mirror of
https://github.com/qdrant/fastembed.git
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969ea29923
* new: Added type stub * chore: Updated stubs * chore: device_id type hint * chore: add -> none to init without args * new: Added workflow type check * chore: Revert added type checkers
57 lines
2.5 KiB
Python
57 lines
2.5 KiB
Python
from fastembed import TextEmbedding, LateInteractionTextEmbedding, SparseTextEmbedding
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from fastembed.sparse.bm25 import Bm25
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from fastembed.rerank.cross_encoder import TextCrossEncoder
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text_embedder = TextEmbedding(cache_dir="models")
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late_interaction_embedder = LateInteractionTextEmbedding(model_name="", cache_dir="models")
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reranker = TextCrossEncoder(model_name="", cache_dir="models")
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sparse_embedder = SparseTextEmbedding(model_name="", cache_dir="models")
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bm25_embedder = Bm25(
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model_name="",
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k=1.0,
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b=1.0,
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avg_len=1.0,
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language="",
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token_max_length=1,
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disable_stemmer=False,
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specific_model_path="models",
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)
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text_embedder.list_supported_models()
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text_embedder.embed(documents=[""], batch_size=1, parallel=1)
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text_embedder.embed(documents="", parallel=None, task_id=1)
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text_embedder.query_embed(query=[""], batch_size=1, parallel=1)
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text_embedder.query_embed(query="", parallel=None)
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text_embedder.passage_embed(texts=[""], batch_size=1, parallel=1)
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text_embedder.passage_embed(texts=[""], parallel=None)
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late_interaction_embedder.list_supported_models()
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late_interaction_embedder.embed(documents=[""], batch_size=1, parallel=1)
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late_interaction_embedder.embed(documents="", parallel=None)
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late_interaction_embedder.query_embed(query=[""], batch_size=1, parallel=1)
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late_interaction_embedder.query_embed(query="", parallel=None)
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late_interaction_embedder.passage_embed(texts=[""], batch_size=1, parallel=1)
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late_interaction_embedder.passage_embed(texts=[""], parallel=None)
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reranker.list_supported_models()
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reranker.rerank(query="", documents=[""], batch_size=1, parallel=1)
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reranker.rerank(query="", documents=[""], parallel=None)
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reranker.rerank_pairs(pairs=[("", "")], batch_size=1, parallel=1)
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reranker.rerank_pairs(pairs=[("", "")], parallel=None)
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sparse_embedder.list_supported_models()
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sparse_embedder.embed(documents=[""], batch_size=1, parallel=1)
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sparse_embedder.embed(documents="", batch_size=1, parallel=None)
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sparse_embedder.query_embed(query=[""], batch_size=1, parallel=1)
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sparse_embedder.query_embed(query="", batch_size=1, parallel=None)
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sparse_embedder.passage_embed(texts=[""], batch_size=1, parallel=1)
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sparse_embedder.passage_embed(texts=[""], batch_size=1, parallel=None)
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bm25_embedder.list_supported_models()
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bm25_embedder.embed(documents=[""], batch_size=1, parallel=1)
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bm25_embedder.embed(documents="", batch_size=1, parallel=None)
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bm25_embedder.query_embed(query=[""], batch_size=1, parallel=1)
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bm25_embedder.query_embed(query="", batch_size=1, parallel=None)
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bm25_embedder.raw_embed(documents=[""])
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