Conversational retrieval qa chain python. chains import ConversationChain.

Sep 26, 2023 · To solve this problem, I had to change the chain type to RetrievalQA and introduce agents and tools. Apr 29, 2024 · retriever = vector. Questions and answers based on a snapshot of the LangChain python docs. chains import ConversationChain. This is necessary to create a standanlone vector to use for retrieval. You can use ConversationBufferMemory with chat_memory set to e. chains import RetrievalQA. agent. Plus, you can still use CRQA or RQA chain and whole lot of other tools with shared memory! Nov 24, 2023 · In the JavaScript version of LangChain, the ConversationalRetrievalQAChain. 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) Jul 10, 2023 · My good friend Justin pointed me in the right direction. Dec 14, 2023 · Im trying to create a conversational chatbot with ConversationalRetrievalChain with prompt template and memory and get error: ValueError: Missing some input keys: {'chat_history'}. 0. from_chain_type(llm, retriever=vectordb. """ from __future__ import annotations import warnings from abc import abstractmethod from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Union from pydantic import Extra, Field, root_validator from 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). If False, inputs are also added to the final outputs. In this example, retriever_infos is a list of dictionaries where each dictionary contains the name, description, and instance of a retriever. Here is an example of combining a retriever with a document chain: You signed in with another tab or window. langchain-community Chain with chat history. From what I understand, you opened this issue regarding the ConversationalRetrievalChain. agents. This section will cover how to implement retrieval in the context of chatbots, but it's worth noting that retrieval is a very subtle and deep topic - we encourage you to explore other parts of the documentation that go into greater depth! May 30, 2023 · qa = ConversationalRetrievalChain. Update #2: I've transitioned to using agents instead and it solves the problem with Conversational Retrieval QA Chain about the chat histories. The easiest way to set this up is simply to specify: Jul 14, 2023 · The Retrieval QA Chain combines the powers of vector databases and LLMs to deliver contextually appropriate answers to user questions. astream_events method. And add the following code to your server. callbacks import StreamingStdOutCallbackHandler import pandas as pd from docx import Document from nltk. chains import RetrievalQA, ConversationalRetrievalChain Aug 3, 2023 · Thank you for your question. LangChain has "Retrieval Agents". 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. from_llm(llm, retriever, return_source_documents=True) Testing the Agent. \ Use three sentences maximum and keep the answer concise. First, it condenses the current question and the chat history into a standalone question. Once enabled, I checked out the object structure in my debugger to learn which field contained the source. It loads a chain that allows you to pass in all of the Mar 10, 2011 · System Info. chains import RetrievalQA qa_chain = RetrievalQA. Now that we have all the components in place, we can build the Conversational Retrieval Chain. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. This function loads the MapReduceDocumentsChain and passes the relevant documents as context to the chain after mapping over all to reduce to just Jun 24, 2023 · Writes a pickle file with the questions and answers about a candidate. agent_toolkits. However, if you're looking to achieve a similar functionality where you want to retrieve answers along with their reference sources, you might need to Apr 5, 2023 · Hi, @samuelwcm!I'm Dosu, and I'm here to help the LangChain team manage their backlog. from_llm( llm, retriever 对话式检索问答（Conversational Retrieval QA）. qa_with_sources import load_qa_with_sources_chain from langchain. as_retriever(), chain_type_kwargs={"prompt": prompt} Aug 17, 2023 · 7. As in the RAG tutorial, we will use create_stuff_documents_chain to generate a question_answer_chain, with input keys context, chat_history, and input-- it accepts the retrieved context alongside the conversation history and query to generate an answer. The idea is that the vector-db-based retriever is just another tool made available to the LLM. from_llm() function not working with a chain_type of "map_reduce". from typing import Any, List, Optional from langchain_core. Fine-tune prompts, configure components, and personalize the experience to align Aug 7, 2023 · from langchain. 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: Apr 1, 2023 · ConversationalRetrievalChain で LangChain の QA にチャット履歴実装. So the index of the list will correspond to the page of the document, e. How can I see the whole conversation if I want to analyze it after the agent. (when calling the chain) My question though: Why is chat_history needed as input if I have memory as hyperparameter in ConversationalRetrievalChain? Jun 3, 2023 · This is because the load_qa_chain for the "map_reduce" chain type is more complex (see source code) compared to the load_qa_chain for the "stuff" chain type (source code). You switched accounts on another tab or window. conversational_retrieval. But now it stopped working. chains . --. __call__ expects a single input dictionary with all the inputs Retrieval and generation Retrieve: Given a user input, relevant splits are retrieved from storage using a Retriever. 