MockDocumentEmbedder

A Document Embedder that returns deterministic embeddings without calling any API, for tests and quick prototypes.

Most common position in a pipeline	In place of a real Document Embedder, in tests and prototypes
Mandatory init variables	None
Mandatory run variables	documents: A list of documents
Output variables	documents: A list of documents enriched with embeddings meta: A dictionary of metadata
API reference	Embedders
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/mock_document_embedder.py
Package name	haystack-ai

Overview

MockDocumentEmbedder is a deterministic, zero-cost drop-in replacement for real Document Embedders such as OpenAIDocumentEmbedder. It implements run, run_async, and serialization like any other embedder but never contacts an external service, which makes it ideal for unit tests, smoke tests, and quick prototypes.

The embedding is selected based on how the component is configured:

• Deterministic (default): With no configuration, each document's embedding is derived from a stable hash of its prepared text. The same text always yields the same unit-length embedding, and different texts yield different embeddings, so the mock works in retrieval pipelines and is reproducible across runs and processes.
• Fixed embedding: Pass an embedding vector. The same vector is assigned to every document.
• Dynamic embedding: Pass an embedding_fn callable that receives the prepared text of a document and returns the embedding. To support serialization, pass a named function.

embedding and embedding_fn are mutually exclusive.

Further optional parameters:

• dimension: The number of dimensions of the deterministic embedding. Defaults to 768. Ignored when embedding or embedding_fn is provided, since their length is determined by the value or callable.
• model: The model name reported in the metadata. Defaults to "mock-model".
• meta: Additional metadata merged into the output meta.
• prefix / suffix: Strings added to the beginning and end of each text before embedding, mirroring real embedders.
• meta_fields_to_embed / embedding_separator: Like real Document Embedders, the metadata fields listed in meta_fields_to_embed are concatenated with the document content before embedding, so the deterministic embedding reflects the embedded metadata.
• progress_bar: Accepted for interface compatibility with real Document Embedders and ignored.

info

The deterministic embeddings are derived from a hash: identical texts get identical vectors and the similarity between different texts is stable but arbitrary. For exact-match retrieval in tests this is exactly what you want. Do not expect semantically similar texts to end up close together.

Use MockDocumentEmbedder for documents and its counterpart MockTextEmbedder for queries. With the default deterministic mode, a query whose text matches a document's content produces the same vector, so the document is retrieved as the top hit.

Usage

On its own

python

from haystack import Document
from haystack.components.embedders import MockDocumentEmbedder

embedder = MockDocumentEmbedder(dimension=8)
result = embedder.run([Document(content="I love pizza!")])
print(result["documents"][0].embedding)  # a deterministic list of 8 floats

In a pipeline

Use it in an indexing pipeline exactly like a real Document Embedder — no API key needed:

python

from haystack import Document, Pipeline
from haystack.components.embedders import MockDocumentEmbedder
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore

document_store = InMemoryDocumentStore()

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", MockDocumentEmbedder(dimension=8))
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder.documents", "writer.documents")

indexing_pipeline.run(
    {
        "embedder": {
            "documents": [
                Document(content="My name is Wolfgang and I live in Berlin"),
                Document(content="I saw a black horse running"),
            ],
        },
    },
)
print(document_store.count_documents())  # 2