filtered-search

name	filtered-search
description	Use when user needs vector search with scalar field filtering. Triggers on: filtered search, filter by category, metadata filter, faceted search, conditional search, attribute filtering, search with constraints.

Filtered Search

Vector semantic search combined with scalar field filtering — find semantically relevant results that also match specific criteria.

When to Activate

Activate this skill when:

• User needs to constrain search results by attributes (category, price, date, etc.)
• User mentions "filter by", "only show", "within range", "where category is"
• User has structured metadata alongside text content
• User wants faceted search like e-commerce product filtering

Do NOT activate when:

• User only needs pure semantic search → use semantic-search
• User needs keyword + semantic fusion → use hybrid-search
• User has no metadata fields to filter on

Interactive Flow

Step 1: Identify Filter Fields

"What attributes do you need to filter by?"

Common Filter Types	Examples
Category/Type	category = "electronics", status = "active"
Numeric Range	price between 100-500, rating >= 4.0
Date/Time	created_at > "2024-01-01", within last 7 days
Tags/Arrays	tags contains "new", skills includes "python"
Boolean	in_stock = true, is_verified = true

Which filter types do you need? (can select multiple)

Step 2: Understand Filter Cardinality

"For each filter field, how many unique values exist?"

Cardinality	Example	Index Strategy
Low (< 100)	category, status	TRIE index
Medium (100-10K)	brand, city	INVERTED index
High (> 10K)	user_id, timestamp	STL_SORT or no index

This affects index design and query performance.

Step 3: Confirm Schema Design

"Based on your requirements, here's the proposed schema:

# Your schema
schema.add_field('category', DataType.VARCHAR, max_length=256)  # TRIE index
schema.add_field('price', DataType.FLOAT)  # STL_SORT index
schema.add_field('tags', DataType.ARRAY, element_type=DataType.VARCHAR)  # INVERTED

Proceed? (yes / adjust [what])"

Core Concepts

Mental Model: Department Store

Think of filtered search as a department store with sections:

• Pure semantic search = "Find me something comfortable" (searches everywhere)
• Filtered search = "Find me something comfortable in the shoe department, under $100"

┌─────────────────────────────────────────────────────────┐
│                    Filtered Search                       │
│                                                          │
│  Query: "comfortable work shoes"                         │
│  Filters: category="shoes", price<=100                   │
│                                                          │
│           ┌─────────────────────────────┐               │
│           │     Step 1: Filter First     │               │
│           │                              │               │
│           │  Full Collection (1M items)  │               │
│           │           │                  │               │
│           │           ▼                  │               │
│           │  category="shoes" (50K)      │               │
│           │           │                  │               │
│           │           ▼                  │               │
│           │  price <= 100 (10K)          │               │
│           └──────────┬──────────────────┘               │
│                      │                                   │
│                      ▼                                   │
│           ┌─────────────────────────────┐               │
│           │  Step 2: Vector Search       │               │
│           │  on 10K filtered items       │               │
│           │                              │               │
│           │  → Semantically match        │               │
│           │    "comfortable work shoes"  │               │
│           └──────────┬──────────────────┘               │
│                      │                                   │
│                      ▼                                   │
│           Top 10 relevant results                        │
└─────────────────────────────────────────────────────────┘

Filter vs Post-Filter

Approach	When Applied	Performance
Pre-filter	Before vector search	✅ Efficient (searches fewer vectors)
Post-filter	After vector search	⚠️ May miss results if limit is small

Milvus uses pre-filtering by default — filters are applied before ANN search.

Why Filtered Search

Scenario	Without Filtering	With Filtering
E-commerce: "laptop under $1000"	Returns $2000 laptops too	Only budget options
Job search: "Python developer in NYC"	Returns SF jobs too	Location-specific
Content: "AI news from this week"	Returns old articles	Recent only

When NOT to Use Filtering

• Filter reduces results too much: If filter leaves < 100 items, vector search adds little value
• Filter is the only criteria: Just use scalar query, no need for vectors
• Dynamic filters change frequently: Consider separate indexes

Implementation

from pymilvus import MilvusClient, DataType
from sentence_transformers import SentenceTransformer

class FilteredSearch:
    def __init__(self, uri: str = "./milvus.db"):
        self.client = MilvusClient(uri=uri)
        self.model = SentenceTransformer('BAAI/bge-large-en-v1.5')
        self.collection_name = "filtered_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("text", DataType.VARCHAR, max_length=65535)
        schema.add_field("category", DataType.VARCHAR, max_length=256)
        schema.add_field("price", DataType.FLOAT)
        schema.add_field("tags", DataType.ARRAY, element_type=DataType.VARCHAR, max_capacity=20, max_length=64)
        schema.add_field("created_at", DataType.INT64)  # Timestamp
        schema.add_field("embedding", DataType.FLOAT_VECTOR, dim=1024)

        index_params = self.client.prepare_index_params()
        index_params.add_index(field_name=, index_type=, metric_type=)
        
        index_params.add_index(field_name=, index_type=)
        index_params.add_index(field_name=, index_type=)

