From what I understand, you opened this issue regarding the ConversationalRetrievalChain. To start, we will set up the retriever we want to use, then turn it into a retriever tool. chain = load_qa_with_sources_chain (OpenAI (temperature=0),. Conversational Retrieval Agents. qa_with_sources. g. Retrieval Agents. "To get a sense of how RAG works, let’s first have a look at Augmented Generation, as it underpins the approach. You signed in with another tab or window. 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. 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，然后从检索器中查找相关文档，最后将这些. Already have an account? Describe the bug When chaining a conversational retrieval QA to a Conversational Agent via a Chain Tool. The algorithm for this chain consists of three parts: 1. What you’ll learn in this course. It initializes the buffer memory based on the provided options and initializes the AgentExecutor with the tools, language model, and memory. Hello, Based on the information you provided and the context from the LangChain repository, there are a couple of ways you can change the final prompt of the ConversationalRetrievalChain without modifying the LangChain source code. Based on the context provided, it seems like the RetrievalQAWithSourcesChain is designed to separate the answer from the sources. In ConversationalRetrievalQA, one retrieval step is done ahead of time. Alhumoud: TAQS: An Arabic Question Similarity System Using Transfer Learning of BERT With BiLSTM The digital footprint of human dialogues in those forumsA conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface which allows users to interact with the system to seek information via multi-turn conversations of natural language, in spoken or written form. Any suggestions what can I do to improve the accuracy of the output? #memory = ConversationEntityMemory(llm=llm, return_mess. With the advancement of AI technologies, we are continually finding ways to utilize them in innovative ways. stanford. A user study reveals that our system leads to a better quality perception by users. #1 Getting Started with GPT-3 vs. Half of the above mentioned process is similar, upto creating an ANN model. e. metadata = {'language': 'DE'}, and use SelfQueryRetriver ( LangChain Documentation). chains. 1. To resolve the type mismatch issue when adding the KBSearchTool to the list of tools in your LangChainJS application, you need to ensure that the KBSearchTool class extends either the StructuredTool or Tool class from the tools. In this example, we load a PDF document in the same directory as the python application and prepare it for processing by. Extends the BaseChain class and implements the ConversationalRetrievalQAChainInput interface. edu {luanyi,hrashkin,reitter,gtomar}@google. EmilioJD closed this as completed on Jun 20. It makes the chat models like GPT-4 or GPT-3. 5 and other LLMs. Table 1: Comparison of MMConvQA with datasets from related research tasks. dosubot bot mentioned this issue on Aug 10. Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. For example, if the class is langchain. ChatCompletion API. In the below example, we will create one from a vector store, which can be created from embeddings. The chain is having trouble remembering the last question that I have made, i. These embeddings can be stored in a vector database such as Chroma, Faiss or Lance. Listen to the audio pronunciation in English. Chatbot Usages in Commerce There are various usages of chatbots in commerce although most chatbots for commerce is focused on customer service. You signed out in another tab or window. For more examples of how to test different embeddings, indexing strategies, and architectures, see the Evaluating RAG Architectures on Benchmark Tasks notebook. To further its capabilities, an output parser that extends from the BaseLLMOutputParser provided by Langchain is integrated with a schema. 9. One thing you can do to speed up is by using only the top similar knowledge retrieved from KB and refine your prompt and set max_interactions to 2-3 depending on your application. For example, there are DocumentLoaders that can be used to convert pdfs, word docs, text files, CSVs, Reddit, Twitter, Discord sources, and much more, into a list of Document's which the LangChain chains are then able to work. Summarization. It first combines the chat history and the question into a single question. from pydantic import BaseModel, validator. {"payload":{"allShortcutsEnabled":false,"fileTree":{"langchain/src/chains/router":{"items":[{"name":"tests","path":"langchain/src/chains/router/tests","contentType. st. , SQL) Code (e. The registry provides configurations to test out common architectures on curated datasets. texts=texts, metadatas=metadatas, embedding=embedding, index_name=index_name, redis_url=redis_url. The memory allows a L arge L anguage M odel (LLM) to remember previous interactions with the user. ConversationalRetrievalChain are performing few steps:. LangChain provides tooling to create and work with prompt templates. From what I understand, you were having trouble changing the system template in conversationalRetrievalChain. And with NVIDIA AI Foundation Endpoints, their applications can be connected to these models running on a fully accelerated stack to test performance. from_llm(OpenAI(temperature=0. [Document(page_content="In 1919 Father James Burns became president of Notre Dame, and in three years he produced an academic revolution that brought the school up to national standards by adopting the elective system and moving away from the university's traditional scholastic and classical emphasis. Interface for the input parameters of the ConversationalRetrievalQAChain class. Answers to customer questions can be drawn from those documents. Chat and Question-Answering (QA) over data are popular LLM use-cases. CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning Zeqiu Wu} Yi Luan Hannah Rashkin David Reitter Hannaneh Hajishirzi}| Mari Ostendorf} Gaurav Singh Tomar }University of Washington Google Research |Allen Institute for AI {zeqiuwu1,hannaneh,ostendor}@uw. . 