FastembedTextEmbedder

Compute text embeddings using Fastembed embedding models.

Key Features

• Uses Fastembed, a lightweight and fast Python embedding library.
• Designed for query pipelines — embeds the user query for semantic search.
• Configurable prefix and suffix strings for instruction-tuned models.
• Supports data-parallel processing options for offline use.
• Works with all Fastembed-compatible text embedding models.

Configuration

1. Drag the FastembedTextEmbedder component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the model name or local path, such as BAAI/bge-small-en-v1.5.
4. Go to the Advanced tab to configure batch size, parallel processing, cache directory, thread count, prefix, suffix, and local files only setting.

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.

Compatible Models

You can find the supported models in the FastEmbed documentation.

Connections

FastembedTextEmbedder receives a text string (the user query) as input. It outputs an embedding list of floats. Connect its output to an embedding retriever to find matching documents. Use the same model as the one used to embed documents in the document store.

Usage Example

This query pipeline uses FastembedTextEmbedder to embed the query for semantic search:

components:
  FastembedTextEmbedder:
    type: haystack_integrations.components.embedders.fastembed.fastembed_text_embedder.FastembedTextEmbedder
    init_parameters:
      model: BAAI/bge-small-en-v1.5
      cache_dir:
      threads:
      prefix: ""
      suffix: ""
      progress_bar: true
      parallel:
      local_files_only: false

  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'default'
          max_chunk_bytes: 104857600
          embedding_dim: 384
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 10

  ChatPromptBuilder:
    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
    init_parameters:
      template:
        - role: system
          content: "You are a helpful assistant answering questions based on the provided documents."
        - role: user
          content: "Documents:\n{% for doc in documents %}\n{{ doc.content }}\n{% endfor %}\n\nQuestion: {{ query }}"

  OpenAIChatGenerator:
    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
          - OPENAI_API_KEY
        strict: false
      model: gpt-4o-mini

  OutputAdapter:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: '{{ replies[0] }}'
      output_type: List[str]

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

connections:
  - sender: FastembedTextEmbedder.embedding
    receiver: embedding_retriever.query_embedding
  - sender: embedding_retriever.documents
    receiver: ChatPromptBuilder.documents
  - sender: ChatPromptBuilder.prompt
    receiver: OpenAIChatGenerator.messages
  - sender: OpenAIChatGenerator.replies
    receiver: OutputAdapter.replies
  - sender: OutputAdapter.output
    receiver: answer_builder.replies
  - sender: embedding_retriever.documents
    receiver: answer_builder.documents

inputs:
  query:
    - FastembedTextEmbedder.text
    - ChatPromptBuilder.query
    - answer_builder.query
  filters:
    - embedding_retriever.filters

outputs:
  documents: embedding_retriever.documents
  answers: answer_builder.answers

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Default	Description
text	str		A string to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		A list of floats representing the embedding of the input text.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	BAAI/bge-small-en-v1.5	Local path or name of the model in Fastembed's model hub, such as BAAI/bge-small-en-v1.5.
cache_dir	Optional[str]	None	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	Optional[int]	None	The number of threads single onnxruntime session can use.
prefix	str	""	A string to add to the beginning of each text.
suffix	str	""	A string to add to the end of each text.
progress_bar	bool	True	If True, displays progress bar during embedding.
parallel	Optional[int]	None	If > 1, data-parallel encoding is 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	bool	False	If True, only use the model files in the cache_dir.

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
text	str		A string to embed.