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fix: inconsistent FLAT/HNSW algorithm recommendations #409

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Merged
merged 2 commits into from
Oct 22, 2025
Merged

fix: inconsistent FLAT/HNSW algorithm recommendations #409

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3dd49e1
fix: updated the hnsw and flat recommendation in schema.rst
nkanu17 Oct 22, 2025
d262326
fix: updated the hnsw and flat recommendation in schema.rst
nkanu17 Oct 22, 2025
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8 changes: 4 additions & 4 deletions docs/api/schema.rst
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Expand Up @@ -160,7 +160,7 @@ HNSW (Hierarchical Navigable Small World) - Graph-based approximate search with

**Use HNSW when:**

- Medium to large datasets (10K-1M+ vectors) requiring high recall rates
- Medium to large datasets (100K-1M+ vectors) requiring high recall rates
- Search accuracy is more important than memory usage
- Need general-purpose vector search with balanced performance
- Cross-platform deployments where hardware-specific optimizations aren't available
Expand Down Expand Up @@ -400,7 +400,7 @@ Algorithm Selection Guide
- Minimal overhead
- Exact but slow for large data
* - **HNSW**
- General purpose (10K-1M+ vectors)
- General purpose (100K-1M+ vectors)
- 95-99% recall, O(log n) search
- Moderate (graph overhead)
- Fast approximate search
Expand All @@ -414,13 +414,13 @@ When to Use Each Algorithm
--------------------------

**Choose FLAT when:**
- Dataset size < 10,000 vectors
- Dataset size < 100,000 vectors
- Exact results are mandatory
- Simple setup is preferred
- Query latency is not critical

**Choose HNSW when:**
- Dataset size 10K - 1M+ vectors
- Dataset size 100K - 1M+ vectors
- Need balanced speed and accuracy
- Cross-platform compatibility required
- Most common choice for production
Expand Down