templates: add rag azure search template (#18143)

- **Description:** Adds a template for performing RAG with the
AzureSearch vectorstore.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A

---------

Co-authored-by: Erick Friis <erickfriis@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

templates/rag-azure-search/.gitignore

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+__pycache__

templates/rag-azure-search/LICENSE

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+MIT License
+
+Copyright (c) 2024 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-azure-search/README.md

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+# rag-azure-search
+
+This template performs RAG on documents using [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) as the vectorstore and Azure OpenAI chat and embedding models.
+
+For additional details on RAG with Azure AI Search, refer to [this notebook](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/vectorstores/azuresearch.ipynb).
+
+
+## Environment Setup
+
+***Prerequisites:*** Existing [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) and [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/overview) resources.
+
+***Environment Variables:***
+
+To run this template, you'll need to set the following environment variables:
+
+***Required:***
+
+- AZURE_SEARCH_ENDPOINT - The endpoint of the Azure AI Search service.
+- AZURE_SEARCH_KEY - The API key for the Azure AI Search service.
+- AZURE_OPENAI_ENDPOINT - The endpoint of the Azure OpenAI service.
+- AZURE_OPENAI_API_KEY - The API key for the Azure OpenAI service.
+- AZURE_EMBEDDINGS_DEPLOYMENT - Name of the Azure OpenAI deployment to use for embeddings.
+- AZURE_CHAT_DEPLOYMENT - Name of the Azure OpenAI deployment to use for chat.
+
+***Optional:***
+
+- AZURE_SEARCH_INDEX_NAME - Name of an existing Azure AI Search index to use. If not provided, an index will be created with name "rag-azure-search".
+- OPENAI_API_VERSION - Azure OpenAI API version to use. Defaults to "2023-05-15". 
+
+## 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-azure-search
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-azure-search
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_azure_search import chain as rag_azure_search_chain
+
+add_routes(app, rag_azure_search_chain, path="/rag-azure-search")
+```
+
+(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 "default"
+```
+
+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-azure-search/playground](http://127.0.0.1:8000/rag-azure-search/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-azure-search")
+```

templates/rag-azure-search/pyproject.toml

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+[tool.poetry]
+name = "rag-azure-search"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain-core = ">=0.1.5"
+langchain-openai = ">=0.0.1"
+azure-search-documents = ">=11.4.0"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.4"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"
+
+[tool.langserve]
+export_module = "rag_azure_search"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/rag-azure-search/rag_azure_search/__init__.py

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

templates/rag-azure-search/rag_azure_search/chain.py

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+import os
+
+from langchain_community.vectorstores.azuresearch import AzureSearch
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
+
+if not os.getenv("AZURE_OPENAI_ENDPOINT"):
+    raise ValueError("Please set the environment variable AZURE_OPENAI_ENDPOINT")
+
+if not os.getenv("AZURE_OPENAI_API_KEY"):
+    raise ValueError("Please set the environment variable AZURE_OPENAI_API_KEY")
+
+if not os.getenv("AZURE_EMBEDDINGS_DEPLOYMENT"):
+    raise ValueError("Please set the environment variable AZURE_EMBEDDINGS_DEPLOYMENT")
+
+if not os.getenv("AZURE_CHAT_DEPLOYMENT"):
+    raise ValueError("Please set the environment variable AZURE_CHAT_DEPLOYMENT")
+
+if not os.getenv("AZURE_SEARCH_ENDPOINT"):
+    raise ValueError("Please set the environment variable AZURE_SEARCH_ENDPOINT")
+
+if not os.getenv("AZURE_SEARCH_KEY"):
+    raise ValueError("Please set the environment variable AZURE_SEARCH_KEY")
+
+
+api_version = os.getenv("OPENAI_API_VERSION", "2023-05-15")
+index_name = os.getenv("AZURE_SEARCH_INDEX_NAME", "rag-azure-search")
+
+embeddings = AzureOpenAIEmbeddings(
+    deployment=os.environ["AZURE_EMBEDDINGS_DEPLOYMENT"],
+    api_version=api_version,
+    chunk_size=1,
+)
+
+vector_store: AzureSearch = AzureSearch(
+    azure_search_endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
+    azure_search_key=os.environ["AZURE_SEARCH_KEY"],
+    index_name=index_name,
+    embedding_function=embeddings.embed_query,
+)
+
+"""
+(Optional) Example document - 
+Uncomment the following code to load the document into the vector store
+or substitute with your own.
+"""
+# import pathlib
+# from langchain.text_splitter import CharacterTextSplitter
+# from langchain_community.document_loaders import TextLoader
+
+# current_file_path = pathlib.Path(__file__).resolve()
+# root_directory = current_file_path.parents[3]
+# target_file_path = \
+#     root_directory / "docs" / "docs" / "modules" / "state_of_the_union.txt"
+
+# loader = TextLoader(str(target_file_path), encoding="utf-8")
+
+# documents = loader.load()
+# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+# docs = text_splitter.split_documents(documents)
+
+# vector_store.add_documents(documents=docs)
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+"""
+
+# Perform a similarity search
+retriever = vector_store.as_retriever()
+
+_prompt = ChatPromptTemplate.from_template(template)
+_model = AzureChatOpenAI(
+    deployment_name=os.environ["AZURE_CHAT_DEPLOYMENT"],
+    api_version=api_version,
+)
+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)