as_retriever() For the Retrieval chain, we got a retriever to fetch documents from the vector store relevant to the user input. class langchain. prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI Now we can build our full QA chain. Jul 3, 2023 · A multi-route chain that uses an LLM router chain to choose amongst retrieval qa chains. This issue is similar to #3425 I am using LangChain v0. # If you don't know the answer, just say that you don't know, don't try to make up an answer. If you don't know the answer, just say that you don't know, don't try to make up an answer. chains import create_retrieval_chain from langchain. embeddings. from_llm function. So how to solve this? In the example below we instantiate our Retriever and query the relevant documents based on the query. In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT) query = "What did the Defaults to None. prompts import PromptTemplate. chains import create_retrieval_chain from langchain. Use a paintbrush in your sentence. The OpenAI LLM node is a wrapper around the OpenAI large language model. example_prompt: converts each example into 1 or more messages through its format_messages method. from langchain_core. Apr 25, 2023 · hetthummar commented on May 7, 2023. You signed in with another tab or window. Use this when you have multiple potential prompts you could use to respond and want to route to just one. g. 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. from_chain_type. 这个示例展示了在索引上进行问答的过程。. The basis of asking questions and getting answered is handled with LangChain’s QA chains. llm, retriever=docsearch. memory import ConversationBufferMemory from langchain. 5-turbo-16k'),db. By the end of this mini course, you will have a better understanding of the different chain nodes available in Flowise and how to use them to build sophisticated conversational AI applications. # {context} Jun 26, 2023 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand Mar 1, 2024 · Panel newbie here. Input variable for Prompt Template won't work with retrieval QA chain. vectorstores import FAISS from langchain. as_retriever(), combine_docs_chain_kwargs={"prompt": prompt} ) Apr 29, 2024 · retriever = vector. See the below example with ref to your provided sample code: qa = ConversationalRetrievalChain. , often with similar embeddings to the input question) Generation: An LLM produces an answer using a prompt that includes the question and the retrieved data; Quickstart Suppose we want a QA app over this blog post. Chroma Sep 26, 2023 · To solve this problem, I had to change the chain type to RetrievalQA and introduce agents and tools. Running inspect. Oct 18, 2023 · The issue I am running into is that I can't use the second option's flexibility with custom prompts. from Note that we have used the built-in chain constructors create_stuff_documents_chain and create_retrieval_chain, so that the basic ingredients to our solution are: retriever; prompt; LLM. When using stuff for text that doesn't surpass the token limit, it works as expected. Nothing else. The last query throws this error: Mar 1, 2024 · And this is the code for Retrieval QA Chain. The PromptTemplate class in LangChain allows you to define a variable number of input variables for a prompt template. You will go through the following steps: Load prompt from Hub; Initialize Chain; Run Chain; Commit any new changes 5 days ago · This class is deprecated. 192 with FAISS vectorstore. The RetrievalQAChain is a chain that combines a Retriever and a QA chain (described above). dev. from_llm(OpenAI(temperature=0. This is done to seggregate the documents during retrieval time. Reload to refresh your session. = None, default_prompt: Optional [PromptTemplate] = None, default_chain: Dec 2, 2023 · Custom prompts for chain types that aren't "stuff" in RetrievalQA; Unable to add qa_prompt to ConversationalRetrievalChain. Now I'm getting some bugs by dealing with QA prompts and doc_chain prompts, imput_variables, chain_types (stuff, refine, map_rerank) and output_parsers. Use the chat history and the new question to create a “standalone question”. Adding chat history The chain we have built uses the input query directly to retrieve relevant How to create multi-prompt chains as well as retrieval QA chains that can handle multiple documents. chains. To use a custom prompt template with a 'persona' variable, you need to modify the prompt_template and PROMPT in the prompt. \ If you don't know the answer, just say that you don't know. 