How to Use ChromaDB for AI Search

ChromaDB gives you AI search with almost no setup

Using ChromaDB for AI search means creating a collection, adding your documents, and querying by meaning instead of exact keywords — and Chroma handles the vector storage and similarity math for you. It is an open-source vector database built to be the fastest way to add semantic search or RAG to an application. You can run it embedded in your process for a prototype or as a server for something shared, and it can even generate embeddings for you, so you write very little glue and get to a working search quickly.

This guide walks the core workflow and the few decisions that actually matter, so you finish able to ask your data questions in plain language. Chroma's appeal is how little ceremony it demands; the trade-off is that very large or high-throughput workloads eventually outgrow it, which is worth knowing before you build something you later have to migrate.

Step 1: Create a collection

A collection is Chroma's container for related documents and their vectors. Create one and decide how embeddings are produced — Chroma can call a default embedding function, or you can supply your own vectors from sentence-transformers or a hosted API for more control over quality.

• Point Chroma at a directory to persist the collection to disk so it survives restarts rather than living only in memory.
• Pick one embedding approach per collection and stay consistent; mixing models within a collection breaks similarity.
• Name collections by purpose so a multi-tenant app can keep different users' data cleanly separated.

Step 2: Add documents with metadata

Add your text along with an id and a metadata dictionary for each item. If you let Chroma embed for you, you pass the raw text; if you embed yourself, you pass the vectors too. The metadata you attach now is what makes filtered search possible later, so it is worth a moment of thought.

Attach rich metadata — source, author, date, category — at insert time. It is what lets you filter searches later, and adding it after the fact means re-ingesting everything.

Step 3: Query by meaning

Query the collection with a question or a vector and Chroma returns the closest documents. Because retrieval is by semantic distance, "how do I reset my password" finds the right help article even if it never uses those exact words — which is the whole point of semantic search over keyword matching.

• Request the number of results you need and read back the documents, distances, and metadata together.
• Use the same embedding path for queries as for stored documents, or the distances become meaningless.
• Treat the distances as a confidence signal — a very weak best match means no good answer exists in the collection.

Step 4: Filter with metadata

Chroma supports filtering on metadata alongside the vector search, so you can restrict results to one source, one date range, or one category. This is essential for multi-tenant apps and for any case where "closest in meaning" must also satisfy "belongs to this user," which is a requirement that sneaks up on most real applications.

• Combine a semantic query with a metadata filter in a single call for efficient, scoped retrieval.
• Filter on access tags to keep one tenant from ever retrieving another tenant's documents.

Step 5: Wire it into RAG

For a chatbot, Chroma is the retrieval layer: embed the user's question, fetch the nearest passages, and pass them to a language model with an instruction to answer only from that context. Frameworks like LangChain and LlamaIndex integrate Chroma directly, so the full loop is a short amount of code rather than a project. Remember Chroma's scope, though — it is excellent for prototypes and small-to-mid workloads, and very large or high-throughput deployments are where a managed engine or pgvector earns its place. Starting on Chroma to validate the product and migrating only when scale demands it is a perfectly sound plan.

Prefer it built and managed for you?

BSH Technologies builds production AI search and RAG on the right store for your scale — ChromaDB where it fits, pgvector or a managed engine where it does not — with metadata filtering, multi-tenant isolation, and grounded answers. To put semantic search into your product, talk to BSH Technologies or explore our AI & automation services.