8,model_name='gpt-3. 04. jasan Asks: How to store chat history using langchain conversationalRetrievalQA chain in a Next JS app? Im creating a text document QA chatbot, Im using Langchainjs along with OpenAI LLM for creating embeddings and Chat and Pinecone as my vector Store. . 1 * 7. See Diagram: After successfully. This node is based on the Retrieval QA Chain node, and it provides a chat history component, allowing you to hold a conversation with the LLM. Generated by DALL-E 2 Table of Contents. Save the new project as “TalkToPDF”. Chat containers can contain other. OpenAI, then the namespace is [“langchain”, “llms”, “openai”] get_num_tokens(text: str) → int ¶. Reload to refresh your session. Abstractive: generate an answer from the context that correctly answers the question. Second, AI simply doesn’t. He also said that she is a consensus. You must provide the AI with the metadata and instruct it to translate any queries/questions to German and use it to retrieve the relevant chunks with the. We introduce a conversational QA architecture that sets the new state of the art on the TREC CAsT 2019. We’re excited to announce streaming support in LangChain. I am using text documents as external knowledge provider via TextLoader. Use an LLM ( GPT-3. It can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing. Sometimes, this isn't needed! If the user is just saying "hi", you shouldn't have to look things up. 5-turbo) to auto-generate question-answer pairs from these docs. Saved searches Use saved searches to filter your results more quicklyCreate an Azure OpenAI, LangChain, ChromaDB, and Chainlit ChatGPT-like application in Azure Container Apps using Terraform. ConversationalRetrievalQAChain vs loadQAStuffChain. Is it possible to have the component called "Conversational Retrieval QA Chain", but that would use a memory buffer ? To remember the rest of the conversation, not only the last prompt. To handle these tasks, a C-KBQA system is designed as a task-oriented dialog system as in Fig. name = 'conversationalRetrievalQAChain' this. LangChain cookbook. Techniques and methods developed for Conversational Question Answering over Knowledge Bases (C-KBQA) are fundamental to the knowledge base search module of a CIR system, as shown in Fig. From what I understand, you were requesting better documentation on the different QA chains in the project. I'd like to combine a ConversationalRetrievalQAChain with - for example - the SerpAPI tool in LangChain. Augmented Generation simply means adding external information to the input prompt fed into the LLM, thereby augmenting the generated response. 51% which is addressed by the paper that it could be improved with more datasets. I am trying to create an customer support system using langchain. 162, code updated. In conclusion, both LangFlow and Flowise provide developers with powerful tools for streamlined language processing. Embeddings play a pivotal role in natural language modeling, particularly in the context of semantic search and retrieval augmented generation (RAG). Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical. There are two common types of question answering tasks: Extractive: extract the answer from the given context. . registry. from langchain. the process of finding and bringing back…. how do i add memory to RetrievalQA. EDIT: My original tool definition doesn't work anymore as of 0. return_messages=True, output_key="answer", input_key="question". llms. 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. Hi, thanks for this amazing tool. e. 2. Those are some cool sources, so lots to play around with once you have these basics set up. from_llm ( llm=OpenAI (temperature=0), retriever=vectorstore. type = 'ConversationalRetrievalQAChain' this. There's been a lot of talk about the best UX for LLM applications, and we believe streaming is at its core. In this article, we will walk through step-by-step a. , PDFs) Structured data (e. But there's no mention of qa_prompt in ConversationalRetrievalChain, or its base chain. They become even more impressive when we begin using them together. An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). Conversational search is one of the ultimate goals of information retrieval. RAG with Agents. Our chatbot starts with the ConversationalRetrievalQA chain, ConversationalRetrievalChain, which builds on RetrievalQAChain to provide a chat history component. This documentation covers the steps to integrate Pinecone, a high-performance vector database, with LangChain, a framework for building applications powered by large language models (LLMs). Extends. The resulting chatbot has an accuracy of 68. Langflow uses LangChain components. vectorstores import Chroma db = Chroma (embedding_function=OpenAIEmbeddings ()) texts = [ """. when I ask "which was my l. Use the chat history and the new question to create a "standalone question". data can include many things, including: Unstructured data (e. You signed in with another tab or window. This post takes you through the most common challenges that customers face when searching internal documents, and gives you concrete guidance on how AWS services can be used to create a generative AI conversational bot that makes internal information more useful. LangChain の ConversationalRetrievalChain の使い方。