> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-ml-crtx-1153-academy-tutorial-rewrites.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Search with filters

> Combine vector similarity with metadata conditions.

Filtered search combines vector similarity with metadata conditions. Results must match both your query similarity and filter criteria.

The filter conditions evaluate alongside vector similarity, not instead of it. VectorAI DB finds similar vectors that also meet your criteria. Use filtered search to combine semantic search with business rules like:

* Price ranges
* Availability status
* Category restrictions

<CodeGroup>
  ```python Python theme={null}
  import asyncio
  from actian_vectorai import AsyncVectorAIClient, FilterBuilder, Field
  import random

  async def main():
      # Connect to VectorAI DB server
      async with AsyncVectorAIClient("localhost:6574") as client:
          # Generate query vector
          query_vector = [random.gauss(0, 1) for _ in range(128)]
          
          # Search with metadata filters
          filter = FilterBuilder()\
              .must(Field("category").eq("electronics"))\
              .must(Field("price").lt(500.0))\
              .build()
          
          # Search with filter
          results = await client.points.search(
              "my_collection",  # Collection name
              vector=query_vector,  # Query vector
              limit=10,  # Number of results
              filter=filter  # Apply filter
          )
          
          # Display results
          for result in results:
              print(f"Product: {result.payload['name']}")
              print(f"Price: ${result.payload['price']}")
              print(f"Score: {result.score}")

  asyncio.run(main())
  ```

  ```javascript JavaScript theme={null}
  import { VectorAIClient, Field } from '@actian/vectorai-client';

  async function main() {
      const client = new VectorAIClient('localhost:6574');

      try {
          // Generate query vector
          const queryVector = Array.from({ length: 128 }, () => Math.random() * 2 - 1);

          // Search with metadata filters
          const filter = new Field('category').eq('electronics')
              .and(new Field('price').lt(500.0));

          // Search with filter
          const results = await client.points.search(
              'my_collection',  // Collection name
              queryVector,      // Query vector
              {
                  limit: 10,    // Number of results
                  filter: filter // Apply filter
              }
          );

          // Display results
          for (const result of results) {
              console.log(`Product: ${result.payload.name}`);
              console.log(`Price: $${result.payload.price}`);
              console.log(`Score: ${result.score}`);
          }
      } finally {
          client.close();
      }
  }

  main().catch(console.error);
  ```
</CodeGroup>

<Tip>
  Learn more about filter syntax and operators in the [filtering](/docs/fundamentals/filtering/filtering) documentation.
</Tip>

Each result includes these fields:

* `id`: The unique identifier of the matching point.
* `score`: Similarity score for points that passed the filter.
* `payload`: Metadata dictionary showing filtered attributes.