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 May 8, 2024 · Split into chunks. Sep 25, 2023 · I understand you're trying to use a custom prompt template with a 'persona' variable in the RetrievalQA chain in LangChain and you're also curious about how the RetrievalQA chain handles custom input variables. You switched accounts on another tab or window. If you don't know the answer, just say that you don't know, don't try Mar 10, 2011 · Chains; Callbacks/Tracing; Async; Reproduction. I also want to perform the QA with a custom prompt as I need the chain's output to be in JSON format for parsing. prompts import PromptTemplate from langchain. import os from langchain. Jul 16, 2023 · PROMPT = PromptTemplate( template=template. embeddings import HuggingFaceEmbeddings. You must only answer with a JSON with the following keys: source_name the source_type, and source_path. It takes in optional parameters for the retriever names, descriptions, prompts, defaults, and additional options. This post explains on a high level how such a Retrieval Augmented Generation pipeline can be implemented. Make sure to pay attention to the chunk_size parameter in TextSplitter. In its initial release (08/05/2023), the hub is limited to prompt management, but we plan to add support for other artifacts soon. {context} Question: {question} Helpful Answer:""" PROMPT = PromptTemplate ( template = prompt_template, input_variables = ["context", "question"] ) # Customized prompt for a specific part of the map-reduce chain custom_prompt_template = """Use the Jul 3, 2023 · The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. I am using Azure openai and langchain in conjunction to build this retrieval engine. vectorstores import Chroma from langchain. chat_models import ChatOpenAI from langchain. MultiPromptChain: This chain routes input between multiple prompts. All the below code is in new_1. prompts import PromptTemplate from langchain_community. Apr 24, 2023 · prompt object is defined as: PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"]) expecting two inputs summaries and question. # Use three sentences maximum and keep the answer as concise as possible. 0. Retrieval: The app retrieves splits from storage (e. question_answering import load_qa_chain # # Prompt # template = """Use the following pieces of context to answer the question at the end. The work-around right now is that I need to edit the langchain in my node_modules directly, so the prompt is now in Apr 11, 2024 · qa_eval_prompt : Prompt for evaluator model that takes as input question-answer pair; qa_eval_prompt_with_context : Similar to above prompt but additionally includes the context as well for the evaluation; Database Initialization. " 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. Now, we’re going to use a RetrievalQA chain to find the answer to a question. Code: from langchain_community. outputs ( Dict[str, str]) – Dictionary of initial chain outputs. getfullargspec(RetrievalQA. You'll see this more clearly when you run the chain with verbose=True. On the Pinecone console you will Dec 19, 2023 · 1. page_content, input_variables=["context", "query"], partial_variables=data ) chain_type_kwargs = {"prompt": PROMPT} qa = RetrievalQA. User: "Show me the details about LG 54" TV model UQ7500". In that same location is a module called prompts. prompts import PromptTemplate Aug 17, 2023 · In the context shared, the MultiRetrievalQAChain class is used, which is a multi-route chain that uses an LLM router chain to choose amongst retrieval QA chains. 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. This was suggested in a similar issue: QA chain is not working properly. output_parsers import StrOutputParser. as_retriever(), chain_type_kwargs=chain_type_kwargs ) response = qa(query) Jul 10, 2023 · Chat History: {chat_history} Follow Up Input: {question} Standalone question:`; // Prompt for the actual question const QA_PROMPT = `You are a helpful AI assistant for sales reps to answer questions about product features and technicals specifications. The resulting prompt template will incorporate both the adjective and noun variables, allowing us to generate prompts like "Please write a creative sentence. Here is the relevant code snippet: destination_chains= {} forr_infoinretriever_infos : prompt=r_info. py which contains both CONDENSE_QUESTION_PROMPT and QA_PROMPT. To create a conversational question-answering chain, you will need a retriever. from_chain_type(llm=llm_chat, chain_type="stuff", retriever=db_test. We build our final rag_chain with create_retrieval_chain. Hello, From your code, it seems like you're on the right track. Apr 16, 2024 · I'm designing a PDF Q/A chatbot using OPEN AI but got some issues. from langchain_community. qa_with_sources. Use the following pieces of context to answer the question at the end. openai import OpenAIEmbeddings from langchain. llms import CTransformers from langchain. load some text documents to a vector store, i used deeplake; load the db; call the function, summarizer(db,"Summarize the mentions of google according to their AI program")(defined in attached file) run for chain_type as stuff, it will work, for map_reduce it will fail in retrieval QA Bot main. We've copied the chain code below, adding an additional system message to the chat prompt template: Using custom prompt for RetrievalQA. \ Use the following pieces of retrieved context to answer the question. I have created a RetrievalQA chain and now want to speed up the process. There are many different types of memory - please see memory docs for the full catalog. This utilizes Langchain’s memory management modules, and I chose the ConversationTokenBufferMemory which keeps a buffer of recent interactions in memory and uses token length to determine when to flush past interactions. ipynb. For example: res = retrievalQA ({ 'query': 'This is my query' }) If the 'typescript_string' key is indeed required, you'll need to include this key