自社ドキュメントなどをベースにQAを作成するときに、ちゃんとチャットの履歴を踏まえてQAを実行させるモジュール。その動作やカスタマイズ方法なども現状分かっている範囲でできる限り詳しく解説（というかメモ）Here, we introduce a simple tool for evaluating QA chains ( see the code here) called auto-evaluator. co LangChain is a powerful, open-source framework designed to help you develop applications powered by a language model, particularly a large. chat_memory. With the data added to the vectorstore, we can initialize the chain. If yes, thats incorrect usage. I have built a knowledge base question and answer system using Conversational Retrieval QA, HNSWLib, and Azure OpenAI API. [1]In-context retrieval augmented generation is a method to improve language model generation by including relevant documents to the model input. # Factory for creating a conversational retrieval QA chain chain_factory = langchain_docs. Next, let’s replace "text file” with “PDF file,” and the new workflow diagram should look like this:Enable “Return Source Documents” in the Conversational Retrieval QA Chain Flowise widget. Computers can solve incredibly complex math problems, yet if we ask GPT-4 to tell us the answer to 4. CoQA contains 127,000+ questions with. from_llm (ChatOpenAI (temperature=0), vectorstore. It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question, then looks up relevant. AI chatbot producing structured output with Next. chains. memory. A chain for scoring the output of a model on a scale of 1-10. . See the task. Share Sort by: Best. In the example below we instantiate our Retriever and query the relevant documents based on the query. This chain takes in chat history (a list of messages) and new questions, and then returns an answer to that question. One of the pieces of external data we wanted to enable question-answering over was our documentation. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. sidebar. from_llm (llm=llm. Example const model = new ChatAnthropic( {}); 8 You can pass your prompt in ConversationalRetrievalChain. See the below example with ref to your provided sample code: qa = ConversationalRetrievalChain. Let’s create one. Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. In essence, the chatbot looks something like above. We've seen in previous chapters how powerful retrieval augmentation and conversational agents can be. llms import OpenAI. g. Text file QnA using conversational retrieval QA chain: Source: can I connect Conversational Retrieval QA Chain with custom tool? I know it's possible to connect a chain to agent using Chain Tool, but when I did this, my chatbot didn't follow all the instructions. These models help developers to build powerful yet responsible Generative AI. Currently, there hasn't been any activity or comments on this issue. from_llm () method with the combine_docs_chain_kwargs param. When. Open up a template called “Conversational Retrieval QA Chain”. Custom ChatGPT Implementation: A custom implementation of ChatGPT made with Next. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on Conversational Question Answering (CQA), wherein a system is. An LLMChain is a simple chain that adds some functionality around language models. It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question, then looks up relevant. . But what I really want is to be able to save and load that ConversationBufferMemory () so that it's persistent between sessions. 🤖. Use an LLM ( GPT-3. These examples show how to compose different Runnable (the core LCEL interface) components to achieve various tasks. py","path":"libs/langchain/langchain. We hope that this repo can serve as a template for developers. """ from typing import Any, Dict, List from langchain. If the question is not related to the context, politely respond that you are teached to only answer questions that are related to the context. ust. OpenAI, then the namespace is [“langchain”, “llms”, “openai”] get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel] ¶. c 2020 Association for Computational Linguistics 960 We present a new dataset for learning to identify follow-up questions, namely LIF. . edu Abstract While recent language models have the abil-With pretrained generative AI models, enterprises can create custom models faster and take advantage of the latest training and inference techniques. You've also mentioned that you've seen a demo that suggests ConversationChain can take in documents, which contradicts your initial understanding. 198 or higher throws an exception related to importing "NotRequired" from. The algorithm for this chain consists of three parts: 1. And then passes those documents and the question to a question-answering chain to return a. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. 