Conversational retrieval chain from llm github. 🦜🔗 Build context-aware reasoning applications.

From what I understand, the issue you reported was about the ConversationalRetrievalChain not utilizing memory for answering questions with references. They said that: They already support RemoteLangChainRetriever Could flowiseai support custom tool for Conversational Retrieval QA Chain. i am creating a chatbot by langchain so i am using a ConversationalRetrievalChain , so i want to determine some prompts to improve my output. Follow this ReadME file to set up a simple langchain agent to chat with your data (in this case - PDF files). Human: This is a friendly conversation between a human and an AI. 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 Mar 10, 2011 · from docs. It provides some logic to create the question_generator chain as well as the combine_docs_chain. Oct 11, 2023 · Issue you'd like to raise. I store the previous messages in my db. I searched the LangChain documentation with the integrated search. ----- Q&A Knowledge Base 1 Q&A Knowledge Base 1. This provides some logic to create the question_generator chain as well as the combine_docs_chain. chains import create_retrieval_chain from langchain. Any suggestions what can I do to improve the accuracy of the output? #memory = ConversationEntityMemory(llm=llm, return_messages=True) memory=ConversationBufferMemory(memory_key="chat_history",output_key='answer') Checked other resources I added a very descriptive title to this question. Another similar issue was encountered when using the ConversationalRetrievalChain. Note that if we input a query that does not require a retrieval step, the agent does not execute one: config = {"configurable": {"thread_id": "abc123"}} from langchain. on Mar 11. To Reproduce Steps to reproduce the behavior: Create flow with Conversational Retrieval QA Chain and some type of vector retriever; send first message; send Aug 12, 2023 · from langchain. I don't know what has changed but now I cannot ask more than 1 question. Dec 21, 2023 · Methods: stream_chat_async: Stream chat using similarity search and AI. 5-turbo") # Data Ingestion. 5-turbo). Jun 23, 2023 · I have some simple python code using conversational retrieval chain with Gradio UI. from_llm method. This is done by Nov 15, 2023 · Issue you'd like to raise. It leverages various NLP techniques, including language models and embeddings, to provide relevant responses. 10. Oct 11, 2023 · @yazanrisheh - I used 2 templates to bring the customization aspect to the Conversational retrieval chain where you can feed in the customized template and try out. To pass context to the ConversationalRetrievalChain, you can use the combine_docs_chain parameter Jun 2, 2023 · System Info LangChain 0. Currently, when using ConversationalRetrievalChain (with the from_llm() function), we have to run the input through a LLMChain with a default "condense_question_prompt" which condenses the chat history and the input to make a standalone question out of it. Jun 17, 2023 · > Entering new StuffDocumentsChain chain > Entering new LLMChain chain Prompt after formatting: System: Use the following pieces of context to answer the users question. Streaming is a feature that allows receiving incremental results in a streaming format when generating long conversations or text. from_llm method to get streamed answers. Ingredients: Chains: create_history_aware_retriever, Learned how to load and prepare data, providing the necessary context to customize Large Language Model (LLM) generations in a tailored manner. The chatbot utilizes a conversational retrieval chain to answer user queries based on the content of embedded documents. 8 Who can help? @hwchase17 @agola11 Information The official example notebooks/scripts My own modified scripts Related Components LLMs/Chat Models Embedding Models Prompts / Prompt Templates / Promp In this example, the PromptTemplate class is used to define the custom prompt. Aug 14, 2023 · Feature request It seems that right now there is no way to pass the filter dynamically on the call of the ConversationalRetrievalChain, the filter can only be specified in the retriever when it's created and used for all the searches. From what I understand, the issue you reported was related to the behavior of streaming:true in ConversationalRetrievalQAChain. 