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docs-website/reference/integrations-api/fastembed.md

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+---
+title: "FastEmbed"
+id: fastembed-embedders
+description: "FastEmbed integration for Haystack"
+slug: "/fastembed-embedders"
+---
+
+
+## haystack_integrations.components.embedders.fastembed.fastembed_document_embedder
+
+### FastembedDocumentEmbedder
+
+FastembedDocumentEmbedder computes Document embeddings using Fastembed embedding models.
+
+The embedding of each Document is stored in the `embedding` field of the Document.
+
+Usage example:
+
+```python
+# To use this component, install the "fastembed-haystack" package.
+# pip install fastembed-haystack
+
+from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder
+from haystack.dataclasses import Document
+
+doc_embedder = FastembedDocumentEmbedder(
+    model="BAAI/bge-small-en-v1.5",
+    batch_size=256,
+)
+
+# Text taken from PubMed QA Dataset (https://huggingface.co/datasets/pubmed_qa)
+document_list = [
+    Document(
+        content=("Oxidative stress generated within inflammatory joints can produce autoimmune phenomena and joint "
+                 "destruction. Radical species with oxidative activity, including reactive nitrogen species, "
+                 "represent mediators of inflammation and cartilage damage."),
+        meta={
+            "pubid": "25,445,628",
+            "long_answer": "yes",
+        },
+    ),
+    Document(
+        content=("Plasma levels of pancreatic polypeptide (PP) rise upon food intake. Although other pancreatic "
+                 "islet hormones, such as insulin and glucagon, have been extensively investigated, PP secretion "
+                 "and actions are still poorly understood."),
+        meta={
+            "pubid": "25,445,712",
+            "long_answer": "yes",
+        },
+    ),
+]
+
+result = doc_embedder.run(document_list)
+print(f"Document Text: {result['documents'][0].content}")
+print(f"Document Embedding: {result['documents'][0].embedding}")
+print(f"Embedding Dimension: {len(result['documents'][0].embedding)}")
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str = "BAAI/bge-small-en-v1.5",
+    cache_dir: str | None = None,
+    threads: int | None = None,
+    prefix: str = "",
+    suffix: str = "",
+    batch_size: int = 256,
+    progress_bar: bool = True,
+    parallel: int | None = None,
+    local_files_only: bool = False,
+    meta_fields_to_embed: list[str] | None = None,
+    embedding_separator: str = "\n",
+) -> None
+```
+
+Create an FastembedDocumentEmbedder component.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – Local path or name of the model in Hugging Face's model hub,
+  such as `BAAI/bge-small-en-v1.5`.
+- **cache_dir** (<code>str | None</code>) – The path to the cache directory.
+  Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+  Defaults to `fastembed_cache` in the system's temp directory.
+- **threads** (<code>int | None</code>) – The number of threads single onnxruntime session can use. Defaults to None.
+- **prefix** (<code>str</code>) – A string to add to the beginning of each text.
+- **suffix** (<code>str</code>) – A string to add to the end of each text.
+- **batch_size** (<code>int</code>) – Number of strings to encode at once.
+- **progress_bar** (<code>bool</code>) – If `True`, displays progress bar during embedding.
+- **parallel** (<code>int | None</code>) – If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+  If 0, use all available cores.
+  If None, don't use data-parallel processing, use default onnxruntime threading instead.
+- **local_files_only** (<code>bool</code>) – If `True`, only use the model files in the `cache_dir`.
+- **meta_fields_to_embed** (<code>list\[str\] | None</code>) – List of meta fields that should be embedded along with the Document content.
+- **embedding_separator** (<code>str</code>) – Separator used to concatenate the meta fields to the Document content.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the component.
+
+#### run
+
+```python
+run(documents: list[Document]) -> dict[str, list[Document]]
+```
+
+Embeds a list of Documents.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of Documents to embed.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: List of Documents with each Document's `embedding` field set to the computed embeddings.
