, evt: gr.SelectData):
        if evt.value == "Chat":
            self.chain = self.conversational_chain()
        elif evt.value == "Q&A":
            self.chain = self.qa_chain()

    def conversational_chain(self, vector_db=VECTOR_DB, **kwargs):
        conversational_chain = ConversationalRetrievalChain.from_llm(
            llm=self.llm,
            retriever=vector_db.as_retriever(),
            # memory=ConversationBufferMemory(),
            condense_question_llm=self.llm,
            return_source_documents=True,
            verbose=False,  # Set to true for debugging
            # TODO: ValueError: Saving of memory is not yet supported.
        )
        return conversational_chain

    def qa_chain(self, vector_db=VECTOR_DB):
        retriever_from_llm = MultiQueryRetriever.from_llm(
            retriever=vector_db.as_retriever(), llm=self.llm
        )
        qa_chain = RetrievalQAWithSourcesChain.from_chain_type(
            self.llm,
            retriever=retriever_from_llm,
            return_source_documents=True,
        )
        return qa_chain

and a dummy app.py file as per below:

import gradio as gr

from utils import ChainManager

INIT_LLM_EVT = gr.SelectData(
    None, {"index": 0, "value": "OpenAI GPT-3.5-turbo", "selected": True}
)
INIT_CHAIN_EVT = gr.SelectData(
    None, {"index": 0, "value": "Q&A", "selected": True}
)
CHAIN_MANAGER = ChainManager(llm_name=INIT_LLM_EVT, chain_type=INIT_CHAIN_EVT)


def response(message: str, chat_history: list, chain=CHAIN_MANAGER.chain):
    return None


with gr.Blocks() as demo:
    with gr.Row():
        chatbot = gr.Chatbot(scale=3, show_label=False)
        with gr.Accordion(label="sources", open=False):
            chunk_source = gr.JSON()  # Need to show chunk, content, page
    with gr.Row():
        user_message = gr.Textbox(
            show_label=False,
            placeholder="Enter your message and press enter",
            scale=3,
        )

    user_message.submit(
        fn=response,
        inputs=[user_message, chatbot],
        outputs=[user_message, chatbot, chunk_source],
    )


demo.queue()
if __name__ == "__main__":
    demo.launch()

When I use gradio app.py I get the saving not supported error. As an FYI this code was when I was familiarising myself with langchain and gradio and since then there has been a lot of refactoring which seems to have eliminitated the issue but I still think it's interesting.

[hwchase17]

i looked into this - this actually doesn't have anything to do with memory, but rather with RetrievalQAWithSourcesChain

Also, i think this is caused by the following lines:

def response(message: str, chat_history: list, chain=CHAIN_MANAGER.chain):
    return None

the issue arises when your using hte chain as a partialed variable value, because then its trying to get serialized. I would just do something like

def response(message: str, chat_history: list, ):
    chain=CHAIN_MANAGER.chain

or something