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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
91 lines
2.5 KiB
Markdown
91 lines
2.5 KiB
Markdown
# RAG - GPT-crawler
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`GPT-crawler` crawls 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 extract 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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``` |