Unlocking the Full Potential of RAG with MongoDB Vector Search

In the rapidly evolving world of artificial intelligence (AI), the integration of Retrieval-Augmented Generation (RAG) has emerged as a game-changer for personalized and context-aware AI assistance. RAG systems leverage the power of large language models (LLMs) in conjunction with information retrieval techniques, enabling AI assistants to provide accurate and relevant responses by seamlessly incorporating users’ private data and real-time information.

While frameworks like LlamaIndex offer excellent functionality for building RAG systems, developers often face challenges when it comes to implementing a robust and customized solution that meets their specific requirements. Most examples online only show the basic setup that gets you started by saving the vector indices on the local disk. But how do you go from there to scaling to terrabytes of data?

Out-of-the-box solutions may not always address the unique needs of an organization or individual, particularly when it comes to data privacy, security, and real-time data integration.

The Importance of Self-Hosted RAG Solutions

One of the primary concerns with relying solely on open-source frameworks is the lack of dedicated support and customization options. As organizations and individuals continue to embrace the power of RAG, the need for tailored solutions that can handle sensitive data and integrate seamlessly with existing systems becomes increasingly important.

By developing and self-hosting their own RAG implementations, developers can ensure complete control over the data flow, security measures, and integration points. This approach not only enhances data privacy but also enables the incorporation of real-time data streams and proprietary knowledge bases, unlocking the full potential of personalized AI assistance.

There are many different Vector Stores available now, for inspiration have a look at this list:

https://docs.llamaindex.ai/en/stable/module_guides/storing/vector_stores/

Leveraging MongoDB’s Vector Search Feature MongoDB, a popular NoSQL database, offers a powerful vector search feature that can be leveraged to build efficient and scalable RAG systems. It is also available to download and run on your own server for free which is an added bonus if you data is sensitive.

By storing and indexing data using vector embeddings, developers can quickly retrieve relevant information based on semantic similarity, enabling their AI assistants to provide more accurate and contextual responses.

Here’s an example of how you can leverage MongoDB’s vector search feature in your RAG implementation:

Imagine you have some data, any data in a document Database, such as MongoDB.

Those who have read my other articles may know that I LOVE Mongo for many reasons. The latest is the fact that is not only a time tested production database, but its Vector search functionality also offers itself perfectly to the sea of emerging LLM applications.

For this example I made an LLM write a fictional story about an exoplanet. This is to assure that the LLM has not seen the data before and MUST rely on the retrieval from the vector database to answer my questions.

The data was ingested into Mongo by using Llama Index’s node and Document classes. While I found a lot of duplicated functionality in that library and none of it did what I wanted to achieve, these 2 classes are worth their weight in gold, because context related nodes are linked with their documents and the metadata option allows your privately hosted LLM to quote the precise source of all your company secrets :-)

Now you can query the data using conventional Mongo Atlas Vector search, no external data connector required at all.

python
pipeline = [
 {
 '$vectorSearch': {
 'index': 'vector_index',
 'path': 'embedding',
 'queryVector': embeddings,
 'numCandidates': 20,
 'limit': 3
 }
 },
 {
 '$project': {
 '_id': 0,
 'id': 1,
 'text': 1,
 'score': {'$meta': 'vectorSearchScore'}
 }
 }
]
results = []
# run pipeline
result = self.mongodb_client\[self.DB_NAME\]\[COLLECTION_NAME\].aggregate(pipeline)
for i in result:
 results.append(i)        

In this example, we first define a pipeline that performs a vector search on our indexed data. The $vectorSearch stage retrieves documents based on the similarity between their embeddings and the provided queryVector. The $project stage then selects the desired fields, including the vector search score, for the retrieved documents.

It’s important to note that for optimal performance and data privacy, we recommend self-hosting your embedding model as well. By keeping your sensitive data and models within your own infrastructure, you can ensure maximum control and security over your RAG system.

Once the data is added and embedded using a self-hosted embedding model of your choice, any query can be embedded using the same model, and the relevant context can be retrieved using the vector search pipeline demonstrated above.

And here is another secret:

You dont need a framework to pass context to the LLM. All LLM’s take a string and return a string. If you follow me you probably know that I am a fan of Open Source LLM’s. They are all different, but really, If you are still reading up till here, it means you are smarter than most. So you can concatonate a string without a framework!

I have had very good QA retrieval results using this string and sending it to Mistral and Lllama2:

qa_context_string = f"""Use the following list of python dictionaries as context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. please also quote the 'id's of the documents / list items that you are quoting in your answer and quote the source using the exact "url" """        

This works fine for me, but I am sure you will come up with even better ways. The possibilies are endless.

Embrace the Future of Personalized AI Assistance As AI continues to permeate various aspects of our lives, the demand for personalized and context-aware AI assistants will only continue to grow. By embracing self-hosted RAG solutions, developers can unlock the full potential of these cutting-edge technologies, providing their users with truly tailored and accurate AI experiences.

While open-source frameworks offer a solid foundation, taking the time to develop and self-host your own RAG implementation ensures complete control over data privacy, security, and real-time data integration. By leveraging powerful tools like MongoDB’s vector search feature and self-hosted embedding models, you can build robust and scalable RAG systems that meet the unique needs of your organization or personal use case.

So, whether you’re a developer seeking to enhance your organization’s AI capabilities or an individual looking to build a personalized AI assistant, consider investing in a self-hosted RAG solution. Unlock the full potential of AI assistance and stay ahead of the curve in this rapidly evolving landscape.

#LLM #RetrievalAugmentedGen #AI #Mongodb #Python