"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Chat Models](/docs/concepts/#chat-models)\n",
"- [Tools](/docs/concepts/#tools)\n",
"- [Agents](/docs/concepts/#agents)\n",
"\n",
":::\n",
"\n",
"By themselves, language models can't take actions - they just output text.\n",
"A big use case for LangChain is creating **agents**.\n",
"Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs to pass them.\n",
@ -28,16 +38,6 @@
"\n",
"In this tutorial we will build an agent that can interact with a search engine. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it.\n",
"\n",
"\n",
"## Concepts\n",
"\n",
"In following this tutorial, you will learn how to:\n",
"\n",
"- Use [language models](/docs/concepts/#chat-models), in particular their tool calling ability\n",
"- Use a Search [Tool](/docs/concepts/#tools) to look up information from the Internet\n",
"- Compose a [LangGraph Agent](/docs/concepts/#agents), which use an LLM to determine actions and then execute them\n",
"- Debug and trace your application using [LangSmith](/docs/concepts/#langsmith)\n",
"\n",
"## End-to-end agent\n",
"\n",
"The code snippet below represents a fully functional agent that uses an LLM to decide which tools to use. It is equipped with a generic search tool. It has conversational memory - meaning that it can be used as a multi-turn chatbot.\n",
"We'll go over an example of how to design and implement an LLM-powered chatbot. \n",
@ -39,18 +49,6 @@
"\n",
"This tutorial will cover the basics which will be helpful for those two more advanced topics, but feel free to skip directly to there should you choose.\n",
"\n",
"\n",
"## Concepts\n",
"\n",
"Here are a few of the high-level components we'll be working with:\n",
"\n",
"- [`Chat Models`](/docs/concepts/#chat-models). The chatbot interface is based around messages rather than raw text, and therefore is best suited to Chat Models rather than text LLMs.\n",
"- [`Prompt Templates`](/docs/concepts/#prompt-templates), which simplify the process of assembling prompts that combine default messages, user input, chat history, and (optionally) additional retrieved context.\n",
"- [`Chat History`](/docs/concepts/#chat-history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to followup questions. \n",
"- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
"\n",
"We'll cover how to fit the above components together to create a powerful conversational chatbot.\n",
"The popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), [GPT4All](https://github.com/nomic-ai/gpt4all), and [llamafile](https://github.com/Mozilla-Ocho/llamafile) underscore the importance of running LLMs locally.\n",
"\n",
"LangChain has [integrations](https://integrations.langchain.com/) with many open-source LLMs that can be run locally.\n",
"PDF files often hold crucial unstructured data unavailable from other sources. They can be quite lengthy, and unlike plain text files, cannot generally be fed directly into the prompt of a language model.\n",
"\n",
"In this tutorial, you'll create a system that can answer questions about PDF files. More specifically, you'll use a [Document Loader](/docs/concepts/#document-loaders) to load text in a format usable by an LLM, then build a retrieval-augmented generation (RAG) pipeline to answer questions, including citations from the source material.\n",
"In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of \"memory\" of past questions and answers, and some logic for incorporating those into its current thinking.\n",
"\n",
"In this guide we focus on **adding logic for incorporating historical messages.** Further details on chat history management is [covered here](/docs/how_to/message_history).\n",
"This page will show how to use query analysis in a basic end-to-end example. This will cover creating a simple search engine, showing a failure mode that occurs when passing a raw user question to that search, and then an example of how query analysis can help address that issue. There are MANY different query analysis techniques and this end-to-end example will not show all of them.\n",
"\n",
"For the purpose of this example, we will do retrieval over the LangChain YouTube videos."
"LangSmith will become increasingly helpful as our application grows in\n",
"complexity.\n",
"\n",
"If you're already familiar with basic retrieval, you might also be interested in\n",
"this [high-level overview of different retrieval techinques](/docs/concepts/#retrieval).\n",
"\n",
"## What is RAG?\n",
"\n",
"RAG is a technique for augmenting LLM knowledge with additional data.\n",
@ -36,7 +39,7 @@
"The most common full sequence from raw data to answer looks like:\n",
"\n",
"### Indexing\n",
"1. **Load**: First we need to load our data. This is done with [DocumentLoaders](/docs/concepts/#document-loaders).\n",
"1. **Load**: First we need to load our data. This is done with [DocumentLoaders](/docs/concepts/#document-loaders).\n",
"2. **Split**: [Text splitters](/docs/concepts/#text-splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won't fit in a model's finite context window.\n",
"3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vectorstores) and [Embeddings](/docs/concepts/#embedding-models) model.\n",
"\n",
@ -930,14 +933,10 @@
"the above sections. Along from the **Go deeper** sources mentioned\n",
"above, good next steps include:\n",
"\n",
"- [Return\n",
" sources](/docs/how_to/qa_sources): Learn\n",
" how to return source documents\n",
"- [Streaming](/docs/how_to/streaming):\n",
" Learn how to stream outputs and intermediate steps\n",
"- [Add chat\n",
" history](/docs/how_to/message_history):\n",
" Learn how to add chat history to your app"
"- [Return sources](/docs/how_to/qa_sources): Learn how to return source documents\n",
"- [Streaming](/docs/how_to/streaming): Learn how to stream outputs and intermediate steps\n",
"- [Add chat history](/docs/how_to/message_history): Learn how to add chat history to your app\n",
"- [Retrieval conceptual guide](/docs/concepts/#retrieval): A high-level overview of specific retrieval techniques"
"Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. Whereas in the latter it is common to generate text that can be searched against a vector database, the approach for structured data is often for the LLM to write and execute queries in a DSL, such as SQL. In this guide we'll go over the basic ways to create a Q&A system over tabular data in databases. We will cover implementations using both chains and agents. These systems will allow us to ask a question about the data in a database and get back a natural language answer. The main difference between the two is that our agent can query the database in a loop as many times as it needs to answer the question.\n",
Jacob Lee
3 weeks ago
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