mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-25 16:13:25 +00:00
docs: Fix broken urls (#16559)
This commit is contained in:
Bob Lin
GitHub
parent
6023953ea7
commit
35e60728b7
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"source": [
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"loader = ToMarkdownLoader.from_api_key(\n",
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" url=\"https://python.langchain.com/en/latest/\", api_key=api_key\n",
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"loader = ToMarkdownLoader(\n",
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" url=\"https://python.langchain.com/docs/get_started/introduction\", api_key=api_key\n",
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")"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"## Contents\n",
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"**LangChain** is a framework for developing applications powered by language models. It enables applications that:\n",
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"\n",
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"- [Getting Started](#getting-started)\n",
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"- [Modules](#modules)\n",
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"- [Use Cases](#use-cases)\n",
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"- [Reference Docs](#reference-docs)\n",
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"- [LangChain Ecosystem](#langchain-ecosystem)\n",
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"- [Additional Resources](#additional-resources)\n",
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"- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)\n",
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"- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)\n",
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"\n",
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"## Welcome to LangChain [\\#](\\#welcome-to-langchain \"Permalink to this headline\")\n",
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"This framework consists of several parts.\n",
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"\n",
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"**LangChain** is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model, but will also be:\n",
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"- **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.\n",
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"- **[LangChain Templates](https://python.langchain.com/docs/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.\n",
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"- **[LangServe](https://python.langchain.com/docs/langserve)**: A library for deploying LangChain chains as a REST API.\n",
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"- **[LangSmith](https://python.langchain.com/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.\n",
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"\n",
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"1. _Data-aware_: connect a language model to other sources of data\n",
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"\n",
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"\n",
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"2. _Agentic_: allow a language model to interact with its environment\n",
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"Together, these products simplify the entire application lifecycle:\n",
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"\n",
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"- **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.\n",
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"- **Productionize**: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.\n",
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"- **Deploy**: Turn any chain into an API with LangServe.\n",
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"\n",
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"The LangChain framework is designed around these principles.\n",
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"## LangChain Libraries [](\\#langchain-libraries \"Direct link to LangChain Libraries\")\n",
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"\n",
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"This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see [here](https://docs.langchain.com/docs/). For the JavaScript documentation, see [here](https://js.langchain.com/docs/).\n",
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"The main value props of the LangChain packages are:\n",
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"\n",
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"## Getting Started [\\#](\\#getting-started \"Permalink to this headline\")\n",
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"1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not\n",
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"2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks\n",
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"\n",
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"How to get started using LangChain to create an Language Model application.\n",
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"Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.\n",
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"\n",
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"- [Quickstart Guide](https://python.langchain.com/en/latest/getting_started/getting_started.html)\n",
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"The LangChain libraries themselves are made up of several different packages.\n",
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"\n",
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"- **`langchain-core`**: Base abstractions and LangChain Expression Language.\n",
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"- **`langchain-community`**: Third party integrations.\n",
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"- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.\n",
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"\n",
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"Concepts and terminology.\n",
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"## Get started [](\\#get-started \"Direct link to Get started\")\n",
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"\n",
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"- [Concepts and terminology](https://python.langchain.com/en/latest/getting_started/concepts.html)\n",
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"[Here’s](https://python.langchain.com/docs/get_started/installation) how to install LangChain, set up your environment, and start building.\n",
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"\n",
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"We recommend following our [Quickstart](https://python.langchain.com/docs/get_started/quickstart) guide to familiarize yourself with the framework by building your first LangChain application.\n",
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"\n",
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"Tutorials created by community experts and presented on YouTube.\n",
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"Read up on our [Security](https://python.langchain.com/docs/security) best practices to make sure you're developing safely with LangChain.\n",
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"\n",
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"- [Tutorials](https://python.langchain.com/en/latest/getting_started/tutorials.html)\n",
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"note\n",
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"\n",
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"These docs focus on the Python LangChain library. [Head here](https://js.langchain.com) for docs on the JavaScript LangChain library.\n",
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"\n",
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"## Modules [\\#](\\#modules \"Permalink to this headline\")\n",
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"## LangChain Expression Language (LCEL) [](\\#langchain-expression-language-lcel \"Direct link to LangChain Expression Language (LCEL)\")\n",
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"\n",
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"These modules are the core abstractions which we view as the building blocks of any LLM-powered application.\n",