in the input dictionary as well. I've made the right decision and opted to advanced using the more granular one, tweaking a lot, and I've achieved a nice result. Question-answering with sources over an index. Chains help the model understand the ongoing conversation and provide coherent and This chain first does a retrieval step to fetch relevant documents, then passes those documents into an LLM to generate a response. If. from_chain_type(), it's a class method used to initialize a BaseRetrievalQA object from a given language model and chain type. 8,model_name='gpt-3. chains import RetrievalQA from langchain. If False, inputs are also added to the final outputs. Hi Everyone, I have the following prompt template which requires an input variable {userName}: const promptTemplate = `Use the following pieces of context to answer the question of {userName} at the end. from_chain_type( llm=llm, chain_type="stuff", retriever=doc_db. Few-shot prompt templates. Usage In the below example, we are using a VectorStore as the Retriever. Here is my code: Apr 2, 2023 · langchain. #1670. Definitely a mix of art and science to arrive on the best way to engineer those prompts in the chain for your specific use case - I am also still learning. Here's how you can do it: First, define the system and human message templates: LangChain Chain Nodes. A common example would May 22, 2023 · Whether it’s law, medicine, engineering, or any other domain, integrating Retrieval QA with prompts offers the potential to enhance professional expertise and decision-making processes. chains. embeddings import HuggingFaceEmbeddings from langchain. Just a follow-up question to your answer for #3. It is used to retrieve documents from a Retriever and then use a QA chain to answer a question based on the retrieved documents. I am using With the correct version of the prompt loaded, we can define our retrieval QA chain. I'm building a document QA bot. Mar 23, 2023 · The main way most people - including us at LangChain - have been doing retrieval is by using semantic search. vectors. I found this helpful thread for the RetrievalQAWithSourcesChain library in python, but does anyone know if it's possible to add a We are using Retrieval QA Chain to answer questions with memory. prompts import PromptTemplate prompt_template = """ Given the question from the user, you must figure out which data source you must use. 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 answering chain to return a Jul 10, 2023 · qa = ConversationalRetrievalChain. Jul 3, 2023 · This chain takes in chat history (a list of messages) and new questions, and then returns an answer to that question. faiss import FAISS from langchain. While the specifics aren't important to this tutorial, you can learn more about Q&A in LangChain by visiting the docs. 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). The hub is a centralized location to manage, version, and share your prompts (and later, other artifacts). There is no chain_type_kwards argument in either load_qa_chain or RetrievalQA. The top search results are passed as context in the prompts of the Large Language Models to generate the answers. I checked in the Azure Portal that deployment is successful and i am able to run in a stand alone prompt. I am running the chain locally on a Macbook Pro (Apple M2 Max) with 64GB RAM, and 12 cores. RetrievalQAWithSourcesChain [source] ¶. 知乎专栏是一个用户可以随心写作和自由表达的平台。 Sep 3, 2023 · Thank you! You are precise and now I'm sure about the differences. :candidate_info The information about a candidate which Dec 7, 2023 · Trying other chain types like "map_reduce" might solve the issue. from_chain_type) shows a chain_type_kwargs argument, which is how you pass a prompt. As for the 'typescript_string' key, I wasn't This way you can select a chain, evaluate it, and avoid worrying about additional moving parts in production. Jun 30, 2023 · Here is my code to initialize the Chain, it should come with the default prompt template: qa_source_chain = RetrievalQAWithSourcesChain. Regarding the usage of RetrievalQA. runnables import RunnablePassthrough. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. Sep 16, 2023 · It does not efficiently chain the response from the relevant documents and for this , we use retrieval qa chain to be able to retrieve the relevant documents and then be able to chain a perfect Apr 29, 2023 · Just answering my question, the difference between having chat_history in RetrievalQA is this in ConversationalRetrievalChain. llm_chain runs with the stand-alone question and the context from the vectorstore retriever. . AI Answer: "xxxx xxxx xxx " (Correct answer) User: "What are its features" AI Answer:: The Toshiba Tvs are goodthey have xxxx features. 