Langchain_Starter_Kit.ipynb

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      "source": [
        "# Langchain Starter Kit",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19WM0B0Pfia2uxtuwp6ePzZVQ9Uk28Oyg?usp=sharing)\n",
        "\n",
        "###### Adapted from [langchain intro](https://colab.research.google.com/github/pinecone-io/examples/blob/master/generation/langchain/handbook/00-langchain-intro.ipynb), Langchain official documentation & awesome-langchain\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Intro to LangChain\n",
        "\n",
        "LangChain is a popular framework that allow users to quickly build apps and pipelines around **L**arge **L**anguage **M**odels. It can be used to for chatbots, **G**enerative **Q**uestion-**A**nwering (GQA), summarization, and much more.\n",
        "\n",
        "The core idea of the library is that we can _\"chain\"_ together different components to create more advanced use-cases around LLMs. Chains may consist of multiple components from several modules:\n",
        "\n",
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IT/ok3/ld1j/5Nr7e/ZE/4J//ALJX7CNprtl+yv4U/wCEXj8TNbvqQ+3Xl757WnmeUf8AS55tm3zpPubc55zgYAPsmvhz9pP/AIKR/sWfsgeN7P4cftJ+OrTwrrd/ZLqMFrPBczM9q8kkSyAwQyLgvE64JB+XpX3HX57/ALWH/BLz9h39t/x9YfE79p7wT/wk+t6Zp6aXb3H9pX9nstY5JJQmy1uYUPzyuclS3PXgYAPKB/wXH/4JUkZ/4XDp3/gFf/8AyNX3n8Bv2iPg7+098NrT4v8AwI1uLxD4bvnligvoUkRHeByjgCRUb5WBB46ivzH/AOIfH/gkX/0Sf/yuav8A/JtfpH+zX+zT8GP2RvhHYfAr9n/Rv7B8LaXJPNbWf2ie62PcyNLIfMuZJZTudifmc9aAPf1bdX5tfFT/AIK+/wDBOP4I/EXV/hP8VfihZaN4g0K4a1vrSW0vHaKZPvKWS3ZTj1ViPev0jTvX5TfGT/giV/wTO/aD+J2s/GT4s/Dp9S8R6/cNdahdrrGpwedM3VtkV2qL9FUCgCt/w/Q/4JQ/9Fk07/wCv/8A5Gr9RfCninQ/G/huw8XeGpvtOnanbxXdrNgqJIJlDo4BwQGUg8gH2r8d/wDiHl/4JF/9Etl/8H2r/wDyZX7BeC/CWheAfCWm+B/C8P2fTdHtYrK0i3M/lwQKERdzszHCgDLEk9yaAN67u4bG3e6uDiOJWdz6KoyTX5UH/guT/wAEqBz/AMLgsP8AwBv/AP5Gr9VL6yg1G1ks7kZjmRo3HqrjBFfjJ/xD2f8ABJM8N8K8j/sN6x/8nUAe0+Bv+CzX/BND4l+OdF+G3gX4qWOo634hv7bTNPtUtL1GnuryRYoowXt1UFnYAZYDmv09BJ6jFfkT8Nv+CFP/AAS3+E/xA0L4p+APhmdP17w1qVpq2nXK61qr+TeWUqzQybHvGR9rqDtcFD0YEcV+u9AHlPxt+Nvwy/Zz+GOqfGX4y6omieGNESOS/v5EeRIVlkSJCVjV3OXdR8qnrX51/wDD9X/gk9/0WTTv/AG//wDkav0I+PnwD+FX7Tnwo1b4IfG3S/7Z8Ma6kcd9ZedLb+asUiyqPMgeORcOit8rjpX5ef8AEPn/AMEj+/wo/LXNYH/t9QB9Wfs+f8FQP2Ev2rfiEvwp/Z4+IVp4n8QtBJdfYoLa6ifyIcb3zNDGuFyO+ea+91bdX5y/sx/8EmP2Bv2OPignxl/Zz8Dt4f8AEcdrNZC6OqaheDyJ8eYvl3NzLHztHO3I7V+jlAH8nX/B1R4G+Cl7+z98P/HPiHWbTTPH2n6jdRaRayxt5up6Y/lLexIyqVLW7yW8qB2XCl9uSxr+G20vLqxuEurORoZYmDxyRnbIki/dZWHIIr+6b/g6k134AQ/s1+BNK8aaSmpfEW81G5g8NzG4njbT7GXyH1CfykdYpC5it4U86N8bi0eGBr+FVYg/7pDmVvup6+tAH9LP/BOz/g46+Pv7PP2D4afteJcfEfwjE8cUWpGTOu2UXQkyOcXiqO0zLISf9b2r9S/+Cqnx2+G37TX7DvjL46/Be/GseGfEi6HdWVyqlGK/aLWNldGAZHR1Ksp5DKRX4/f8E6v+Dd79pv8AaqsrT4nftHtP8MPBVwFljinj/wCJ3exMNymK3kwsCN03zgN0Koy81+vH/BT74AfC/wDZT/Ya8b/AP4JaeNL8NeHDoVvaQFi7Hfc2kkkjseWkkld5HbuzHAA4rfD/AMSPqj7fw4/5KXL/APr7D/0pH8l9Aoor629tT/VtbXZ+pX/BI/4E6X8Wv2r7Xx94zt0k8JfDu3fxDqkkozEr24P2cN7+YPMH/XM9MV9b/wDBTHxB4d/bj/ZK8Hf8FBPAdkLe40bUL3w9r0YUBoYGmP2VpNvPylkK8ZImrqfgT4r+FX/BPv8A4Ji23ir4w+EB411D4/XRSTSVvn0h30pY227p4Y2lCbOdoG4+d12k47P9gP4//sk/tMeG/HX7AXw5+FDfC2z+IWmXVxDMNfudZSS/gQbdq3Ua+XKqLvVs4Yx8jIFctRuU7n8qZ7neY1s/qcV4fDTlQw1RQU048ns43jWunJSvdvVRa9xHzj/wTp8K/D39mr9kjx//AMFJvHmiR6/rXh25TTPDFteLvgW5kMa+agzncZZdpcDcoRsEZNfCPi3/AIKYft1+MfGo8dX3xK1e0uUlMkcFhL9ktFU4+U28eIXA7b0bvnNfpt+yJ4Ivv2hf2BPjT/wTvhEMPxE8K6u99p2nFxEbl7SZGZQzEAkzwyRvk/KHQHHDV+W3wq+L/wC2l+wb411jwf8ADqXUPBGv640FtdWl3pMU08/kFigEd5bynC72GY1O7PG4AGtI1GraHvZVPA4/GZvicbSp18apr2cKsrR9jyprlbUrLVvRO709E8FfHr4tftJftqfDP4h/G3WpNd1lPEOhWi3Biig/cxXkZVdkKIv3mYk4ySa/UD/gp1J/wUkX9tfxqvwY/wCFlf8ACKiWz/s7+w11T+zvL+x2+7yfspMX392/aB82c/NmvSP+CmNzf6r+3P8Asxav4oIbUbpdCnuGAC7pZb6F2OFAHJPpXyH/AMFXP2o/2l/A/wDwUB+InhLwT8RvE+kaVZy6etvY2OrXVrbQq1jbthIo5FQZJLNgcsSe9Dqe+pM8CjjZZ1meWV8DhKEE6FS0KmtOKVSK93RfLRaXND/glVq3xjvf+CpXhi3+Pk+sz+J7Szvobtdflne9jHkGSNZBcnzF+RwVB/hIr3j/AIKXfCXwb+2V8Ib39vL4G2qJr/gu9u9C8d2MCbWDWEhiFzsHIUKqvuOT5TruxsNfLH/BIXxj4n8cf8FHfD/i3xxqN1rOrXVrftPe30zXFxKVtWQF5JCWYgAAZPQAVg/srftjWv7Jn7cvjY+Moze+CPFWvalpXiKzbDRvbPPKqyleNxhyx64KM46kVNRe9zX0Z6ObZXmUOIpZnlySr4XD0nyQ0hOLc+eCXZpe72dj2XwDqUtj/wAES9f1nSWdLmz8b2s0M0ReOWKSOWFldHUAqykZBru/+CKv7Q/x9+I37bMHhf4ieOPEHiDTzoWoTC21LU7m5gEkfl7W8p5DHkZODt719Oftz/suaP8Asq/8ExPHHgz4e3Ud74V1XxfZ6vokiPvAsb0wuqZXI+RtyDn5lVW6mvz3/wCCE5I/b2tv+xf1T+UdLmbsrnm0JYLOOEc9zSVNOTnVlFtaxvGL67eZ8NfHX9pv9o7xL428T+GNf+IHiW+0mfUbyGSxudVuZrdollZQhjeQqUAGNrAivlSu++Kv/JTvEf8A2Fb3/wBHvXA1tGTvZn9IcNZZhsFgKUMNTUVKMW7JLVpdgr+9X9k349/Cb9mL9nHTfjZ8cdXTQfC2i+E9La9v5EeRYfPNpBHlIleRt0kij5VPXmv4Kq/vI/Zd/Z/+Ev7Un7Nmm/An45aT/bfhfXfCmlfbbLz5rbzPsr2dzH+8t3ilXbLEjfK4ztwcqSD5ucaQVj+afpU/7jgPWX6HRa3/AMHCf/BJjRUYr8TZbwr2ttE1Vs/i1oo/WvJNc/4OXv8Agl/o4b7LqniHUdv/AD66S/P0814/1xX0ppX/AAQs/wCCVGjxiO3+EGnyY/imvL+dvzlunr0Kz/4I9/8ABMjTdot/gr4YcDr5tqZf/Q2avAP4gPyT+IH/AAda/seaUJLb4Y/Dzxdr1wnAN99k06F/QqyzXD4/3o1PtXznb/8AByP+2z8bwY/2Vv2ZZ9Rnf/UkNf68Dnp8tpb2/oejc1/UD8OP2Iv2Ofg95TfCv4WeE/D0kIAWax0a0hmOOhaQRb2PuWJr6ehhht4xDAoRF4CqMAfhQB/HNH8Z/wDg5/8A2wJ00rwx4StfhNp02RNOLODR41Vux/tB7m/XoeY03jP0rpPDn/BuT+1j+0JeW/iP/goR+0TqPiCdWEj2dk9zqxDf9M7m+eMREYHAt2X06V/XxgHkiloA/E39nX/g31/4Jm/s9XdrrR8IT+NtTtAQl14ouDfLycnNugitj0Gd0JzX7JeG/Dmh+E9Li0DwzaQafp9soSC1tokhhiUdkRAqgfQV0FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHhX7S37RXw4/ZP8Agnrn7QHxdkuIfDnh5InvXtYTPKqzypCu2NeWy8ij8apfss/tMfDH9sL4E6H+0V8G5bibw54h+0/ZGuoTBMfslxJbSbo25GJInA9RzXwf/wAF2/8AlFH8Xf8Ar003/wBOVpXLf8G+KBP+CQ3wjx3Gu/8Ap5vqAP2booooAKKKKACiiigAooooAKKKKACiiigAooooAQ9K/k+/4KDf8f8A4Y/7fv8A2hX9YJ6V+LHxU/Yi8SfGK4tLjxjoFzJ9h8zyfKuEjx5u3dn5xn7or6jg/N6OWZnTxlf4Y329LHyfGmUV80yqrgcPbnla1/Jo/nKZs8UB8DFfu7/w67sP+hbv/wDwMX/45R/w67sP+hbv/wDwMX/45X7x/wARdyfrGX3H4X/xCDOf5o/e/wDI/CPzP8/5FNZt1fu9/wAOu7D/AKFu/wD/AAMX/wCOUf8ADruw/wChbv8A/wADF/8AjlJeLuT/AMsvuD/iD+c/zR+9/wCR+D9PD4GK/d3/AIdd2H/Qt3//AIGL/wDHKP8Ah13Yf9C3f/8AgYv/AMcpvxeyd7xl9wn4QZz/ADR+9/5H4R+Z/n/IprNur93/APh1tp//AELV/wD+Bif/AByk/wCHXdh/0Ld//wCBi/8AxykvF3J/5ZfcSvCLOe8fvf8Akfg/Tw+Biv3e/wC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      ],
