# RAG - MongoDB 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") ``` If you want to set up an ingestion pipeline, you can add the following code to your `server.py` file: ```python from rag_mongo import ingest as rag_mongo_ingest add_routes(app, rag_mongo_ingest, path="/rag-mongo-ingest") ``` (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 ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_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 deployment overview page and connecting to you database ![Screenshot highlighting the 'Connect' button in MongoDB Atlas.](_images/connect.png) "MongoDB Atlas Connect Button" We then look at the drivers available ![Screenshot showing the MongoDB Atlas drivers section for connecting to the database.](_images/driver.png) "MongoDB Atlas Drivers Section" Among which we will see our URI listed ![Screenshot displaying an example of a MongoDB URI in the connection instructions.](_images/uri.png) "MongoDB URI Example" 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 ![Screenshot of the MongoDB Atlas interface showing the cluster overview with a 'Connect' button.](_images/cluster.png) "MongoDB Atlas Cluster Overview" We can then navigate to where all our collections are listed ![Screenshot of the MongoDB Atlas interface showing the collections overview within a database.](_images/collections.png) "MongoDB Atlas Collections Overview" We can then find the collection we want and look at the search indexes for that collection ![Screenshot showing the search indexes section in MongoDB Atlas for a specific collection.](_images/search-indexes.png) "MongoDB Atlas Search Indexes" That should likely be empty, and we want to create a new one: ![Screenshot highlighting the 'Create Index' button in MongoDB Atlas.](_images/create.png) "MongoDB Atlas Create Index Button" We will use the JSON editor to create it ![Screenshot showing the JSON Editor option for creating a search index in MongoDB Atlas.](_images/json_editor.png) "MongoDB Atlas JSON Editor Option" And we will paste the following JSON in: ```text { "mappings": { "dynamic": true, "fields": { "embedding": { "dimensions": 1536, "similarity": "cosine", "type": "knnVector" } } } } ``` ![Screenshot of the JSON configuration for a search index in MongoDB Atlas.](_images/json.png) "MongoDB Atlas Search Index JSON Configuration" 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!