To retrieve it back, yes, the same embedding model must be used to generate two vector and compare their similarity. It seems like you're encountering a problem when trying to return source documents using ConversationalRetrievalChain with ConversationBufferWindowMemory. This function would check the type of the chain and format the input accordingly. A more efficient solution could be to create a wrapper function that can handle both types of inputs. run function is not returning source documents. It first takes the raw text as input and used the prepare_vectorstore function to create the vectorstore. 67 to 0. Oct 16, 2023 · pdf_loader = DirectoryLoader(directory_path, glob="**/*. 162, code updated. In langchain version 0. For the Conversational retrieval chain, we have to get the retriever fetch Oct 28, 2023 · Feature request Module: langchain. May 18, 2023 · edited. from_llm() method with the combine_docs_chain_kwargs param. prepare_agent: it is used to create the pandas dataframe agent in case user uploads a csv file. And how figured out the issue looking at the Langchain source code for the original/default prompt templates for each Chain type. Based on the context provided, there are two main ways to pass the actual chat history to the _acall method of the ConversationalRetrievalChain class. The same method is already implemented differently in many chains, which continues to create errors in related chains. In that same location is a module called prompts. Mar 6, 2024 · In this example, allowed_metadata is a dictionary that specifies the metadata criteria documents must meet to be included in the filtering process. Any thoughts would be greatly appreciated. ConversationalRetrievalChain [source] ¶ Bases: BaseConversationalRetrievalChain [Deprecated] Chain for having a conversation based on retrieved documents. schema import OutputParserException, PromptValue # Assuming you have an instance of ConversationalRetrievalChain and a parser conversational_chain = ConversationalRetrievalChain Sep 21, 2023 · The BufferMemory is used to store the chat history. Is there any way of twerking this prompt so that it should give email of customer support that I will provide in prompt. 238 it used to return sources but this seems to be broken in the releases since then. ConversationalRetrievalChain uses condense_question_prompt to find the question. acall({"question": query}) Expected behavior. Oct 30, 2023 · In response to Dosubot: As per the documentation here when using qa = ConversationalRetrievalChain. base. chains import (. Jun 22, 2023 · Another user provided some guidance on reading the Langchain code to understand the different keywords used in different prompt templates for different chain types. llms import OpenAI from langchain. namespace = namespace; // Create a chain that uses the OpenAI LLM and Pinecone vector store. . fromLLM(openai, vectorstore); const chain = new ChainTool({. Jun 16, 2023 · Understanding `collapse_prompt` in the map_reduce `load_qa_chain` in ConversationalRetrievalChain In the context of a ConversationalRetrievalChain, when using chain_type = "map_reduce", I am unsure how collapse_prompt should be set up. If you are using OpenAI's model for creating embeddings then it will surely have a different range for relevant and irrelevant questions than any hugging face-based model. The corrected code is: Jun 19, 2023 · ConversationChain does not have memory to remember historical conversation #2653. prepare_chain: This function is used to prepare the conversation_retrieval_chain. 🦜🔗 Build context-aware reasoning applications. Aug 13, 2023 · Yes, it is indeed possible to combine a simple chat agent that answers user questions with a document retrieval chain for specific inquiries from your documents in the LangChain framework. pdf", show_progress=True, use_multithreading=True, silent_errors=True, loader_cls = PyPDFLoader) documents = pdf_loader. Also, it's worth mentioning that you can pass an alternative prompt for the question generation chain that also returns parts of the chat history relevant to the answer. You can create a custom retriever that wraps around the original retriever and applies the filtering. Jun 20, 2023 · The Conversational Chain has a LLM baked in i think? The other one can be used as a Tool for Agents. Jul 12, 2023 · You signed in with another tab or window. May 4, 2023 · You can pass your prompt in ConversationalRetrievalChain. On the other hand, ConversationalRetrievalChain is specifically designed for answering questions based on documents. from_llm(OpenAI(temperature=0), vectorstore. bing_chain_types. The solution was to replace OpenAI with ChatOpenAI when working with a chat model (like gpt-3. In ChatOpenAI from LangChain, setting the streaming variable to True enables this functionality. Another similar issue was encountered when using the ConversationalRetrievalChain. stuff_prompt import PROMPT_SELECTOR from langchain. combine_documents. conversational_retrieval is where ConversationalRetrievalChain lives in the Langchain source code. Increasing this limit will allow the model to store more information. Mar 10, 2011 · chain_type="stuff", retriever=retriever, verbose=True, memory=memory,) #async result = await qna. Check the attached file, there I described the issue in detail. Apr 2, 2023 · langchain. Jun 23, 2023 · I