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docs-website/docs/pipeline-components/embedders/fastembeddocumentembedder.mdx

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+---
+title: "FastembedDocumentEmbedder"
+id: fastembeddocumentembedder
+slug: "/fastembeddocumentembedder"
+description: "This component computes the embeddings of a list of documents using the models supported by FastEmbed."
+---
+
+# FastembedDocumentEmbedder
+
+This component computes the embeddings of a list of documents using the models supported by FastEmbed.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx)  in an indexing pipeline                  |
+| **Mandatory run variables**            | `documents`: A list of documents                                                            |
+| **Output variables**                   | `documents`: A list of documents (enriched with embeddings)                                 |
+| **API reference**                      | [FastEmbed](/reference/fastembed-embedders)                                                        |
+| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/fastembed |
+| **Package name** | `fastembed-haystack` |
+
+</div>
+
+This component should be used to embed a list of documents. To embed a string, use the [`FastembedTextEmbedder`](fastembedtextembedder.mdx).
+
+## Overview
+
+`FastembedDocumentEmbedder` computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses embedding [models supported by FastEmbed](https://qdrant.github.io/fastembed/examples/Supported_Models/).
+
+The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector that represents the query is compared with those of the documents in order to find the most similar or relevant documents.
+
+### Compatible models
+
+You can find the original models in the [FastEmbed documentation](https://qdrant.github.io/fastembed/).
+
+Nowadays, most of the models in the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) are compatible with FastEmbed. You can look for compatibility in the [supported model list](https://qdrant.github.io/fastembed/examples/Supported_Models/).
+
+### Installation
+
+To start using this integration with Haystack, install the package with:
+
+```shell
+pip install fastembed-haystack
+```
+
+### Parameters
+
+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.
+
+```python
+cache_dir= "/your_cacheDirectory"
+embedder = FastembedDocumentEmbedder(
+	*model="*BAAI/bge-large-en-v1.5",
+	cache_dir=cache_dir,
+	threads=2
+)
+```
+
+If you want to use the data parallel encoding, you can set the parameters `parallel` and `batch_size`.
+
+- If parallel > 1, data-parallel encoding will be used. This is recommended for offline encoding of large datasets.
+- If parallel is 0, use all available cores.
+- If None, don't use data-parallel processing; use default `onnxruntime` threading instead.
+
+:::tip
+If you create a Text Embedder and a Document Embedder based on the same model, Haystack uses the same resource behind the scenes to save resources.
+:::
+
+### Embedding Metadata
+
+Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.
+
+You can do this easily by using the Document Embedder:
+
+```python
+from haystack.preview import Document
+from haystack_integrations.components.embedders.fastembed import (
+    FastembedDocumentEmbedder,
+)
+
+doc = Document(
+    text="some text",
+    metadata={"title": "relevant title", "page number": 18},
+)
+
+embedder = FastembedDocumentEmbedder(
+    model="BAAI/bge-small-en-v1.5",
+    batch_size=256,
+    metadata_fields_to_embed=["title"],
+)
+
+docs_w_embeddings = embedder.run(documents=[doc])["documents"]
+```
+
+## Usage
+
+### On its own
+
+```python
+from haystack.dataclasses import Document
+from haystack_integrations.components.embedders.fastembed import (
+    FastembedDocumentEmbedder,
+)
+
+document_list = [
+    Document(content="I love pizza!"),
+    Document(content="I like spaghetti"),
+]
+
+doc_embedder = FastembedDocumentEmbedder()
+
+result = doc_embedder.run(document_list)
+print(result["documents"][0].embedding)
+
+# [-0.04235665127635002, 0.021791068837046623, ...]
+```
+
+### In a pipeline
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.writers import DocumentWriter
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.document_stores.types import DuplicatePolicy
+from haystack_integrations.components.embedders.fastembed import (
+    FastembedDocumentEmbedder,
+    FastembedTextEmbedder,
+)
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [
+    Document(content="My name is Wolfgang and I live in Berlin"),
+    Document(content="I saw a black horse running"),
+    Document(content="Germany has many big cities"),
+    Document(content="fastembed is supported by and maintained by Qdrant."),
+]
+
+document_embedder = FastembedDocumentEmbedder()
+writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component("document_embedder", document_embedder)
+indexing_pipeline.add_component("writer", writer)
+indexing_pipeline.connect("document_embedder", "writer")
+
+indexing_pipeline.run({"document_embedder": {"documents": documents}})
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", FastembedTextEmbedder())
+query_pipeline.add_component(
+    "retriever",
+    InMemoryEmbeddingRetriever(document_store=document_store),
+)
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+query = "Who supports fastembed?"
+
+result = query_pipeline.run({"text_embedder": {"text": query}})
+
+print(result["retriever"]["documents"][0])  # noqa: T201
+
+# Document(id=...,
+# content: 'fastembed is supported by and maintained by Qdrant.',
+# score: 0.758..)
+```
+
+## Additional References
+
+🧑‍🍳 Cookbook: [RAG Pipeline Using FastEmbed for Embeddings Generation](https://haystack.deepset.ai/cookbook/rag_fastembed)