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**Description:** **TextEmbed** is a high-performance embedding inference server designed to provide a high-throughput, low-latency solution for serving embeddings. It supports various sentence-transformer models and includes the ability to deploy image and text embedding models. TextEmbed offers flexibility and scalability for diverse applications. - **PyPI Package:** [TextEmbed on PyPI](https://pypi.org/project/textembed/) - **Docker Image:** [TextEmbed on Docker Hub](https://hub.docker.com/r/kevaldekivadiya/textembed) - **GitHub Repository:** [TextEmbed on GitHub](https://github.com/kevaldekivadiya2415/textembed) **PR Description** This PR adds functionality for embedding documents and queries using the `TextEmbedEmbeddings` class. The implementation allows for both synchronous and asynchronous embedding requests to a TextEmbed API endpoint. The class handles batching and permuting of input texts to optimize the embedding process. **Example Usage:** ```python from langchain_community.embeddings import TextEmbedEmbeddings # Initialise the embeddings class embeddings = TextEmbedEmbeddings(model="your-model-id", api_key="your-api-key", api_url="your_api_url") # Define a list of documents documents = [ "Data science involves extracting insights from data.", "Artificial intelligence is transforming various industries.", "Cloud computing provides scalable computing resources over the internet.", "Big data analytics helps in understanding large datasets.", "India has a diverse cultural heritage." ] # Define a query query = "What is the cultural heritage of India?" # Embed all documents document_embeddings = embeddings.embed_documents(documents) # Embed the query query_embedding = embeddings.embed_query(query) # Print embeddings for each document for i, embedding in enumerate(document_embeddings): print(f"Document {i+1} Embedding:", embedding) # Print the query embedding print("Query Embedding:", query_embedding) --------- Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> |
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