Penyaringan dengan Metadata
Manfaatkan metadata untuk menyaring pencarian kemiripan dan menambahkan batasan kontekstual ke kueri Anda.
Penyaringan dengan Metadata adalah pelajaran Vector Databases: Pinecone, Weaviate & pgvector gratis di CoddyKit. Ini adalah pelajaran 1 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Vector Databases: Pinecone, Weaviate & pgvector, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Vector Databases: Pinecone, Weaviate & pgvector mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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.
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Penyaringan dengan Metadata” gratis?
Ya — teks lengkap “Penyaringan dengan Metadata” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Vector Databases: Pinecone, Weaviate & pgvector, upgrade ke CoddyKit PRO. Kursus Vector Databases: Pinecone, Weaviate & pgvector mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Penyaringan dengan Metadata”?
Manfaatkan metadata untuk menyaring pencarian kemiripan dan menambahkan batasan kontekstual ke kueri Anda. Kamu berlatih Vector Databases: Pinecone, Weaviate & pgvector dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai Vector Databases: Pinecone, Weaviate & pgvector?
Tidak diperlukan pengalaman sebelumnya. Vector Databases: Pinecone, Weaviate & pgvector di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 1 dari 4.
Berapa lama pelajaran “Penyaringan dengan Metadata” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran Vector Databases: Pinecone, Weaviate & pgvector ini?
Ya. Setiap pelajaran Vector Databases: Pinecone, Weaviate & pgvector menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Penyaringan dengan Metadata
- Mengelola Ruang Nama
- Pembaruan dan Penghapusan Waktu Nyata
- Pencarian Hibrida dengan Vektor Jarang-Padat