1. Limit your prompt within the border of the document or use the default prompt which works same way. . - GitHub - JRC1995/Chatbot: Hybrid Conversational Bot based on both neural retrieval and neural generative mechanism with TTS. Here, we are going to use Cheerio Web Scraper node to scrape links from a. Hi, @miha-bhaskaran!I'm Dosu, and I'm helping the LangChain team manage our backlog. Compare the output of two models (or two outputs of the same model). From almost the beginning we've added support for. Reload to refresh your session. Can do multiple retrieval steps. Open-Retrieval Conversational Question Answering Chen Qu1 Liu Yang1 Cen Chen2 Minghui Qiu3 W. Let’s see how it works. I mean, it was working, but didn't care about my system message. However, what is passed in only question (as query) and NOT summaries. 3. qa = ConversationalRetrievalChain. {"payload":{"allShortcutsEnabled":false,"fileTree":{"docs/extras/use_cases/question_answering/how_to":{"items":[{"name":"code","path":"docs/extras/use_cases/question. Conversational Retrieval Agents. They consider using ConversationalRetrievalQA which works in a chat-like manner instead of a single-time prompt. The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. A base class for evaluators that use an LLM. 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', 'chat_history'}). from_documents (docs, embeddings) Now create the memory buffer and initialize the chain: memory = ConversationBufferMemory (memory_key="chat_history",. llms. You can go to Copilot's settings and turn on "Debug mode" at the bottom for more console messages!,dporrnlqjirudprylhwrzdwfk wrjhwkhuzlwkpidplo :rxog xsuhihuwrwud qhz dfwlrqprylh dvodvwwlph" (pp wklvwlph,zdqwrqh wkdw,fdqzdwfkzlwkp fkloguhqSearch ACM Digital Library. 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. Conversational agent for a chat model which utilize chat specific prompts and buffer memory. In collaboration with University of Amsterdam. ConversationalRetrievalChainの概念. chat_message lets you insert a multi-element chat message container into your app. One way is to input multiple smaller documents, after they have been divided into chunks, and operate over them with a MapReduceDocumentsChain. Reload to refresh your session. A Multi-document chatbot is basically a robot friend that can read lots of different stories or articles and then chat with you about them, giving you the scoop on all they’ve learned. Are you using the chat history as a context inside your prompt template. "Chain conversational_retrieval_chain expects multiple inputs, cannot use 'run'" To Reproduce Steps to reproduce the behavior: Follo. . Langflow uses LangChain components. this. st. It first combines the chat history. Get the namespace of the langchain object. The chain is having trouble remembering the last question that I have made, i. 208' which somebody pointed. RAG. One of the first demo’s we ever made was a Notion QA Bot, and Lucid quickly followed as a way to do this over the internet. A ContextualCompressionRetriever which wraps another Retriever along with a DocumentCompressor and automatically compresses the retrieved documents of the base Retriever. This guide will show you how to: Finetune DistilBERT on the SQuAD dataset for extractive question answering. In this article we will walk through step-by-step a coded. Use the following pieces of context to answer the question at the end. After that, you can generate a SerpApi API key. PROMPT = """. A Comparison of Question Rewriting Methods for Conversational Passage Retrieval. Unlike the machine comprehension module (Chap. Working together, with our mutual focus on flexibility and ease of use, we found that LangChain and Chroma were a perfect fit. I use Chromadb as a vectorstore to store the chat history and search relevant pieces of information when needed. , "D", as you mentioned on your comment), the response should only include information from that particular document without interference from the content of other documents (A, B, C, E), you should store and query the embeddings for each. Source code for langchain. Answer:" output = prompt_node. from langchain. Github repo QnA using conversational retrieval QA chain. filter(Type="RetrievalTask") Name. data can include many things, including: Unstructured data (e. 5 and other LLMs. Introduction; Useful Resources; Hardware; Agent Code - Configuration - Import Packages - Check GPU is Enabled - Hugging Face Login - The Retriever - Language Generation Pipeline - The Agent; Testing the agent; Conclusion; Introduction. Stack used - Using Conversational Retrieval QA | 🦜️🔗 Langchain The knowledge base are bunch of pdfs → Embeddings are generated via openai ada → saved in Pinecone. chains import ConversationalRetrievalChain 3 4 model = ChatOpenAI (model='gpt-3. How can I create a bot, that will send a response based on custom data. When a user query comes, it goes with ConversationalRetrievalQAChain with chat history LLM used in langchain is openai turbo 3. You can add your custom prompt with the combine_docs_chain_kwargs parameter: combine_docs_chain_kwargs= {"prompt": prompt} You can change your code. 1 from langchain. langchain. As i didn't find anything about used prompts in docs I was looking for them in repo and there are two. " The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. Langflow uses LangChain components. 🤖. Pre-requisites#The Embeddings and Completions endpoints are a great combination to use when building a question-answering or chatbot application. In this sample, I demonstrate how to quickly build chat applications using Python and leveraging powerful technologies such as OpenAI ChatGPT models, Embedding models, LangChain framework, ChromaDB vector. New comments cannot be posted. Enthusiastic and skilled software professional proficient in ASP. ChatOpenAI class provides more chat-related methods, such as completion_with_retry,. I am using conversational retrieval chain with memory, but I am getting incorrect answers for trivial questions. 