      "metadata": {
        "id": "HZUM8KoC717Y"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "As of 13 July:\n",
        "* **Model IO**:\n",
        "Prompts: Templatize, dynamically select, and manage model inputs\n",
        "Language models: Make calls to language models through common interfaces\n",
        "Output parsers: Extract information from model outputs\n",
        "\n",
        "* **Data connection**: Document loaders: Load documents from many different sources\n",
        "Document transformers: Split documents, drop redundant documents, and more\n",
        "Text embedding models: Take unstructured text and turn it into a list of floating point numbers\n",
        "Vector stores: Store and search over embedded data\n",
        "Retrievers: Query your data\n",
        "\n",
        "* **Chains**: Generically as a sequence of calls to components, which can include other chains. The base interface is simple: This idea of composing components together in a chain is simple but powerful. It drastically simplifies and makes more modular the implementation of complex applications, which in turn makes it much easier to debug, maintain, and improve your applications.\n",
        "\n",
        "* **Memory**: By default, Chains and Agents are stateless, meaning that they treat each incoming query independently (like the underlying LLMs and chat models themselves). In some applications, like chatbots, it is essential to remember previous interactions, both in the short and long-term. The Memory class does exactly that. LangChain provides memory components in two forms.\n",
        "\n",
        "* **Agents**: Some applications require a flexible chain of calls to LLMs and other tools based on user input. The Agent interface provides the flexibility for such applications. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next. There are two main types of agents: (i) Action agents: at each timestep, decide on the next action using the outputs of all previous actions.(ii) Plan-and-execute agents: decide on the full sequence of actions up front, then execute them all without updating the plan\n",
        "\n",
        "* **Tools**: the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do\n",
        "Toolkits: wrappers around collections of tools that can be used together a specific use case. For example, in order for an agent to interact with a SQL database it will likely need one tool to execute queries and another to inspect tables\n",
        "\n",
        "* **Callbacks**: LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.\n",
        "\n",
        "* **Ecosystem, Integrations**\n",
        "\n"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get inspired! Examples of applications built using Langchain (see [here](https://github.com/kyrolabs/awesome-langchain) for more)\n",
        "\n",
        "- [AudioGPT](https://github.com/AIGC-Audio/AudioGPT): Understanding and Generating Speech, Music, Sound, and Talking Head ![GitHub Repo stars](https://img.shields.io/github/stars/AIGC-Audio/AudioGPT?style=social)\n",
        "- [Paper QA](https://github.com/whitead/paper-qa): LLM Chain for answering questions from documents with citations ![GitHub Repo stars](https://img.shields.io/github/stars/whitead/paper-qa?style=social)\n",
        "- [Chat Langchain](https://github.com/hwchase17/chat-langchain): locally hosted chatbot specifically focused on question answering over the LangChain documentation ![GitHub Repo stars](https://img.shields.io/github/stars/hwchase17/chat-langchain?style=social)\n",
        "- [Langchain Chat](https://github.com/zahidkhawaja/langchain-chat-nextjs): another Next.js frontend for LangChain Chat. ![GitHub Repo stars](https://img.shields.io/github/stars/zahidkhawaja/langchain-chat-nextjs?style=social)\n",
        "- [Book GPT](https://github.com/fraserxu/book-gpt): drop a book, start asking question. ![GitHub Repo stars](https://img.shields.io/github/stars/fraserxu/book-gpt?style=social)\n",
        "- [Chat LangchainJS](https://github.com/sullivan-sean/chat-langchainjs): NextJS version of Chat Langchain ![GitHub Repo stars](https://img.shields.io/github/stars/sullivan-sean/chat-langchainjs?style=social)\n",
        "- [Doc Search](https://github.com/namuan/dr-doc-search): converse with book - Built with GPT-3 ![GitHub Repo stars](https://img.shields.io/github/stars/namuan/dr-doc-search?style=social)\n",
        "- [Fact Checker](https://github.com/jagilley/fact-checker): fact-checking LLM outputs with langchain ![GitHub Repo stars](https://img.shields.io/github/stars/jagilley/fact-checker?style=social)\n",
        "- [MM ReAct](https://github.com/microsoft/MM-REACT): Multi Modal ReAct Design\n",
        "- [QABot](https://github.com/hardbyte/qabot): Query local or remote files or databases with natural language queries powered by langchain and openai ![GitHub Repo stars](https://img.shields.io/github/stars/hardbyte/qabot?style=social)\n",
        "- [GPT Automator](https://github.com/chidiwilliams/GPT-Automator): Your voice-controlled Mac assistant. ![GitHub Repo stars](https://img.shields.io/github/stars/chidiwilliams/GPT-Automator?style=social)\n",
        "- [Teams LangchainJS](https://github.com/SidU/teams-langchain-js): Demonstration of LangChainJS with Teams / Bot Framework bots ![GitHub Repo stars](https://img.shields.io/github/stars/SidU/teams-langchain-js?style=social)\n",
        "- [ChatGPT](https://github.com/biff-ai/chatgpt-langchainjs-example): ChatGPT & langchain example for node.js & Docker ![GitHub Repo stars](https://img.shields.io/github/stars/biff-ai/chatgpt-langchainjs-example?style=social)\n",
        "- [FlowGPT](https://github.com/nilooy/flowgpt): Generate diagram with AI ![GitHub Repo stars](https://img.shields.io/github/stars/nilooy/flowgpt?style=social)\n",
        "- [langchain-text-summarizer](https://github.com/alphasecio/langchain-text-summarizer): A sample streamlit application summarizing text using LangChain ![GitHub Repo stars](https://img.shields.io/github/stars/alphasecio/langchain-text-summarizer?style=social)\n",
