## mongo-parent-document-retrieval

This template performs RAG using MongoDB and OpenAI. It does a more advanced form of RAG called Parent-Document Retrieval.

In this form of retrieval, a large document is first split into medium sized chunks. From there, those medium size chunks are split into small chunks. Embeddings are created for the small chunks. When a query comes in, an embedding is created for that query and compared to the small chunks. But rather than passing the small chunks directly to the LLM for generation, the medium-sized chunks from whence the smaller chunks came are passed. This helps enable finer-grained search, but then passing of larger context (which can be useful during generation).

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

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

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

To use this package, you should first have the LangChain CLI installed:

```
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 mongo-parent-document-retrieval
```

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

```
langchain app add mongo-parent-document-retrieval
```

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

```
from mongo_parent_document_retrieval import chain as mongo_parent_document_retrieval_chainadd_routes(app, mongo_parent_document_retrieval_chain, path="/mongo-parent-document-retrieval")
```

(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 DO NOT already have a Mongo Search Index you want to connect to, see `MongoDB Setup` section below before proceeding. Note that because Parent Document Retrieval uses a different indexing strategy, it's likely you will want to run this new setup.

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

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/mongo-parent-document-retrieval/playground](http://127.0.0.1:8000/mongo-parent-document-retrieval/playground)

We can access the template from code with:

```
from langserve.client import RemoteRunnablerunnable = RemoteRunnable("http://localhost:8000/mongo-parent-document-retrieval")
```

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

## MongoDB Setup[​](#mongodb-setup "Direct link to 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

We then look at the drivers available

Among which we will see our URI listed

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

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

```
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:

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

We can then navigate to where all our collections are listed

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

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

We will use the JSON editor to create it

And we will paste the following JSON in:

```
{  "mappings": {    "dynamic": true,    "fields": {      "doc_level": [        {          "type": "token"        }      ],      "embedding": {        "dimensions": 1536,        "similarity": "cosine",        "type": "knnVector"      }    }  }}
```

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!