# rag-gpt-crawler GPT-crawler will crawl websites to produce files for use in custom GPTs or other apps (RAG). This template uses [gpt-crawler](https://github.com/BuilderIO/gpt-crawler) to build a RAG app ## Environment Setup Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. ## Crawling Run GPT-crawler to extact content from a set of urls, using the config file in GPT-crawler repo. Here is example config for LangChain use-case docs: ``` export const config: Config = { url: "https://python.langchain.com/docs/use_cases/", match: "https://python.langchain.com/docs/use_cases/**", selector: ".docMainContainer_gTbr", maxPagesToCrawl: 10, outputFileName: "output.json", }; ``` Then, run this as described in the [gpt-crawler](https://github.com/BuilderIO/gpt-crawler) README: ``` npm start ``` And copy the `output.json` file into the folder containing this README. ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package rag-gpt-crawler ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-gpt-crawler ``` And add the following code to your `server.py` file: ```python from rag_chroma import chain as rag_gpt_crawler add_routes(app, rag_gpt_crawler, path="/rag-gpt-crawler") ``` (Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/rag-gpt-crawler/playground](http://127.0.0.1:8000/rag-gpt-crawler/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-gpt-crawler") ```