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* Add TurboQuant quantization documentation (v1.18.0) Adds a new TurboQuant section to the quantization guide covering the four encoding options (bits1/bits1_5/bits2/bits4), automatic asymmetric quantization, distance metric support, and the automatic TQ+ precision enhancement for sealed segments. Updates the comparison table and method-selection guidance to recommend TurboQuant over Binary and Scalar Quantization for new collections. Adds HTTP-only snippets for basic setup and explicit bit-depth selection. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Consistently use title case for headers * Restructure doc to lead with TurboQuant * Clarify rescoring * Edits * Soften TQ advice * Updates * Fix * Apply suggestions from code review Co-authored-by: Jojii <15957865+JojiiOfficial@users.noreply.github.com> * Review feedback * Add Go/Java/C# code snippets * Add list of 4 quantization methods to introduction * Review feedback * Stronger advice for TQ4 * Add Python snippets * Add Rust snippets * Add TS snippets * Update recommendation table * Update production checklist * Remove link to article --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Jojii <15957865+JojiiOfficial@users.noreply.github.com>
16 lines
427 B
Python
16 lines
427 B
Python
from qdrant_client import QdrantClient, models
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# @hide-start
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client = QdrantClient(url="http://localhost:6333")
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# @hide-end
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client.create_collection(
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collection_name="{collection_name}",
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vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),
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quantization_config=models.TurboQuantization(
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turbo=models.TurboQuantQuantizationConfig(
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always_ram=True,
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),
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),
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)
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