mirror of
https://github.com/hwchase17/langchain
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79 lines
2.8 KiB
Markdown
79 lines
2.8 KiB
Markdown
# rag-momento-vector-index
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This template performs RAG using Momento Vector Index (MVI) and OpenAI.
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> 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.
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To sign up and access MVI, visit the [Momento Console](https://console.gomomento.com/).
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## Environment Setup
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This template uses Momento Vector Index as a vectorstore and requires that `MOMENTO_API_KEY`, and `MOMENTO_INDEX_NAME` are set.
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Go to the [console](https://console.gomomento.com/) to get an API key.
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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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-momento-vector-index
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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-momento-vector-index
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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_momento_vector_index import chain as rag_momento_vector_index_chain
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add_routes(app, rag_momento_vector_index_chain, path="/rag-momento-vector-index")
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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-momento-vector-index/playground](http://127.0.0.1:8000/rag-momento-vector-index/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-momento-vector-index")
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```
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## Indexing Data
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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.
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