Multi-Vector Search

Search across multiple vector fields simultaneously — find items that match across different semantic aspects like title, description, and reviews.

When to Activate

Activate this skill when:

•User has multiple text fields per item (title + description, question + answer)
•User wants to search different aspects with different weights
•User mentions "multi-vector", "title and content", "multiple embeddings"
•User's items have semantically distinct parts that should be searched together

Do NOT activate when:

•User has single text field → use semantic-search
•User needs keyword + semantic → use hybrid-search
•User has image + text → use multimodal-retrieval

Interactive Flow

Step 1: Identify Vector Fields

"What text fields do you have per item?"

Common Patterns	Fields
Products	title, description, reviews
Documents	title, abstract, body
Q&A	question, answer
Resumes	skills, experience, education
News	headline, body

List your fields: ___

Step 2: Determine Field Importance

"How important is each field for search relevance?"

Field	Importance	Suggested Weight
Title	High (exact matches)	0.4 - 0.5
Description	Medium (detailed info)	0.3 - 0.4
Reviews	Low (supplementary)	0.1 - 0.2

Note: Weights should sum to 1.0

Step 3: Confirm Configuration

"Based on your requirements:

•Vector fields: title_embedding, content_embedding
•Weights: 0.5, 0.5 (balanced)
•Fusion: RRF (recommended)

Proceed? (yes / adjust [what])"

Core Concepts

Mental Model: Multi-Criteria Job Interview

Think of multi-vector search as evaluating a candidate on multiple criteria:

•Resume alone might miss communication skills
•Interview alone might miss technical depth
•Both together give complete picture

code

┌─────────────────────────────────────────────────────────┐
│                  Multi-Vector Search                     │
│                                                          │
│  Item: Product Listing                                   │
│  ┌─────────────┬─────────────┬─────────────┐           │
│  │   Title     │ Description │   Reviews   │           │
│  │  "iPhone    │ "Latest     │ "Great      │           │
│  │   15 Pro"   │  Apple..."  │  camera!"   │           │
│  └──────┬──────┴──────┬──────┴──────┬──────┘           │
│         │             │             │                   │
│         ▼             ▼             ▼                   │
│    ┌─────────┐  ┌─────────┐  ┌─────────┐              │
│    │ Embed   │  │ Embed   │  │ Embed   │              │
│    └────┬────┘  └────┬────┘  └────┬────┘              │
│         │            │            │                    │
│         ▼            ▼            ▼                    │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐              │
│  │title_vec │ │ desc_vec │ │review_vec│              │
│  │ (1024d)  │ │ (1024d)  │ │ (1024d)  │              │
│  └────┬─────┘ └────┬─────┘ └────┬─────┘              │
│       │            │            │                     │
│       └────────────┼────────────┘                     │
│                    │                                   │
│                    ▼                                   │
│  Query: "good camera phone"                            │
│                    │                                   │
│           ┌───────┴────────┐                          │
│           │  Search Each   │                          │
│           │  Vector Field  │                          │
│           └───────┬────────┘                          │
│                   │                                    │
│                   ▼                                    │
│           ┌───────────────┐                           │
│           │ Score Fusion  │  RRF or Weighted          │
│           └───────┬───────┘                           │
│                   │                                    │
│                   ▼                                    │
│           Combined Results                             │
│           (matches across all aspects)                 │
└─────────────────────────────────────────────────────────┘

Why Multiple Vectors?

Single Vector	Multi-Vector
Embeds entire item as one	Preserves distinct semantic aspects
Title dominates or gets lost	Title and description weighted separately
Can't tune importance	Can adjust weights per field
Simpler	More flexible

When Multi-Vector Helps

Scenario	Problem with Single Vector	Multi-Vector Solution
Product search	Long description dilutes title	Title vector captures exact name
Academic papers	Abstract and body mixed	Search abstract for overview, body for details
Q&A matching	Question semantics lost in answer	Match question-to-question separately