11 LangChain: 0. In ChatOpenAI from LangChain, setting the streaming variable to True enables this functionality. qa agent_executor = create_react_agent(llm, tools, checkpointer=memory) This is all we need to construct a conversational RAG agent. There's no need to add another agent or make changes to your existing LLMChains. Please correct me if I was wrong. In addition, there are a few solved issues that might be relevant to your case: ConversationalRetrievalChain doesn't work with ConversationEntityMemory + SQLiteEntityStore : The issue was resolved by providing an extra key Apr 29, 2024 · LangChain provides us with Conversational Retrieval Chain that works not just on the recent input, but the whole chat history. In creating an app, ideally, you would like to let's say db = SQLDatabase. Hi community, I am developing an app to interact (Q&A) with several documents previously embedded and stored into a MongoDB Atlas cluster. index_id=kendraId, top_k=5, region_name=region, user_context={"Token": userToken} ) prompt_template = """. _qa_task: Helper method to execute the QA task in a separate thread. http_chat_async: Perform one-time HTTP chat using similarity search and AI. May 10, 2023 · here I have tryAgent() function that will retrieve the chunked dataset from DeepLake and use it in ConversationalRetrievalChain I tried to add : retriever. Contribute to langchain-ai/langchain development by creating an account on GitHub. Map-Reduce: Processes the splits using a map-reduce approach. I used the GitHub search to find a similar question and # Create a conversational retrieval chain from the language model and the retriever conversation_retrieval_chain = ConversationalRetrievalChain. Apr 26, 2023 · Hello! I am building an ai assistant, with the help of langchain's ConversationRetrievalChain. Parameters. 188 Python 3. if there is more than 1 output keys: use the relevant output key for the chain for example in ConversationalRetrievalChain Nov 24, 2023 · retriever = AmazonKendraRetriever(. Those documents (and original inputs) are then passed to an LLM to generate Dec 26, 2023 · Based on the information you've provided and the context of similar issues in the LangChain repository, it seems like the ConversationalRetrievalChain. Mar 31, 2023 · If you are using memory with each chain type. ConversationalRetrievalChain. Contribute to langchain-ai/langchainjs development by creating an account on GitHub. py file of the conversational_retrieval directory . You signed out in another tab or window. The LLM will be fed with the data retrieved from embedding step in the form of text. The _aget_docs method is used to asynchronously retrieve documents relevant to the given question, while the _acall method is used to asynchronously execute the chain. I am still learning it actually You can upload documents in txt, pdf, CSV, or docx formats and chat with your data. But now it stopped working. Apr 24, 2024 · chain = ConversationalRetrievalChain. Openapi schema parsing: Instead of parsing the openapi schema, the llama_with_retrieval. The AI is talkative and provides specific details from its context but limits it to 240 tokens. Both will Jul 10, 2023 · In this code, FilteredRetriever is a simple wrapper that delegates the retrieval to the original retriever, and then filters the results based on the source path. I wanted to let you know that we are marking this issue as stale. I provided a response suggesting that there might be missing or extra fields in the router configuration. This Oct 16, 2023 · pdf_loader = DirectoryLoader(directory_path, glob="**/*. If you want to replace it completely, you can override the default prompt template: Sep 2, 2023 · No, the problem is still here. Other users, such as @alexandermariduena and @harshil21 , have also faced the same issue and suggested possible solutions. . Hi people, I'm using ConversationalRetrievalChain without any modifications, and in 90% of the cases, it responds by repeating words and entire phrases, like in the examples below: const createRetrieverChain = (llm: BaseLanguageModel, retriever: Runnable, rephrasePrompt: string) => // Small speed/accuracy optimization: no need to rephrase the first question // since there shouldn't be any meta-references to prior chat history Sep 2, 2023 · If you want to continue the conversation, start your reply with @dosu-bot. from_llm unable to print the final answer with chain_Type=refine. The formatted prompt with context then gets passed to the LLM and a response is generated. Let's observe its behavior. The solution was to replace OpenAI with ChatOpenAI when working with a chat model (like gpt-3. The ConversationalRetrievalChain should be able to manage the context of the conversation and route the queries accordingly. \n\nChat History:\n\nHuman: what does the third amendment say?