From c6bbb440115686e7bd936aeeab571788fa9f27e6 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Sat, 25 Oct 2025 12:52:13 +0000
Subject: [PATCH] Optimize SearchQueryVector.as_dict
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The optimization replaces a dictionary comprehension approach with direct conditional assignment, eliminating the overhead of creating an intermediate dictionary and iterating through it.

**Key Changes:**
- **Eliminated intermediate dictionary creation**: The original code created a full dictionary with all fields first, then filtered out `None` values using a dictionary comprehension
- **Direct conditional construction**: The optimized version builds the result dictionary incrementally, only adding keys when their values are not `None`
- **Single attribute access**: Each attribute (`self.values`, `self.sparse_values`, `self.sparse_indices`) is accessed only once and stored in a local variable

**Performance Benefits:**
The line profiler shows the original dictionary comprehension (`{k: v for k, v in d.items() if v is not None}`) consumed 60% of the total runtime. This operation involved:
1. Creating `d.items()` iterator
2. Checking each key-value pair for `None`
3. Building a new dictionary

The optimized version eliminates this expensive iteration entirely, resulting in a **118% speedup** (from 50.7μs to 23.3μs).

**Test Case Performance:**
The optimization is particularly effective for cases with many `None` fields - the annotated tests show 158-203% improvements when most or all fields are `None`, as the optimized version skips unnecessary dictionary operations entirely.
---
 .../db_data/dataclasses/search_query_vector.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/pinecone/db_data/dataclasses/search_query_vector.py b/pinecone/db_data/dataclasses/search_query_vector.py
index d829102f6..4fa2ee350 100644
--- a/pinecone/db_data/dataclasses/search_query_vector.py
+++ b/pinecone/db_data/dataclasses/search_query_vector.py
@@ -30,9 +30,15 @@ def as_dict(self) -> dict:
         """
         Returns the SearchQueryVector as a dictionary.
         """
-        d = {
-            "values": self.values,
-            "sparse_values": self.sparse_values,
-            "sparse_indices": self.sparse_indices,
-        }
-        return {k: v for k, v in d.items() if v is not None}
+        # Directly construct the dictionary only with non-None attributes
+        result = {}
+        values = self.values
+        if values is not None:
+            result["values"] = values
+        sparse_values = self.sparse_values
+        if sparse_values is not None:
+            result["sparse_values"] = sparse_values
+        sparse_indices = self.sparse_indices
+        if sparse_indices is not None:
+            result["sparse_indices"] = sparse_indices
+        return result