langchain/templates/rag-mongo/README.md

# rag-mongo

This template performs RAG using MongoDB and OpenAI.

## Environment Setup

You should export two environment variables, one being your MongoDB URI, the other being your OpenAI API KEY.
If you do not have a MongoDB URI, see the `Setup Mongo` section at the bottom for instructions on how to do so.

```shell
export MONGO_URI=...
export OPENAI_API_KEY=...
```

## 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-mongo
```

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

```shell
langchain app add rag-mongo
```

And add the following code to your `server.py` file:
```python
from rag_mongo import chain as rag_mongo_chain

add_routes(app, rag_mongo_chain, path="/rag-mongo")
```

(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 DO NOT already have a Mongo Search Index you want to connect to, see `MongoDB Setup` section below before proceeding.

If you DO have a MongoDB Search index you want to connect to, edit the connection details in `rag_mongo/chain.py`

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

We can access the template from code with:

```python
from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-mongo")
```

For additional context, please refer to [this notebook](https://colab.research.google.com/drive/1cr2HBAHyBmwKUerJq2if0JaNhy-hIq7I#scrollTo=TZp7_CBfxTOB).


## MongoDB Setup

Use this step if you need to setup your MongoDB account and ingest data.
We will first follow the standard MongoDB Atlas setup instructions [here](https://www.mongodb.com/docs/atlas/getting-started/).

1. Create an account (if not already done)
2. Create a new project (if not already done)
3. Locate your MongoDB URI.

This can be done by going to the deployement overview page and connecting to you database

![connect.png](_images/connect.png)

We then look at the drivers available

![driver.png](_images/driver.png)

Among which we will see our URI listed

![uri.png](_images/uri.png)

Let's then set that as an environment variable locally:

```shell
export MONGO_URI=...
```

4. Let's also set an environment variable for OpenAI (which we will use as an LLM)

```shell
export OPENAI_API_KEY=...
```

5. Let's now ingest some data! We can do that by moving into this directory and running the code in `ingest.py`, eg:

```shell
python ingest.py
```

Note that you can (and should!) change this to ingest data of your choice

6. We now need to set up a vector index on our data.

We can first connect to the cluster where our database lives

![cluster.png](_images%2Fcluster.png)

We can then navigate to where all our collections are listed

![collections.png](_images%2Fcollections.png)

We can then find the collection we want and look at the search indexes for that collection

![search-indexes.png](_images%2Fsearch-indexes.png)

That should likely be empty, and we want to create a new one:

![create.png](_images%2Fcreate.png)

We will use the JSON editor to create it

![json_editor.png](_images%2Fjson_editor.png)

And we will paste the following JSON in:

```text
 {
   "mappings": {
     "dynamic": true,
     "fields": {
       "embedding": {
         "dimensions": 1536,
         "similarity": "cosine",
         "type": "knnVector"
       }
     }
   }
 }
```
![json.png](_images%2Fjson.png)

From there, hit "Next" and then "Create Search Index". It will take a little bit but you should then have an index over your data!