\nAssistant: I do not have access to the context necessary to answer that Jul 3, 2023 · Convenience method to load chain from LLM and retriever. As a result, we can't use memory in chains and it lets Azure open AI not useful enough on Flowise AI. You need to modify the question_generator chain to pass the previous answer as an input variable. llm, retriever=merger_retriever, chain_type=self. 🦜🔗 Build context-aware reasoning applications. Reload to refresh your session. LangChain 🦜️🔗: Harnessing the power of LangChain, the chatbot exhibits natural language processing capabilities. combine_documents_chain. Nov 16, 2023 · You can find more details about this solution in this issue. when trying to pass ChatOpenAI to RetrievalQAChain or ConversationalRetrievalQAChain suggests upgrading the Feature request. Jul 19, 2023 · This process involves creating a standalone question from the chat history and the new question, retrieving relevant documents based on this standalone question, and then passing these documents to a language model to generate a final response. 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. Python: 3. from_llm(providedllm,retriever=retriever) , does ConversationalRetrievalChain also use LLM ? if so what llm does it use ? is it gpt-3. fromLLM function is used to create a QA chain that can answer questions based on the text from the 'state_of_the_union. The ConversationalRetrievalQAChain. pdf", show_progress=True, use_multithreading=True, silent_errors=True, loader_cls = PyPDFLoader) documents = pdf_loader. Aug 11, 2023 · Saved searches Use saved searches to filter your results more quickly May 18, 2023 · edited. Relevant documents will be retrieved and sent to the LLM along with your follow-up questions for accurate answers. Apr 13, 2023 · Because mostly we use embedding to transform [text -> vector (aka. Here are some potential causes and resolutions: The question_generator chain might be taking a long time to generate a new question. May 23, 2023 · chat_history in conversation_retrieval dosen't seem to be working when I was trying to implement a solution with conversation_retrieval_chain, I'm getting "A single string input was passed in, but this chain expects multiple inputs ({'question', Aug 4, 2023 · TypeError: llm. from_template (template) # Create the memory object memory = ConversationBufferMemory (memory_key = 'chat_history', return_messages = True, output_key = 'answer') # Assuming you have a retriever instance retriever = BaseRetriever # Replace Oct 30, 2023 · You can find this in the _get_chat_history function in the base. The following code examples are gathered through the Langchain python documentation and docstrings on some of their classes. This will allow the ConversationalRetrievalChain to use the ConversationBufferMemory for storing and retrieving conversation history. How's everything in your corner of the universe? Based on the information you've provided and the context from the LangChain repository, it seems like you're trying to enable streaming in the ConversationalRetrievalChain. Jan 10, 2024 · In this example, llamaCPP_retriever is an instance of your llamaCPP retriever. py simply expects a /query endpoint to retrieve additional context to ground the LLM. You can use this FilteredRetriever in place of the original retriever when creating the ConversationalRetrievalChain. To retrieve it back, yes, the same embedding model must be used to generate two vector and compare their similarity. 5-turbo ? Aug 18, 2023 · So far, the only workaround that I found out is querying the chain using an external chat history, like this: chain({"question": query, "chat_history":"dummy chat history"}) Thank you in advance for your help. llm_chain_type, verbo Sep 29, 2023 · I have just ask langchainjs for making external request in Conversational Retrieval QA Chain like custom tool. template) This will print out the prompt, which will comes from here. Great to see you back here, diving into the world of conversational AI with us. e. chains. Nov 20, 2023 · Hi, @0ENZO, I'm helping the LangChain team manage their backlog and am marking this issue as stale. However, you can achieve the desired functionality by combining the ConversationalRetrievalChain with OpenAI's ChatCompletion. If you don't know the answer, just say that you don't know, don't try to make up an answer. You switched accounts on another tab or window. I built a FastAPI endpoint where users can ask questions from the ai. _modelType is not a function suggests using LLM from langchain instead of llama-node as the latter is missing the _modelType property. _publish_chat_history: Publish chat history to Kafka. To reach this goal, I wrote this code: db_client = MongoCl Jan 26, 2024 · In the above code, the ConversationBufferMemory instance is passed to the ConversationalRetrievalChain constructor via the memory argument. A similar problem was addressed by modifying the func parameter in the Tool class to include the chain function with the return_source_documents=True argument. System Prompt: Constructs the prompt for the LLM. 