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Vector Databases: Pinecone, Weaviate & pgvector · Lesson

Filtering with Metadata

Utilize metadata to refine your similarity searches, adding contextual constraints to your queries.

Filtering with Metadata is a free Vector Databases: Pinecone, Weaviate & pgvector lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Vector Databases: Pinecone, Weaviate & pgvector learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

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.

Frequently asked questions

Is the “Filtering with Metadata” lesson free?

Yes — the full text of “Filtering with Metadata” is free to read here on the web, and the Vector Databases: Pinecone, Weaviate & pgvector course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Vector Databases: Pinecone, Weaviate & pgvector course, upgrade to CoddyKit PRO.

What will I learn in “Filtering with Metadata”?

Utilize metadata to refine your similarity searches, adding contextual constraints to your queries. You practise Vector Databases: Pinecone, Weaviate & pgvector with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Vector Databases: Pinecone, Weaviate & pgvector?

No prior experience is required. Vector Databases: Pinecone, Weaviate & pgvector on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Filtering with Metadata” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Vector Databases: Pinecone, Weaviate & pgvector lesson?

Yes. Every Vector Databases: Pinecone, Weaviate & pgvector lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Filtering with Metadata
  2. Managing Namespaces
  3. Real-time Updates & Deletions
  4. Hybrid Search with Sparse-Dense Vectors
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