        "- [Langchain Chat Websocket](https://github.com/pors/langchain-chat-websockets): About LangChain LLM chat with streaming response over websockets ![GitHub Repo stars](https://img.shields.io/github/stars/pors/langchain-chat-websockets?style=social)\n",
        "- [langchain_yt_tools](https://github.com/venuv/langchain_yt_tools): Langchain tools to search/extract/transcribe text transcripts of Youtube videos ![GitHub Repo stars](https://img.shields.io/github/stars/venuv/langchain_yt_tools?style=social)\n",
        "- [SmartPilot](https://github.com/jaredkirby/SmartPilot): A Python program leveraging OpenAI's language models to generate, analyze, and select the best answer to a given question ![GitHub Repo stars](https://img.shields.io/github/stars/jaredkirby/SmartPilot?style=social)\n",
        "- [Howdol](https://github.com/bborn/howdoi.ai): a helpful chatbot that can answer questions ![GitHub Repo stars](https://img.shields.io/github/stars/bborn/howdoi.ai?style=social)\n",
        "- [MrsStax](https://github.com/normandmickey/MrsStax): QA Slack Bot ![GitHub Repo stars](https://img.shields.io/github/stars/normandmickey/MrsStax?style=social)\n",
        "- [ThoughtSource⚡](https://github.com/OpenBioLink/ThoughtSource): A framework for the science of machine thinking ![GitHub Repo stars](https://img.shields.io/github/stars/OpenBioLink/ThoughtSource?style=social)\n",
        "- [ChatGPT Langchain](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain): ChatGPT clone using langchain on Huggingface\n",
        "- [Chat Math Techniques](https://huggingface.co/spaces/JavaFXpert/gpt-math-techniques): langchain chat with math techniques on Huggingface\n",
        "- [Notion QA](https://github.com/hwchase17/notion-qa): Notion Question-Answering Bot ![GitHub Repo stars](https://img.shields.io/github/stars/hwchase17/notion-qa?style=social)\n",
        "- [QNimGPT](https://huggingface.co/spaces/rituthombre/QNim): Play Nim against an IBM Quantum Computer simulator or OpenAI GPT-3.5\n",
        "- [ChatPDF](https://github.com/akshata29/chatpdf): ChatGPT + Enterprise data with Azure OpenAI ![GitHub Repo stars](https://img.shields.io/github/stars/akshata29/chatpdf?style=social)\n",
        "- [Chat with Scanned Documents](https://github.com/tony-xlh/Chat-with-Scanned-Documents): A demo chatting with documents scanned with Dynamic Web TWAIN.\n",
        "- [snowChat ❄️](https://github.com/kaarthik108/snowChat): Chat with you're snowflake database ![GitHub Repo stars](https://img.shields.io/github/stars/kaarthik108/snowChat?style=social)\n",
        "- [DB GPT](https://github.com/csunny/DB-GPT): Interact your data and environment using the local GPT, no data leaks, 100% privately, 100% security ![GitHub Repo stars](https://img.shields.io/github/stars/csunny/DB-GPT?style=social)\n",
        "- [Psychic](https://github.com/psychic-api/psychic): Universal APIs for unstructured data. Sync documents from SaaS tools to a SQL or vector database, where they can be easily queried by AI applications like ChatGPT. ![GitHub Repo stars](https://img.shields.io/github/stars/psychic-api/psychic?style=social)\n",
        "- [Airtable-QnA](https://github.com/ikram-shah/airtable-qna): 🌟 a question-answering tool for your Airtable content\n",
        "- [WingmanAI](https://github.com/e-johnstonn/wingmanAI): tool for interacting with real-time transcription of both system and microphone audio\n",
        "- [TutorGPT](https://github.com/plastic-labs/tutor-gpt): Dynamic few-shot metaprompting for the task of tutoring. ![GitHub Repo stars](https://img.shields.io/github/stars/plastic-labs/tutor-gpt?style=social)\n",
        "- [Cheshire Cat](https://github.com/cheshire-cat-ai/core): Custom AGI boT with ready-to-use chat integration and plugins development platform. ![GitHub Repo stars](https://img.shields.io/github/stars/cheshire-cat-ai/core?style=social)\n",
        "- [Got Chaat Bot](https://github.com/parker84/GoT-chat-bot): Repo for creating GoT Chatbots (ex: talk with Tyrion Lannister) ![GitHub Repo stars](https://img.shields.io/github/stars/parker84/GoT-chat-bot?style=social)\n",
        "- [Dialoqbase](https://github.com/n4ze3m/dialoqbase): web application that allows you to create custom chatbots with your own knowledge base ![GitHub Repo stars](https://img.shields.io/github/stars/n4ze3m/dialoqbase?style=social)\n",
        "- [CSV-AI 🧠](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/snowflake.html): CSV-AI is the ultimate app powered by LangChain that allows you to unlock hidden insights in your CSV files.\n",
        "- [MindGeniusAI](https://github.com/xianjianlf2/MindGeniusAI): Auto generate MindMap with ChatGPT ![GitHub Repo stars](https://img.shields.io/github/stars/xianjianlf2/MindGeniusAI?style=social)\n",
        "- [Robby-Chatbot](https://github.com/yvann-hub/Robby-chatbot): AI chatbot 🤖 for chat with CSV, PDF, TXT files 📄 and YTB videos 🎥 | using Langchain🦜 | OpenAI | Streamlit ⚡.\n",
        "- [AI Chatbot](https://github.com/vercel-labs/ai-chatbot): A full-featured, hackable Next.js AI chatbot built by Vercel Labs ![GitHub Repo stars](https://img.shields.io/github/stars/vercel-labs/ai-chatbot?style=social)\n",
        "- [Instrukt](https://github.com/blob42/Instrukt): A fully-fledged AI environment in the terminal. Build, test and instruct agents. ![GitHub Repo stars](https://img.shields.io/github/stars/blob42/Instrukt?style=social)\n",
        "- [OpenChat](https://github.com/openchatai/OpenChat/): LLMs custom-chatbots console ⚡. ![GitHub Repo stars](https://img.shields.io/github/stars/openchatai/OpenChat?style=social)\n",
        "- [Twitter Agent](https://github.com/ahmedbesbes/twitter-agent/): Scrape tweets, summarize them and chat with them in an interactive terminal\n",
        "- [GPT Migrate](https://github.com/0xpayne/gpt-migrate): Easily migrate your codebase from one framework or language to another."