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"LCEL is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.\n",
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"\n",
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"For each module LangChain provides standard, extendable interfaces. LanghChain also provides external integrations and even end-to-end implementations for off-the-shelf use.\n",
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"- **[Overview](https://python.langchain.com/docs/expression_language/)**: LCEL and its benefits\n",
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"- **[Interface](https://python.langchain.com/docs/expression_language/interface)**: The standard interface for LCEL objects\n",
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"- **[How-to](https://python.langchain.com/docs/expression_language/how_to)**: Key features of LCEL\n",
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"- **[Cookbook](https://python.langchain.com/docs/expression_language/cookbook)**: Example code for accomplishing common tasks\n",
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"\n",
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"The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.\n",
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"## Modules [](\\#modules \"Direct link to Modules\")\n",
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"\n",
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"The modules are (from least to most complex):\n",
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"LangChain provides standard, extendable interfaces and integrations for the following modules:\n",
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"\n",
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"- [Models](https://python.langchain.com/docs/modules/model_io/models/): Supported model types and integrations.\n",
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"#### [Model I/O](https://python.langchain.com/docs/modules/model_io/) [](\\#model-io \"Direct link to model-io\")\n",
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"\n",
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"- [Prompts](https://python.langchain.com/en/latest/modules/prompts.html): Prompt management, optimization, and serialization.\n",
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"Interface with language models\n",
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"\n",
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"- [Memory](https://python.langchain.com/en/latest/modules/memory.html): Memory refers to state that is persisted between calls of a chain/agent.\n",
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"#### [Retrieval](https://python.langchain.com/docs/modules/data_connection/) [](\\#retrieval \"Direct link to retrieval\")\n",
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"\n",
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"- [Indexes](https://python.langchain.com/en/latest/modules/data_connection.html): Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.\n",
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"Interface with application-specific data\n",
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"\n",
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"- [Chains](https://python.langchain.com/en/latest/modules/chains.html): Chains are structured sequences of calls (to an LLM or to a different utility).\n",
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"#### [Agents](https://python.langchain.com/docs/modules/agents/) [](\\#agents \"Direct link to agents\")\n",
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"\n",
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"- [Agents](https://python.langchain.com/en/latest/modules/agents.html): An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.\n",
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"Let models choose which tools to use given high-level directives\n",
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"\n",
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"- [Callbacks](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html): Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.\n",
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"## Examples, ecosystem, and resources [](\\#examples-ecosystem-and-resources \"Direct link to Examples, ecosystem, and resources\")\n",
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"\n",
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"### [Use cases](https://python.langchain.com/docs/use_cases/question_answering/) [](\\#use-cases \"Direct link to use-cases\")\n",
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"\n",
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"## Use Cases [\\#](\\#use-cases \"Permalink to this headline\")\n",
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"Walkthroughs and techniques for common end-to-end use cases, like:\n",
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"\n",
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"Best practices and built-in implementations for common LangChain use cases:\n",
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"- [Document question answering](https://python.langchain.com/docs/use_cases/question_answering/)\n",
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"- [Chatbots](https://python.langchain.com/docs/use_cases/chatbots/)\n",
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"- [Analyzing structured data](https://python.langchain.com/docs/use_cases/sql/)\n",
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"- and much more...\n",
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"\n",
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"- [Autonomous Agents](https://python.langchain.com/en/latest/use_cases/autonomous_agents.html): Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.\n",
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"### [Integrations](https://python.langchain.com/docs/integrations/providers/) [](\\#integrations \"Direct link to integrations\")\n",
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"\n",
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"- [Agent Simulations](https://python.langchain.com/en/latest/use_cases/agent_simulations.html): Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.\n",
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"LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](https://python.langchain.com/docs/integrations/providers/).\n",
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"\n",
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"- [Personal Assistants](https://python.langchain.com/en/latest/use_cases/personal_assistants.html): One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.\n",
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"### [Guides](https://python.langchain.com/docs/guides/debugging) [](\\#guides \"Direct link to guides\")\n",
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"\n",
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"- [Question Answering](https://python.langchain.com/en/latest/use_cases/question_answering.html): Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.\n",
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"Best practices for developing with LangChain.\n",
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"\n",
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"- [Chatbots](https://python.langchain.com/en/latest/use_cases/chatbots.html): Language models love to chat, making this a very natural use of them.\n",
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"### [API reference](https://api.python.langchain.com) [](\\#api-reference \"Direct link to api-reference\")\n",
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"\n",
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"- [Querying Tabular Data](https://python.langchain.com/en/latest/use_cases/tabular.html): Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).\n",
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"Head to the reference section for full documentation of all classes and methods in the LangChain and LangChain Experimental Python packages.\n",
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"\n",
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"- [Code Understanding](https://python.langchain.com/en/latest/use_cases/code.html): Recommended reading if you want to use language models to analyze code.\n",
