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141 lines
5.9 KiB
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141 lines
5.9 KiB
Plaintext
---
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title: "FastembedSparseTextEmbedder"
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id: fastembedsparsetextembedder
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slug: "/fastembedsparsetextembedder"
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description: "Use this component to embed a simple string (such as a query) into a sparse vector."
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---
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# FastembedSparseTextEmbedder
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Use this component to embed a simple string (such as a query) into a sparse vector.
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| :------------------------------------- | :------------------------------------------------------------------------------------------ |
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| **Most common position in a pipeline** | Before a sparse embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline |
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| **Mandatory run variables** | “text”: A string |
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| **Output variables** | “sparse_embedding”: A [`SparseEmbedding`](/docs/data-classes#sparseembedding) object |
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| **API reference** | [FastEmbed](/reference/fastembed-embedders) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/fastembed |
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For embedding lists of documents, use the [`FastembedSparseDocumentEmbedder`](/docs/fastembedsparsedocumentembedder), which enriches the document with the computed sparse embedding.
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## Overview
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`FastembedSparseTextEmbedder` transforms a string into a sparse vector using sparse embedding [models](https://qdrant.github.io/fastembed/examples/Supported_Models/#supported-sparse-text-embedding-models) supported by FastEmbed.
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When you perform sparse embedding retrieval, use this component first to transform your query into a sparse vector. Then, the sparse embedding Retriever will use the vector to search for similar or relevant documents.
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### Compatible Models
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You can find the supported models in the [FastEmbed documentation](https://qdrant.github.io/fastembed/examples/Supported_Models/#supported-sparse-text-embedding-models).
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Currently, supported models are based on SPLADE, a technique for producing sparse representations for text, where each non-zero value in the embedding is the importance weight of a term in the BERT WordPiece vocabulary. For more information, see [our docs](../retrievers.mdx#sparse-embedding-based-retrievers) that explain sparse embedding-based Retrievers further.
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### Installation
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To start using this integration with Haystack, install the package with:
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```shell
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pip install fastembed-haystack
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```
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### Parameters
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You can set the path where the model will be stored in a cache directory. Also, you can set the number of threads a single `onnxruntime` session can use:
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```python
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cache_dir= "/your_cacheDirectory"
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embedder = FastembedSparseTextEmbedder(
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model="prithivida/Splade_PP_en_v1",
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cache_dir=cache_dir,
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threads=2
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)
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```
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If you want to use the data parallel encoding, you can set the `parallel` parameter.
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- If `parallel` > 1, data-parallel encoding will be used. This is recommended for offline encoding of large datasets.
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- If `parallel` is 0, use all available cores.
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- If None, don't use data-parallel processing; use the default `onnxruntime` threading instead.
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:::tip
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If you create both a Sparse Text Embedder and a Sparse Document Embedder based on the same model, Haystack utilizes a shared resource behind the scenes to conserve resources.
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:::
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## Usage
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### On its own
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```python
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from haystack_integrations.components.embedders.fastembed import FastembedSparseTextEmbedder
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text = """It clearly says online this will work on a Mac OS system.
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The disk comes and it does not, only Windows.
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Do Not order this if you have a Mac!!"""
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text_embedder = FastembedSparseTextEmbedder(model="prithivida/Splade_PP_en_v1")
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text_embedder.warm_up()
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sparse_embedding = text_embedder.run(text)["sparse_embedding"]
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```
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### In a pipeline
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Currently, sparse embedding retrieval is only supported by `QdrantDocumentStore`.
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First, install the package with:
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```shell
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pip install qdrant-haystack
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```
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Then, try out this pipeline:
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```python
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from haystack import Document, Pipeline
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from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
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from haystack_integrations.components.retrievers.qdrant import QdrantSparseEmbeddingRetriever
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from haystack_integrations.components.embedders.fastembed import FastembedSparseTextEmbedder, FastembedSparseDocumentEmbedder, FastembedTextEmbedder
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document_store = QdrantDocumentStore(
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":memory:",
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recreate_index=True,
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use_sparse_embeddings=True
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)
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documents = [
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Document(content="My name is Wolfgang and I live in Berlin"),
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Document(content="I saw a black horse running"),
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Document(content="Germany has many big cities"),
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Document(content="fastembed is supported by and maintained by Qdrant."),
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]
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sparse_document_embedder = FastembedSparseDocumentEmbedder(
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model="prithivida/Splade_PP_en_v1"
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)
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sparse_document_embedder.warm_up()
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documents_with_sparse_embeddings = sparse_document_embedder.run(documents)["documents"]
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document_store.write_documents(documents_with_sparse_embeddings)
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query_pipeline = Pipeline()
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query_pipeline.add_component("sparse_text_embedder", FastembedSparseTextEmbedder())
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query_pipeline.add_component("sparse_retriever", QdrantSparseEmbeddingRetriever(document_store=document_store))
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query_pipeline.connect("sparse_text_embedder.sparse_embedding",
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"sparse_retriever.query_sparse_embedding")
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query = "Who supports fastembed?"
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result = query_pipeline.run({"sparse_text_embedder": {"text": query}})
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print(result["sparse_retriever"]["documents"][0]) # noqa: T201
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## Document(id=...,
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## content: 'fastembed is supported by and maintained by Qdrant.',
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## score: 0.561..)
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```
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## Additional References
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:cook: Cookbook: [Sparse Embedding Retrieval with Qdrant and FastEmbed](https://haystack.deepset.ai/cookbook/sparse_embedding_retrieval)
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