fastembed-haystack 1.4.1

Haystack 2.x component to embed strings and Documents using fastembed embedding model

fastembed-haystack

---

Table of Contents

• Installation
• Usage
• License

Installation

pip install fastembed-haystack

Usage

You can use FastembedTextEmbedder and FastembedDocumentEmbedder by importing as:

from haystack_integrations.components.embedders.fastembed import FastembedTextEmbedder

text = "fastembed is supported by and maintained by Qdrant."
text_embedder = FastembedTextEmbedder(
    model="BAAI/bge-small-en-v1.5"
)
text_embedder.warm_up()
embedding = text_embedder.run(text)["embedding"]

from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder
from haystack import Document

embedder = FastembedDocumentEmbedder(
    model="BAAI/bge-small-en-v1.5",
)
embedder.warm_up()
doc = Document(content="fastembed is supported by and maintained by Qdrant.", meta={"long_answer": "no",})
result = embedder.run(documents=[doc])

You can use FastembedSparseTextEmbedder and FastembedSparseDocumentEmbedder by importing as:

from haystack_integrations.components.embedders.fastembed import FastembedSparseTextEmbedder

text = "fastembed is supported by and maintained by Qdrant."
text_embedder = FastembedSparseTextEmbedder(
    model="prithivida/Splade_PP_en_v1"
)
text_embedder.warm_up()
embedding = text_embedder.run(text)["sparse_embedding"]

from haystack_integrations.components.embedders.fastembed import FastembedSparseDocumentEmbedder
from haystack import Document

embedder = FastembedSparseDocumentEmbedder(
    model="prithivida/Splade_PP_en_v1",
)
embedder.warm_up()
doc = Document(content="fastembed is supported by and maintained by Qdrant.", meta={"long_answer": "no",})
result = embedder.run(documents=[doc])

You can use FastembedRanker by importing as:

from haystack import Document

from haystack_integrations.components.rankers.fastembed import FastembedRanker

query = "Who is maintaining Qdrant?"
documents = [
    Document(
        content="This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc."
    ),
    Document(content="fastembed is supported by and maintained by Qdrant."),
]

ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-6-v2")
ranker.warm_up()
reranked_documents = ranker.run(query=query, documents=documents)["documents"]

print(reranked_documents[0])

# Document(id=...,
#  content: 'fastembed is supported by and maintained by Qdrant.',
#  score: 5.472434997558594..)

License

fastembed-haystack is distributed under the terms of the Apache-2.0 license.