Implementation

python

from pymilvus import MilvusClient, DataType, AnnSearchRequest, RRFRanker
from sentence_transformers import SentenceTransformer

class MultiVectorSearch:
    def __init__(self, uri: str = "./milvus.db"):
        self.client = MilvusClient(uri=uri)
        self.model = SentenceTransformer('BAAI/bge-large-en-v1.5')
        self.dim = 1024
        self.collection_name = "multi_vector_search"
        self._init_collection()

    def _init_collection(self):
        if self.client.has_collection(self.collection_name):
            return

        schema = self.client.create_schema()
        schema.add_field("id", DataType.INT64, is_primary=True, auto_id=True)
        schema.add_field("title", DataType.VARCHAR, max_length=1024)
        schema.add_field("content", DataType.VARCHAR, max_length=65535)
        schema.add_field("title_embedding", DataType.FLOAT_VECTOR, dim=self.dim)
        schema.add_field("content_embedding", DataType.FLOAT_VECTOR, dim=self.dim)

        index_params = self.client.prepare_index_params()
        index_params.add_index(field_name="title_embedding", index_type="AUTOINDEX", metric_type="COSINE")
        index_params.add_index(field_name="content_embedding", index_type="AUTOINDEX", metric_type="COSINE")

        self.client.create_collection(
            collection_name=self.collection_name,
            schema=schema,
            index_params=index_params
        )

    def add(self, items: list):
        """Add data
        items: [{"title": "...", "content": "..."}]
        """
        titles = [item["title"] for item in items]
        contents = [item["content"] for item in items]

        title_embeddings = self.model.encode(titles).tolist()
        content_embeddings = self.model.encode(contents).tolist()

        data = []
        for item, title_emb, content_emb in zip(items, title_embeddings, content_embeddings):
            data.append({
                "title": item["title"],
                "content": item["content"],
                "title_embedding": title_emb,
                "content_embedding": content_emb
            })

        self.client.insert(collection_name=self.collection_name, data=data)

    def search(self, query: str, limit: int = 10, mode: str = "balanced"):
        """Multi-vector search
        mode: "balanced" | "title" | "content"
        """
        query_embedding = self.model.encode(query).tolist()

        # Title vector search
        title_req = AnnSearchRequest(
            data=[query_embedding],
            anns_field="title_embedding",
            param={"metric_type": "COSINE"},
            limit=limit * 2
        )

        # Content vector search
        content_req = AnnSearchRequest(
            data=[query_embedding],
            anns_field="content_embedding",
            param={"metric_type": "COSINE"},
            limit=limit * 2
        )

        # RRF fusion
        results = self.client.hybrid_search(
            collection_name=self.collection_name,
            reqs=[title_req, content_req],
            ranker=RRFRanker(k=60),
            limit=limit,
            output_fields=["title", "content"]
        )

        return [{"title": hit["entity"]["title"],
                 "content": hit["entity"]["content"][:200] + "...",
                 "score": hit["distance"]} for hit in results[0]]

    def search_title_only(self, query: str, limit: int = 10):
        """Fast search on title only"""
        embedding = self.model.encode(query).tolist()
        results = self.client.search(
            collection_name=self.collection_name,
            data=[embedding],
            anns_field="title_embedding",
            limit=limit,
            output_fields=["title", "content"]
        )
        return [{"title": hit["entity"]["title"], "score": hit["distance"]} for hit in results[0]]

# Usage
search = MultiVectorSearch()

# Add articles
search.add([
    {
        "title": "Milvus Vector Database Getting Started",
        "content": "Milvus is an open-source vector database designed for AI applications..."
    },
    {
        "title": "RAG System Architecture Design",
        "content": "Retrieval-Augmented Generation combines retrieval and generation..."
    }
])

# Multi-vector search
results = search.search("how to use vector database")

# Title-only search (faster)
results = search.search_title_only("Milvus")

Weight Strategies

Scenario-Based Weights

Scenario	Title	Description	Reviews	Why
Product Lookup	0.5	0.3	0.2	Users often know product name
Feature Search	0.3	0.5	0.2	Looking for specific features
Review-Based	0.2	0.3	0.5	"What do people say about X"
Academic Papers	0.4	0.4	0.2	Title and abstract equally important