👍 2 dosubot[bot] and decoderdot reacted with thumbs up emoji 👎 3 dosubot[bot], jriihi, and sbarman25 reacted with thumbs down emoji This repository demonstrates how to use a Vector Store retriever in a conversational chain with LangChain, using the vector store Chroma. This chain takes in conversation history and then uses that to generate a search query which is passed to the underlying retriever. Oct 17, 2023 · In this example, "second_prompt" is the placeholder for the second prompt. Here's how it was approached: 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. Jan 16, 2024 · Example prompt created by LangChain from my conversation: "Human: Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. i want to give the bot name ,character and behave (system message prompt ) users use different languages how can i let the bot take user input then translate it to English then parse Mar 10, 2010 · Hi, @DhavalThkkar!I'm Dosu, and I'm helping the LangChain team manage their backlog. But there's no mention of qa_prompt in ConversationalRetrievalChain, or its base chain Sep 3, 2023 · Provide a system message to prime the llm; Retrieve documents and call stuff documents chain on those; Call the conversational retrieval chain and run it to get an answer. The LLM model contains its own embedding step Mar 11, 2024 · nedala10. fromLLM. The first method involves using a ChatMemory instance, such as ConversationBufferWindowMemory, to manage the chat history. _get_qa_chain: Get the conversational retrieval chain for handling chat. Hey there, @PriyaranjanMarathe!Great to see you back and diving into more queries. Azure open AI can only do retrieval QA on Flowise and it can't work on conversational retrieval chain. from_llm method in LangChain is a convenience method that loads a ConversationalRetrievalChain from a language model and a retriever. Question: User query is processed. Using this RunnableSequence we can pass questions, and chat history to the model for informed conversational question answering. So in this case "context" gets filled with the results from the retriever and "question" with the "query" value, when calling the chain Description. Add a parameter to ConversationalRetrievalChain to skip the condense question prompt procedure. Here's a simple way to do it: Nov 11, 2023 · In the context shared, it's also mentioned that you can use a separate language model for each call, use a custom prompt for condensing the question, return source documents, use a search_distance parameter, use a map_reduce chain, use a question answering with sources chain, stream output to stdout, and specify a get_chat_history function May 12, 2023 · You signed in with another tab or window. Jul 29, 2023 · When using Conversational Retrieval QA Chain after sending the second message in a chat I always get back "Cannot set properties of undefined (setting 'llm')" no matter what I've tried. Aug 31, 2023 · Based on my understanding, you were experiencing errors while trying to integrate a conversational retrieval chain with router chains. agents. if the chain output has only one key memory will get the output by default. This is the bug here I think but it works. llm (BaseLanguageModel) – The default language model to use at every part of this chain (eg in both the question generation and the answering) Hi, @colintoh!I'm helping the LangChain team manage their backlog and I wanted to let you know that we are marking this issue as stale. llm_chain. The first query will always work. txt' file. prompt. The ConversationalRetrievalChain class uses this retriever to fetch relevant documents based on the generated question. Please note that this modification requires you to keep track of the previous answer and pass it as an input to the question_generator chain. Nov 8, 2023 · Return metadata when use qdrant as retriever in ConversationalRetrievalChain. . My code: def create_chat_agent(): llm = ChatOpenAI(temperature=0, model_name="gpt-3. Mastered Data Retrieval and Presentation Techniques 💡 Acquired techniques to retrieve and present data to the LLM in useful ways, enhancing the efficiency of a conversational retrieval chain. It was working not long ago, with recorded proof (in my YT video). Jul 19, 2023 · You can replace your current RetrievalQA chain with the ConversationalRetrievalChain. These tools help manage and retrieve data efficiently, making them essential for AI applications. from_llm I am using ConversationalRetrievalChain. Store: Stores the conversation and relevant splits. Chat History: Sep 28, 2023 · In this template, {previous_answer} is a placeholder for the previous answer generated by the LLM. 