voyage-embedders-haystack 2.0.0

Haystack component to embed strings and Documents using VoyageAI Embedding models.

Voyage Embedders and Rankers - Haystack

Custom components for Haystack for creating embeddings and reranking documents using the Voyage Models.

Voyage’s embedding models are state-of-the-art in retrieval accuracy. These models outperform top performing embedding models like intfloat/e5-mistral-7b-instruct and OpenAI/text-embedding-3-large on the MTEB Benchmark.

What's New (v2.0.0)

• Haystack 3.x compatibility: All components now work with haystack-ai>=3.0.0.
• Lazy initialization: Components now use warm_up() for lazy client creation. The client property auto-initializes on first access.
• Async support: All components now have async def run() methods for Haystack 3.x async pipelines.
• Document immutability: Document embedders now use dataclasses.replace() to return new Document instances instead of mutating them in-place.

See the full Changelog for all releases.

Requirements

• Python 3.10 or higher
• Voyage AI API Key

Installation

pip install voyage-embedders-haystack

Usage

You can use Voyage Embedding models with multiple components:

• VoyageTextEmbedder: For generating embeddings for queries.
• VoyageDocumentEmbedder: For creating semantic embeddings for documents in your indexing pipeline.
• VoyageContextualizedDocumentEmbedder: For creating contextualized embeddings where document chunks are embedded together to preserve context and improve retrieval accuracy.
• VoyageMultimodalEmbedder: For creating multimodal embeddings that can encode text, images, and videos into a shared vector space.

The Voyage Reranker models can be used with the VoyageRanker component.

Multimodal Embeddings

The VoyageMultimodalEmbedder uses Voyage's multimodal embedding model (voyage-multimodal-3.5) to encode text, images, and videos into a shared vector space. This enables cross-modal similarity search where you can find images using text queries or find related content across different modalities.

Key features:

• Supports text, images (PIL Images, ByteStream), and videos
• Inputs can combine multiple modalities (e.g., text + image)
• Variable output dimensions: 256, 512, 1024 (default), 2048
• Recommended model: voyage-multimodal-3.5

Usage example:

from haystack.dataclasses import ByteStream
from haystack_integrations.components.embedders.voyage_embedders import VoyageMultimodalEmbedder
from voyageai.video_utils import Video

# Text-only embedding
embedder = VoyageMultimodalEmbedder(model="voyage-multimodal-3.5")
result = embedder.run(inputs=[["What is in this image?"]])

# Mixed text and image embedding
image_bytes = ByteStream.from_file_path("image.jpg")
result = embedder.run(inputs=[["Describe this image:", image_bytes]])

# Video embedding
video = Video.from_path("video.mp4", model="voyage-multimodal-3.5")
result = embedder.run(inputs=[["Describe this video:", video]])

Contextualized Chunk Embeddings

The VoyageContextualizedDocumentEmbedder uses Voyage's contextualized embedding models to encode document chunks "in context" with other chunks from the same document. This approach preserves semantic relationships between chunks and reduces context loss, leading to improved retrieval accuracy.

Key features:

• Documents are grouped by a metadata field (default: source_id)
• Chunks from the same source document are embedded together
• Maintains semantic connections between related chunks
• Recommended model: voyage-context-3

For detailed usage examples, see the contextualized embedder example.

Once you've selected the suitable component for your specific use case, initialize the component with the model name and VoyageAI API key. You can also set the environment variable VOYAGE_API_KEY instead of passing the API key as an argument. To get an API key, please see the Voyage AI website.

Information about the supported models, can be found on the Voyage AI Documentation.

Example

You can find all the examples in the examples folder.

Below is the example Semantic Search pipeline that uses the Simple Wikipedia Dataset from HuggingFace.

Load the dataset:

# Install HuggingFace Datasets using "pip install datasets"
from datasets import load_dataset
from haystack import Pipeline
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.writers import DocumentWriter
from haystack.dataclasses import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore

# Import Voyage Embedders
from haystack_integrations.components.embedders.voyage_embedders import VoyageDocumentEmbedder, VoyageTextEmbedder

# Load first 100 rows of the Simple Wikipedia Dataset from HuggingFace
dataset = load_dataset("pszemraj/simple_wikipedia", split="validation[:100]")

docs = [
    Document(
        content=doc["text"],
        meta={
            "title": doc["title"],
            "url": doc["url"],
        },
    )
    for doc in dataset
]

Index the documents to the InMemoryDocumentStore using the VoyageDocumentEmbedder and DocumentWriter:

doc_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
retriever = InMemoryEmbeddingRetriever(document_store=doc_store)
doc_writer = DocumentWriter(document_store=doc_store)

doc_embedder = VoyageDocumentEmbedder(
    model="voyage-4",
    input_type="document",
)
text_embedder = VoyageTextEmbedder(model="voyage-4", input_type="query")

# Indexing Pipeline
indexing_pipeline = Pipeline()
indexing_pipeline.add_component(instance=doc_embedder, name="DocEmbedder")
indexing_pipeline.add_component(instance=doc_writer, name="DocWriter")
indexing_pipeline.connect("DocEmbedder", "DocWriter")

indexing_pipeline.run({"DocEmbedder": {"documents": docs}})

print(f"Number of documents in Document Store: {len(doc_store.filter_documents())}")
print(f"First Document: {doc_store.filter_documents()[0]}")
print(f"Embedding of first Document: {doc_store.filter_documents()[0].embedding}")

Query the Semantic Search Pipeline using the InMemoryEmbeddingRetriever and VoyageTextEmbedder:

text_embedder = VoyageTextEmbedder(model="voyage-4", input_type="query")

# Query Pipeline
query_pipeline = Pipeline()
query_pipeline.add_component(instance=text_embedder, name="TextEmbedder")
query_pipeline.add_component(instance=retriever, name="Retriever")
query_pipeline.connect("TextEmbedder.embedding", "Retriever.query_embedding")

# Search
results = query_pipeline.run({"TextEmbedder": {"text": "Which year did the Joker movie release?"}})

# Print text from top result
top_result = results["Retriever"]["documents"][0].content
print("The top search result is:")
print(top_result)

Contributing

We welcome contributions from the community! Please take a look at our contributing guide for more details on how to get started.

Pull requests are welcome. For major changes, please open an issue first to discuss the proposed changes.

License

voyage-embedders-haystack is distributed under the terms of the Apache-2.0 license.

Maintained by Ashwin Mathur.