      ],
      "metadata": {
        "id": "G2zJHRWx8jUs"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNaXrEPOhbuL"
      },
      "source": [
        "# Using LLMs in LangChain\n",
        "\n",
        "LangChain supports several LLM providers, like Hugging Face and OpenAI. As of now, OpenAI > everything else, but the decision to choose a model depends on a myriad of factors beyond cost - tasks, privacy, ownership, speed, fine-tuning ability and so on.\n",
        "\n",
        "Let's start our exploration of LangChain by learning how to use a few of these different LLM integrations."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hugging Face (Open Source)\n",
        "\n",
        "We first need to install additional prerequisite libraries:"
      ],
      "metadata": {
        "id": "KjF9g7xXAh5S"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LWA15ZkVjg80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3869b5cb-bce2-48f6-8750-18f3f69cfdd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.6/73.6 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qU huggingface_hub\n",
        "!pip install -qU langchain\n",
        "!pip install -qU openai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-whfR5Tjf1O"
      },
      "source": [
        "For Hugging Face models we need a Hugging Face Hub API token. We can find this by first getting an account at [HuggingFace.co](https://huggingface.co/) and clicking on our profile in the top-right corner > click *Settings* > click *Access Tokens* > click *New Token* > set *Role* to *write* > *Generate* > copy and paste the token below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sRGTytxCjKaW"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'HF_TOKEN_HERE'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exAl3iQgnAra"
      },
      "source": [
        "We can then generate text using a HF Hub model (we'll use `google/flan-t5-base`) using the Inference API built into Hugging Face Hub.\n",
        "\n",
        "_(The default Inference API doesn't use specialized hardware and so can be slow and cannot run larger models like `bigscience/bloom-560m` or `google/flan-t5-xxl`)_"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HuggingFace LLM Leaderboard\n",
        "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard"
      ],
      "metadata": {
        "id": "M-aLdd3sVfOS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7yubiSJhIfs",
        "outputId": "3be28cd6-071b-4099-d230-ae28494e2842"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "he was hungry\n"
          ]
        }
      ],
      "source": [
        "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
        "\n",
        "# initialize HF LLM\n",
        "flan_t5 = HuggingFaceHub(\n",
        "    repo_id=\"google/flan-t5-base\",\n",
        "    model_kwargs={\"temperature\":1e-10}\n",
        ")\n",
        "\n",
        "# build prompt template for simple question-answering\n",
        "template = \"\"\"Question: {question}\n",
        "\n",
        "Answer: \"\"\"\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
        "\n",
        "llm_chain = LLMChain(\n",
        "    prompt=prompt,\n",
        "    llm=flan_t5\n",
        ")\n",
        "\n",
        "question = \"Why did the chicken cross the road?\"\n",
        "\n",
        "print(llm_chain.run(question))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpdXG9YtzrLJ"
      },
      "source": [
        "## OpenAI\n",
        "\n",
        "Start by installing additional prerequisites:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fOo9qQvDgkz"
      },
      "source": [
        "We can also use OpenAI's generative models. The process is similar, we need to\n",
        "give our API key which can be retrieved by signing up for an account on the\n",
        "[OpenAI website](https://openai.com/api/) (see top-right of page). We then pass the API key below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "deWmOJecfbBr"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.environ['OPENAI_API_KEY'] = 'OPENAI_KEY_HERE'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2AWnaTCP0Ryg"
      },
      "source": [
        "Then we decide on which model we'd like to use, there are several options but we will go with `text-davinci-003`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZhQSDoYe0ly4"
      },
      "outputs": [],
      "source": [
        "from langchain.llms import OpenAI\n",
        "\n",
        "davinci = OpenAI(model_name='text-davinci-003')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGL2zs3uEVj6"
      },
      "source": [
        "We'll use the same simple question-answer prompt template as before with the Hugging Face example. The only change is that we now pass our OpenAI LLM `davinci`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVSsC3iGEPAp",
        "outputId": "d9cef3d2-d2af-41ad-f4ab-7f556f1a3e59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " To get to the other side.\n"
          ]
        }
      ],
      "source": [
        "llm_chain = LLMChain(\n",
        "    prompt=prompt,\n",
        "    llm=davinci\n",
        ")\n",
        "\n",
        "print(llm_chain.run(question))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LLMs and Chat Models are subtly but importantly different. LLMs in LangChain refer to pure text completion models. The APIs they wrap take a string prompt as input and output a string completion. OpenAI's GPT-3 is implemented as an LLM. Chat models are often backed by LLMs but tuned specifically for having conversations. And, crucially, their provider APIs expose a different interface than pure text completion models. Instead of a single string, they take a list of chat messages as input. Usually these messages are labeled with the speaker (usually one of \"System\", \"AI\", and \"Human\"). And they return a (\"AI\") chat message as output. GPT-4 and Anthropic's Claude are both implemented as Chat Models.\n",
        "\n",
        "To make it possible to swap LLMs and Chat Models, both implement the Base Language Model interface. This exposes common methods \"predict\", which takes a string and returns a string, and \"predict messages\", which takes messages and returns a message. If you are using a specific model it's recommended you use the methods specific to that model class (i.e., \"predict\" for LLMs and \"predict messages\" for Chat Models), but if you're creating an application that should work with different types of models the shared interface can be helpful."
      ],
      "metadata": {
        "id": "ZdAwGfzEXz3u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Illustration of the \"stochastic parrot nature\" of LLMs -- despite how powerful they are, the ground truth will always be dependent on a particular context based on some latest information or domain knowledge!\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "\n",
        "gpt35turbo = ChatOpenAI(model_name='gpt-3.5-turbo-16k')\n",
        "#gpt4 = ChatOpenAI(model_name='gpt-4')\n",
        "\n",
        "from langchain.schema import (\n",
        "    AIMessage,\n",
        "    HumanMessage,\n",
        "    SystemMessage\n",
        ")\n",
        "\n",
        "gpt35turbo([HumanMessage(content=question)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGKWdj4poLsR",
        "outputId": "87ed6fd4-2e0a-45c0-c020-3e1188d4b66a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='There are many possible answers to this classic joke, but here are a few popular ones:\\n\\n1. To get to the other side: This is the traditional punchline, where the chicken crossing the road is seen as a simple and straightforward action.\\n\\n2. To escape danger or a predator: The chicken may have been trying to avoid a threat or danger on one side of the road and decided to cross to the other side for safety.\\n\\n3. To explore or seek something: The chicken may have been curious and wanted to explore beyond its usual surroundings, so it crossed the road to discover new areas or find food.\\n\\n4. To reach its destination: The chicken may have had a specific goal or destination in mind on the other side of the road, such as a feeding area or a chicken coop.\\n\\nUltimately, the answer to this joke is open to interpretation and can vary depending on comedic timing and context.', additional_kwargs={}, example=False)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This is a context-aware question. How programmatically provide LLMs with that information?\n",
        "gpt35turbo([HumanMessage(content=\"How do I use chains in Langchain?\")])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09NSDJID-FB8",
        "outputId": "696d944a-f955-4e1c-ba8a-ea7d19f4aa79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='Using chains in Langchain is quite simple. Here are the steps:\\n\\n1. Install the Langchain app: First, you need to download and install the Langchain app on your device from the respective app store (e.g., Google Play Store for Android or the Apple App Store for iOS).\\n\\n2. Sign up or log in: Open the app and either sign up for a new account or log in if you already have one.\\n\\n3. Create a new chain: To create a new chain, tap on the \"+\" icon or the \"Create Chain\" button. Give your chain a name and optionally add a description.\\n\\n4. Add participants: Next, you need to add participants to your chain. You can either invite your existing Langchain contacts or type in the email addresses of the people you want to invite. Langchain will send them an invitation to join the chain.\\n\\n5. Start a conversation: Once participants have joined the chain, you can start a conversation by tapping on the chain\\'s name. This will open the chat interface where you can exchange messages with the other participants.\\n\\n6. Use translation features: Langchain offers translation features to help overcome language barriers. You can type or speak in your preferred language, and Langchain will automatically translate it for the other participants based on their language preferences. You can also choose to translate incoming messages into your preferred language.\\n\\n7. Customize settings: Langchain allows you to customize various settings for each chain. You can change the chain\\'s name, description, language preferences, and notification preferences. Additionally, you can mute or leave a chain if needed.\\n\\n8. Explore additional features: Langchain provides additional features like voice notes, image sharing, and location sharing. You can use these features to enhance your communication within the chain.\\n\\nRemember to respect other participants\\' privacy and follow community guidelines while using Langchain. Enjoy your multilingual conversations using the app!', additional_kwargs={}, example=False)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Connection / Document Loaders\n",