2 min read Feb 14, 2023. chat_message's first parameter is the name of the message author, which can be. If your goal is to ensure that when you query for information related to a specific PDF document (e. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on conversational. It involves defining input and partial variables within a prompt template. """Question-answering with sources over an index. ConversationalRetrievalQA - a chatbot that does a retrieval step to start - is one of our most popular chains. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. This is done with the goals of (1) allowing retrievers constructed elsewhere to be used more easily in LangChain, (2) encouraging more experimentation with alternative The registry provides configurations to test out common architectures on curated datasets. py","path":"langchain/chains/qa_with_sources/__init. ; A number of extra context features, context/0, context/1 etc. In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. {"payload":{"allShortcutsEnabled":false,"fileTree":{"langchain/src/chains/router":{"items":[{"name":"tests","path":"langchain/src/chains/router/tests","contentType. Our chatbot starts with the ConversationalRetrievalQA chain, ConversationalRetrievalChain, which builds on RetrievalQAChain to provide a chat history component. This video goes through. Excuse me, I would like to ask you some questions. Distributing Routes allows organizations to democratize access to LLMs while also ensuring user behavior doesn't abuse or take. CoQA is pronounced as coca . a) Previous framework typically has three stages: entailment reasoning based decision-making, span extraction and question rephrasing. A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest Ruohong Zhang ♠∗ Luyu Gao Chen Zheng Zhen Fan Guokun Lai Zheng Zhang♣ Fangzhou Ai♢ Yiming Yang♠ Hongxia Yang ♠CMU, ♣Emory University, ♢UC San Diego, TikTok Abstractebayeson Jun 15. callbacks import get_openai_callback Traceback (most recent call last):To get started, let’s install the relevant packages. js. from_chain_type(. Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. ConversationalRetrievalQAChain Class ConversationalRetrievalQAChain Class for conducting conversational question-answering tasks with a retrieval [email protected] - a chatbot that does a retrieval step to start - is one of our most popular chains. py","path":"langchain/chains/qa_with_sources/__init. Learn more. 9,. Chat Models take a list of chat messages as input - this list commonly referred to as a prompt. RLHF is an evolving fine-tuning technique that uses human feedback to ensure that a model produces the desired output. vectors. Hello everyone. Please reduce the length of the messages or completion. LangChain strives to create model agnostic templates to make it easy to. After that, you can pass the context along with the question to the openai. Bruce Croft1 Mohit Iyyer1 1 University of Massachusetts Amherst 2 Ant Financial 3 Alibaba Group This notebook walks through a few ways to customize conversational memory. Open comment sort options. Hybrid Conversational Bot based on both neural retrieval and neural generative mechanism with TTS. In order to remember the chat I using ConversationalRetrievalChain with list of chatsYou can add your custom prompt with the combine_docs_chain_kwargs parameter: combine_docs_chain_kwargs={"prompt": prompt}. from_llm(). Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment. Hello! To improve the performance and accuracy of my document QA application, I want to add a prompt template but I'm unsure on how to incorporate LLMChain + Retrieval QA. Current methods rely on the dual-encoder architecture to embed contextualized vectors of questions in conversations. Thanks for the reply and the explanation, it's more clear for me how the , I'm trying to build and API endpoint capable of receive a question and give a response based on some . ) # First we add a step to load memory. 这个示例展示了在索引上进行问答的过程。. . This flow is used to upsert all information from a website to a vector database, then have LLM answer user's question by looking up from the vector database. as_retriever ()) Here is the logic: Start a new variable "chat_history" with. 0. However, every time I send a new message, I always have to wait for about 30 seconds before receiving a reply. You signed out in another tab or window. You signed in with another tab or window. I found this helpful thread for the RetrievalQAWithSourcesChain library in python, but does anyone know if it's possible to add a custom prompt template for. <br>Experienced in developing secure web applications and conducting comprehensive security audits. langchain ライブラリの ConversationalRetrievalChainはシンプルな質問応答モデルの実装を実現する方法の一つです。. llms import OpenAI. hk, pascale@ece. Click “Upload File” in “PDF File” and upload a sample pdf file titled “Introduction to AWS Security”. ", New Prompt:Write 3 paragraphs…. memory import ConversationBufferMemory. Our chatbot starts with the ConversationalRetrievalQA chain, ConversationalRetrievalChain, which builds on RetrievalQAChain to provide a chat history component. {"payload":{"allShortcutsEnabled":false,"fileTree":{"langchain/src/chains":{"items":[{"name":"api","path":"langchain/src/chains/api","contentType":"directory"},{"name.