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. loadQAStuffChain is a function that creates a QA chain that uses a language model to generate an answer to a question given some context. 对话式检索问答链（ConversationalRetrievalQA chain）是在检索问答链（RetrievalQAChain）的基础上提供了一个聊天历史组件。. I need a URL. google. The agent has verbose=True parameter and I can see the conversation happening in console. Jan 18, 2024 · The weird thing is, that it is working with a LLM-Chain from Langchain without Retrieval: from langchain. chain = ConversationalRetrievalChain. messages import SystemMessage from langchain_core. qa_chain = RetrievalQA. # Create the chat prompt templates. base. fromLLM method is equivalent to the Python ConversationalRetrievalChain. This combine_documents_chain is then used to create and return a new BaseRetrievalQA instance. In this process, a numerical vector (an embedding) is calculated for all documents, and those vectors are then stored in a vector database (a database optimized for storing and querying vectors). messages[0]. edu. prompts. chat_models import ChatOpenAI from langchain. A retrieval-based question-answering chain, which integrates with a retrieval component and allows you to configure input parameters and perform question-answering tasks. SQLChatMessageHistory (or Redis like I am using). The environment provides the documents and the retriever information. May 20, 2023 · In terms of Python types, it will return a List[Document]. # RetrievalQA. Therefore, the retriever needs to have a query We omit the conversational aspect to keep things more manageable for the lower-powered local model: ```python # from langchain. More or less they are wrappers over one another. Jan 10, 2024 · Initializing the Conversational Chain. Empowering Your Bot: The beauty of Flowise lies in its customization options. A retrieval-based question-answering chain, which integrates with a Vectara retrieval component and allows you to configure input parameters and perform question-answering tasks. chains import LLMChain,QAWithSourcesChain. With the data added to the vectorstore, we can initialize the chain. question at the end. prompt. from_llm function. To create a conversational question-answering chain, you will need a retriever. Nov 22, 2023 · Definitions. I developed a script that worked just fine, it was as follows: This worked very well, until I tried to use it in a new app with Streamlit. This method creates a new instance of ConversationalRetrievalQAChain from a BaseLanguageModel and a BaseRetriever. corpus import stopwords import os. 5-turbo) for generating the question. The process involves using a ConversationalRetrievalChain to handle user queries. prompts import PromptTemplate. Oct 16, 2023 · Retrieval QA Chain Now, we’re going to use a RetrievalQA chain to find the answer to a question. openai import OpenAIEmbeddings from langchain. In the example below we instantiate our Retriever and query the relevant documents based on the query. research. Here we use create_stuff_documents_chain to generate a question_answer_chain, with input keys context, chat_history, and input -- it accepts the retrieved context alongside the conversation history and query to generate an answer. This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. Incoming queries are then vectorized as For a more advanced setup, you can refer to the LangChain documentation on creating retrieval chains and combining them with conversational models. I was expecting a behavior similar to the Conversational Chain. Reload to refresh your session. The main langchain package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture. text_splitter import CharacterTextSplitter from langchain. Nov 16, 2023 · It works perfectly. memory import ConversationBufferMemory from langchain. It serves as the backbone for maintaining context in ongoing dialogues, ensuring that the AI model can provide coherent and contextually relevant responses. question_answering import load_qa_chain template = """ {Your_Prompt} Sep 8, 2023 · I have a starter code to run an agent with retrieval_qa chain. It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question May 4, 2023 · Hi @Nat. The first query will always work. This method will stream output from all "events" in the chain, and can be quite verbose. First, the two first lines of code that perform a similatiry search breaks the code with this error: InvalidRequestError: '$. You can use ChatPromptTemplate, for setting the context you can use HumanMessage and AIMessage prompt. May 8, 2024 · Split into chunks. Aug 24, 2023 · there's no direct "create_qa_with_sources_chain" function or "AnswerWithSources" class in popular NLP libraries like Hugging Face's Transformers or Langchain's Conversational Retrieval Agent. vectorstores import FAISS from langchain. LangChain is a framework for developing applications powered by large language models (LLMs). We build our final rag_chain with create_retrieval_chain. Image Credit to hbs. chains import RetrievalQA from langchain. import os from langchain. I don't know what has changed but now I cannot ask more than 1 question. May 8, 2023 · Colab: https://colab. Meaning that ConversationalRetrievalChain is the conversation version of RetrievalQA. E. Use LangGraph to build stateful agents with Jul 3, 2023 · class langchain. Dec 1, 2023 · Based on the context provided and the issues found in the LangChain repository, you can add system and human prompts to the RetrievalQA chain by creating a ChatPromptTemplate and passing it to the ConversationalRetrievalChain. conversation. If you want to add this to an existing project, you can just run: langchain app add rag-conversation. We create a memory object so that the agent can remember previous interactions. __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. Bases: LLMChain. qa Sep 6, 2023 · Enable “Return Source Documents” in the Conversational Retrieval QA Chain Flowise widget. 