Editing Embeddings Vector Dbs (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Embedding Model Comparison
! Model !! Dimensions !! Speed !! Quality !! Cost
|-
| text-embedding-3-small (OpenAI) || 1536 || Fast (API) || Very high || Paid per token
|-
| text-embedding-3-large (OpenAI) || 3072 || Fast (API) || Highest || More expensive
|-
| BAAI/BGE-M3 || 1024 || Moderate (local) || Very high || Free (self-hosted)
|-
| all-MiniLM-L6-v2 || 384 || Very fast (local) || Good || Free (self-hosted)
|-
| E5-mistral-7b || 4096 || Slow (large model) || Excellent || Free (GPU needed)
|}

'''Failure modes and pitfalls:'''
* '''Embedding model-retrieval mismatch''' — The embedding model used to index documents must be identical to the one used to embed queries. Using different models produces nonsensical results.
* '''Chunking artifacts''' — Important context split across chunks leads to poor retrievals. If an answer spans two chunks, neither may score high enough to be retrieved.
* '''Embedding stale data''' — If documents are updated but re-embedding is not triggered, the index serves outdated information. Implement change detection and incremental re-indexing.
* '''Dimensionality curse''' — In very high dimensions, all vectors tend to become equidistant from each other, degrading nearest-neighbor search quality. Use models with well-calibrated dimensionalities.
* '''Semantic gap''' — Embeddings capture distributional semantics but may miss precise numerical facts, dates, or codes. Combine with structured filters or keyword search.
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