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"### [Developer's guide](https://python.langchain.com/docs/contributing) [](\\#developers-guide \"Direct link to developers-guide\")\n",
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"\n",
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"- [Interacting with APIs](https://python.langchain.com/en/latest/use_cases/apis.html): Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.\n",
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"Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.\n",
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"\n",
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"- [Extraction](https://python.langchain.com/en/latest/use_cases/extraction.html): Extract structured information from text.\n",
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"\n",
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"- [Summarization](https://python.langchain.com/en/latest/use_cases/summarization.html): Compressing longer documents. A type of Data-Augmented Generation.\n",
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"\n",
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"- [Evaluation](https://python.langchain.com/en/latest/use_cases/evaluation.html): Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.\n",
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"\n",
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"\n",
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"## Reference Docs [\\#](\\#reference-docs \"Permalink to this headline\")\n",
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"\n",
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"Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n",
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"\n",
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"- [Reference Documentation](https://python.langchain.com/en/latest/reference.html)\n",
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"\n",
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"\n",
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"## LangChain Ecosystem [\\#](\\#langchain-ecosystem \"Permalink to this headline\")\n",
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"\n",
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"Guides for how other companies/products can be used with LangChain.\n",
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"\n",
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"- [LangChain Ecosystem](https://python.langchain.com/en/latest/ecosystem.html)\n",
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"\n",
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"\n",
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"## Additional Resources [\\#](\\#additional-resources \"Permalink to this headline\")\n",
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"\n",
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"Additional resources we think may be useful as you develop your application!\n",
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"\n",
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"- [LangChainHub](https://github.com/hwchase17/langchain-hub): The LangChainHub is a place to share and explore other prompts, chains, and agents.\n",
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"\n",
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"- [Gallery](https://python.langchain.com/en/latest/additional_resources/gallery.html): A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\n",
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"\n",
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"- [Deployments](https://python.langchain.com/en/latest/additional_resources/deployments.html): A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\n",
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"\n",
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"- [Tracing](https://python.langchain.com/en/latest/additional_resources/tracing.html): A guide on using tracing in LangChain to visualize the execution of chains and agents.\n",
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"\n",
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"- [Model Laboratory](https://python.langchain.com/en/latest/additional_resources/model_laboratory.html): Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\n",
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"\n",
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"- [Discord](https://discord.gg/6adMQxSpJS): Join us on our Discord to discuss all things LangChain!\n",
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"\n",
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"- [YouTube](https://python.langchain.com/en/latest/additional_resources/youtube.html): A collection of the LangChain tutorials and videos.\n",
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"\n",
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"- [Production Support](https://forms.gle/57d8AmXBYp8PP8tZA): As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.\n"
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"Head to the [Community navigator](https://python.langchain.com/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.\n"
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]
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}
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],
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@ -198,7 +174,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5dde17e7",
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"id": "7c89b313-adb6-4aa2-9cd8-952a5724a2ce",
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"metadata": {},
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"outputs": [],
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"source": []
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@ -220,7 +196,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"\n",
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"This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your OpenAI requests.\n",
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"\n",
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"Another example is [here](https://python.langchain.com/en/latest/ecosystem/promptlayer.html)."
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"Another example is [here](https://python.langchain.com/docs/integrations/providers/promptlayer)."
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.11.6"
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},
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"vscode": {
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"interpreter": {
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"id": "134a0785",
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"metadata": {},
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"source": [
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"# Chat Over Documents with Vectara\n",
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"\n",
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"This notebook is based on the [chat_vector_db](https://github.com/hwchase17/langchain/blob/master/docs/modules/chains/index_examples/chat_vector_db.html) notebook, but using Vectara as the vector database."
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"# Chat Over Documents with Vectara"
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]
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},
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{
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"metadata": {},
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"source": [
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"# Logging to file\n",
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"This example shows how to print logs to file. It shows how to use the `FileCallbackHandler`, which does the same thing as [`StdOutCallbackHandler`](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html#using-an-existing-handler), but instead writes the output to file. It also uses the `loguru` library to log other outputs that are not captured by the handler."
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"This example shows how to print logs to file. It shows how to use the `FileCallbackHandler`, which does the same thing as [`StdOutCallbackHandler`](https://python.langchain.com/docs/modules/callbacks/#get-started), but instead writes the output to file. It also uses the `loguru` library to log other outputs that are not captured by the handler."
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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