feat: add a rag template for momento vector index (langchain-ai#12757)

# Description
Add a RAG template showcasing Momento Vector Index as a vector store.
Includes a project directory and README.

# **Twitter handle** 

Tag the company @momentohq for a mention and @mlonml for the
contribution.

templates/rag-momento-vector-index/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-momento-vector-index/README.md

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+# rag-momento-vector-index
+
+This template performs RAG using Momento Vector Index (MVI) and OpenAI.
+
+> MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. Combine with other Momento services such as Momento Cache to cache prompts and as a session store or Momento Topics as a pub/sub system to broadcast events to your application.
+
+To sign up and access MVI, visit the [Momento Console](https://console.gomomento.com/).
+
+## Environment Setup
+
+This template uses Momento Vector Index as a vectorstore and requires that `MOMENTO_API_KEY`, and `MOMENTO_INDEX_NAME` are set.
+
+Go to the [console](https://console.gomomento.com/) to get an API key.
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U "langchain-cli[serve]"
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package rag-momento-vector-index
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-momento-vector-index
+```
+
+And add the following code to your `server.py` file:
+
+```python
+from rag_momento_vector_index import chain as rag_momento_vector_index_chain
+
+add_routes(app, rag_momento_vector_index_chain, path="/rag-momento-vector-index")
+```
+
+(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-momento-vector-index/playground](http://127.0.0.1:8000/rag-momento-vector-index/playground)
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-momento-vector-index")
+```
+
+## Indexing Data
+
+We have included a sample module to index data. That is available at `rag_momento_vector_index/ingest.py`. You will see a commented out line in `chain.py` that invokes this. Uncomment to use.

templates/rag-momento-vector-index/pyproject.toml

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+[tool.poetry]
+name = "rag-momento-vector-index"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.313, <0.1"
+momento = "^1.12.0"
+openai = "^0.28.1"
+tiktoken = "^0.5.1"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.4"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"
+
+[tool.poetry.group.index.dependencies]
+bs4 = "^0.0.1"
+
+[tool.poetry.group.test.dependencies]
+langserve = "^0.0.21"
+
+[tool.langserve]
+export_module = "rag_momento_vector_index"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/rag-momento-vector-index/rag_momento_vector_index/__init__.py

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

templates/rag-momento-vector-index/rag_momento_vector_index/chain.py

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+import os
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.vectorstores import MomentoVectorIndex
+from momento import (
+    CredentialProvider,
+    PreviewVectorIndexClient,
+    VectorIndexConfigurations,
+)
+
+API_KEY_ENV_VAR_NAME = "MOMENTO_API_KEY"
+if os.environ.get(API_KEY_ENV_VAR_NAME, None) is None:
+    raise Exception(f"Missing `{API_KEY_ENV_VAR_NAME}` environment variable.")
+
+MOMENTO_INDEX_NAME = os.environ.get("MOMENTO_INDEX_NAME", "langchain-test")
+
+### Sample Ingest Code - this populates the vector index with data
+### Run this on the first time to seed with data
+# from rag_momento_vector_index import ingest
+# ingest.load(API_KEY_ENV_VAR_NAME, MOMENTO_INDEX_NAME)
+
+
+vectorstore = MomentoVectorIndex(
+    embedding=OpenAIEmbeddings(),
+    client=PreviewVectorIndexClient(
+        configuration=VectorIndexConfigurations.Default.latest(),
+        credential_provider=CredentialProvider.from_environment_variable(
+            API_KEY_ENV_VAR_NAME
+        ),
+    ),
+    index_name=MOMENTO_INDEX_NAME,
+)
+retriever = vectorstore.as_retriever()
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# RAG
+model = ChatOpenAI()
+chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-momento-vector-index/rag_momento_vector_index/ingest.py

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+### Ingest code - you may need to run this the first time
+import os
+
+from langchain.document_loaders import WebBaseLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import MomentoVectorIndex
+from momento import (
+    CredentialProvider,
+    PreviewVectorIndexClient,
+    VectorIndexConfigurations,
+)
+
+
+def load(API_KEY_ENV_VAR_NAME: str, index_name: str) -> None:
+    if os.environ.get(API_KEY_ENV_VAR_NAME, None) is None:
+        raise Exception(f"Missing `{API_KEY_ENV_VAR_NAME}` environment variable.")
+
+    # Load
+    loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+    data = loader.load()
+
+    # Split
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+    all_splits = text_splitter.split_documents(data)
+
+    # Add to vectorDB
+    MomentoVectorIndex.from_documents(
+        all_splits,
+        embedding=OpenAIEmbeddings(),
+        client=PreviewVectorIndexClient(
+            configuration=VectorIndexConfigurations.Default.latest(),
+            credential_provider=CredentialProvider.from_environment_variable(
+                API_KEY_ENV_VAR_NAME
+            ),
+        ),
+        index_name=index_name,
+    )