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 Using agents. To achieve this, you can use the MultiRetrievalQAChain class. language_models import BaseLanguageModel from langchain_core. Langchain, an innovative natural language processing library, opens the door to fascinating conversational experiences with datasets in Python. load_qa_chain uses Dynamic Document each time it's called; RetrievalQA get it from the Embedding space of document; VectorstoreIndexCreator is the wrapper of 2. Hence, I used load_qa_chain but with load_qa_chain, I am unable to use memory. Nov 15, 2023 · …and create a conversational retrieval chain from langchain. It formats the prompt template using the input key values provided and passes the formatted string to GPT4All , LLama-V2 , or another specified LLM. run command is executed. input' is invalid. embeddings. use SQLite instead for testing This chain takes in conversation history and then uses that to generate a search query which is passed to the underlying retriever. The benefits that a conversational retrieval agent has are: Doesn't always look up documents in the retrieval system. openai_functions. It initializes Hugging Face embeddings, creates a vector store using FAISS (a It then performs the standard retrieval steps of looking up relevant documents from the retriever and passing those documents and the question into a question answering chain to return a response. 10. All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations. Sep 14, 2023 · convR_qa = ConversationalRetrievalChain(retriever=customRetriever, memory=memory, question_generator=question_generator_chain, combine_docs_chain=qa_chain, return_source_documents=True, return_generated_question=True, verbose=True )`. as_retriever() For the Retrieval chain, we got a retriever to fetch documents from the vector store relevant to the user input. Now we can build our full QA chain. from langchain. To create a new LangChain project and install this as the only package, you can do: langchain app new my-app --package rag-conversation. Python: 3. 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. Nov 13, 2023 · I am working with the LangChain library in Python to build a conversational AI that selects the best candidates based on their resumes. Returns. Sep 2, 2023 · In this code, FinalStreamingStdOutCallbackHandler is instantiated with default parameters, which means the final answer will be prefixed with "Final Answer:" and all Feb 27, 2024 · Conversational Chain Construction: The core of the system is a Conversational Retrieval Chain, where LangChain’s conversational model interacts with the document retriever. from_llm method. Sep 5, 2023 · conversational_chain = ConversationalRetrievalChain(retriever=retriever,question_generator=question_generator,combine_docs_chain=doc_chain,memory=memory,rephrase_question=False,verbose=True,return_source_documents=True,) then you should be able to get file name from metadata like this 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. 主要なクラスは以下の通りです。. Aug 14, 2023 · this is my code: # Define the system message template. In this Definitions. 11 LangChain: 0. chat_models import ChatOpenAI. agents. Aug 13, 2023 · from langchain. [ Deprecated] Chain to have a conversation and load context from memory. ConversationalRetrievalChain [source] ¶ Bases: BaseConversationalRetrievalChain [Deprecated] Chain for having a conversation based on retrieved documents. text_splitter import RecursiveCharacterTextSplitter from langchain. :param file_key The key - file name used to retrieve the pickle file. Jun 27, 2023 · ConversationalRetrievalChain vs LLMChain. Next, we will use the high level constructor for this type of agent. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations . create_retrieval_chain: Retriever: This chain takes in a user inquiry, which is then passed to the retriever to fetch relevant documents. このシステムは、質問に対して関連するドキュメントを検索し、それらのドキュメントから回答を抽出することができます。. Use the following pieces of context and chat history to answer the. 7" and “max_length = 512”. Retrieval-Based Chatbots: Retrieval-based chatbots are chatbots that generate responses by selecting pre-defined responses from a database or a set of possible Jul 3, 2023 · inputs ( Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain memory. def print_letter_by_letter(text): May 6, 2023 · A conversational agent will access the conversation history and only use the . """. Initialize the chain. Below is the working code sample. For the Conversational retrieval chain, we have to get the retriever fetch documents relevant not only to the user input but also to the chat history. BaseRetrievalQA: この抽象 May 16, 2023 · My problem is, each time when I execute conv_chain({"question": prompt, "chat_history": chat_history}), it is creating a new ConversationalRetrievalChain that is, in the log, I get Entering new ConversationalRetrievalChain chain > message. My chain needs to consider the context from a set of documents (resumes) for its decision-making process. I wanted to let you know that we are marking this issue as stale. 