        "That is why we need to augment LLMs with facts by connecting it to document loaders. The community has contributed a ton of data connectors, including structured and non-structured types. Check them out before coding your own data parsers from scratch!\n",
        "\n",
        "https://python.langchain.com/docs/modules/data_connection/document_loaders"
      ],
      "metadata": {
        "id": "HPIYn4CVXg2m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example 1: Recursive URL Loader"
      ],
      "metadata": {
        "id": "fdUkFAwYYqh9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader\n",
        "\n",
        "url = 'https://python.langchain.com/docs/modules/chains/'\n",
        "loader = RecursiveUrlLoader(url=url)\n",
        "docs = loader.load()"
      ],
      "metadata": {
        "id": "KQsQZTZ3ZAan"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5nK_V7Rmj8k",
        "outputId": "e40b6472-5ad5-4e1e-8633-4e65fb324b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2mR86HNni11",
        "outputId": "f35cf2d0-bdcf-4bb4-925f-e2bbead846c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='\\n\\n\\n\\n\\nTagging | ğŸ¦œï¸�ğŸ”— Langchain\\n\\n\\n\\n\\n\\nSkip to main contentğŸ¦œï¸�ğŸ”— LangChainJS/TS DocsGitHubCTRLKGet startedIntroductionInstallationQuickstartTutorialsModulesModel I/â€‹OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainExtractionFLAREGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesUse casesGuidesEcosystemAdditional resourcesAPI referenceModulesChainsAdditionalTaggingOn this pageTaggingThe tagging chain uses the OpenAI functions parameter to specify a schema to tag a document with. This helps us make sure that the model outputs exactly tags that we want, with their appropriate types.The tagging chain is to be used when we want to tag a passage with a specific attribute (i.e. what is the sentiment of this message?)from langchain.chat_models import ChatOpenAIfrom langchain.chains import create_tagging_chain, create_tagging_chain_pydanticfrom langchain.prompts import ChatPromptTemplate    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It\\'s recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")Simplest approach, only specifying typeâ€‹We can start by specifying a few properties with their expected type in our schemaschema = {    \"properties\": {        \"sentiment\": {\"type\": \"string\"},        \"aggressiveness\": {\"type\": \"integer\"},        \"language\": {\"type\": \"string\"},    }}chain = create_tagging_chain(schema, llm)As we can see in the examples, it correctly interprets what we want but the results vary so that we get, for example, sentiments in different languages (\\'positive\\', \\'enojado\\' etc.).We will see how to control these results in the next section.inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"chain.run(inp)    {\\'sentiment\\': \\'positive\\', \\'language\\': \\'Spanish\\'}inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"chain.run(inp)    {\\'sentiment\\': \\'enojado\\', \\'aggressiveness\\': 1, \\'language\\': \\'Spanish\\'}inp = \"Weather is ok here, I can go outside without much more than a coat\"chain.run(inp)    {\\'sentiment\\': \\'positive\\', \\'aggressiveness\\': 0, \\'language\\': \\'English\\'}More controlâ€‹By being smart about how we define our schema we can have more control over the model\\'s output. Specifically we can define:possible values for each propertydescription to make sure that the model understands the propertyrequired properties to be returnedFollowing is an example of how we can use enum, description and required to control for each of the previously mentioned aspects:schema = {    \"properties\": {        \"sentiment\": {\"type\": \"string\", \"enum\": [\"happy\", \"neutral\", \"sad\"]},        \"aggressiveness\": {            \"type\": \"integer\",            \"enum\": [1, 2, 3, 4, 5],            \"description\": \"describes how aggressive the statement is, the higher the number the more aggressive\",        },        \"language\": {            \"type\": \"string\",            \"enum\": [\"spanish\", \"english\", \"french\", \"german\", \"italian\"],        },    },    \"required\": [\"language\", \"sentiment\", \"aggressiveness\"],}chain = create_tagging_chain(schema, llm)Now the answers are much better!inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"chain.run(inp)    {\\'sentiment\\': \\'happy\\', \\'aggressiveness\\': 0, \\'language\\': \\'spanish\\'}inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"chain.run(inp)    {\\'sentiment\\': \\'sad\\', \\'aggressiveness\\': 10, \\'language\\': \\'spanish\\'}inp = \"Weather is ok here, I can go outside without much more than a coat\"chain.run(inp)    {\\'sentiment\\': \\'neutral\\', \\'aggressiveness\\': 0, \\'language\\': \\'english\\'}Specifying schema with Pydanticâ€‹We can also use a Pydantic schema to specify the required properties and types. We can also send other arguments, such as \\'enum\\' or \\'description\\' as can be seen in the example below.By using the create_tagging_chain_pydantic function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types.from enum import Enumfrom pydantic import BaseModel, Fieldclass Tags(BaseModel):    sentiment: str = Field(..., enum=[\"happy\", \"neutral\", \"sad\"])    aggressiveness: int = Field(        ...,        description=\"describes how aggressive the statement is, the higher the number the more aggressive\",        enum=[1, 2, 3, 4, 5],    )    language: str = Field(        ..., enum=[\"spanish\", \"english\", \"french\", \"german\", \"italian\"]    )chain = create_tagging_chain_pydantic(Tags, llm)inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"res = chain.run(inp)res    Tags(sentiment=\\'sad\\', aggressiveness=10, language=\\'spanish\\')PreviousDocument QANextVector store-augmented text generationSimplest approach, only specifying typeMore controlSpecifying schema with PydanticCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.\\n\\n\\n\\n', metadata={'source': 'https://python.langchain.com/docs/modules/chains/additional/tagging', 'title': 'Tagging | ğŸ¦œï¸�ğŸ”— Langchain', 'description': 'The tagging chain uses the OpenAI functions parameter to specify a schema to tag a document with. This helps us make sure that the model outputs exactly tags that we want, with their appropriate types.', 'language': 'en'})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vector stores\n",
        "\n",
        "One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search for you.\n",
        "\n",
        "This walkthrough uses the FAISS vector database, which makes use of the Facebook AI Similarity Search (FAISS) library.\n",
        "\n"
      ],
      "metadata": {
        "id": "b4c7oolMcsZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install faiss-cpu\n",
        "!pip install tiktoken"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee0kjwSAc8-z",
        "outputId": "6006071e-db7f-4014-b30e-bcc5ff482316"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.7.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m97.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faiss-cpu\n",
            "Successfully installed faiss-cpu-1.7.4\n",
            "Collecting tiktoken\n",
            "  Downloading tiktoken-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken) (2022.10.31)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from tiktoken) (2.27.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (3.4)\n",
            "Installing collected packages: tiktoken\n",
            "Successfully installed tiktoken-0.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.embeddings.openai import OpenAIEmbeddings\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.vectorstores import FAISS\n",
        "\n",
        "embeddings = OpenAIEmbeddings()\n",
        "db = FAISS.from_documents(docs, embeddings)"
      ],
      "metadata": {
        "id": "ssPjY6wWc2hj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load documents as Vectorstore for querying"
      ],
      "metadata": {
        "id": "qG1yDGVVn5zI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use\n",
        "db.similarity_search(\"How to use chains for document retrieval?\")"
      ],
      "metadata": {
        "id": "zye2bWRIdGl8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1b1abdc-d5ed-4b9f-ce0e-dc6c05c83fd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content=\"\\n\\n\\n\\n\\nPopular | ğŸ¦œï¸�ğŸ”— Langchain\\n\\n\\n\\n\\n\\nSkip to main contentğŸ¦œï¸�ğŸ”— LangChainJS/TS DocsGitHubCTRLKGet startedIntroductionInstallationQuickstartTutorialsModulesModel I/â€‹OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesUse casesGuidesEcosystemAdditional resourcesAPI referenceModulesChainsPopularPopularğŸ“„ï¸� API chainsAPIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.ğŸ“„ï¸� Retrieval QAThis example showcases question answering over an index.ğŸ“„ï¸� Conversational Retrieval QAThe ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.ğŸ“„ï¸� Using OpenAI functionsThis walkthrough demonstrates how to incorporate OpenAI function-calling API's in a chain. We'll go over:ğŸ“„ï¸� SQLThis example demonstrates the use of the SQLDatabaseChain for answering questions over a SQL database.ğŸ“„ï¸� SummarizationA summarization chain can be used to summarize multiple documents. One way is to input multiple smaller documents, after they have been divided into chunks, and operate over them with a MapReduceDocumentsChain. You can also choose instead for the chain that does summarization to be a StuffDocumentsChain, or a RefineDocumentsChain.PreviousMap re-rankNextAPI chainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.\\n\\n\\n\\n\", metadata={'source': 'https://python.langchain.com/docs/modules/chains/popular/', 'title': 'Popular | ğŸ¦œï¸�ğŸ”— Langchain', 'language': 'en'}),\n",
