mirror of https://github.com/hwchase17/langchain
Add template for gpt-crawler (#13625)
Template for RAG using [gpt-crawler](https://github.com/BuilderIO/gpt-crawler). --------- Co-authored-by: Erick Friis <erick@langchain.dev>pull/13608/head
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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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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LangSmith is currently in private beta, you can sign up [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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```
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[tool.poetry]
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name = "rag-gpt-crawler"
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version = "0.1.0"
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description = ""
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authors = [
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"Lance Martin <lance@langchain.dev>",
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]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = ">=0.0.325"
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openai = "<2"
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tiktoken = ">=0.5.1"
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chromadb = ">=0.4.14"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.15"
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[tool.langserve]
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export_module = "rag_gpt_crawler"
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export_attr = "chain"
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[build-system]
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requires = [
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"poetry-core",
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]
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build-backend = "poetry.core.masonry.api"
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "681a5d1e",
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"metadata": {},
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"source": [
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"## Run Template\n",
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"\n",
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"In `server.py`, set -\n",
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"```\n",
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"add_routes(app, chain_rag_conv, path=\"/rag-gpt-crawler\")\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"\n",
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"rag_app = RemoteRunnable(\"http://localhost:8001/rag-gpt-crawler\")\n",
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"rag_app.invoke(\"How does summarization work?\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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from rag_gpt_crawler.chain import chain
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__all__ = ["chain"]
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import json
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from pathlib import Path
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.prompts import ChatPromptTemplate
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema import Document
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores import Chroma
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# Load output from gpt crawler
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path_to_gptcrawler = Path(__file__).parent.parent / "output.json"
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data = json.loads(Path(path_to_gptcrawler).read_text())
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docs = [
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Document(
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page_content=dict_["html"],
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metadata={"title": dict_["title"], "url": dict_["url"]},
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)
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for dict_ in data
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]
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# Split
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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all_splits = text_splitter.split_documents(docs)
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# Add to vectorDB
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vectorstore = Chroma.from_documents(
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documents=all_splits,
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collection_name="rag-gpt-builder",
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embedding=OpenAIEmbeddings(),
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)
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retriever = vectorstore.as_retriever()
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# LLM
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model = ChatOpenAI()
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# RAG chain
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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