vectara rag mq (#13214)

Description: another Vectara template for MultiQuery RAG flow
Twitter handle: @ofermend

Fixes to #13106

---------

Co-authored-by: Ofer Mendelevitch <ofer@vectara.com>
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>

templates/rag-vectara-multiquery/LICENSE

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+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

templates/rag-vectara-multiquery/README.md

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+
+# rag-vectara-multiquery
+
+This template performs multiquery RAG with vectara.
+
+## Environment Setup
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+Also, ensure the following environment variables are set:
+* `VECTARA_CUSTOMER_ID`
+* `VECTARA_CORPUS_ID`
+* `VECTARA_API_KEY`
+
+## 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-vectara
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-vectara
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_vectara import chain as rag_vectara_chain
+
+add_routes(app, rag_vectara_chain, path="/rag-vectara")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [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=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "vectara-demo"
+```
+
+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-vectara/playground](http://127.0.0.1:8000/rag-vectara/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-vectara")
+```

templates/rag-vectara-multiquery/pyproject.toml

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+[tool.poetry]
+name = "rag-vectara-multiquery"
+version = "0.1.0"
+description = ""
+authors = [
+    "Ofer Mendelevitch <ofer@vectara.com>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.325"
+openai = "^0.28.1"
+tiktoken = "^0.5.1"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.15"
+[tool.poetry.group.dev.dependencies.python-dotenv]
+extras = [
+    "cli",
+]
+version = "^1.0.0"
+
+[tool.langserve]
+export_module = "rag_vectara_multiquery"
+export_attr = "chain"
+
+[build-system]
+requires = [
+    "poetry-core",
+]
+build-backend = "poetry.core.masonry.api"

templates/rag-vectara-multiquery/rag_vectara_multiquery.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "8692a430",
+   "metadata": {},
+   "source": [
+    "# Run Template\n",
+    "\n",
+    "In `server.py`, set -\n",
+    "```\n",
+    "add_routes(app, chain_ext, path=\"/rag-vectara-multiquery\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41db5e30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langserve.client import RemoteRunnable\n",
+    "\n",
+    "rag_app_vectara = RemoteRunnable(\"http://localhost:8000/rag-vectara-multiquery\")\n",
+    "rag_app_vectara.invoke(\"How does agent memory work?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.6 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

templates/rag-vectara-multiquery/rag_vectara_multiquery/__init__.py

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+from rag_vectara_multiquery.chain import chain
+
+__all__ = ["chain"]

templates/rag-vectara-multiquery/rag_vectara_multiquery/chain.py

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+import os
+
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.vectorstores import Vectara
+
+if os.environ.get("VECTARA_CUSTOMER_ID", None) is None:
+    raise Exception("Missing `VECTARA_CUSTOMER_ID` environment variable.")
+if os.environ.get("VECTARA_CORPUS_ID", None) is None:
+    raise Exception("Missing `VECTARA_CORPUS_ID` environment variable.")
+if os.environ.get("VECTARA_API_KEY", None) is None:
+    raise Exception("Missing `VECTARA_API_KEY` environment variable.")
+
+# If you want to ingest data then use this code.
+# Note that no document chunking is needed, as this is
+# done efficiently in the Vectara backend.
+# Note: you will need to install beautifulsoup4 to ingest
+
+# from langchain.document_loaders import WebBaseLoader
+# from langchain.embeddings import OpenAIEmbeddings
+# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+# docs = loader.load()
+# vec_store = Vectara.from_documents(docs, embedding=OpenAIEmbeddings())
+# retriever = vec_store.as_retriever()
+
+# Otherwise, if data is already loaded into Vectara then use this code:
+llm = ChatOpenAI(temperature=0)
+retriever = MultiQueryRetriever.from_llm(retriever=Vectara().as_retriever(), llm=llm)
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# RAG
+model = ChatOpenAI()
+chain = (
+    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)