它首先将聊天历史（可以是显式传入的或从提供的内存中检索到的）和问题合并成一个独立的问题 Aug 27, 2023 · Alternatively, you can use load_qa_chain with memory and a custom prompt. txt documents when it thinks that the query is related to the Tool description. Jul 15, 2023 · I wasn't able to do that with ConversationalRetrievalChain as it was not allowing for multiple custom inputs in custom prompt. g. Retrieval is a common technique chatbots use to augment their responses with data outside a chat model's training data. llm, retriever=vectorstore. Those documents (and original inputs) are then passed to an LLM to generate pip install -U langchain-cli. 4月 1, 2023. qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff", prompt=GERMAN_QA_PROMPT, document_prompt=GERMAN_DOC_PROMPT) chain = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=retriever, reduce_k_below_max_tokens=True, max_tokens_limit=3375, return_source_documents=True) from Aug 10, 2023 · Aug 10, 2023. from_template(""". Aug 1, 2023 · Step 6: Create a Conversational Retrieval Chain ⛓️. Nov 21, 2023 · The registry provides configurations to test out common architectures on curated datasets. as_retriever() qa = ConversationalRetrievalChain. chains. from_chain_type(. Oct 16, 2023 · from langchain. Aug 3, 2023 · Let's compare this to the ConversationalRetrievalQA chain that most people use. ConversationChain [source] ¶. To do this, we prepared our LLM model with “temperature = 0. :candidate_info The information about a candidate which Sep 1, 2023 · Below is the code that stores history by default, if there is no answer in doc store, it will fetch result from llm. These are NOT third party integrations. llms import LlamaCpp. We then use those returned relevant documents to pass as context to the loadQAMapReduceChain. The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. Sep 3, 2023 · This chain has two steps. By leveraging a collection of documents and applying sophisticated searching and language processing techniques, it strives to provide users with highly accurate and reliable information retrieval and question answering capabilities. We will pass the prompt in via the chain_type_kwargs argument. chains import ConversationalRetrievalChain retriever=qdrant. return_only_outputs ( bool) – Whether to only return the chain outputs. chains import LLMChain. But wait… the source is the file that was chunked and uploaded to Pinecone. Make sure to pay attention to the chunk_size parameter in TextSplitter. Updated the Retrieval-Generation Chain: I updated the rag_chain to use the new history_aware_retriever and question_answer_chain. 229 SO: Windows, Linux Ubuntu and Mac. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Standalone question generation is required in the context of building a new question when an indirect follow-up question is asked in Chat Mar 23, 2023 · The main way most people - including us at LangChain - have been doing retrieval is by using semantic search. And now we can build our full QA chain. : ``` memory = ConversationBufferMemory( chat_memory=RedisChatMessageHistory( session_id=conversation_id, url=redis_url, key_prefix="your_redis_index_prefix" ), memory_key="chat_history", return_messages=True ) ´´´ You can e. You signed out in another tab or window. template = fixed_prompt. system_template = """End every answer should end with " This is the according to 10th article". This class uses an LLMRouterChain to choose amongst multiple retrieval Apr 30, 2024 · Modified the Question-Answering Chain: I updated the question_answer_chain to use the new system prompt. May 30, 2023 · Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist. Nov 18, 2023 · memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True) CONDENSE_QUESTION_PROMPT = PromptTemplate. outputs ( Dict[str, str]) – Dictionary of initial chain outputs. Many of the following guides assume you fully understand Introduction. tokenize import sent_tokenize, word_tokenize from collections import Counter from nltk. This is my code: `from llama_cpp import Llama. Below is a list of the available tasks at the time of writing. The condense_question_prompt parameter in Python corresponds to the May 11, 2023 · このコードは、質問応答 (QA)システムを実装したPythonのクラス定義です。. 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 Retrieval. memory import ConversationBufferMemory. Apr 21, 2023 · Source code for langchain. LangChain では、 EmbeddingAPI を使って vector search とその結果を LLM に与えて QA Bot を構築したり、あるいは ChatGPT のような記憶・履歴 (Memory)を実装して、自然な対話を行う便利な Nov 8, 2023 · > Entering new LLMChain chain Prompt after formatting: System: Answer the user question using the provided context and chat history. This chain builds on top of RetrievalQAChain to include a chat history component to facilitate conversational interactions. vectorstores import Chroma from langchain. Jun 23, 2023 · I have some simple python code using conversational retrieval chain with Gradio UI. 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. 5 days ago · Source code for langchain. \ If you don't know the answer, just say that you don't know. Feb 17, 2024 · Conversational Retrieval QA Chain. 