              " Document(page_content='\\n\\n\\n\\n\\nRetrieval QA | ğŸ¦œï¸�ğŸ”— Langchain\\n\\n\\n\\n\\n\\nSkip to main contentğŸ¦œï¸�ğŸ”— LangChainJS/TS DocsGitHubCTRLKGet startedIntroductionInstallationQuickstartTutorialsModulesModel I/â€‹OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesUse casesGuidesEcosystemAdditional resourcesAPI referenceModulesChainsPopularRetrieval QARetrieval QAThis example showcases question answering over an index.from langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromaloader = TextLoader(\"../../state_of_the_union.txt\")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_documents(texts, embeddings)qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever())query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" 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. He also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"Chain Typeâ€‹You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see this notebook.There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"map_reduce\", retriever=docsearch.as_retriever())query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" The president said that Judge Ketanji Brown Jackson is one of our nation\\'s top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"The above way allows you to really simply change the chain_type, but it doesn\\'t provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQA chain with the combine_documents_chain parameter. For example:from langchain.chains.question_answering import load_qa_chainqa_chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" The president said that Ketanji Brown Jackson 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. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"Custom Promptsâ€‹You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chainfrom langchain.prompts import PromptTemplateprompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don\\'t know the answer, just say that you don\\'t know, don\\'t try to make up an answer.{context}Question: {question}Answer in Italian:\"\"\"PROMPT = PromptTemplate(    template=prompt_template, input_variables=[\"context\", \"question\"])chain_type_kwargs = {\"prompt\": PROMPT}qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" Il presidente ha detto che Ketanji Brown Jackson Ã¨ una delle menti legali piÃ¹ importanti del paese, che continuerÃ\\xa0 l\\'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani.\"Return Source Documentsâ€‹Additionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), return_source_documents=True)query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"query\": query})result[\"result\"]    \" The president said that Ketanji Brown Jackson is one of the nation\\'s top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"result[\"source_documents\"]    [Document(page_content=\\'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while youâ€™re at it, pass the Disclose Act so Americans can know who is funding our elections. \\\\n\\\\nTonight, Iâ€™d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyerâ€”an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\\\n\\\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\\\n\\\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nationâ€™s top legal minds, who will continue Justice Breyerâ€™s legacy of excellence.\\', lookup_str=\\'\\', metadata={\\'source\\': \\'../../state_of_the_union.txt\\'}, lookup_index=0),     Document(page_content=\\'A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since sheâ€™s been nominated, sheâ€™s received a broad range of supportâ€”from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\\\n\\\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\\\n\\\\nWe can do both. At our border, weâ€™ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\\\n\\\\nWeâ€™ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\\\n\\\\nWeâ€™re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\\\n\\\\nWeâ€™re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\\', lookup_str=\\'\\', metadata={\\'source\\': \\'../../state_of_the_union.txt\\'}, lookup_index=0),     Document(page_content=\\'And for our LGBTQ+ Americans, letâ€™s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\\\n\\\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\\\n\\\\nWhile it often appears that we never agree, that isnâ€™t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\\\n\\\\nAnd soon, weâ€™ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\\\n\\\\nSo tonight Iâ€™m offering a Unity Agenda for the Nation. Four big things we can do together.  \\\\n\\\\nFirst, beat the opioid epidemic.\\', lookup_str=\\'\\', metadata={\\'source\\': \\'../../state_of_the_union.txt\\'}, lookup_index=0),     Document(page_content=\\'Tonight, Iâ€™m announcing a crackdown on these companies overcharging American businesses and consumers. \\\\n\\\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\\\n\\\\nThat ends on my watch. \\\\n\\\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\\\n\\\\nWeâ€™ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\\\n\\\\nLetâ€™s pass the Paycheck Fairness Act and paid leave.  \\\\n\\\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\\\n\\\\nLetâ€™s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jillâ€”our First Lady who teaches full-timeâ€”calls Americaâ€™s best-kept secret: community colleges.\\', lookup_str=\\'\\', metadata={\\'source\\': \\'../../state_of_the_union.txt\\'}, lookup_index=0)]Alternatively, if our document have a \"source\" metadata key, we can use the RetrievalQAWithSourceChain to cite our sources:docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])from langchain.chains import RetrievalQAWithSourcesChainfrom langchain import OpenAIchain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever())chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)    {\\'answer\\': \\' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\\\\n\\',     \\'sources\\': \\'31-pl\\'}PreviousAPI chainsNextConversational Retrieval QACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.\\n\\n\\n\\n', metadata={'source': 'https://python.langchain.com/docs/modules/chains/popular/vector_db_qa', 'title': 'Retrieval QA | ğŸ¦œï¸�ğŸ”— Langchain', 'description': 'This example showcases question answering over an index.', 'language': 'en'}),\n",
              " Document(page_content='\\n\\n\\n\\n\\nDynamically selecting from multiple retrievers | ğŸ¦œï¸�ğŸ”— Langchain\\n\\n\\n\\n\\n\\nSkip to main contentğŸ¦œï¸�ğŸ”— LangChainJS/TS DocsGitHubCTRLKGet startedIntroductionInstallationQuickstartTutorialsModulesModel I/â€‹OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainExtractionFLAREGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesUse casesGuidesEcosystemAdditional resourcesAPI referenceModulesChainsAdditionalDynamically selecting from multiple retrieversDynamically selecting from multiple retrieversThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. 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.from langchain.chains.router import MultiRetrievalQAChainfrom langchain.llms import OpenAIfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import FAISSsou_docs = TextLoader(\\'../../state_of_the_union.txt\\').load_and_split()sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()pg_docs = TextLoader(\\'../../paul_graham_essay.txt\\').load_and_split()pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()personal_texts = [    \"I love apple pie\",    \"My favorite color is fuchsia\",    \"My dream is to become a professional dancer\",    \"I broke my arm when I was 12\",    \"My parents are from Peru\",]personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()retriever_infos = [    {        \"name\": \"state of the union\",         \"description\": \"Good for answering questions about the 2023 State of the Union address\",         \"retriever\": sou_retriever    },    {        \"name\": \"pg essay\",         \"description\": \"Good for answering questions about Paul Graham\\'s essay on his career\",         \"retriever\": pg_retriever    },    {        \"name\": \"personal\",         \"description\": \"Good for answering questions about me\",         \"retriever\": personal_retriever    }]chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)print(chain.run(\"What did the president say about the economy?\"))            > Entering new MultiRetrievalQAChain chain...    state of the union: {\\'query\\': \\'What did the president say about the economy in the 2023 State of the Union address?\\'}    > Finished chain.     The president said that the economy was stronger than it had been a year prior, and that the American Rescue Plan helped create record job growth and fuel economic relief for millions of Americans. He also proposed a plan to fight inflation and lower costs for families, including cutting the cost of prescription drugs and energy, providing investments and tax credits for energy efficiency, and increasing access to child care and Pre-K.print(chain.run(\"What is something Paul Graham regrets about his work?\"))            > Entering new MultiRetrievalQAChain chain...    pg essay: {\\'query\\': \\'What is something Paul Graham regrets about his work?\\'}    > Finished chain.     Paul Graham regrets that he did not take a vacation after selling his company, instead of immediately starting to paint.print(chain.run(\"What is my background?\"))            > Entering new MultiRetrievalQAChain chain...    personal: {\\'query\\': \\'What is my background?\\'}    > Finished chain.     Your background is Peruvian.print(chain.run(\"What year was the Internet created in?\"))            > Entering new MultiRetrievalQAChain chain...    None: {\\'query\\': \\'What year was the Internet created in?\\'}    > Finished chain.    The Internet was created in 1969 through a project called ARPANET, which was funded by the United States Department of Defense. However, the World Wide Web, which is often confused with the Internet, was created in 1989 by British computer scientist Tim Berners-Lee.PreviousDynamically selecting from multiple promptsNextRetrieval QA using OpenAI functionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.\\n\\n\\n\\n', metadata={'source': 'https://python.langchain.com/docs/modules/chains/additional/multi_retrieval_qa_router', 'title': 'Dynamically selecting from multiple retrievers | ğŸ¦œï¸�ğŸ”— Langchain', 'description': 'This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. 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.', 'language': 'en'}),\n",