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. It can only do simple Q&A in such case. list of number)]. base_compressor=compressor,base_retriever=retriever), memory=memory, combine_docs_chain_kwargs={"prompt": qa_prompt}, output_key="result", Oct 24, 2023 · Feature request. Getting Argument of type 'ChatOpenAI' is not assignable to parameter of type 'BaseLLM'. conversational_retrieval is where ConversationalRetrievalChain lives in the Langchain source code. combine_documents import create_stuff_documents_chain from langchain. Sep 21, 2023 · A LLM chain to judge "whether this question needs externel knowledge, return true or false"; Then, depending on the results, let's choose a chain from below: a LLM chain to answer generic question like "Q: Thank you very much!" and it returns "You are welcome!"; A BaseCombineDocumentsChain to for RAG like "Q: Introduce the causes of leukemia?" If I have to eliminate using this conversational retrieval chain and use a custom chain, how can I do that with all the functions that I am getting from the above code? if someone can help with great details it will be very helpful as I have been working around this for a few days and couldn't find any fix, I don't worry about about the way of Jul 7, 2023 · I am using conversational retrieval chain with memory, but I am getting incorrect answers for trivial questions. from_llm and I want to use the Qdrant vector store as a retriever. For more details, you can refer to the source code in the langchainjs repository. from_llm(llm=model, retriever=retriever, return_source_documents=True,combine_docs_chain_kwargs={"prompt": qa_prompt}) I am obviously not a developer, but it works (and I must say that the documentation on Langchain is very very difficult to follow) Oct 8, 2023 · If then only "context" and "question" are left, you can call the chain with dbqa({"query": "Test Query"}). agent_toolkits import create_pbi_chat_agent, create_conversational_retrieval_agent # Create a chat agent chat_agent = create_pbi_chat_agent () # Create a document retrieval agent retrieval_agent = create_conversational_retrieval_agent () # Combine the two agents def combined_agent (user_input): # First, try to answer the Sep 21, 2023 · The BufferMemory is used to store the chat history. The corrected code is: Dec 27, 2023 · It takes as arguments a language model (llm), a vector store (vectorstore), a prompt for condensing the question (condense_question_prompt), a chain type (chain_type), optional keyword arguments for the document chain (combine_docs_chain_kwargs), callbacks (callbacks), and any additional keyword arguments (**kwargs). Finally, we pipe the result of the LLM call to an output parser which formats the response into a readable string. from_llm method is returning a new question instead of the expected answer when you pass in chat_history because it's designed to generate a new standalone question for retrieval. create_retrieval_chain: Retriever: This chain takes in a user inquiry, which is then passed to the retriever to fetch relevant documents. from_llm (. search_kwargs['max_tokens_limit'] = 2000 memory = ConversationTokenBufferMemory(llm=llm_davinci) in order to set the limit_token. Apr 18, 2023 · First, it might be helpful to view the existing prompt template that is used by your chain: print ( chain. 0. In that same location is a module called prompts. get_prompt(llm) #based on LLM model, it will return either the regular PromptTemplate version or the chat (ChatPromptTemplate). Throughout this project, we examined each component of the RAG system from document loader to conversational retrieval chain. 🦜🔗 Build context-aware reasoning applications 🦜🔗. I want to add a config chain prior to the ConversationalRetrievalChain that is going to process the query and set the retriever's search kwargs ( k, fetch_k, lambda_mult ) depending on the question, how can I do that and pass the parameters from the config chain output Dec 21, 2023 · These methods are used to handle the execution of the chain in an asynchronous manner. py which contains both CONDENSE_QUESTION_PROMPT and QA_PROMPT. from_llm(llm=self. 5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True) # Split into chunks text_splitter May 30, 2023 · qa = ConversationalRetrievalChain. llm=llm, chain_type="stuff", retriever=ContextualCompressionRetriever(. Hello, I am using ConcersationalRetrievalChain with the following configuration ConversationalRetrievalChain. 