mirror of
https://github.com/hwchase17/langchain
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92 lines
2.5 KiB
Markdown
92 lines
2.5 KiB
Markdown
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# rag-gpt-crawler
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GPT-crawler will crawl websites to produce files for use in custom GPTs or other apps (RAG).
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This template uses [gpt-crawler](https://github.com/BuilderIO/gpt-crawler) to build a RAG app
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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## Crawling
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Run GPT-crawler to extact content from a set of urls, using the config file in GPT-crawler repo.
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Here is example config for LangChain use-case docs:
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```
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export const config: Config = {
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url: "https://python.langchain.com/docs/use_cases/",
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match: "https://python.langchain.com/docs/use_cases/**",
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selector: ".docMainContainer_gTbr",
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maxPagesToCrawl: 10,
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outputFileName: "output.json",
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};
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```
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Then, run this as described in the [gpt-crawler](https://github.com/BuilderIO/gpt-crawler) README:
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```
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npm start
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```
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And copy the `output.json` file into the folder containing this README.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-gpt-crawler
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-gpt-crawler
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```
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And add the following code to your `server.py` file:
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```python
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from rag_chroma import chain as rag_gpt_crawler
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add_routes(app, rag_gpt_crawler, path="/rag-gpt-crawler")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-gpt-crawler")
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``` |