              " Document(page_content='\\n\\n\\n\\n\\nDocument QA | ğŸ¦œï¸�ğŸ”— Langchain\\n\\n\\n\\n\\n\\nSkip to main contentğŸ¦œï¸�ğŸ”— LangChainJS/TS DocsGitHubCTRLKGet startedIntroductionInstallationQuickstartTutorialsModulesModel I/â€‹OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainExtractionFLAREGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesUse casesGuidesEcosystemAdditional resourcesAPI referenceModulesChainsAdditionalDocument QAOn this pageDocument QAHere we walk through how to use LangChain for question answering over a list of documents. Under the hood we\\'ll be using our Document chains.Prepare Dataâ€‹First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromafrom langchain.docstore.document import Documentfrom langchain.prompts import PromptTemplatefrom langchain.indexes.vectorstore import VectorstoreIndexCreatorwith open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]).as_retriever()    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.query = \"What did the president say about Justice Breyer\"docs = docsearch.get_relevant_documents(query)from langchain.chains.question_answering import load_qa_chainfrom langchain.llms import OpenAIQuickstartâ€‹If you just want to get started as quickly as possible, this is the recommended way to do it:chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")query = \"What did the president say about Justice Breyer\"chain.run(input_documents=docs, question=query)    \\' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.\\'If you want more control and understanding over what is happening, please see the information below.The stuff Chainâ€‹This sections shows results of using the stuff Chain to do question answering.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'output_text\\': \\' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.\\'}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don\\'t know the answer, just say that you don\\'t know, don\\'t try to make up an answer.{context}Question: {question}Answer in Italian:\"\"\"PROMPT = PromptTemplate(    template=prompt_template, input_variables=[\"context\", \"question\"])chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'output_text\\': \\' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.\\'}The map_reduce Chainâ€‹This sections shows results of using the map_reduce Chain to do question answering.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'output_text\\': \\' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.\\'}Intermediate StepsWe can also return the intermediate steps for map_reduce chains, should we want to inspect them. This is done with the return_map_steps variable.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'intermediate_steps\\': [\\' \"Tonight, Iâ€™d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyerâ€”an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"\\',      \\' A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since sheâ€™s been nominated, sheâ€™s received a broad range of supportâ€”from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\\',      \\' None\\',      \\' None\\'],     \\'output_text\\': \\' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.\\'}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.question_prompt_template = \"\"\"Use the following portion of a long document to see if any of the text is relevant to answer the question. Return any relevant text translated into italian.{context}Question: {question}Relevant text, if any, in Italian:\"\"\"QUESTION_PROMPT = PromptTemplate(    template=question_prompt_template, input_variables=[\"context\", \"question\"])combine_prompt_template = \"\"\"Given the following extracted parts of a long document and a question, create a final answer italian. If you don\\'t know the answer, just say that you don\\'t know. Don\\'t try to make up an answer.QUESTION: {question}========={summaries}=========Answer in Italian:\"\"\"COMBINE_PROMPT = PromptTemplate(    template=combine_prompt_template, input_variables=[\"summaries\", \"question\"])chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'intermediate_steps\\': [\"\\\\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell\\'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.\",      \\'\\\\nNessun testo pertinente.\\',      \\' Non ha detto nulla riguardo a Justice Breyer.\\',      \" Non c\\'Ã¨ testo pertinente.\"],     \\'output_text\\': \\' Non ha detto nulla riguardo a Justice Breyer.\\'}Batch SizeWhen using the map_reduce chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:llm = OpenAI(batch_size=5, temperature=0)The refine Chainâ€‹This sections shows results of using the refine Chain to do question answering.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'output_text\\': \\'\\\\n\\\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which he said would be the most sweeping investment to rebuild America in history and would help the country compete for the jobs of the 21st Century.\\'}Intermediate StepsWe can also return the intermediate steps for refine chains, should we want to inspect them. This is done with the return_refine_steps variable.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'intermediate_steps\\': [\\'\\\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.\\',      \\'\\\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.\\',      \\'\\\\n\\\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans.\\',      \\'\\\\n\\\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.\\'],     \\'output_text\\': \\'\\\\n\\\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.\\'}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.refine_prompt_template = (    \"The original question is as follows: {question}\\\\n\"    \"We have provided an existing answer: {existing_answer}\\\\n\"    \"We have the opportunity to refine the existing answer\"    \"(only if needed) with some more context below.\\\\n\"    \"------------\\\\n\"    \"{context_str}\\\\n\"    \"------------\\\\n\"    \"Given the new context, refine the original answer to better \"    \"answer the question. \"    \"If the context isn\\'t useful, return the original answer. Reply in Italian.\")refine_prompt = PromptTemplate(    input_variables=[\"question\", \"existing_answer\", \"context_str\"],    template=refine_prompt_template,)initial_qa_template = (    \"Context information is below. \\\\n\"    \"---------------------\\\\n\"    \"{context_str}\"    \"\\\\n---------------------\\\\n\"    \"Given the context information and not prior knowledge, \"    \"answer the question: {question}\\\\nYour answer should be in Italian.\\\\n\")initial_qa_prompt = PromptTemplate(    input_variables=[\"context_str\", \"question\"], template=initial_qa_template)chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True,                     question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'intermediate_steps\\': [\\'\\\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha reso omaggio al suo servizio.\\',      \"\\\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l\\'importanza di avanzare la libertÃ\\xa0 e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.\",      \"\\\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l\\'importanza di avanzare la libertÃ\\xa0 e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l\\'approvazione dell\\'Equality Act. Ha inoltre sottolineato l\\'importanza di lavorare insieme per sconfiggere l\\'epidemia di oppiacei.\",      \"\\\\n\\\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l\\'importanza di avanzare la libertÃ\\xa0 e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l\\'approvazione dell\\'Equality Act. Ha inoltre sottolineato l\\'importanza di lavorare insieme per sconfiggere l\\'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l\\'economia dal\"],     \\'output_text\\': \"\\\\n\\\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l\\'importanza di avanzare la libertÃ\\xa0 e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l\\'approvazione dell\\'Equality Act. Ha inoltre sottolineato l\\'importanza di lavorare insieme per sconfiggere l\\'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l\\'economia dal\"}The map-rerank Chainâ€‹This sections shows results of using the map-rerank Chain to do question answering with sources.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True)query = \"What did the president say about Justice Breyer\"results = chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)results[\"output_text\"]    \\' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.\\'results[\"intermediate_steps\"]    [{\\'answer\\': \\' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.\\',      \\'score\\': \\'100\\'},     {\\'answer\\': \\' This document does not answer the question\\', \\'score\\': \\'0\\'},     {\\'answer\\': \\' This document does not answer the question\\', \\'score\\': \\'0\\'},     {\\'answer\\': \\' This document does not answer the question\\', \\'score\\': \\'0\\'}]Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.from langchain.output_parsers import RegexParseroutput_parser = RegexParser(    regex=r\"(.*?)\\\\nScore: (.