{ "cells": [ { "cell_type": "markdown", "id": "77b854df", "metadata": {}, "source": [ "# 2Markdown\n", "\n", ">[2markdown](https://2markdown.com/) service transforms website content into structured markdown files.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "497736aa", "metadata": {}, "outputs": [], "source": [ "# You will need to get your own API key. See https://2markdown.com/login\n", "\n", "api_key = \"\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "009e0036", "metadata": {}, "outputs": [], "source": [ "from langchain.document_loaders import ToMarkdownLoader" ] }, { "cell_type": "code", "execution_count": 5, "id": "910fb6ee", "metadata": {}, "outputs": [], "source": [ "loader = ToMarkdownLoader.from_api_key(\n", " url=\"https://python.langchain.com/en/latest/\", api_key=api_key\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "ac8db139", "metadata": {}, "outputs": [], "source": [ "docs = loader.load()" ] }, { "cell_type": "code", "execution_count": 8, "id": "706304e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "## Contents\n", "\n", "- [Getting Started](#getting-started)\n", "- [Modules](#modules)\n", "- [Use Cases](#use-cases)\n", "- [Reference Docs](#reference-docs)\n", "- [LangChain Ecosystem](#langchain-ecosystem)\n", "- [Additional Resources](#additional-resources)\n", "\n", "## Welcome to LangChain [\\#](\\#welcome-to-langchain \"Permalink to this headline\")\n", "\n", "**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", "\n", "1. _Data-aware_: connect a language model to other sources of data\n", "\n", "2. _Agentic_: allow a language model to interact with its environment\n", "\n", "\n", "The LangChain framework is designed around these principles.\n", "\n", "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", "\n", "## Getting Started [\\#](\\#getting-started \"Permalink to this headline\")\n", "\n", "How to get started using LangChain to create an Language Model application.\n", "\n", "- [Quickstart Guide](https://python.langchain.com/en/latest/getting_started/getting_started.html)\n", "\n", "\n", "Concepts and terminology.\n", "\n", "- [Concepts and terminology](https://python.langchain.com/en/latest/getting_started/concepts.html)\n", "\n", "\n", "Tutorials created by community experts and presented on YouTube.\n", "\n", "- [Tutorials](https://python.langchain.com/en/latest/getting_started/tutorials.html)\n", "\n", "\n", "## Modules [\\#](\\#modules \"Permalink to this headline\")\n", "\n", "These modules are the core abstractions which we view as the building blocks of any LLM-powered application.\n", "\n", "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", "\n", "The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.\n", "\n", "The modules are (from least to most complex):\n", "\n", "- [Models](https://python.langchain.com/docs/modules/model_io/models/): Supported model types and integrations.\n", "\n", "- [Prompts](https://python.langchain.com/en/latest/modules/prompts.html): Prompt management, optimization, and serialization.\n", "\n", "- [Memory](https://python.langchain.com/en/latest/modules/memory.html): Memory refers to state that is persisted between calls of a chain/agent.\n", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "\n", "## Use Cases [\\#](\\#use-cases \"Permalink to this headline\")\n", "\n", "Best practices and built-in implementations for common LangChain use cases:\n", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [Extraction](https://python.langchain.com/en/latest/use_cases/extraction.html): Extract structured information from text.\n", "\n", "- [Summarization](https://python.langchain.com/en/latest/use_cases/summarization.html): Compressing longer documents. A type of Data-Augmented Generation.\n", "\n", "- [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", "\n", "\n", "## Reference Docs [\\#](\\#reference-docs \"Permalink to this headline\")\n", "\n", "Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n", "\n", "- [Reference Documentation](https://python.langchain.com/en/latest/reference.html)\n", "\n", "\n", "## LangChain Ecosystem [\\#](\\#langchain-ecosystem \"Permalink to this headline\")\n", "\n", "Guides for how other companies/products can be used with LangChain.\n", "\n", "- [LangChain Ecosystem](https://python.langchain.com/en/latest/ecosystem.html)\n", "\n", "\n", "## Additional Resources [\\#](\\#additional-resources \"Permalink to this headline\")\n", "\n", "Additional resources we think may be useful as you develop your application!\n", "\n", "- [LangChainHub](https://github.com/hwchase17/langchain-hub): The LangChainHub is a place to share and explore other prompts, chains, and agents.\n", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [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", "\n", "- [Discord](https://discord.gg/6adMQxSpJS): Join us on our Discord to discuss all things LangChain!\n", "\n", "- [YouTube](https://python.langchain.com/en/latest/additional_resources/youtube.html): A collection of the LangChain tutorials and videos.\n", "\n", "- [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" ] } ], "source": [ "print(docs[0].page_content)" ] }, { "cell_type": "code", "execution_count": null, "id": "5dde17e7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }