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).
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.
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
from mongo_parent_document_retrieval import chain as mongo_parent_document_retrieval_chain
add_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. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
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
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
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/mongo-parent-document-retrieval/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/mongo-parent-document-retrieval")
For additional context, please refer to this notebook.
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.
- Create an account (if not already done)
- Create a new project (if not already done)
- 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:
- Let's also set an environment variable for OpenAI (which we will use as an LLM)
- Let's now ingest some data! We can do that by moving into this directory and running the code in
Note that you can (and should!) change this to ingest data of your choice
- 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:
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!