mirror of https://github.com/hwchase17/langchain
Vectara RAG template (#12975)
- **Description:** RAG template using Vectara - **Twitter handle:** @ofermendpull/12753/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-vectara
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This template performs RAG with vectara.
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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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Also, ensure the following environment variables are set:
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* `VECTARA_CUSTOMER_ID`
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* `VECTARA_CORPUS_ID`
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* `VECTARA_API_KEY`
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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-vectara
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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-vectara
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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_vectara import chain as rag_vectara_chain
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add_routes(app, rag_vectara_chain, path="/rag-vectara")
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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 "vectara-demo"
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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-vectara/playground](http://127.0.0.1:8000/rag-vectara/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-vectara")
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```
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[tool.poetry]
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name = "rag-vectara"
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version = "0.1.0"
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description = ""
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authors = [
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"Ofer Mendelevitch <ofer@vectara.com>",
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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 = "^0.28.1"
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tiktoken = "^0.5.1"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.15"
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[tool.poetry.group.dev.dependencies.python-dotenv]
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extras = [
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"cli",
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]
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version = "^1.0.0"
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[tool.langserve]
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export_module = "rag_vectara"
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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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"attachments": {},
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"cell_type": "markdown",
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"id": "8692a430",
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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_ext, path=\"/rag-vectara\")\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": "41db5e30",
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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_vectara = RemoteRunnable(\"http://localhost:8000/rag-vectara\")\n",
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"rag_app_vectara.invoke(\"How does agent memory 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 3.11.6 64-bit",
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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": "3.11.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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}
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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_vectara.chain import chain
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__all__ = ["chain"]
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import os
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from langchain.chat_models import ChatOpenAI
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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.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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from langchain.vectorstores import Vectara
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if os.environ.get("VECTARA_CUSTOMER_ID", None) is None:
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raise Exception("Missing `VECTARA_CUSTOMER_ID` environment variable.")
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if os.environ.get("VECTARA_CORPUS_ID", None) is None:
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raise Exception("Missing `VECTARA_CORPUS_ID` environment variable.")
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if os.environ.get("VECTARA_API_KEY", None) is None:
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raise Exception("Missing `VECTARA_API_KEY` environment variable.")
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# If you want to ingest data then use this code.
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# Note that no document chunking is needed, as this is
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# done efficiently in the Vectara backend.
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# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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# docs = loader.load()
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# vec_store = Vectara.from_document(docs)
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# retriever = vec_store.as_retriever()
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# Otherwise, if data is already loaded into Vectara then use this code:
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retriever = Vectara().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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# RAG
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model = ChatOpenAI()
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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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