# voyage-large-2 Source: https://zilliz.com/ai-models/voyage-large-2 Published: 2024-08-05T19:58:18.309Z ## Model attributes | Attribute | Value | | --- | --- | | Provider | Voyage AI | | Integration | Milvus Integrated | | Task | Embedding | | Modality | Text | | Similarity metric | Any (Normalized) | | License | Proprietary | | Dimensions | 1536 | | Max input tokens | 16000 | | Price | $ 0.12/1M tokens | ## Introduction to the voyage-large-2 model `voyage-large-2` is Voyage AI's general-purpose text embedding model optimized for retrieval quality (e.g., better than OpenAI V3 Large). It is also ideal for tasks like summarization, clustering, and classification. Comparing `voyage-large-2` with other popular embedding models by Voyage AI: | | | | | | ----------------------- | :---------------------: | :-----------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | Model | Context Length (tokens) | Embedding Dimension | Description | | voyage-large-2-instruct | 16000 | 1024 | Top of [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Instruction-tuned general-purpose embedding model optimized for clustering, classification, and retrieval. | | voyage-multilingual-2 | 32000 | 1024 | Optimized for multilingual retrieval and RAG. | | voyage-code-2 | 16000 | 1536 | Optimized for code retrieval (17% better than alternatives). | | voyage-large-2 | 16000 | 1536 | General-purpose embedding model that is optimized for retrieval quality (e.g., better than OpenAI V3 Large). | | [voyage-2](https://zilliz.com/ai-models/voyage-2) | 4000 | 1024 | General-purpose embedding model optimized for a balance between cost, latency, and retrieval quality. | ## How to generate vector embeddings with voyage-large-2 1. [**PyMilvus**](https://github.com/milvus-io/pymilvus): the Python SDK for[ Milvus](https://zilliz.com/what-is-milvus) that seamlessly integrates the `voyage-large-2` model. 2. Voyage AI Python package: the Python SDK offered by Voyage AI. Once the vector embeddings are generated, they can be stored in [Zilliz Cloud](https://zilliz.com/cloud) (a fully managed vector database service powered by Milvus) and used for [semantic similarity search](https://zilliz.com/glossary/semantic-search). Here are four key steps: 1. [**Sign up**](https://cloud.zilliz.com/signup) for a Zilliz Cloud account for free. 2. **[Set up a serverless cluster](https://docs.zilliz.com/docs/create-cluster#set-up-a-free-cluster)** and obtain the [Public Endpoint and API Key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details). 3. **Create a vector collection** and insert your vector embeddings. 4. **Run a semantic search** on the stored embeddings. ### Generate vector embeddings via PyMilvus and insert them into Zilliz Cloud for semantic search ``` from pymilvus import model, MilvusClient ef = model.dense.VoyageEmbeddingFunction( model_name="voyage-large-2", api_key="your-voyage-api-key", ) # Generate embeddings for documents docs = [ "Artificial intelligence was founded as an academic discipline in 1956.", "Alan Turing was the first person to conduct substantial research in AI.", "Born in Maida Vale, London, Turing was raised in southern England." ] docs_embeddings = ef.encode_documents(docs) # Generate embeddings for queries queries = ["When was artificial intelligence founded", "Where was Alan Turing born?"] query_embeddings = ef.encode_queries(queries) # Connect to Zilliz Cloud with Public Endpoint and API Key client = MilvusClient( uri=ZILLIZ_PUBLIC_ENDPOINT, token=ZILLIZ_API_KEY) COLLECTION = "documents" if client.has_collection(collection_name=COLLECTION): client.drop_collection(collection_name=COLLECTION) client.create_collection( collection_name=COLLECTION, dimension=ef.dim, auto_id=True) for doc, embedding in zip(docs, docs_embeddings): client.insert(COLLECTION, {"text": doc, "vector": embedding}) results = client.search( collection_name=COLLECTION, data=query_embeddings, consistency_level="Strong", output_fields=["text"]) ``` For more information, refer to our [PyMilvus Embedding Model documentation](https://milvus.io/docs/embeddings.md).   ### Generate vector embeddings with Voyage AI Python package and insert them into Zilliz Cloud for semantic search ``` import voyageai from pymilvus import MilvusClient vo = voyageai.Client(api_key="your-voyage-api-key") # Generate embeddings for documents docs = [ "Artificial intelligence was founded as an academic discipline in 1956.", "Alan Turing was the first person to conduct substantial research in AI.", "Born in Maida Vale, London, Turing was raised in southern England." ] doc_embeddings = vo.embed(docs, model="voyage-large-2", input_type="document").embeddings # Generate embeddings for queries queries = ["When was artificial intelligence founded", "Where was Alan Turing born?"] query_embeddings = vo.embed(docs, model="voyage-large-2", input_type="query").embeddings # Connect to Zilliz Cloud with Public Endpoint and API Key client = MilvusClient( uri=ZILLIZ_PUBLIC_ENDPOINT, token=ZILLIZ_API_KEY) COLLECTION = "documents" if client.has_collection(collection_name=COLLECTION): client.drop_collection(collection_name=COLLECTION) client.create_collection( collection_name=COLLECTION, dimension=1536, auto_id=True) for doc, embedding in zip(docs, docs_embeddings): client.insert(COLLECTION, {"text": doc, "vector": embedding}) results = client.search( collection_name=COLLECTION, data=query_embeddings, consistency_level="Strong", output_fields=["text"]) ``` For more information, refer to [Voyage AI Embedding Guide](https://docs.voyageai.com/docs/embeddings#python-api).