should be able to provide custom context to my conversational retrieval chain, without custom prompt it works and gets good answers from vector db, but I cant use custom prompts The text was updated successfully, but these errors were encountered: Jul 8, 2023 · Based on my understanding, you were experiencing issues with the accuracy of the output when using the conversational retrieval chain with memory. May 12, 2023 · System Info Hi i am using ConversationalRetrievalChain with agent and agent. Before we close this issue, we wanted to check if it is still relevant to the latest version of the LangChain repository. chains import ConversationalRetrievalChain from langchain. Based on the similar issues and solutions found in the LangChain repository, you can achieve this by using the ConversationalRetrievalChain class in Yes, there is a method to use gemini-pro with ConversationalRetrievalChain. create_retrieval_chain focuses on retrieving relevant documents based on the conversation history. # Depending on the memory type and configuration, the chat history format may differ. 5-turbo) for generating the question. However, I can suggest a workaround. CHAT_TURN_TYPE = Union[Tuple[str, str], BaseMessage] Nov 29, 2023 · Textbox ( lines=5, label="Chat History" ), ], outputs="text" ) iface. The problem is that, under this setting, I Apr 16, 2023 · Hello, 1)debug the score of the search You can try to call similarity_search_with_score(query) on your vectore store, but that would be outside the retrieval chain 2)debug the final prompt send to OpenAI You can easily do that by having “verbose=True” You’ll see the full prompt logged into the terminal (or notebook) output Hope it helps New version lacks backwards compatibility for externally passing chat_history to conversational retrieval chain #2029 Closed OmriNach opened this issue Jul 20, 2023 · 2 comments · Fixed by #2030 Nov 16, 2023 · You can find more details about this solution in this issue. I'm trying to use a ConversationalRetrievalChain along with a ConversationBufferMemory and return_source_documents set to True. The Gradio interface is configured to Jul 10, 2023 · The filter argument you're trying to use in search_kwargs isn't a supported feature of the as_retriever method or the underlying retrieval system. Alternately, if there was a way for the chain to simple read from the BufferMemory, I could just manage inserting messages outside the chain. May 29, 2023 · The simple answer to this is different models which create embeddings have different ranges of numbers to judge the similarity. The following code examples are gathered through the Langchain python documentation and docstrings on some of their classes. Also, same question like @blazickjp is there a way to add chat memory to this ?. It might be beneficial to update to the latest version and see if the issue persists. Nov 20, 2023 · Hi, @0ENZO, I'm helping the LangChain team manage their backlog and am marking this issue as stale. You can find more details about the Oct 23, 2023 · from langchain. cls, llm: BaseLanguageModel, retriever: BaseRetriever, Jan 26, 2024 · Issue with current documentation: import os import qdrant_client from dotenv import load_dotenv from langchain. The code executes without any error Description. def from_llm(. dosubot bot mentioned this issue on Nov 7, 2023. May 5, 2024 · This involves modifying the chain to include a mechanism for parsing and utilizing the JSON structured output produced by your model. chains. Is this by functionality or is it a missing feature? def llm_answer(query): chat_history = [] result = qa({"quest Jan 26, 2024 · 🤖. Here is the method in the code: @classmethod def from_chain_type (. This allows the QA chain to answer meta questions with the additional context. This chain includes web access and other tools. Jul 19, 2023 · While changing the prompts could potentially standardize the input across all routes, it might require significant modifications to your existing codebase. The LLM model contains its own embedding step Jul 20, 2023 · Hi, @wolfassi123!I'm Dosu, and I'm helping the LangChain team manage their backlog. It generates responses based on the context of the conversation and doesn't necessarily rely on document retrieval. memory import ConversationBufferMemory from langchain. Mar 9, 2024 · File "D:\LLM projects\ask-multiple-pdfs-main\ask-multiple-pdfs-main\app. md The SequentialChain class in the LangChain framework is a type of Chain where the outputs of one chain feed directly into the next. chains import LLMChain from langchain. chat, vectorStore. Apr 7, 2023 · edited. Here is an example of combining a retriever with a document chain: You signed in with another tab or window. Here's an example of how you can do this: from langchain. Jul 18, 2023 · The ConversationChain is a more versatile chain designed for managing conversations. Jun 29, 2023 · System Info ConversationalRetrievalChain with Question Answering with sources llm = OpenAI(temperature=0) question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa Aug 17, 2023 · Issue you'd like to raise. However, it does not work properly in RetrievalQA or ConversationalRetrievalChain. Sep 14, 2023 · System Info. You can use the GoogleGenerativeAI class from the langchain_google_genai module to create an instance of the gemini-pro model. Aug 