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. def format_docs(docs): The chain did pretty well, and in the previous section we were able to use the playground to come up with a proposed fix to the problem. Creating a QA Chain. This function loads the MapReduceDocumentsChain and passes the relevant documents as context to the chain after mapping over all to reduce to just Oct 16, 2023 · Retrieval QA Chain. temperature) retriever = self. [ ] Chat with Langchain's AI using retrieval QA and vector store. Your contribution Nov 20, 2023 · At the moment I’m writing this post, the langchain documentation is a bit lacking in providing simple examples of how to pass custom prompts to some of the built-in chains. get ( "prompt" ) retriever=r_info [ "retriever" ] chain=RetrievalQA. This is done so that this question can be passed into the retrieval step to fetch relevant 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. Eg. But there's no mention of qa_prompt in ConversationalRetrievalChain, or its base chain You can create custom RetrievalQA chains by providing different prompts and retrievers as shown in the from_retrievers class method in the MultiRetrievalQAChain class. You can only chose one. document_loaders import PyPDFLoader, DirectoryLoader. The chat history is not sent to the combine_docs_chain at all, since it was summarized by the question_generator. Explore the code, options, and API of this powerful chain. conversational_retrieval is where ConversationalRetrievalChain lives in the Langchain source code. For instance, PDF File with Apple FORM-10K uploaded can have a metadata object {source: apple}, whereas PDF File with Tesla FORM-10K uploaded can have {source: tesla} . py file Mar 25, 2023 · これを行う主な方法は、「Retrieval Augmented Generation」と呼ばれる手法です。ユーザーの質問を言語モデルに直接渡すだけでなく、システムが質問への回答に関連するドキュメントを検索し、元の質問と一緒に言語モデルに渡して、応答を生成します。 1-1. He also said that she is a consensus May 6, 2023 · Then the combine_docs_chain. Memory is a class that gets called at the start and at the end of every chain. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand Jun 24, 2023 · Writes a pickle file with the questions and answers about a candidate. The from_retrievers class method is used to create an instance of this class. 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 6, 2023 · llm = ChatOpenAI(model_name=self. For more information, check out the docs or reach out to support@langchain. Incoming queries are then vectorized as Jun 21, 2023 · from langchain. Aug 1, 2023 · Once our custom prompts are defined, we can initialize the Conversational Retrieval Chain. You signed out in another tab or window. return_only_outputs ( bool) – Whether to only return the chain outputs. This will simplify the process of incorporating chat history. I want add prompt to it that it must only reply from the document and avoid making up the answer Starting with a dict with the input query, add the retrieved docs in the "context" key; Feed both the query and context into a RAG chain and add the result to the dict. The hub is a centralized location to manage, version, and share your prompts (and later, other The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. from langchain. code-block:: python from langchain. I have already visited other stack overflow questions but none of them helped me. 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. Langchain's documentation does not Oct 20, 2023 · 🤖. Returns. 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 prompt_template = """ You are an assistant whose role is to define and categorize situations using formal definitions available to you. as_retriever(search_kwargs={"k": 5}) chain Oct 8, 2023 · Here's how you can modify your code to resolve this issue: # Ensure 'context' is included in the dictionary passed to qa_chain result = qa_chain ({ "query": myquery, "app": myapp, "context": "your_context_here" }) In the above code, replace "your_context_here" with the actual context to be used. chains import RetrievalQA import chainlit as cl DB_FAISS_PATH = "vectorstores/db_faiss" # Context: {context}/ custom_prompt_template = """Use To resolve this issue, ensure that the input dictionary you're passing to the RetrievalQA chain includes a 'query' key. Bases: BaseQAWithSourcesChain. In this tutorial, you learned how to use the hub to manage prompts for a retrieval QA chain. . The chain_type argument is a string that specifies the type of chain to load. See below for an example implementation using `create_retrieval_chain`: . as_retriever(), memory=memory) creating a chatbot for replying in a document. model_name, temperature=self. A static method that creates an instance of MultiRetrievalQAChain from a BaseLanguageModel and a set of retrievers. At the start, memory loads variables and passes them along in the chain. prompts import PromptTemplate prompt_template = """Use the following pieces of context to answer the question at the end. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. The basic components of the template are: examples: A list of dictionary examples to include in the final prompt. We initialize a simple vector database using FAISS and Open AI embeddings. The algorithm for this chain consists of three parts: 1. However, what is passed in only question (as query) and NOT summaries. Let's re-run the evaluation with the new prompt to see how it behaves overall. Regarding the "prompt" parameter in the "chain_type_kwargs", it is used to initialize the LLMChain in the "from_llm" method of the BaseRetrievalQA class. from_llm; Issue: ConversationalRetrievalChain - issue with passing the CONDENSE_QUESTION_PROMPT for working chat history; How to add a custom message/prompt template; Retrieval QA and