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. load() print(str(len(documents))+ " documents loaded") llm = ChatOpenAI(temperature = 0, model_name='gpt-3. The LLMChain instance is used to generate a new question for retrieval based on the current question and chat history. i. const CONDENSE_PROMPT = `Given the history of the conversation and a follow up question, rephrase the follow up question to be a standalone question. from_uri(DATABASE_URI) in one endpoint, and use the db in other endpoints, The second endpoint could be what users get access to, so in Chat History: {chat_history} ""Follow up question: {question}") condense_question_prompt = PromptTemplate. Performance: LLaMA has multiple model sizes and utilizing the 7B model is better for speed, however may be worse for prompt responses than the 65B model. Jun 25, 2024 · inputs = { "question": question, "chat_history": chat_history } # Call the chain output = conversational_chain ( inputs ) print ( output) This code sets up a conversational retrieval chain that integrates the chat history into the question generation process, ensuring that the LLM can generate a standalone question for document retrieval [1]. Based on the context provided, there are two main ways to pass the actual chat history to the _acall method of the ConversationalRetrievalChain class. 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. chains import create_history_aware_retriever May 3, 2023 · To anyone who found that the LLM input isn't aligned even if rephrase_question is set to False, I notice that although the question for LLM itself keeps unchanged, the query for retrieving docs uses the rephrased question (as shown in code below), which results in degraded retrieval and generation result in my case. Built-in Memory The delay in the get_conversation_chain function could be caused by several factors, including the time taken to generate a new question, retrieve documents, and combine documents. Motivation. stuff_prompt import create_stuff_prompt_selector STUFF_PROMPT_SELECTOR = create_stuff_prompt_selector(ui_input= ui_input) #adds the ui_input to the prompt stuff_prompt = STUFF_PROMPT_SELECTOR. from_llm(llm, retriever) # Function to process a user prompt May 6, 2023 · You signed in with another tab or window. Falcon-7B LLM: The use of the 8-bit quantized Falcon-7B LLM enhances the efficiency and performance of the chatbot's language understanding. 229 SO: Windows, Linux Ubuntu and Mac. 🤖. Apr 5, 2023 · From what I understand, you opened this issue regarding the ConversationalRetrievalChain. from_llm() function not working with a chain_type of "map_reduce". If the follow up question does not need context, return the exact same text back. Dec 16, 2023 · dosubot bot commented on Dec 16, 2023. _TEMPLATE = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. However, it does not work properly in RetrievalQA or ConversationalRetrievalChain. - flojud/DocsChat Jul 3, 2023 · what is the difference between ConversationalRetrievalChain(retriever=retriever) and ConversationalRetrievalChain. If you don't know the answer, just say that you don't know. {context} Qu Feb 25, 2024 · The default prompt configuration causes the LLM to translate my question into an entirely different language, as seen here: I want to provide a custom prompt to the LLMChain that includes both the context and the user's question. Aug 7, 2023 · Its primary function is to retrieve relevant documents based on a given question and chat history, and it does not have any functionality for modifying the tone of the conversation. Hope you're doing well! Based on your question, it seems like you want to keep the ConversationalRetrievalChain instance "qa" in memory within a Flask application, so that it doesn't need to be called every time a request is received. It looks like you're trying to retrieve source documents using the create_retriever_tool and create_openai_tools_agent functions but are encountering issues. But, once the k most similar vectors have been retrieved, I don't want the page_conten Mar 10, 2011 · System Info. The diagram below illustrates the Conversational Retrieval Chain implemented in this project: Chat History: Maintains the context of the conversation. Do Conversational Retrieval QA Chain support custom tool? #2747 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 Oct 30, 2023 · The ConversationalRetrievalChain. Standalone question generation is required in the context of building a new question when an indirect follow-up question is asked in Chat Apr 2, 2023 · langchain. You need to pass the second prompt when you are using the create_prompt method. ey mu jc cu rx be fx mb vd ft