+
+**Raises:**
+
+- <code>TypeError</code> – If the input is not a list of Documents.
+
+## haystack_integrations.components.embedders.fastembed.fastembed_sparse_document_embedder
+
+### FastembedSparseDocumentEmbedder
+
+FastembedSparseDocumentEmbedder computes Document embeddings using Fastembed sparse models.
+
+Usage example:
+
+```python
+from haystack_integrations.components.embedders.fastembed import FastembedSparseDocumentEmbedder
+from haystack.dataclasses import Document
+
+sparse_doc_embedder = FastembedSparseDocumentEmbedder(
+    model="prithivida/Splade_PP_en_v1",
+    batch_size=32,
+)
+
+# Text taken from PubMed QA Dataset (https://huggingface.co/datasets/pubmed_qa)
+document_list = [
+    Document(
+        content=("Oxidative stress generated within inflammatory joints can produce autoimmune phenomena and joint "
+                 "destruction. Radical species with oxidative activity, including reactive nitrogen species, "
+                 "represent mediators of inflammation and cartilage damage."),
+        meta={
+            "pubid": "25,445,628",
+            "long_answer": "yes",
+        },
+    ),
+    Document(
+        content=("Plasma levels of pancreatic polypeptide (PP) rise upon food intake. Although other pancreatic "
+                 "islet hormones, such as insulin and glucagon, have been extensively investigated, PP secretion "
+                 "and actions are still poorly understood."),
+        meta={
+            "pubid": "25,445,712",
+            "long_answer": "yes",
+        },
+    ),
+]
+
+result = sparse_doc_embedder.run(document_list)
+print(f"Document Text: {result['documents'][0].content}")
+print(f"Document Sparse Embedding: {result['documents'][0].sparse_embedding}")
+print(f"Sparse Embedding Dimension: {len(result['documents'][0].sparse_embedding)}")
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str = "prithivida/Splade_PP_en_v1",
+    cache_dir: str | None = None,
+    threads: int | None = None,
+    batch_size: int = 32,
+    progress_bar: bool = True,
+    parallel: int | None = None,
+    local_files_only: bool = False,
+    meta_fields_to_embed: list[str] | None = None,
+    embedding_separator: str = "\n",
+    model_kwargs: dict[str, Any] | None = None,
+) -> None
+```
+
+Create an FastembedDocumentEmbedder component.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – Local path or name of the model in Hugging Face's model hub,
+  such as `prithivida/Splade_PP_en_v1`.
+- **cache_dir** (<code>str | None</code>) – The path to the cache directory.
+  Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+  Defaults to `fastembed_cache` in the system's temp directory.
+- **threads** (<code>int | None</code>) – The number of threads single onnxruntime session can use.
+- **batch_size** (<code>int</code>) – Number of strings to encode at once.
+- **progress_bar** (<code>bool</code>) – If `True`, displays progress bar during embedding.
+- **parallel** (<code>int | None</code>) – If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+  If 0, use all available cores.
+  If None, don't use data-parallel processing, use default onnxruntime threading instead.
+- **local_files_only** (<code>bool</code>) – If `True`, only use the model files in the `cache_dir`.
+- **meta_fields_to_embed** (<code>list\[str\] | None</code>) – List of meta fields that should be embedded along with the Document content.
+- **embedding_separator** (<code>str</code>) – Separator used to concatenate the meta fields to the Document content.
+- **model_kwargs** (<code>dict\[str, Any\] | None</code>) – Dictionary containing model parameters such as `k`, `b`, `avg_len`, `language`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the component.
+
+#### run
+
+```python
+run(documents: list[Document]) -> dict[str, list[Document]]
+```
+
+Embeds a list of Documents.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of Documents to embed.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: List of Documents with each Document's `sparse_embedding`
+  field set to the computed embeddings.
+
+**Raises:**
+
+- <code>TypeError</code> – If the input is not a list of Documents.
