base on GPT4 & LangChain Chatbot for large PDF docs # GPT-4 & LangChain - Create a ChatGPT Chatbot for Your PDF Files Use the new GPT-4 api to build a chatGPT chatbot for multiple Large PDF files. Tech stack used includes LangChain, Pinecone, Typescript, Openai, and Next.js. LangChain is a framework that makes it easier to build scalable AI/LLM apps and chatbots. Pinecone is a vectorstore for storing embeddings and your PDF in text to later retrieve similar docs. [Tutorial video](https://www.youtube.com/watch?v=ih9PBGVVOO4) [Join the discord if you have questions](https://discord.gg/E4Mc77qwjm) The visual guide of this repo and tutorial is in the `visual guide` folder. **If you run into errors, please review the troubleshooting section further down this page.** Prelude: Please make sure you have already downloaded node on your system and the version is 18 or greater. ## Development 1. Clone the repo or download the ZIP ``` git clone [github https url] ``` 2. Install packages First run `npm install yarn -g` to install yarn globally (if you haven't already). Then run: ``` yarn install ``` After installation, you should now see a `node_modules` folder. 3. Set up your `.env` file - Copy `.env.example` into `.env` Your `.env` file should look like this: ``` OPENAI_API_KEY= PINECONE_API_KEY= PINECONE_ENVIRONMENT= PINECONE_INDEX_NAME= ``` - Visit [openai](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) to retrieve API keys and insert into your `.env` file. - Visit [pinecone](https://pinecone.io/) to create and retrieve your API keys, and also retrieve your environment and index name from the dashboard. 4. In the `config` folder, replace the `PINECONE_NAME_SPACE` with a `namespace` where you'd like to store your embeddings on Pinecone when you run `npm run ingest`. This namespace will later be used for queries and retrieval. 5. In `utils/makechain.ts` chain change the `QA_PROMPT` for your own usecase. Change `modelName` in `new OpenAI` to `gpt-4`, if you have access to `gpt-4` api. Please verify outside this repo that you have access to `gpt-4` api, otherwise the application will not work. ## Convert your PDF files to embeddings **This repo can load multiple PDF files** 1. Inside `docs` folder, add your pdf files or folders that contain pdf files. 2. Run the script `yarn run ingest` to 'ingest' and embed your docs. If you run into errors troubleshoot below. 3. Check Pinecone dashboard to verify your namespace and vectors have been added. ## Run the app Once you've verified that the embeddings and content have been successfully added to your Pinecone, you can run the app `npm run dev` to launch the local dev environment, and then type a question in the chat interface. ## Troubleshooting In general, keep an eye out in the `issues` and `discussions` section of this repo for solutions. **General errors** - Make sure you're running the latest Node version. Run `node -v` - Try a different PDF or convert your PDF to text first. It's possible your PDF is corrupted, scanned, or requires OCR to convert to text. - `Console.log` the `env` variables and make sure they are exposed. - Make sure you're using the same versions of LangChain and Pinecone as this repo. - Check that you've created an `.env` file that contains your valid (and working) API keys, environment and index name. - If you change `modelName` in `OpenAI`, make sure you have access to the api for the appropriate model. - Make sure you have enough OpenAI credits and a valid card on your billings account. - Check that you don't have multiple OPENAPI keys in your global environment. If you do, the local `env` file from the project will be overwritten by systems `env` variable. - Try to hard code your API keys into the `process.env` variables if there are still issues. **Pinecone errors** - Make sure your pinecone dashboard `environment` and `index` matches the one in the `pinecone.ts` and `.env` files. - Check that you've set the vector dimensions to `1536`. - Make sure your pinecone namespace is in lowercase. - Pinecone indexes of users on the Starter(free) plan are deleted after 7 days of inactivity. To prevent this, send an API request to Pinecone to reset the counter before 7 days. - Retry from scratch with a new Pinecone project, index, and cloned repo. ## Credit Frontend of this repo is inspired by [langchain-chat-nextjs](https://github.com/zahidkhawaja/langchain-chat-nextjs) ", Assign "at most 3 tags" to the expected json: {"id":"3292","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"