Filtrado mediante metadatos
Utilice metadatos para perfeccionar sus búsquedas por similitud y añadir restricciones contextuales a sus consultas.
Filtrado mediante metadatos es una lección gratuita de Vector Databases: Pinecone, Weaviate & pgvector en CoddyKit. Esta es la lección 1 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Vector Databases: Pinecone, Weaviate & pgvector, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Vector Databases: Pinecone, Weaviate & pgvector incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
Filter Your Vector Searches
Imagine searching for products, but only wanting "electronics" that are "under $50". This is where metadata filtering comes in handy!
Vector databases let you store extra information, called metadata, alongside your vectors. This lesson teaches you how to use this metadata to refine your similarity searches in Pinecone.
Understanding Metadata
Metadata is simply "data about data." In Pinecone, it's a set of key-value pairs attached to each vector.
- Key: A string representing a property (e.g.,
"category","price","author"). - Value: Can be a string, number, boolean, or even a list of strings/numbers.
It helps describe the item your vector represents.
Why Filter Searches?
Filtering is crucial for getting more precise and relevant search results.
- Precision: Narrow down results to only what's relevant (e.g., "red shoes").
- Context: Add specific conditions beyond just vector similarity (e.g., "articles published last year").
- Efficiency: Reduce the number of vectors considered, potentially speeding up searches for large datasets.
Adding Metadata to Vectors
When you add (or "upsert") vectors into Pinecone, you can include a metadata dictionary. This makes the extra data searchable later.
You learned about upserting in a previous lesson. Here's a quick reminder of how metadata is attached:
from pinecone import Pinecone, Index
import os
# Assume Pinecone is initialized (replace with your actual setup)
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
# Example vector with metadata
vectors_to_upsert = [
{
"id": "item1",
"values": [0.1, 0.2, 0.3], # Placeholder vector
"metadata": {"genre": "fiction", "year": 2023, "price": 19.99}
},
{
"id": "item2",
"values": [0.4, 0.5, 0.6],
"metadata": {"genre": "non-fiction", "year": 2022, "price": 25.50}
}
]
# index.upsert(vectors=vectors_to_upsert) # Uncomment to actually upsert
print("Metadata structure for upserting shown.")Filtering with Exact Matches
The simplest way to filter is to look for exact matches on a metadata field. You specify the field name and its desired value.
For example, to find all items with "genre": "fiction", you'd use {"genre": "fiction"} in your query's filter parameter.
from pinecone import Pinecone, Index
import os
# Assume index is already set up and has data
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
# Query for items where genre is exactly 'fiction'
query_vector = [0.1, 0.2, 0.3] # Your query embedding
# results = index.query(
# vector=query_vector,
# top_k=3,
# filter={"genre": "fiction"}
# )
print("Querying with filter: {'genre': 'fiction'}")
# print(results) # Uncomment to see resultsFiltering by Ranges
You can also filter by numerical ranges using special Pinecone operators. These are useful for values like prices, dates, or ratings.
$gt: greater than$gte: greater than or equal to$lt: less than$lte: less than or equal to
For example, {"price": {"$lt": 20.0}} finds items cheaper than $20.
from pinecone import Pinecone, Index
import os
# Assume index with 'price' metadata
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
query_vector = [0.1, 0.2, 0.3]
# Find items published after 2022
# results = index.query(
# vector=query_vector,
# top_k=5,
# filter={"year": {"$gt": 2022}}
# )
print("Querying with filter: {'year': {'$gt': 2022}}")
# print(results) # Uncomment to see resultsFiltering with Lists
Sometimes you need to filter based on whether a value is present (or not present) in a list of options. Pinecone provides $in and $nin operators for this.
$in: value is one of the specified options.$nin: value is NOT one of the specified options.
Example: {"category": {"$in": ["electronics", "clothing"]}}
from pinecone import Pinecone, Index
import os
# Assume index with 'tag' metadata
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
query_vector = [0.1, 0.2, 0.3]
# Find items belonging to 'fiction' OR 'poetry'
# results = index.query(
# vector=query_vector,
# top_k=5,
# filter={"genre": {"$in": ["fiction", "poetry"]}}
# )
print("Querying with filter: {'genre': {'$in': ['fiction', 'poetry']}}")
# print(results) # Uncomment to see resultsCombining Filter Conditions
You can combine multiple filter conditions to create complex queries. By default, multiple conditions at the same level are treated as an AND operation.
