Vector Databases: Pinecone, Weaviate & pgvector · Lektion

Mit Metadaten filtern

Nutzen Sie Metadaten, um Ihre Ähnlichkeitssuchen zu verfeinern und Ihren Abfragen kontextbezogene Einschränkungen hinzuzufügen.

Mit Metadaten filtern ist eine kostenlose Vector Databases: Pinecone, Weaviate & pgvector-Lektion auf CoddyKit. Dies ist Lektion 1 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des Vector Databases: Pinecone, Weaviate & pgvector-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der Vector Databases: Pinecone, Weaviate & pgvector-Kurs umfasst insgesamt 4 Lektionen.

Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.

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 results

Filtering 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 results

Filtering 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 results

Combining 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 results

Using $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 results

Advanced 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 results

Test 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 $or for complex logic.

Next, we'll explore how to organize your data even further using Pinecone's namespaces.

Häufig gestellte Fragen

Ist die Lektion „Mit Metadaten filtern“ kostenlos?

Ja — der vollständige Text von „Mit Metadaten filtern“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des Vector Databases: Pinecone, Weaviate & pgvector-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der Vector Databases: Pinecone, Weaviate & pgvector-Kurs umfasst insgesamt 4 Lektionen.

Was lerne ich in „Mit Metadaten filtern“?

Nutzen Sie Metadaten, um Ihre Ähnlichkeitssuchen zu verfeinern und Ihren Abfragen kontextbezogene Einschränkungen hinzuzufügen. Du übst Vector Databases: Pinecone, Weaviate & pgvector mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.

Brauche ich Erfahrung, um Vector Databases: Pinecone, Weaviate & pgvector zu starten?

Keine Vorkenntnisse erforderlich. Vector Databases: Pinecone, Weaviate & pgvector auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 1 von 4.

Wie lange dauert die Lektion „Mit Metadaten filtern“?

Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.

Kann ich in dieser Vector Databases: Pinecone, Weaviate & pgvector-Lektion Code schreiben und ausführen?

Ja. Jede Vector Databases: Pinecone, Weaviate & pgvector-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.

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