8:06 pm. I am using Jul 3, 2023 · The main difference between this method and Chain. Here is the method in the code: @classmethod def from_chain_type (. py file: 1 day ago · combine_docs_chain ( Runnable[Dict[str, Any], str]) – Runnable that takes inputs and produces a string output. How to add memory to load_qa_chain or How to implement ConversationalRetrievalChain with custom prompt with multiple inputs. On the other hand, if you want to respond based on the conversation history and document context simultaneously, then might want to try a custom chain and prompt. It was working not long ago, with recorded proof (in my YT video). llm_chain. chains import LLMChain llm_chain = LLMChain( llm=llm, prompt= prompt_temp, verbose=True, ) test = llm_chain({"type_string": types, "input": question}) test This works and I am getting a correct response. tags = ["contextualize_q_chain"]) qa_system_prompt = """You are an assistant for question-answering tasks. Apr 29, 2024 · Definition: Langchain Conversational Memory is a specialized module within the Langchain framework designed to manage the storage and retrieval of conversational data. Here is an example: Here is an example: from langchain . To stream intermediate output, we recommend use of the async . memory import BaseMemory from langchain_core. This includes setting up a retriever, creating a document chain, and handling query transformations for follow-up questions. \ Use the following pieces of retrieved context to answer the question. But now, I want to combine my chain with an agent, where agent can decide whether to retrieve or not depends on Aug 25, 2023 · Create a conversational retriever chain using the LLM and retriever. prompts import PromptTemplate Aug 1, 2023 · Each time ConversationalRetrievalChain receives your query in conversation, it will rephrase the question, and retrieves documents from your vector store (It is FAISS in your case), and returns answers generated by LLMs (It is OpenAI in your case). llms import OpenAI from langchain. It seems like you're trying to chain RetrievalQA with other simple chains in the LangChain framework, and you're having trouble because RetrievalQA doesn't seem to accept output_keys. The initialize_chain function sets up the conversational retrieval chain. But when I am try to use the RetrievalQA chain then it only works with cli and not streaming the tokens to the chainlit ui. The simplest Q&A chain implementation we can use is the load_qa_chain. If you don't know the answer, just say that you don't know, don't try to make up an answer. as_retriever()) Now, we call qa_chain with the question that we want to ask. Generate: A ChatModel / LLM produces an answer using a prompt that includes the question and the retrieved data; Table of contents Quickstart: We recommend starting here. And the chat_history array looks like, multiple nested arrays : . Sometimes, this isn't needed! If the user is just saying "hi", you shouldn't have to look things up; Can do multiple retrieval steps. So in my example, you'd have one "tool" to retrieve relevant data and another "tool" to execute an internet search. Setting the right chunk size is critical for RAG performance, as much of a RAG pipeline’s success is based on the retrieval step finding the right context for generation. Next, split the documents into separate chunks. chat import MessagesPlaceholder Leading into the retrieval step, our history_aware_retriever will rephrase this question using the conversation's context to ensure that the retrieval is meaningful. chains import ConversationalRetrievalChain,RetrievalQA from langchain Using in a chain We can create a summarization chain with either model by passing in the retrieved docs and a simple prompt. messages = [. Here's how you can do it: First, define the system and human message templates: May 18, 2023 · There are 4 methods in LangChain using which we can retrieve the QA over Documents. class langchain. ''' conversational_agent. The inputs to this will be any original inputs to this chain, a new context key with the retrieved documents, and chat_history (if not present in the inputs) with a value of [] (to easily enable conversational retrieval. com/drive/1gyGZn_LZNrYXYXa-pltFExbptIe7DAPe?usp=sharingIn this video I look at how to load multiple docs into a single Jul 16, 2023 · import openai import numpy as np import pandas as pd import os from langchain. Mar 8, 2024 · Create a Retrieval QA Chain RetrievalQA is a method for question-answering tasks, utilizing an index to retrieve relevant documents or text chunks, it suits for straightforward Q&A applications. If you cannot find the answer from the pieces of context, just say that you don't know, don't try to make up an answer. , documents[0] for the first page, documents[1] for the second page, and so on. """Chain for chatting with a vector database. \ {context}""" qa_prompt = ChatPromptTemplate Apr 29, 2023 · Just answering my question, the difference between having chat_history in RetrievalQA is this in ConversationalRetrievalChain. conversational_agent('Who is virat Kohli') Apr 25, 2023 · hetthummar commented on May 7, 2023. question_answering import load_qa_chain # # Prompt # template = """Use the following pieces of context to answer the question at the end. I am working in a notebook. This chain is responsible for answering the user’s question based on the retrieved context and the chat history. ao sx nm vp zr uj dj xc he lk