mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-05 02:48:30 +02:00
Abdon Pijpelinkandxzfc
ea0de4c12e
* Initial Rust snippets * Update content for Rust * Upgrade Rust to 1.94.0; test Edge code snippets against dev * Temporarily test against Edge shim crate * Move to EdgeShardConfig and EdgeVectorParams * Add docs for payload indexing, filtering, facet(), and optimize() * De-emphasize on-device use case * Update link to Rust examples on Github * Flattened API * Switch to new/create and load to initialize shards * Fixups for released packages * Mention recover_partial_snapshot on method list * Link to Github dev branch for examples --------- Co-authored-by: xzfc <xzfcpw@gmail.com>
88 lines
1.9 KiB
Python
88 lines
1.9 KiB
Python
# @block-start download-models
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from fastembed import ImageEmbedding, TextEmbedding
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TEXT_MODEL_NAME='Qdrant/clip-ViT-B-32-text'
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VISION_MODEL_NAME='Qdrant/clip-ViT-B-32-vision'
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MODELS_DIR="./qdrant-edge-directory/models"
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ImageEmbedding(
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model_name=VISION_MODEL_NAME,
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cache_dir=MODELS_DIR
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)
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TextEmbedding(
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model_name=TEXT_MODEL_NAME,
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cache_dir=MODELS_DIR
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)
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# @block-end download-models
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# @block-start initialize-edge-shard
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from pathlib import Path
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from qdrant_edge import (
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Distance,
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EdgeConfig,
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EdgeShard,
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EdgeVectorParams,
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)
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SHARD_DIRECTORY = "./qdrant-edge-directory"
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VECTOR_DIMENSION = 512
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VECTOR_NAME="my-vector"
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Path(SHARD_DIRECTORY).mkdir(parents=True, exist_ok=True)
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config = EdgeConfig(
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vectors={
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VECTOR_NAME: EdgeVectorParams(
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size=VECTOR_DIMENSION,
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distance=Distance.Cosine,
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)
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}
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)
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edge_shard = EdgeShard.create(SHARD_DIRECTORY, config)
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# @block-end initialize-edge-shard
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# @block-start embed-and-store-image
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from pathlib import Path
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from qdrant_edge import Point, UpdateOperation
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import uuid
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IMAGES_DIR = "images"
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image_model = ImageEmbedding(
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model_name=VISION_MODEL_NAME,
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cache_dir=MODELS_DIR,
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local_files_only=True
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)
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embeddings = list(image_model.embed([Path(IMAGES_DIR) / "temp.jpg"]))[0]
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point = Point(
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id=str(uuid.uuid4()),
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vector={VECTOR_NAME: embeddings.tolist()}
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)
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edge_shard.update(UpdateOperation.upsert_points([point]))
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# @block-end embed-and-store-image
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# @block-start query-with-text-embedding
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from qdrant_edge import Query, QueryRequest
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text_model = TextEmbedding(
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model_name=TEXT_MODEL_NAME,
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cache_dir=MODELS_DIR,
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local_files_only=True
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)
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embeddings = list(text_model.embed(["<search terms>"]))[0]
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results = edge_shard.query(
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QueryRequest(
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query=Query.Nearest(embeddings.tolist(),using=VECTOR_NAME),
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limit=10,
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with_vector=False,
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with_payload=True
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)
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)
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# @block-end query-with-text-embedding
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