27, 2023 · Another way is to create the ConversationalRetrievalChain without the combine_docs_chain_kwargs and memory parameters. Streaming is a feature that allows receiving incremental results in a streaming format when generating long conversations or text. from_llm method. fromLLM(. I don't want bot to say. Contribute to langchain-ai/langchain development by creating an account on GitHub. py which contains both CONDENSE_QUESTION_PROMPT and QA_PROMPT. You switched accounts on another tab or window. loadQAStuffChain is a function that creates a QA chain that uses a language model to generate an answer to a question given some context. ConversationalRetrievalChain qa = ConversationalRetrievalChain( retriever=self. This dictionary is then passed to the run method of your ConversationalRetrievalChain instance. The LLM will be fed with the data retrieved from embedding step in the form of text. For more details, you can refer to the source code in the langchainjs repository. asRetriever(), {. May 6, 2023 · You signed in with another tab or window. The template parameter is a string that defines the structure of the prompt, and the input_variables parameter is a list of variable names that will be replaced in the template. This class is used to create a pipeline of chains where the output of one chain is used as the input for the next chain in the sequence. Hello @yen111445!Nice to see you back here again. from_chain_type but without memory The text was updated successfully, but these errors were encountered: Jul 3, 2023 · class langchain. For your requirement to reply to greetings but not to irrelevant questions, you can use the response_if_no_docs_found parameter in the from_llm method of ConversationalRetrievalChain. From what I understand, you raised an issue about combining LLM Chains and ConversationalRetrievalChains in an agent's routes. m trying to do a bot that answer questions from a chromadb , i have stored multiple pdf files with metadata like the filename and candidate name , my problem is when i use conversational retrieval chain the LLM model just receive page_content without the metadata , i want the LLM model to be aware of the page_content with its metadata like filename and candidate name here is my code Sep 7, 2023 · The ConversationalRetrievalQAChain is initialized with two models, a slower model ( gpt-4) for the main retrieval and a faster model ( gpt-3. Mar 9, 2016 · you need to look, for each chain type (stuff, refine, map_reduce & map_rerank) for the correct input vars for each prompt. How Adding a prompt template to conversational retrieval chain giving the code: `template= """Use the following pieces of context to answer the question at the end. May 13, 2023 · I've tried every combination of all the chains and so far the closest I've gotten is ConversationalRetrievalChain, but without custom prompts, and RetrievalQA. The PromptTemplate class in LangChain allows you to define a variable number of input variables for a prompt template. ", chain: vectorchain, }); return chain; Jul 3, 2023 · class langchain. Ensure that the custom retriever's get_relevant_documents method returns a list of Document objects, as the rest of the chain expects documents in this format. predict: it uses the toggle isCSV to Nov 13, 2023 · Currently, the ConversationalRetrievalChain updates the context by creating a new standalone question from the chat history and the new question, retrieving relevant documents based on this new question, and then generating a final response based on these documents and either the new question or the original question and chat history. Apr 29, 2024 · For the Retrieval chain, we got a retriever to fetch documents from the vector store relevant to the user input. From what I understand, you encountered validation errors for the ConversationalRetrievalChain in the provided code, and Dosubot provided a detailed response explaining the incorrect usage of the ConversationalRetrievalChain class and offered guidance on resolving the errors. The parse method should take the output of the chain and transform it into the desired format. # This needs to be consolidated. retry import RetryOutputParser from langchain. Any advices ? Last option I know would be to write my own custom chain which accepts sources and also preserve memory. The first method involves using a ChatMemory instance, such as ConversationBufferWindowMemory, to manage the chat history. Oct 13, 2023 · However, within the context of a ConversationalRetrievalQAChain I can't figure out a way to specify additional_kwargs. txt' file. This function doesn't directly handle multiple questions for a single PDF document. stuff import StuffDocumentsChain # This controls how each document will be formatted. Ensure Compatibility with Chain Methods: After adapting the chain to accept structured outputs, verify that all methods within the chain that interact with the model's output are compatible with structured data Dec 2, 2023 · In this example, the PromptTemplate class is used to define the custom prompt. Mar 10, 2011 · Same working principle as in the source files combine_docs_chain = load_qa_chain(llm = llm, chain_type = 'stuff', prompt = stuff_prompt ) #create a custom combine_docs_chain Create the ConversationalRetrievalChain. dosubot bot mentioned this issue on Sep 23, 