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95 lines
4.4 KiB
Plaintext
95 lines
4.4 KiB
Plaintext
---
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title: "MockTextEmbedder"
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id: mocktextembedder
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slug: "/mocktextembedder"
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description: "A Text Embedder that returns deterministic embeddings without calling any API, for tests and quick prototypes."
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---
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# MockTextEmbedder
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A Text Embedder that returns deterministic embeddings without calling any API, for tests and quick prototypes.
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<div className="key-value-table">
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| --- | --- |
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| **Most common position in a pipeline** | In place of a real Text Embedder, in tests and prototypes |
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| **Mandatory init variables** | None |
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| **Mandatory run variables** | `text`: A string |
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| **Output variables** | `embedding`: A list of float numbers <br /> <br />`meta`: A dictionary of metadata |
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| **API reference** | [Embedders](/reference/embedders-api) |
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| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/mock_text_embedder.py |
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| **Package name** | `haystack-ai` |
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</div>
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## Overview
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`MockTextEmbedder` is a deterministic, zero-cost drop-in replacement for real Text Embedders such as `OpenAITextEmbedder`. 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.
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The embedding is selected based on how the component is configured:
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- **Deterministic (default)**: With no configuration, the embedding is derived from a stable hash of the input 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.
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- **Fixed embedding**: Pass an `embedding` vector. The same vector is returned for every input.
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- **Dynamic embedding**: Pass an `embedding_fn` callable that receives the prepared text (after `prefix`/`suffix` are applied) and returns the embedding. To support serialization, pass a named function.
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`embedding` and `embedding_fn` are mutually exclusive.
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Further optional parameters:
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- `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.
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- `model`: The model name reported in the metadata. Defaults to `"mock-model"`.
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- `meta`: Additional metadata merged into the output `meta`.
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- `prefix` / `suffix`: Strings added to the beginning and end of the text before embedding, mirroring real embedders.
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:::info
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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.
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:::
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Use `MockTextEmbedder` for queries and its counterpart [`MockDocumentEmbedder`](mockdocumentembedder.mdx) for documents. 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.
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## Usage
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### On its own
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```python
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from haystack.components.embedders import MockTextEmbedder
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embedder = MockTextEmbedder(dimension=8)
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result = embedder.run("I love pizza!")
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print(result["embedding"]) # a deterministic list of 8 floats
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```
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### In a pipeline
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A retrieval pipeline built with mock embedders runs without any API key and always returns the same result for the same input:
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```python
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from haystack import Document, Pipeline
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from haystack.components.embedders import MockDocumentEmbedder, MockTextEmbedder
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from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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document_store = InMemoryDocumentStore()
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documents = [
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Document(content="My name is Wolfgang and I live in Berlin"),
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Document(content="I saw a black horse running"),
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]
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indexed = MockDocumentEmbedder(dimension=8).run(documents=documents)
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document_store.write_documents(indexed["documents"])
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query_pipeline = Pipeline()
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query_pipeline.add_component("text_embedder", MockTextEmbedder(dimension=8))
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query_pipeline.add_component(
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"retriever",
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InMemoryEmbeddingRetriever(document_store=document_store),
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)
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query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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result = query_pipeline.run(
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{"text_embedder": {"text": "I saw a black horse running"}},
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)
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print(result["retriever"]["documents"][0].content) # "I saw a black horse running"
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```
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