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# rag-singlestoredb
This template performs RAG using SingleStoreDB and OpenAI.
## Environment Setup
This template uses SingleStoreDB as a vectorstore and requires that `SINGLESTOREDB_URL` is set. It should take the form `admin:password@svc-xxx.svc.singlestore.com:port/db_name`
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
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pip install -U langchain-cli
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```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package rag-singlestoredb
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add rag-singlestoredb
```
And add the following code to your `server.py` file:
```python
from rag_singlestoredb import chain as rag_singlestoredb_chain
add_routes(app, rag_singlestoredb_chain, path="/rag-singlestoredb")
```
(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-singlestoredb/playground ](http://127.0.0.1:8000/rag-singlestoredb/playground )
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-singlestoredb")
```