+
+## haystack_integrations.components.embedders.fastembed.fastembed_sparse_text_embedder
+
+### FastembedSparseTextEmbedder
+
+FastembedSparseTextEmbedder computes string embedding using fastembed sparse models.
+
+Usage example:
+
+```python
+from haystack_integrations.components.embedders.fastembed import FastembedSparseTextEmbedder
+
+text = ("It clearly says online this will work on a Mac OS system. "
+        "The disk comes and it does not, only Windows. Do Not order this if you have a Mac!!")
+
+sparse_text_embedder = FastembedSparseTextEmbedder(
+    model="prithivida/Splade_PP_en_v1"
+)
+
+sparse_embedding = sparse_text_embedder.run(text)["sparse_embedding"]
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str = "prithivida/Splade_PP_en_v1",
+    cache_dir: str | None = None,
+    threads: int | None = None,
+    progress_bar: bool = True,
+    parallel: int | None = None,
+    local_files_only: bool = False,
+    model_kwargs: dict[str, Any] | None = None,
+) -> None
+```
+
+Create a FastembedSparseTextEmbedder component.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – Local path or name of the model in Fastembed's model hub, such as `prithivida/Splade_PP_en_v1`
+- **cache_dir** (<code>str | None</code>) – The path to the cache directory.
+  Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+  Defaults to `fastembed_cache` in the system's temp directory.
+- **threads** (<code>int | None</code>) – The number of threads single onnxruntime session can use. Defaults to None.
+- **progress_bar** (<code>bool</code>) – If `True`, displays progress bar during embedding.
+- **parallel** (<code>int | None</code>) – If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+  If 0, use all available cores.
+  If None, don't use data-parallel processing, use default onnxruntime threading instead.
+- **local_files_only** (<code>bool</code>) – If `True`, only use the model files in the `cache_dir`.
+- **model_kwargs** (<code>dict\[str, Any\] | None</code>) – Dictionary containing model parameters such as `k`, `b`, `avg_len`, `language`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the component.
+
+#### run
+
+```python
+run(text: str) -> dict[str, SparseEmbedding]
+```
+
+Embeds text using the Fastembed model.
+
+**Parameters:**
+
+- **text** (<code>str</code>) – A string to embed.
+
+**Returns:**
+
+- <code>dict\[str, SparseEmbedding\]</code> – A dictionary with the following keys:
+- `embedding`: A list of floats representing the embedding of the input text.
+
+**Raises:**
+
+- <code>TypeError</code> – If the input is not a string.
+
+## haystack_integrations.components.embedders.fastembed.fastembed_text_embedder
+
+### FastembedTextEmbedder
+
+FastembedTextEmbedder computes string embedding using fastembed embedding models.
+
+Usage example:
+
+```python
+from haystack_integrations.components.embedders.fastembed import FastembedTextEmbedder
+
+text = ("It clearly says online this will work on a Mac OS system. "
+        "The disk comes and it does not, only Windows. Do Not order this if you have a Mac!!")
+
+text_embedder = FastembedTextEmbedder(
+    model="BAAI/bge-small-en-v1.5"
+)
+
+embedding = text_embedder.run(text)["embedding"]
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str = "BAAI/bge-small-en-v1.5",
+    cache_dir: str | None = None,
+    threads: int | None = None,
+    prefix: str = "",
+    suffix: str = "",
+    progress_bar: bool = True,
+    parallel: int | None = None,
+    local_files_only: bool = False,
+) -> None
+```
+
+Create a FastembedTextEmbedder component.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – Local path or name of the model in Fastembed's model hub, such as `BAAI/bge-small-en-v1.5`
+- **cache_dir** (<code>str | None</code>) – The path to the cache directory.
+  Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+  Defaults to `fastembed_cache` in the system's temp directory.
+- **threads** (<code>int | None</code>) – The number of threads single onnxruntime session can use. Defaults to None.
+- **prefix** (<code>str</code>) – A string to add to the beginning of each text.
+- **suffix** (<code>str</code>) – A string to add to the end of each text.
