OllamaDocumentEmbedder

Calculate document embeddings using Ollama models.

Key Features

• Uses Ollama to run embedding models locally, without external API services.
• Stores the computed embedding in the embedding field of each document.
• Compatible with embedding models available in Ollama's library.
• Default model is nomic-embed-text.
• Supports embedding metadata fields alongside document content.

Configuration

Before using this component, make sure you have a running Ollama instance with the embedding model pulled.

1. Drag the OllamaDocumentEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   • Set the model name. The model must already be available in your running Ollama instance. See other pre-built models in Ollama's library.
   • Set the url to point to your Ollama server (default: http://localhost:11434).
4. Go to the Advanced tab to configure timeout, batch_size, meta_fields_to_embed, and embedding_separator.

Embedding Models in Query Pipelines and Indexes

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Connections

OllamaDocumentEmbedder accepts a list of documents as input. In an indexing pipeline, connect it to converters such as TextFileToDocument or preprocessors such as DocumentSplitter.

It outputs a list of documents with the embedding field populated. Connect its documents output to DocumentWriter to store the embedded documents.

To embed a query string instead of documents, use OllamaTextEmbedder.

Source Code

To check this component's source code, open document_embedder.py in the Haystack Core Integrations repository.

Usage Examples

Basic Configuration

  OllamaDocumentEmbedder:
    type: haystack_integrations.components.embedders.ollama.document_embedder.OllamaDocumentEmbedder
    init_parameters:
      model: nomic-embed-text
      url: http://localhost:11434
      timeout: 120
      prefix: ''
      suffix: ''
      progress_bar: true
      embedding_separator: "\n"
      batch_size: 32

In this index, OllamaDocumentEmbedder receives documents from DocumentSplitter and embeds them. It then sends the embedded documents to DocumentWriter. The index uses the nomic-embed-text model, which means OllamaTextEmbedder in the query pipeline must use the same model.

components:
  TextFileToDocument:
    type: haystack.components.converters.txt.TextFileToDocument
    init_parameters:
      encoding: utf-8
      store_full_path: false
  DocumentSplitter:
    type: haystack.components.preprocessors.document_splitter.DocumentSplitter
    init_parameters:
      split_by: word
      split_length: 200
      split_overlap: 0
      split_threshold: 0
      splitting_function:
  OllamaDocumentEmbedder:
    type: haystack_integrations.components.embedders.ollama.document_embedder.OllamaDocumentEmbedder
    init_parameters:
      model: nomic-embed-text
      url: http://localhost:11434
      generation_kwargs:
      timeout: 120
      prefix: ''
      suffix: ''
      progress_bar: true
      meta_fields_to_embed:
      embedding_separator: "\n"
      batch_size: 32
  DocumentWriter:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: ollama-embeddings-index
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
          similarity: cosine
      policy: NONE

connections:
- sender: TextFileToDocument.documents
  receiver: DocumentSplitter.documents
- sender: DocumentSplitter.documents
  receiver: OllamaDocumentEmbedder.documents
- sender: OllamaDocumentEmbedder.documents
  receiver: DocumentWriter.documents

max_runs_per_component: 100

metadata: {}

inputs:
  files:
  - TextFileToDocument.sources

Parameters

Inputs

Parameter	Type	Default	Description
documents	List[Document]		Documents to be converted to an embedding.
generation_kwargs	Optional[Dict[str, Any]]	None	Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, etc. See the Ollama docs.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		Documents with their embeddings added to the embedding field.
meta	Dict[str, Any]		Metadata about the request, including the model name.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	nomic-embed-text	The name of the model to use. The model should be available in the running Ollama instance.
url	str	http://localhost:11434	The URL of a running Ollama instance.
generation_kwargs	Optional[Dict[str, Any]]	None	Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, and others. See the available arguments in Ollama docs.
timeout	int	120	The number of seconds before throwing a timeout error from the Ollama API.
prefix	str		A string to add at the beginning of each text.
suffix	str		A string to add at the end of each text.
progress_bar	bool	True	If True, shows a progress bar when running.
meta_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed along with the document text.
embedding_separator	str	\n	Separator used to concatenate the metadata fields to the document text.
batch_size	int	32	Number of documents to process at once.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
documents	List[Document]		Documents to be converted to an embedding.
generation_kwargs	Optional[Dict[str, Any]]	None	Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, etc. See the Ollama docs.