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Abdon PijpelinkandClaude Sonnet 4.6
ffe50340fa
* docs(edge): add "Modify the Vector Schema" step to quickstart Documents the new Edge 0.7 API for adding and removing named vector fields on an existing shard without recreating it, with Python and Rust snippets. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(edge): mention quantization support in quickstart Adds a note after the EdgeConfig snippet that Edge supports all four quantization methods (Scalar, Product, Binary, TurboQuant), with a link to the quantization guide. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(edge): add On-Device BM25 page for Edge 0.7 New guide covering the built-in BM25 sparse embedder: configuring a sparse vector shard, creating a Bm25/EdgeBm25 embedder, embedding and upserting documents, and querying. Includes Python and Rust snippets. Also adds the page to the Edge index navigation table. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(edge): add WAL segment size configuration to quickstart Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Upgrade code snippets to Edge 0.7.2 * Review feedback --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
from pathlib import Path
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SHARD_DIRECTORY = "./qdrant-edge-bm25"
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Path(SHARD_DIRECTORY).mkdir(parents=True, exist_ok=True) # @hide
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# @block-start configure-bm25-shard
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from qdrant_edge import (
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EdgeConfig,
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EdgeShard,
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EdgeSparseVectorParams,
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Modifier,
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)
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config = EdgeConfig(
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sparse_vectors={"text": EdgeSparseVectorParams(modifier=Modifier.Idf)},
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)
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shard = EdgeShard.create(SHARD_DIRECTORY, config)
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# @block-end configure-bm25-shard
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# @block-start create-bm25
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from qdrant_edge import Bm25, Bm25Config
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bm25 = Bm25(Bm25Config(language="english"))
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# @block-end create-bm25
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# @block-start embed-and-upsert
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from qdrant_edge import Point, UpdateOperation
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shard.update(UpdateOperation.upsert_points([
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Point(1, {"text": bm25.embed_document("the quick brown fox")}, {"title": "Article 1"}),
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Point(2, {"text": bm25.embed_document("a lazy dog sleeps")}, {"title": "Article 2"}),
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Point(3, {"text": bm25.embed_document("foxes are clever")}, {"title": "Article 3"}),
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]))
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shard.optimize()
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# @block-end embed-and-upsert
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# @block-start query-with-bm25
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from qdrant_edge import Query, QueryRequest
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query_vector = bm25.embed_query("clever fox")
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results = shard.query(QueryRequest(
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query=Query.Nearest(query_vector, using="text"),
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limit=3,
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with_payload=True,
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))
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# @block-end query-with-bm25
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