+- **progress_bar** (<code>bool</code>) – If `True`, displays progress bar during embedding.
+- **parallel** (<code>int | None</code>) – If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+  If 0, use all available cores.
+  If None, don't use data-parallel processing, use default onnxruntime threading instead.
+- **local_files_only** (<code>bool</code>) – If `True`, only use the model files in the `cache_dir`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the component.
+
+#### run
+
+```python
+run(text: str) -> dict[str, list[float]]
+```
+
+Embeds text using the Fastembed model.
+
+**Parameters:**
+
+- **text** (<code>str</code>) – A string to embed.
+
+**Returns:**
+
+- <code>dict\[str, list\[float\]\]</code> – A dictionary with the following keys:
+- `embedding`: A list of floats representing the embedding of the input text.
+
+**Raises:**
+
+- <code>TypeError</code> – If the input is not a string.
+
+## haystack_integrations.components.rankers.fastembed.late_interaction_ranker
+
+### FastembedLateInteractionRanker
+
+Ranks Documents based on their similarity to the query using ColBERT models via Fastembed.
+
+Uses late interaction (MaxSim) scoring to compute token-level similarity between
+query and document embeddings, then ranks documents accordingly.
+
+See https://qdrant.github.io/fastembed/examples/Supported_Models/ for supported models.
+
+Usage example:
+
+```python
+from haystack import Document
+from haystack_integrations.components.rankers.fastembed import FastembedLateInteractionRanker
+
+ranker = FastembedLateInteractionRanker(model_name="colbert-ir/colbertv2.0", top_k=2)
+
+docs = [Document(content="Paris"), Document(content="Berlin")]
+query = "What is the capital of germany?"
+output = ranker.run(query=query, documents=docs)
+print(output["documents"][0].content)
+
+# Berlin
+```
+
+#### __init__
+
+```python
+__init__(
+    model_name: str = "colbert-ir/colbertv2.0",
+    top_k: int = 10,
+    cache_dir: str | None = None,
+    threads: int | None = None,
+    batch_size: int = 64,
+    parallel: int | None = None,
+    local_files_only: bool = False,
+    meta_fields_to_embed: list[str] | None = None,
+    meta_data_separator: str = "\n",
+    score_threshold: float | None = None,
+) -> None
+```
+
+Creates an instance of the 'FastembedLateInteractionRanker'.
+
+**Parameters:**
+
+- **model_name** (<code>str</code>) – Fastembed ColBERT model name. Check the list of supported models in the
+  [Fastembed documentation](https://qdrant.github.io/fastembed/examples/Supported_Models/).
+- **top_k** (<code>int</code>) – The maximum number of documents to return.
+- **cache_dir** (<code>str | None</code>) – The path to the cache directory.
+  Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+  Defaults to `fastembed_cache` in the system's temp directory.
+- **threads** (<code>int | None</code>) – The number of threads single onnxruntime session can use. Defaults to None.
+- **batch_size** (<code>int</code>) – Number of strings to encode at once.
+- **parallel** (<code>int | None</code>) – If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+  If 0, use all available cores.
+  If None, don't use data-parallel processing, use default onnxruntime threading instead.
+- **local_files_only** (<code>bool</code>) – If `True`, only use the model files in the `cache_dir`.
+- **meta_fields_to_embed** (<code>list\[str\] | None</code>) – List of meta fields that should be concatenated
+  with the document content for reranking.
+- **meta_data_separator** (<code>str</code>) – Separator used to concatenate the meta fields
+  to the Document content.
+- **score_threshold** (<code>float | None</code>) – If provided, only documents with a score above the threshold are returned.
+  Note that ColBERT scores are unnormalized sums and typically range from 3 to 25.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FastembedLateInteractionRanker
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – The dictionary to deserialize from.
+
+**Returns:**
+
+- <code>FastembedLateInteractionRanker</code> – The deserialized component.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the component.