prompt templates Jun 15, 2023 · Retrieval QA and prompt templates. combine_documents import create_stuff_documents_chain qa_system_prompt = """You are an assistant for question-answering tasks. We then use those returned relevant documents to pass as context to the loadQAMapReduceChain. We will start with the retriever definition. In my example code, where I'm using RetrievalQA, I'm passing in my prompt (QA_CHAIN_PROMPT) as an argument, however the {context} and {prompt} values are yet to be filled in (since it is passing in the original string). Oct 23, 2023 · The OpenAI LLM and the Conversational Retrieval QA (crqa) chain help us tie the whole chain of actions together. To do this, we prepared our LLM model with “temperature = 0. Configure Chain With the correct version of the prompt loaded, we can define our retrieval QA chain. These chains are used to store and manage the conversation history and context for the chatbot or language model. The data retriever will just be our embedded vector store we created earlier. Next, split the documents into separate chunks. The document and the chatbot is supposed to support Indonesian. Keep the answer as concise as possible. as_retriever(), Jul 3, 2023 · inputs ( Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain memory. as_retriever()) Here is the source chain object, I guess default template for this chain is hacked? Aug 30, 2023 · First documents are searched, for example via classic MB25/keyword searches or via neural information seek. By default, the StuffDocumentsChain is used as the # from langchain. Using an example set Jan 16, 2024 · Demonstrating Multi Prompt chain Define 3 prompt template: a) Load docs, create chroma db using embedding, use it as a retriever, create Retrieval QA. May 13, 2023 · Condense question is the prompt that processes user input and chat history. 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 Click the Additional Parameters of PDF File Loader, and specify metadata object. " Jun 8, 2023 · I'm having trouble setting up the successful usage of a custom QA prompt template that includes input variables with my RetrievalQA. Your output must be as exact to the reference ground truth information as possible. At the end, it saves any returned variables. We can create this in a few lines of code. Aug 21, 2023 · Thanks for your reply. While the existing 1 day ago · combine_docs_chain ( Runnable[Dict[str, Any], str]) – Runnable that takes inputs and produces a string output. vectorstores. In this example, we create two prompt templates, template1 and template2, and then combine them using the + operator to create a composite template. 7" and “max_length = 512”. In this walkthrough, you will get started using the hub to manage prompts for a retrieval QA chain. It is an alternative method to fromRetrievers and provides more flexibility in configuring the underlying chains. retrieval. MultiRetrievalQAChain: Retriever If you don't know the answer, just say that you don't know, don't try to make up an answer. Jun 22, 2023 · I'm trying to perform QA on a large block of text and so using map_reduce or refine is preferable over stuff. Specifically we show how to use the MultiRetrievalQAChain to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it. Therefore, the retriever needs to have a query Dec 13, 2023 · from langchain. At the moment, the generation of the text takes too long (1-2minutes) with the qunatized Mixtral 8x7B-Instruct model from "TheBloke". Dec 11, 2023 · Code is as follows: template = """Use the following pieces of context to answer the question at the end. 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. In the context of chatbots and large language models, "chains" typically refer to sequences of text or conversation turns. combine_documents import create_stuff_documents_chain from langchain_core. from_llm( llm=OpenAI(temperature=0), retriever=vectorstore. But from what I see, LangChain use English in the prompt that's used in the QARetrieval Module. For retrieval, we set k as 3 (return top Oct 20, 2023 · I am retrieving the results from my internal Db but for this example I have added an open URL. :param file_key The key - file name used to retrieve the pickle file. 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. from_llm() method with the combine_docs_chain_kwargs param. I have written the following Panel application for an LLM to query on a vector database: import os, dotenv, openai, panel from langchain. memory import ConversationBufferMemory from langchain_openai import ChatOpenAI, OpenAIEmbeddings from 检索型问答（Retrieval QA）. May 4, 2023 · You can pass your prompt in ConversationalRetrievalChain. zip Jan 18, 2024 · The weird thing is, that it is working with a LLM-Chain from Langchain without Retrieval: from langchain. Use three sentences maximum. 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. 2. These are a set of classes and helper functions that aid us in building a QA system on top of a data retriever object. If the "prompt" parameter is not provided, the method This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. Combine docs is how the output/response back to the user is handled after the retrieval happens. rz ap wl ld yb tr jw vn pl rp