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chore(docs): sync up README from docarray
Signed-off-by: dev-bot@jina.ai <Jina Dev Bot>
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‎README.md

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- :fire: Offers native support for **[NumPy](https://github.com/numpy/numpy)**, **[PyTorch](https://github.com/pytorch/pytorch)**, **[TensorFlow](https://github.com/tensorflow/tensorflow)**, and **[JAX](https://github.com/google/jax)**, catering specifically to **model training scenarios**.
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- :zap: Based on **[Pydantic](https://github.com/pydantic/pydantic)**, and instantly compatible with web and microservice frameworks like **[FastAPI](https://github.com/tiangolo/fastapi/)** and **[Jina](https://github.com/jina-ai/jina/)**.
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- :package: Provides support for vector databases such as **[Weaviate](https://weaviate.io/), [Qdrant](https://qdrant.tech/), [ElasticSearch](https://www.elastic.co/de/elasticsearch/), [Redis](https://redis.io/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**.
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- :package: Provides support for vector databases such as **[Weaviate](https://weaviate.io/), [Qdrant](https://qdrant.tech/), [ElasticSearch](https://www.elastic.co/de/elasticsearch/), **[Redis](https://redis.io/)**, **[Mongo Atlas](https://www.mongodb.com/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**.
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- :chains: Allows data transmission as JSON over **HTTP** or as **[Protobuf](https://protobuf.dev/)** over **[gRPC](https://grpc.io/)**.
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## Installation
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- :mag: **Neural search** applications
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- :bulb: **Recommender systems**
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Currently, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, **[Redis](https://redis.io/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come!
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Currently, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, **[Redis](https://redis.io/)**, **[Mongo Atlas](https://www.mongodb.com/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come!
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The Document Index interface lets you index and retrieve Documents from multiple vector databases, all with the same user interface.
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- **Production-ready:** The new Document Indexes are a much thinner wrapper around the various vector DB libraries, making them more robust and easier to maintain
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- **Increased flexibility:** We strive to support any configuration or setting that you could perform through the DB's first-party client
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For now, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, **[Redis](https://redis.io/)**, Exact Nearest Neighbour search and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come.
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For now, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, **[Redis](https://redis.io/)**, **[Mongo Atlas](https://www.mongodb.com/)**, Exact Nearest Neighbour search and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come.
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</details>
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- [Milvus](https://milvus.io)
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- ExactNNMemorySearch as a local alternative with exact kNN search.
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- [HNSWlib](https://github.com/nmslib/hnswlib) as a local-first ANN alternative
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- [Mongo Atlas](https://www.mongodb.com/)
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An integration of [OpenSearch](https://opensearch.org/) is currently in progress.
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embeddings = OpenAIEmbeddings()
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# Define a document schema
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class MovieDoc(BaseDoc):
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title: str
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QdrantDocumentIndex,
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ElasticDocIndex,
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RedisDocumentIndex,
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MongoDBAtlasDocumentIndex,
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)
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# Select a suitable backend and initialize it with data

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