+
+#### run
+
+```python
+run(
+    query: str, documents: list[Document], top_k: int | None = None
+) -> dict[str, list[Document]]
+```
+
+Returns a list of documents ranked by their similarity to the given query using ColBERT MaxSim scoring.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – The input query to compare the documents to.
+- **documents** (<code>list\[Document\]</code>) – A list of documents to be ranked.
+- **top_k** (<code>int | None</code>) – The maximum number of documents to return.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: A list of documents closest to the query, sorted from most similar to least similar.
+
+**Raises:**
+
+- <code>ValueError</code> – If `top_k` is not > 0.
+
+## haystack_integrations.components.rankers.fastembed.ranker
+
+### FastembedRanker
+
+Ranks Documents based on their similarity to the query using Fastembed models.
+
+See https://qdrant.github.io/fastembed/examples/Supported_Models/ for supported models.
+
+Documents are indexed from most to least semantically relevant to the query.
+
+Usage example:
+
+```python
+from haystack import Document
+from haystack_integrations.components.rankers.fastembed import FastembedRanker
+
+ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-6-v2", top_k=2)
+
+docs = [Document(content="Paris"), Document(content="Berlin")]
+query = "What is the capital of germany?"
+output = ranker.run(query=query, documents=docs)
+print(output["documents"][0].content)
+
+# Berlin
+```
+
+#### __init__
+
+```python
+__init__(
+    model_name: str = "Xenova/ms-marco-MiniLM-L-6-v2",
+    top_k: int = 10,
+    cache_dir: str | None = None,
+    threads: int | None = None,
+    batch_size: int = 64,
+    parallel: int | None = None,
+    local_files_only: bool = False,
+    meta_fields_to_embed: list[str] | None = None,
+    meta_data_separator: str = "\n",
+    score_threshold: float | None = None,
+) -> None
+```
+
+Creates an instance of the 'FastembedRanker'.
+
+**Parameters:**
+
+- **model_name** (<code>str</code>) – Fastembed model name. Check the list of supported models in the [Fastembed documentation](https://qdrant.github.io/fastembed/examples/Supported_Models/).
+- **top_k** (<code>int</code>) – The maximum number of documents to return.
+- **cache_dir** (<code>str | None</code>) – The path to the cache directory.
+  Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+  Defaults to `fastembed_cache` in the system's temp directory.
+- **threads** (<code>int | None</code>) – The number of threads single onnxruntime session can use. Defaults to None.
+- **batch_size** (<code>int</code>) – Number of strings to encode at once.
+- **parallel** (<code>int | None</code>) – If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+  If 0, use all available cores.
+  If None, don't use data-parallel processing, use default onnxruntime threading instead.
+- **local_files_only** (<code>bool</code>) – If `True`, only use the model files in the `cache_dir`.
+- **meta_fields_to_embed** (<code>list\[str\] | None</code>) – List of meta fields that should be concatenated
+  with the document content for reranking.
+- **meta_data_separator** (<code>str</code>) – Separator used to concatenate the meta fields
+  to the Document content.
+- **score_threshold** (<code>float | None</code>) – If provided, only documents with a score above the threshold are returned.
+  Applied after `top_k`, so the output may contain fewer than `top_k` documents.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FastembedRanker
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – The dictionary to deserialize from.
+
+**Returns:**
+
+- <code>FastembedRanker</code> – The deserialized component.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the component.
+
+#### run
+
+```python
+run(
+    query: str, documents: list[Document], top_k: int | None = None
+) -> dict[str, list[Document]]
+```
+
+Returns a list of documents ranked by their similarity to the given query, using FastEmbed.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – The input query to compare the documents to.
+- **documents** (<code>list\[Document\]</code>) – A list of documents to be ranked.
+- **top_k** (<code>int | None</code>) – The maximum number of documents to return.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: A list of documents closest to the query, sorted from most similar to least similar.
+
+**Raises:**
+
+- <code>ValueError</code> – If `top_k` is not > 0.