2023. i. as_retriever(), combine_docs_chain_kwargs={"prompt": prompt} Sep 2, 2023 · Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt, apredict, apredict_messages, generate_prompt, invoke, predict, predict_messages (type=type_error) Expected behavior. TS #2639. It involves creating an Mar 31, 2023 · Key values to be excluded from the methods mentioned above are also accepted as arguments, so clear unification of input_key and output_key is necessary to prevent branching problems in each chain. It's useful for tasks like similarity search and Apr 4, 2023 · const vectorchain = VectorDBQAChain. {context} Qu May 1, 2023 · template = """Given the following conversation respond to the best of your ability in a pirate voice and end every sentence with Ay Ay Matey Chat History: {chat Jul 3, 2023 · Hello, Based on the names, I would think RetrievalQA or RetrievalQAWithSourcesChain is best served to support a question/answer based support chatbot, but we are getting good results with Conversat Nov 12, 2023 · It uses the load_qa_chain function to create a combine_documents_chain based on the provided chain type and language model. list of number)]. ConversationalRetrievalChain() got multiple values for keyword argument 'question_generator'', 'SystemError' `Qtemplate = ( "Combine the chat history and follow up question into " We read every piece of feedback, and take your input very seriously. Dec 16, 2023 · dosubot bot commented on Dec 16, 2023. fromLLM function is used to create a QA chain that can answer questions based on the text from the 'state_of_the_union. memory: new BufferMemory({. Instead, it initializes a BaseRetrievalQA object by loading a question-answering chain based on the provided chain_type and chain_type_kwargs. py", line 67, in get_conversation_chain conversation_chain = ConversationalRetrievalChain. as Oct 20, 2023 · 🤖. But there's no mention of qa_prompt in ConversationalRetrievalChain, or its base chain Apr 25, 2023 · EDIT: My original tool definition doesn't work anymore as of 0. This modification allows the ConversationalRetrievalChain to use the content from a file for retrieval instead of the original retriever. humanPrefix: "I want you to act as a document that I am having a conversation with. vectorstores i _TEMPLATE = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. Dosubot provided a detailed response with potential solutions and requested specific information to provide a more tailored solution. launch () In this example, the chat_interface function takes a dictionary as input, which contains both the question and the chat history. Nov 3, 2023 · From what I understand, you raised this issue regarding delays in response and the display of rephrased queries to the user in the conversational retrieval chain. chains. conversational_retrieval. vectorStore. vector_store. Hi, I have been learning LangChain for the last month and I have been struggling in the last week to "guarantee" ConversationalRetrievalChain only answers based on the knowledge added on embeddings. May 26, 2023 · import {loadQAMapReduceChain} from "langchain/chains/load"; const question_generator_template = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. @classmethod. The from_retrievers method of MultiRetrievalQAChain creates a RetrievalQA chain for each retriever and routes the input to one of these chains based on the retriever name. Memory Classes: AgentExecutor uses specific memory classes to manage chat history and intermediate steps, while create_retrieval_chain relies on the RunnableWithMessageHistory class to manage chat history. Let's dive into this issue you're experiencing. This combine_documents_chain is then used to create and return a new BaseRetrievalQA instance. It involves creating an Jul 19, 2023 · To pass context to the ConversationalRetrievalChain, you can use the combine_docs_chain parameter when initializing the chain. Hello, Thank you for bringing this issue to our attention. load() print(str(len(documents))+ " documents loaded") llm = ChatOpenAI(temperature = 0, model_name='gpt-3. The ConversationalRetrievalQAChain. Nov 15, 2023 · Issue you'd like to raise. There have been some discussions on this issue, with nithinreddyyyyyy seeking suggestions on how to improve the accuracy. Hello, From your code, it seems like you're on the right track. question_answering. I hope the answer provided by ConversationalRetrievalChain makes sense and does not contain repetitions of the question or entire phrases. Apr 13, 2023 · Because mostly we use embedding to transform [text -> vector (aka. prompts import PromptTemplate from langchain. 72. I hope this To improve the memory of the Retrieval QA Chain, you can consider the following modifications: Increase the max_tokens_limit: This variable determines the maximum number of tokens that can be stored in the memory. It's a good choice for chatbots and other conversational applications. I used mine so my Agent can use the Pinecone VetorBase of me shouldh he need to load some Information into the buffer memory. claude-v2) for ConversationalRetrievalQAChain. e. Currently, I was doing it in two steps, getting the answer from this chain and then chat chai with the answer and custom prompt + memory to provide the final reply. from_llm() object with the custom combine_docs_chain May 12, 2023 · from langchain. Based on the code you've provided, it seems like you're trying to store the history of the conversation using the ConversationBufferMemory class and then retrieve it in the next iteration of the conversation. 