Handling Multi-Field Search with ChromaDB

Welcome back! In our previous lesson, we explored reranking, a technique that refines search results by ordering them based on relevance. Today, we will focus on multi-field search, an approach that enhances semantic search capabilities by allowing queries across multiple document fields, such as title and content. This lesson will build on your existing knowledge of vector search and introduce you to the practical implementation of multi-field search using ChromaDB.

Multi-field search is crucial in scenarios where documents contain rich information spread across different fields. By searching across these fields, we can provide more comprehensive and relevant results to users. Let's dive into how this works and how you can implement it in your projects.

Let's walk through the process of setting up ChromaDB for multi-field search. We'll use the provided code example to guide us through each step.

First, we load our documents and initialize a ChromaDB client and create a collection with an embedding function. This collection will store our documents, each containing multiple fields like title, content, category, tags, and date. Here's how you can do it:

from chromadb import Client
from chromadb.utils import embedding_functions
from data import load_documents

# Load documents
documents = load_documents("./data/corpus.json")
print(f"Loaded {len(documents)} documents.")

# Set up Chroma
model_name = "sentence-transformers/all-MiniLM-L6-v2"
embed_func = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model_name)

client = Client()
collection = client.get_or_create_collection("document_collection", embedding_function=embed_func)

Next, we add documents to the collection using a batch add function. Each document includes fields such as id, content, and metadata (which can include a title, category, tags, and etc.). This structure allows us to perform multi-field searches effectively.

# Batch add function
def batch_add_to_chroma(collection, documents, batch_size=50):
    print("Starting batch insert into ChromaDB...")
    for i in range(0, len(documents), batch_size):
        batch = documents[i:i + batch_size]
        print(f"Inserting batch {i} to {i + len(batch)}...")
        collection.add(
            documents=[doc["content"] for doc in batch],
            ids=[str(doc["id"]) for doc in batch],
            metadatas=[{
                "title": doc["title"],
                "category": doc.get("category", "unknown"),
                "tags": ",".join(doc.get("tags", [])),
                "date": doc.get("date", "unknown")
            } for doc in batch]
        )
    print("Finished inserting all batches.")

# Add data to Chroma
batch_add_to_chroma(collection, documents)