Published: 2025-05-16
When building AI chatbots for customer support, one of the biggest challenges is providing accurate, contextual information about company processes, products, and policies. Out-of-the-box language models don't know your specific business information, and you can't fit everything into a single prompt.
Vector databases solve this by storing your company's knowledge as high-dimensional vectors that can be quickly searched for semantic similarity. When a customer asks a question, you can retrieve the most relevant context and provide it to your AI model for generating accurate responses.
I've worked with vector databases in two main scenarios: using cloud providers and running my own vector storage in Postgres.
My first major experience was with Pinecone, and it was incredibly straightforward to get started. Within 30 minutes, we were embedding items and retrieving them successfully. The managed nature meant we didn't need to worry about database indexes or infrastructure - it just worked.
We were able to scale rapidly, storing hundreds of thousands of records in a matter of days without any downtime to our servers. The speed and reliability were impressive, especially for a production customer support system where downtime wasn't an option.
Key Learning: Namespaces
One major lesson learned was around Pinecone's namespace feature. When we started, namespaces weren't well supported, so we began without using them at all. Once namespace support matured, we realized we needed them for better data organization, but transitioning wasn't straightforward. We eventually got it done, but it would have been much easier to plan for namespaces from the beginning.
For a side project, I wanted to explore a more cost-effective, self-hosted approach while building a profile search system. The goal was to allow users to find profiles based on simple text searches that could understand intent and context beyond just keyword matching.
Hybrid Search Strategy: I implemented a dual approach that combined traditional filtering with vector similarity search. When a user submits a query, I send it to ChatGPT to parse and extract structured filters, while simultaneously embedding the query for semantic search against profile summaries.
The interesting part was how I weighted the results: vector similarity gets a 0.9 weight while the filter score gets 0.1, then I sort results based on this combined score. This approach is powerful because profiles with AI-generated summaries might contain relevant information that matches the query semantically, even if they don't perfectly match all the explicit filters. The result is more comprehensive search results than either approach would provide alone.
Scaling Challenges: As the dataset grew to over 250,000 records, I hit a major performance wall. Vector searches became incredibly slow - to the point where queries wouldn't even finish. This forced me to learn about vector indexing the hard way.
Creating the necessary indexes was its own challenge. Running on a small database instance (256MB RAM, 0.1 CPU), the indexing process was painfully slow. I had to temporarily scale up to 4GB RAM and 1 CPU just to get the indexes built in a reasonable timeframe. Once the indexes were in place and I scaled back down, search performance was acceptable again.
In practice, I've used vector databases to help companies provide better customer support by:
Hybrid Search Pipeline: For the profile search system, the architecture works like this:
This approach gives us the best of both worlds - the precision of structured filters with the comprehensiveness of semantic search.
Plan for Namespaces Early: If your vector database supports namespaces (like Pinecone), plan for them from the beginning. Migrating to use namespaces later is possible but adds unnecessary complexity.
Managed Services Win on Speed to Market: Pinecone's managed approach allowed us to go from zero to production-ready vector search in under an hour. When you're building customer-facing features, this speed is invaluable.
Scale Matters: Being able to add hundreds of thousands of records without impacting server performance was crucial for our customer support use case. Users couldn't afford downtime while we built out the knowledge base.
Hybrid Search is Powerful: Combining vector similarity with traditional filtering and weighting the results appropriately can provide much better search experiences than either approach alone. The 0.9/0.1 weighting worked well for profiles, but this ratio should be tuned based on your specific use case.
Plan for Scale from Day One: With self-hosted solutions, you need to think about indexing strategy early. At 250,000+ records, unindexed vector searches become unusable. Also, budget for temporary resource scaling when building indexes - what seems like adequate resources for normal operations may be insufficient for index creation.
Pinecone (Cloud) Advantages:
Postgres + pgvector (Self-Hosted) Advantages:
Self-Hosted Challenges:
The choice ultimately depends on your priorities: Pinecone for speed and scale, Postgres for control and cost-effectiveness.
Vector databases are becoming increasingly important for AI applications. As models get better and more companies adopt AI for customer service, having a robust knowledge retrieval system becomes critical for providing accurate, helpful responses.
I'm currently looking for work, if you have a role that you think I'd be a good fit for feel free to reach out to me on LinkedIn.