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

     ():
        
        texts = [item[]  item  items]
        embeddings = .model.encode(texts).tolist()
         item, emb  (items, embeddings):
            item[] = emb
        .client.insert(collection_name=.collection_name, data=items)

     ():
        
        embedding = .model.encode(query).tolist()

        
        expr_parts = []
         filters:
               filters:
                expr_parts.append()
               filters:
                expr_parts.append()
               filters:
                expr_parts.append()
               filters:
                 tag  filters[]:
                    expr_parts.append()
               filters:
                expr_parts.append()

        filter_expr = .join(expr_parts)  expr_parts  

        results = .client.search(
            collection_name=.collection_name,
            data=[embedding],
            =filter_expr,
            limit=limit,
            output_fields=[, , , ]
        )

         [{: hit[][],
                 : hit[][],
                 : hit[][],
                 : hit[]}  hit  results[]]


search = FilteredSearch()


search.add([
    {: , : , : , : [, ], : },
    {: , : , : , : [, ], : },
    {: , : , : , : [], : },
])


results = search.search(
    query=,
    filters={: , : , : }
)

Filter Expression Syntax

# Comparison operators
'price == 100'              # Equals
'price != 100'              # Not equals
'price > 100'               # Greater than
'price >= 100'              # Greater or equal
'price < 100'               # Less than
'price <= 100'              # Less or equal

# String matching
'category == "electronics"' # Exact match
'title like "iPhone%"'      # Prefix match
'title like "%Pro%"'        # Contains

# IN operator
'category in ["phones", "laptops", "tablets"]'

# Array operations
'array_contains(tags, "new")'           # Array contains value
'array_contains_all(tags, ["a", "b"])'  # Contains all
'array_contains_any(tags, ["a", "b"])'  # Contains any

# Logical operators
'price >= 100 and price <= 1000'        # AND
'category == "phones" or category == "tablets"'  # OR
'not (price > 1000)'                     # NOT

# Complex expression
'category == "phones" and price >= 500 and array_contains(tags, "5G")'

Index Strategy Guide

Field Type	Cardinality	Index Type	Use Case
VARCHAR	Low (< 100)	TRIE	Category, status, type
VARCHAR	High	INVERTED	Tags, keywords
INT/FLOAT	Any	STL_SORT	Numeric ranges
ARRAY	Any	INVERTED	Array contains
BOOL	2	None needed	Boolean flags

Index Creation Example

index_params = self.client.prepare_index_params()

# Vector index (required)
index_params.add_index("embedding", index_type="AUTOINDEX", metric_type="COSINE")

# Scalar indexes (optional but recommended for frequently filtered fields)
index_params.add_index("category", index_type="TRIE")      # Low cardinality string
index_params.add_index("price", index_type="STL_SORT")     # Numeric range
index_params.add_index("tags", index_type="INVERTED")      # Array contains

Common Pitfalls

❌ Pitfall 1: Over-Filtering

Problem: Filter returns 0 results

Why: Filter conditions too restrictive

Fix: Check filter cardinality before searching

# First check how many items match the filter
count = client.query(
    collection_name="products",
    filter='category == "rare_category" and price < 10',
    output_fields=["count(*)"]
)
# If count is 0, relax filters

❌ Pitfall 2: Missing Scalar Index

Problem: Filtered search is slow

Why: No index on frequently filtered field

Fix: Add appropriate index type

# Add index for the filtered field
index_params.add_index("category", index_type="TRIE")

❌ Pitfall 3: Wrong Index Type

Problem: Index doesn't improve performance

Why: Using TRIE for high-cardinality field

Fix: Match index type to cardinality

• Low cardinality → TRIE
• High cardinality → INVERTED
• Numeric range → STL_SORT

❌ Pitfall 4: SQL Injection in Filters

Problem: User input directly in filter expression

Why: Security vulnerability

Fix: Validate and sanitize user input

# BAD - vulnerable to injection
filter_expr = f'category == "{user_input}"'

# GOOD - validate input
VALID_CATEGORIES = ["phones", "laptops", "tablets"]
if user_input in VALID_CATEGORIES:
    filter_expr = f'category == "{user_input}"'

When to Level Up

Need	Upgrade To
Keyword matching + filters	hybrid-search with filters
Multiple text fields	multi-vector-search with filters
Complex multi-hop queries	agentic-rag with tool-based filtering

References

• Filter expression syntax: references/filter-optimization.md
• Index configuration: core:indexing
• Vertical guides: verticals/