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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
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    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
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    *   [How to split code](/v0.2/docs/how_to/code_splitter)
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    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
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    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
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    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
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    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
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    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
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    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
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*   [How-to guides](/v0.2/docs/how_to/)
*   How to return sources

On this page

How to return sources
=====================

Prerequisites

This guide assumes familiarity with the following:

*   [Retrieval-augmented generation](/v0.2/docs/tutorials/rag/)

Often in Q&A applications it’s important to show users the sources that were used to generate the answer. The simplest way to do this is for the chain to return the Documents that were retrieved in each generation.

We’ll be using the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng for retrieval content this notebook.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

### Dependencies[​](#dependencies "Direct link to Dependencies")

We’ll use an OpenAI chat model and embeddings and a Memory vector store in this walkthrough, but everything shown here works with any [ChatModel](/v0.2/docs/concepts/#chat-models) or [LLM](/v0.2/docs/concepts#llms), [Embeddings](/v0.2/docs/concepts#embedding-models), and [VectorStore](/v0.2/docs/concepts#vectorstores) or [Retriever](/v0.2/docs/concepts#retrievers).

We’ll use the following packages:

    npm install --save langchain @langchain/openai cheerio

We need to set environment variable `OPENAI_API_KEY`:

    export OPENAI_API_KEY=YOUR_KEY

### LangSmith[​](#langsmith "Direct link to LangSmith")

Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with [LangSmith](https://smith.langchain.com/).

Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:

    export LANGCHAIN_TRACING_V2=trueexport LANGCHAIN_API_KEY=YOUR_KEY

Chain without sources[​](#chain-without-sources "Direct link to Chain without sources")
---------------------------------------------------------------------------------------

Here is the Q&A app we built over the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng in the [Quickstart](/v0.2/docs/tutorials/qa_chat_history/).

    import "cheerio";import { CheerioWebBaseLoader } from "langchain/document_loaders/web/cheerio";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";import { pull } from "langchain/hub";import { ChatPromptTemplate } from "@langchain/core/prompts";import { formatDocumentsAsString } from "langchain/util/document";import {  RunnableSequence,  RunnablePassthrough,} from "@langchain/core/runnables";import { StringOutputParser } from "@langchain/core/output_parsers";const loader = new CheerioWebBaseLoader(  "https://lilianweng.github.io/posts/2023-06-23-agent/");const docs = await loader.load();const textSplitter = new RecursiveCharacterTextSplitter({  chunkSize: 1000,  chunkOverlap: 200,});const splits = await textSplitter.splitDocuments(docs);const vectorStore = await MemoryVectorStore.fromDocuments(  splits,  new OpenAIEmbeddings());// Retrieve and generate using the relevant snippets of the blog.const retriever = vectorStore.asRetriever();const prompt = await pull<ChatPromptTemplate>("rlm/rag-prompt");const llm = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });const ragChain = RunnableSequence.from([  {    context: retriever.pipe(formatDocumentsAsString),    question: new RunnablePassthrough(),  },  prompt,  llm,  new StringOutputParser(),]);

Let’s see what this prompt actually looks like:

    console.log(prompt.promptMessages.map((msg) => msg.prompt.template).join("\n"));

    You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.Question: {question}Context: {context}Answer:

    await ragChain.invoke("What is task decomposition?");

    "Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. I"... 208 more characters

Adding sources[​](#adding-sources "Direct link to Adding sources")
------------------------------------------------------------------

With LCEL, we can easily pass the retrieved documents through the chain and return them in the final response:

    import {  RunnableMap,  RunnablePassthrough,  RunnableSequence,} from "@langchain/core/runnables";import { formatDocumentsAsString } from "langchain/util/document";const ragChainFromDocs = RunnableSequence.from([  RunnablePassthrough.assign({    context: (input) => formatDocumentsAsString(input.context),  }),  prompt,  llm,  new StringOutputParser(),]);let ragChainWithSource = new RunnableMap({  steps: { context: retriever, question: new RunnablePassthrough() },});ragChainWithSource = ragChainWithSource.assign({ answer: ragChainFromDocs });await ragChainWithSource.invoke("What is Task Decomposition");

    {  question: "What is Task Decomposition",  context: [    Document {      pageContent: "Fig. 1. Overview of a LLM-powered autonomous agent system.\n" +        "Component One: Planning#\n" +        "A complicated ta"... 898 more characters,      metadata: {        source: "https://lilianweng.github.io/posts/2023-06-23-agent/",        loc: { lines: [Object] }      }    },    Document {      pageContent: 'Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are'... 887 more characters,      metadata: {        source: "https://lilianweng.github.io/posts/2023-06-23-agent/",        loc: { lines: [Object] }      }    },    Document {      pageContent: "Agent System Overview\n" +        "                \n" +        "                    Component One: Planning\n" +        "                 "... 850 more characters,      metadata: {        source: "https://lilianweng.github.io/posts/2023-06-23-agent/",        loc: { lines: [Object] }      }    },    Document {      pageContent: "Resources:\n" +        "1. Internet access for searches and information gathering.\n" +        "2. Long Term memory management"... 456 more characters,      metadata: {        source: "https://lilianweng.github.io/posts/2023-06-23-agent/",        loc: { lines: [Object] }      }    }  ],  answer: "Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. I"... 256 more characters}

Check out the [LangSmith trace](https://smith.langchain.com/public/f07e78b6-cafc-41fd-af54-892c92263b09/r) here to see the internals of the chain.

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You’ve now learned how to return sources from your QA chains.

Next, check out some of the other guides around RAG, such as [how to stream responses](/v0.2/docs/how_to/qa_streaming).

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*   [Setup](#setup)
    *   [Dependencies](#dependencies)
    *   [LangSmith](#langsmith)
*   [Chain without sources](#chain-without-sources)
*   [Adding sources](#adding-sources)
*   [Next steps](#next-steps)

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