For explicit OR operations, you use the $or operator. For example, to find items with category="books" AND price < 20:
from pinecone import Pinecone, Index
import os
# Assume index with 'category' and 'price' metadata
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
query_vector = [0.1, 0.2, 0.3]
# Find books cheaper than $20 (AND operation)
# results = index.query(
# vector=query_vector,
# top_k=5,
# filter={"category": "books", "price": {"$lt": 20.0}}
# )
print("Querying with filter: {'category': 'books', 'price': {'$lt': 20.0}}")
# print(results) # Uncomment to see resultsUsing $or for Flexible Filters
To perform an OR search, you use the $or operator. This operator takes a list of filter conditions, and if any of them are true, the item is included.
Example: Find items where category="electronics" OR category="home goods":
from pinecone import Pinecone, Index
import os
# Assume index with 'category' metadata
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
query_vector = [0.1, 0.2, 0.3]
# Find items where category is 'electronics' OR 'home goods'
# results = index.query(
# vector=query_vector,
# top_k=5,
# filter={
# "$or": [
# {"category": "electronics"},
# {"category": "home goods"}
# ]
# }
# )
print("Querying with $or filter...")
# print(results) # Uncomment to see resultsAdvanced Combined Filters
You can combine $and (implicit or explicit) and $or operators for very powerful and specific filtering. This allows you to build complex search logic.
For example, to find items that are "fiction" AND ("published after 2020" OR "price under $15"):
from pinecone import Pinecone, Index
import os
# Assume index with 'genre', 'year', 'price' metadata
# pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
# index = pc.Index("my-index")
query_vector = [0.1, 0.2, 0.3]
# Complex filter: fiction AND (year > 2020 OR price < 15)
# results = index.query(
# vector=query_vector,
# top_k=5,
# filter={
# "genre": "fiction",
# "$or": [
# {"year": {"$gt": 2020}},
# {"price": {"$lt": 15.0}}
# ]
# }
# )
print("Querying with complex AND/OR filter...")
# print(results) # Uncomment to see resultsTest Your Filtering Knowledge
Which Pinecone filter would you use to find items that are either in the "books" category or have a "rating" of at least 4.5?
Recap: Mastering Metadata Filters
Great job! You've learned how to use metadata to significantly enhance your similarity searches in Pinecone.
- Metadata: Key-value pairs stored with your vectors.
- Filter Types: Equality, range (
$gt,$lt, etc.), and list ($in,$nin). - Combining Filters: Use implicit AND or explicit
$orfor complex logic.
Next, we'll explore how to organize your data even further using Pinecone's namespaces.
Preguntas frecuentes
¿La lección «Filtrado mediante metadatos» es gratis?
Sí — el texto completo de «Filtrado mediante metadatos» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Vector Databases: Pinecone, Weaviate & pgvector, actualiza a CoddyKit PRO. El curso de Vector Databases: Pinecone, Weaviate & pgvector incluye 4 lecciones en total.
¿Qué aprenderé en «Filtrado mediante metadatos»?
Utilice metadatos para perfeccionar sus búsquedas por similitud y añadir restricciones contextuales a sus consultas. Practicas Vector Databases: Pinecone, Weaviate & pgvector con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar Vector Databases: Pinecone, Weaviate & pgvector?
No se requiere experiencia previa. Vector Databases: Pinecone, Weaviate & pgvector en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 1 de 4.
¿Cuánto tiempo toma la lección «Filtrado mediante metadatos»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de Vector Databases: Pinecone, Weaviate & pgvector?
Sí. Cada lección de Vector Databases: Pinecone, Weaviate & pgvector incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Filtrado mediante metadatos
- Gestión de namespaces
- Actualizaciones y eliminaciones en tiempo real
- Búsqueda híbrida con vectores sparse-dense