Dynamic Weight Selection

python

def get_weights_for_query(query: str) -> tuple:
    """Automatically select weights based on query characteristics."""

    query_lower = query.lower()
    word_count = len(query.split())

    # Short queries → likely looking for specific item by name
    if word_count <= 3:
        return (0.7, 0.3)  # Favor title

    # Questions → need detailed content
    if query_lower.startswith(('how', 'what', 'why', 'when')):
        return (0.3, 0.7)  # Favor content

    # Default balanced
    return (0.5, 0.5)

Using Weighted Ranker

python

from pymilvus import WeightedRanker

# Instead of RRF, use weighted fusion
ranker = WeightedRanker(0.6, 0.4)  # 60% title, 40% content

results = self.client.hybrid_search(
    collection_name=self.collection_name,
    reqs=[title_req, content_req],
    ranker=ranker,  # Weighted instead of RRF
    limit=limit,
    output_fields=["title", "content"]
)

Performance Optimization

Adaptive Search Strategy

python

def smart_search(self, query: str, mode: str = "auto"):
    """Automatically choose search strategy."""

    if mode == "auto":
        # Short query → title only (faster)
        if len(query.split()) <= 2:
            return self.search_title_only(query)
        # Long query → full multi-vector
        else:
            return self.search(query)
    elif mode == "title":
        return self.search_title_only(query)
    else:
        return self.search(query)

Embedding Caching

python

from functools import lru_cache

class OptimizedMultiVectorSearch(MultiVectorSearch):
    @lru_cache(maxsize=1000)
    def _get_embedding(self, text: str) -> tuple:
        """Cache embeddings for repeated queries."""
        return tuple(self.model.encode(text).tolist())

    def search(self, query: str, limit: int = 10):
        query_embedding = list(self._get_embedding(query))
        # ... rest of search logic

Common Pitfalls

❌ Pitfall 1: Too Many Vector Fields

Problem: Created 10 vector fields, search is slow

Why: Each field requires a separate ANN search

Fix: Limit to 2-4 most important fields

python

# BAD - too many fields
schema.add_field("title_vec", ...)
schema.add_field("subtitle_vec", ...)
schema.add_field("description_vec", ...)
schema.add_field("summary_vec", ...)
schema.add_field("tags_vec", ...)
schema.add_field("category_vec", ...)

# GOOD - consolidated
schema.add_field("title_vec", ...)  # Title + subtitle
schema.add_field("content_vec", ...)  # Description + summary

❌ Pitfall 2: Same Embedding for Different Length Texts

Problem: Title vector and body vector have similar embeddings

Why: Model truncates long text, short text embeds fully

Fix: Consider different models or chunk long content

python

# For very long content, consider chunking
chunks = [content[i:i+500] for i in range(0, len(content), 500)]
chunk_embeddings = self.model.encode(chunks)
# Store best chunk or average

❌ Pitfall 3: Ignoring Empty Fields

Problem: Items with empty descriptions cause errors

Why: Empty string produces invalid embedding

Fix: Handle empty fields

python

def _safe_embed(self, text: str) -> list:
    if not text or not text.strip():
        return [0.0] * self.dim  # Zero vector for empty
    return self.model.encode(text).tolist()

❌ Pitfall 4: Mismatched Weights

Problem: Weights don't sum to 1.0

Why: Can cause unexpected score scaling

Fix: Always normalize weights

python

def normalize_weights(*weights):
    total = sum(weights)
    return tuple(w / total for w in weights)

weights = normalize_weights(0.6, 0.3, 0.2)  # (0.55, 0.27, 0.18)

When to Level Up

Need	Upgrade To
Add keyword matching	Combine with `hybrid-search`
Filter by attributes	Add filter parameter
Different modalities (image + text)	`multimodal-retrieval`
Better precision	Add `core:rerank` step

References

•Fusion strategies: hybrid-search/references/fusion-strategies.md
•Embedding models: core:embedding
•Vertical guides: verticals/