## rag\_lantern

This template performs RAG with Lantern.

[Lantern](https://lantern.dev/) is an open-source vector database built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL). It enables vector search and embedding generation inside your database.

## Environment Setup[​](#environment-setup "Direct link to Environment Setup")

Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.

To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key.

To find your `LANTERN_URL` and `LANTERN_SERVICE_KEY`, head to your Lantern project's [API settings](https://lantern.dev/dashboard/project/_/settings/api).

*   `LANTERN_URL` corresponds to the Project URL
*   `LANTERN_SERVICE_KEY` corresponds to the `service_role` API key

```
export LANTERN_URL=export LANTERN_SERVICE_KEY=export OPENAI_API_KEY=
```

## Setup Lantern Database[​](#setup-lantern-database "Direct link to Setup Lantern Database")

Use these steps to setup your Lantern database if you haven't already.

1.  Head to [https://lantern.dev](https://lantern.dev/) to create your Lantern database.
    
2.  In your favorite SQL client, jump to the SQL editor and run the following script to setup your database as a vector store:
    
    ```
    -- Create a table to store your documentscreate table  documents (    id uuid primary key,    content text, -- corresponds to Document.pageContent    metadata jsonb, -- corresponds to Document.metadata    embedding REAL[1536] -- 1536 works for OpenAI embeddings, change as needed  );-- Create a function to search for documentscreate function match_documents (  query_embedding REAL[1536],  filter jsonb default '{}') returns table (  id uuid,  content text,  metadata jsonb,  similarity float) language plpgsql as $$#variable_conflict use_columnbegin  return query  select    id,    content,    metadata,    1 - (documents.embedding <=> query_embedding) as similarity  from documents  where metadata @> filter  order by documents.embedding <=> query_embedding;end;$$;
    ```
    

## Setup Environment Variables[​](#setup-environment-variables "Direct link to Setup Environment Variables")

Since we are using [`Lantern`](https://python.langchain.com/docs/integrations/vectorstores/lantern) and [`OpenAIEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/openai), we need to load their API keys.

## Usage[​](#usage "Direct link to Usage")

First, install the LangChain CLI:

```
pip install -U langchain-cli
```

To create a new LangChain project and install this as the only package, you can do:

```
langchain app new my-app --package rag-lantern
```

If you want to add this to an existing project, you can just run:

```
langchain app add rag-lantern
```

And add the following code to your `server.py` file:

```
from rag_lantern.chain import chain as rag_lantern_chainadd_routes(app, rag_lantern_chain, path="/rag-lantern")
```

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/). If you don't have access, you can skip this section

```
export LANGCHAIN_TRACING_V2=trueexport 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:

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-lantern/playground](http://127.0.0.1:8000/rag-lantern/playground)

We can access the template from code with:

```
from langserve.client import RemoteRunnablerunnable = RemoteRunnable("http://localhost:8000/rag-lantern")
```