Filtrer avec les métadonnées
Utilisez les métadonnées pour affiner vos recherches par similarité et ajouter des contraintes contextuelles à vos requêtes.
Filtrer avec les métadonnées est une leçon Vector Databases: Pinecone, Weaviate & pgvector gratuite sur CoddyKit. Ceci est la leçon 1 sur 4. Tu peux lire la leçon complète ci-dessous gratuitement — puis la pratiquer en direct dans le navigateur avec un éditeur de code intégré et un tuteur IA 24/7. Elle fait partie du parcours d'apprentissage Vector Databases: Pinecone, Weaviate & pgvector, et ta progression se synchronise sur le web et l'application CoddyKit. Le cours Vector Databases: Pinecone, Weaviate & pgvector comprend 4 leçons au total.
Certaines parties de cette leçon n'ont pas encore été traduites et s'affichent en anglais.
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.
Questions Fréquemment Posées
La leçon « Filtrer avec les métadonnées » est-elle gratuite ?
Oui — le texte complet de « Filtrer avec les métadonnées » est gratuit à lire ici sur le web. Pour la pratiquer de manière interactive (un éditeur de code intégré et un tuteur IA 24/7) et déverrouiller le reste du cours Vector Databases: Pinecone, Weaviate & pgvector, passe à CoddyKit PRO. Le cours Vector Databases: Pinecone, Weaviate & pgvector comprend 4 leçons au total.
Qu'est-ce que j'apprendrai dans « Filtrer avec les métadonnées » ?
Utilisez les métadonnées pour affiner vos recherches par similarité et ajouter des contraintes contextuelles à vos requêtes. Tu pratiques Vector Databases: Pinecone, Weaviate & pgvector avec du code pratique que tu exécutes directement dans le navigateur, et un tuteur IA 24/7 répond à tes questions au fur et à mesure que tu avances dans la leçon.
Dois-je avoir de l'expérience pour commencer Vector Databases: Pinecone, Weaviate & pgvector ?
Aucune expérience préalable n'est requise. Vector Databases: Pinecone, Weaviate & pgvector sur CoddyKit est structuré pour les débutants jusqu'aux apprenants avancés, donc tu peux commencer ici ou depuis le début et avancer à ton rythme. Ceci est la leçon 1 sur 4.
Combien de temps prend la leçon « Filtrer avec les métadonnées » ?
La plupart des leçons CoddyKit prennent environ 5–10 minutes. Chacune est courte et interactive, tu progresses régulièrement et tu repiques exactement où tu t'es arrêté sur le web et l'app.
Peux-tu écrire et exécuter du code dans cette leçon Vector Databases: Pinecone, Weaviate & pgvector ?
Oui. Chaque leçon Vector Databases: Pinecone, Weaviate & pgvector inclut un éditeur de code intégré, tu écris et exécutes du vrai code directement dans ton navigateur et tu reçois des retours IA instantanés — aucune configuration locale requise.
Toutes les leçons de ce cours
- Filtrer avec les métadonnées
- Gérer les espaces de noms
- Mises à jour et suppressions en temps réel
- Recherche hybride avec vecteurs creux et denses