*)\",    output_keys=[\"answer\", \"score\"],)prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don\\'t know the answer, just say that you don\\'t know, don\\'t try to make up an answer.In addition to giving an answer, also return a score of how fully it answered the user\\'s question. This should be in the following format:Question: [question here]Helpful Answer In Italian: [answer here]Score: [score between 0 and 100]Begin!Context:---------{context}---------Question: {question}Helpful Answer In Italian:\"\"\"PROMPT = PromptTemplate(    template=prompt_template,    input_variables=[\"context\", \"question\"],    output_parser=output_parser,)chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True, prompt=PROMPT)query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'intermediate_steps\\': [{\\'answer\\': \\' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.\\',       \\'score\\': \\'100\\'},      {\\'answer\\': \\' Il presidente non ha detto nulla sulla Giustizia Breyer.\\',       \\'score\\': \\'100\\'},      {\\'answer\\': \\' Non so.\\', \\'score\\': \\'0\\'},      {\\'answer\\': \\' Non so.\\', \\'score\\': \\'0\\'}],     \\'output_text\\': \\' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.\\'}Document QA with sourcesâ€‹We can also perform document QA and return the sources that were used to answer the question. To do this we\\'ll just need to make sure each document has a \"source\" key in the metadata, and we\\'ll use the load_qa_with_sources helper to construct our chain:docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))])query = \"What did the president say about Justice Breyer\"docs = docsearch.similarity_search(query)from langchain.chains.qa_with_sources import load_qa_with_sources_chainchain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {\\'output_text\\': \\' The president thanked Justice Breyer for his service.\\\\nSOURCES: 30-pl\\'}PreviousQuestion-Answering CitationsNextTaggingDocument QA with sourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.\\n\\n\\n\\n', metadata={'source': 'https://python.langchain.com/docs/modules/chains/additional/question_answering', 'title': 'Document QA | ğŸ¦œï¸�ğŸ”— Langchain', 'description': \"Here we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our Document chains.\", 'language': 'en'})]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Putting it all together using Chains!\n",
        "\n",
        "Using an LLM in isolation is fine for simple applications, but more complex applications require chaining LLMs - either with each other or with other components.\n",
        "\n",
        "LangChain provides the Chain interface for such \"chained\" applications. We define a Chain very generically as a sequence of calls to components, which can include other chains. The base interface is simple:\n",
        "\n"
      ],
      "metadata": {
        "id": "8lrsZuFbpeXs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example: Retrieval QA Chain\n",
        "\n",
        "Question-Answering over an index aka vectorstore.\n",
        "\n"
      ],
      "metadata": {
        "id": "X2FVkx-fpmGk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "qa = RetrievalQA.from_chain_type(llm=gpt35turbo, chain_type=\"stuff\", retriever=db.as_retriever(), return_source_documents=True)"
      ],
      "metadata": {
        "id": "af3iL5ejqCRn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = qa({\"query\": \"How do I use chains in Langchain?\"})"
      ],
      "metadata": {
        "id": "KSXzBCEKqBEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(result['result'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J51VcjR8qHhQ",
        "outputId": "73d022d2-18e1-4f74-f66a-70734cded6e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To use chains in Langchain, you can follow these steps:\n",
            "\n",
            "1. Install Langchain: Start by installing Langchain on your system. You can find the installation instructions in the Langchain documentation.\n",
            "\n",
            "2. Import the necessary modules: Depending on the specific chain you want to use, you will need to import the relevant modules. For example, if you want to use an API chain, you may need to import the `APIChain` module.\n",
            "\n",
            "3. Initialize the chain: Create an instance of the chain you want to use. This may involve providing additional arguments such as an API key or other configuration options.\n",
            "\n",
            "4. Run the chain: Use the `run()` method of the chain instance to execute the chain logic. This method may take input prompts or arguments depending on the chain's functionality.\n",
            "\n",
            "Here is a basic example of using an API chain to retrieve information:\n",
            "\n",
            "```python\n",
            "from langchain.chains import APIChain\n",
            "\n",
            "# Initialize the API chain\n",
            "chain = APIChain()\n",
            "\n",
            "# Run the chain with a question\n",
            "result = chain.run(\"What is the weather like today?\")\n",
            "\n",
            "# Print the result\n",
            "print(result)\n",
            "```\n",
            "\n",
            "Remember to refer to the Langchain documentation for more details on specific chains and their usage.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bonus tips & tricks for developing on Langchain and working with LLMs\n",
        "\n",
        "In just a few lines of code above, we've seen how powerful LLMs can become once we start 'chaining' them with additional steps, data augmentation, and connecting to other applications in an ecosystem or providing your business domain knowledge. The possibilities are endless!\n",
        "\n",
        "## On Langchain\n",
        "* While Langchain abstracts away a lot of the code behind the scenes, it can be extremely frustrating to debug when things go wrong. Documentation isn't the best so read the code / [API reference](https://api.python.langchain.com/en/latest/api_reference.html). Also check out [langchain workflow visualizer](https://github.com/amosjyng/langchain-visualizer)\n",
        "* As you can see building a chatbot/AI application has never been easier. This means barriers to entry are much, much lower. If you're considering building startups using this there are probably many others out there with the same idea so figure out what's your moat\n",
        "* Sounds obvious, but check out data connectors / loaders before writing your own\n",
        "* In production, freeze your library versions! This thing moves so fast you'll get deprecation warning in a couple of weeks lol\n",
        "* \"English is the new programming language\". [Prompt engineering guide](https://github.com/dair-ai/Prompt-Engineering-Guide)\n",
        "* Get creative with your prompts. Experimement chaining together differerent workflows (such as getting another AI to check the answer, improve it, factcheck, etc.)\n",
        "* There are some non-LLM tasks such as using simple IF-ELSE / rule-based approaches to solving problems within your workflow.\n",
        "\n",
        "\n",
        "## On LLMs\n",
        "\n",
        "\n",
        "* Despite a gazillion metrics for evaluating LLMs, human evaluation is still the best. If it works, it works\n",
        "* In order of ascending difficulty (read: cost!):\n",
        "\n",
        " * Prompt engineering -> Fine-tuning -> Training a model from scratch\n",
        " * Unless you have a spare NVIDIA P100 sitting around, figure out if your use case works using Prompt Engineering first!! Most people assume that you need to train a model from scratch for the LLM to learn domain knowledge, which is untrue. There are many well-documented approaches such as ICL (see below)\n",
        " * Drawing similarities to traditional machine learning, this is the 'hyperparameter tuning' phase with higher degrees of freedom and complexity, also a form of dark arts\n",
        "\n",
        "\n",
        "## On proving business ROI\n",
        "* **Always know what problem you are trying to solve first. Are you generating revenue? Are you saving costs? How much? Don't force an AI use case. If you have a hammer everything looks like a nail!**\n",
        "* Build fast, simple POCs that work FIRST before deploying. That way it is much easier to get stakeholder buy-in and the resources you need for projects\n",
        "* It is tiresome and not good for your mental health to try to keep up with everything all at once, take care of yourself :)"
      ],
      "metadata": {
        "id": "Q_j14_lursYJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Readings (from [ICL approaches](https://towardsdatascience.com/in-context-learning-approaches-in-large-language-models-9c0c53b116a1))\n",
        "\n",
        "[1] A Survey of Large Language Models, https://arxiv.org/pdf/2303.18223.pdf\n",
        "\n",
        "[2] A Survey on In-Context Learning, https://arxiv.org/pdf/2301.00234.pdf\n",
        "\n",
        "[3] Emergent Abilities of Large Language Models, https://arxiv.org/pdf/2206.07682.pdf\n",
        "\n",
        "[4] Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers, https://arxiv.org/pdf/2212.10559.pdf\n",
        "\n",
        "[5] An Explanation of In-context Learning as Implicit Bayesian Inference, http://ai.stanford.edu/blog/understanding-incontext/\n",
        "\n",
        "[6] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, https://arxiv.org/pdf/2201.11903.pdf\n",
        "\n",
        "[7] Large Language Models are Zero-shot Reasoners, https://arxiv.org/pdf/2205.11916.pdf\n",
        "\n",
        "[8] In-context learning and induction heads. Transformer Circuits, 2022. https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html .\n",
        "\n",
        "[9] Self-consistency improves chain-of-thought reasoning in LLM, https://arxiv.org/pdf/2203.11171.pdf\n",
        "\n",
        "[10] Tree of Thoughts, https://arxiv.org/pdf/2305.10601.pdf\n",
        "\n",
        "[11] Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data https://arxiv.org/pdf/2302.12822.pdf\n",
        "\n",
        "[12] Automatic Chain-of-Thought Prompting in Large Language Models, https://arxiv.org/pdf/2210.03493.pdf\n",
        "\n",
        "[13] Large Language models can Self Improve, https://www.arxiv-vanity.com/papers/2210.11610/\n",
        "\n",
        "[14] Lost in the Middle: How Language Models Use Long Contexts, https://arxiv.org/abs/2307.03172"
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