5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True) # Split into chunks text_splitter May 13, 2023 · First, the prompt that condenses conversation history plus current user input (condense_question_prompt), and second, the prompt that instructs the Chain on how to return a final response to the user (which happens in the combine_docs_chain). -Best- Oct 17, 2023 · In this example, "second_prompt" is the placeholder for the second prompt. You need to pass the second prompt when you are using the create_prompt method. This parameter should be an instance of a chain that combines documents, such as the StuffDocumentsChain. Not working with claude model (anthropic. Aug 29, 2023 · return cls(\nTypeError: langchain. const chain = ConversationalRetrievalQAChain. Oct 4, 2023 · (Source: Conversational Retrieval QA with sources cannot return source) Unfortunately, I couldn't find any changes made to the RetrievalQAWithSourcesChain in the updates between version 0. . Jun 30, 2023 · vectorStore. Mar 13, 2023 · I want to pass documents like we do with load_qa_with_sources_chain but I want memory so I was trying to do same thing with conversation chain but I don't see a way to pass documents along with it. Jun 24, 2024 · For a more advanced setup, you can refer to the LangChain documentation on creating retrieval chains and combining them with conversational models. Closed. 308. 236 (which you are using) and the latest version 0. You can find more information about the SequentialChain class in the libs I am using Conversational Retrieval Chain to make conversation bot with my documents. from_llm similar to how models from VertexAI are used with ChatVertexAI or VertexAI by specifying the model_name. Aug 3, 2023 · The RetrievalQA. This is done so that this question can be passed into the retrieval step to fetch relevant documents. 5-turbo). conversational_retrieval import ConversationalRetrievalChain from langchain. from_llm(File "d:\llm projects\ask-multiple-pdfs-main\ask-multiple-pdfs-main\llm\lib\site-packages\langchain\chains\conversational_retrieval\base. See the below example with ref to your provided sample code: llm=OpenAI(temperature=0), retriever=vectorstore. Chat History: Aug 4, 2023 · Getting Argument of type 'ChatOpenAI' is not assignable to parameter of type 'BaseLLM'. I hope you're doing well. name: "vector-chain", description: "QA chain that uses a vector store to retrieve documents and then uses OpenAI to answer questions. py", line 212, in from_llm return cls 🦜🔗 Build context-aware reasoning applications. This includes setting up a retriever, creating a document chain, and handling query transformations for follow-up questions. chat_vector_db: This chain is used for storing and retrieving vectors in a chat context. Reload to refresh your session. when trying to pass ChatOpenAI to RetrievalQAChain or ConversationalRetrievalQAChain suggests upgrading the LangChain package from version 0. I don't know when it does not know something. I wanted to let you know that we are marking this issue as stale. have a look at this snipped from ConversationalRetrievalChain class. I have a question&answer over docs chatbot application, that uses the RetrievalQAWithSourcesChain and ChatPromptTemplate. from_chain_type function is used to create an instance of BaseRetrievalQA using a specified chain type. Jul 17, 2023 · conversational_retrieval: This chain is designed for multi-turn conversations where the context includes the history of the conversation. Systemrole promt in my chain. 0. Its default prompt is CONDENSE_QUESTION_PROMPT. {"payload":{"allShortcutsEnabled":false,"fileTree":{"langchain/src/chains":{"items":[{"name":"api","path":"langchain/src/chains/api","contentType":"directory"},{"name Nov 8, 2023 · Regarding the ConversationalRetrievalChain class in LangChain, it handles the flow of conversation and memory through a three-step process: It uses the chat history and the new question to create a "standalone question". In this example, retriever_infos is a list of dictionaries where each dictionary contains the name, description, and instance of a retriever. as_retriever(), memory=memory) we do not need to pass history at all. Then, manually set the SystemMessagePromptTemplate for the llm_chain in the combine_docs_chain of the ConversationalRetrievalChain: Sep 3, 2023 · Retrieve documents and call stuff documents chain on those; Call the conversational retrieval chain and run it to get an answer. The metadata_based_get_input function checks if a document's metadata matches the allowed metadata before including it in the filtering process. Oct 21, 2023 · 🤖. For the Conversational retrieval chain, we have to get the retriever fetch Yes, the Conversational Retrieval QA Chain does support the use of custom tools for making external requests such as getting orders or collecting customer data. output_parsers. This is possible through the use of the RemoteLangChainRetriever class, which is designed to retrieve documents from a remote source using a JSON-based API. If you don't know the answer, just say that you don't know. You signed out in another tab or window. uh ep pv na xh lw nm br vq mi