From b49a3ee40fdcdcaae7e04e931d42ecd236223ad2 Mon Sep 17 00:00:00 2001 From: Joan Fontanals Martinez Date: Mon, 24 Jul 2023 05:21:14 +0200 Subject: [PATCH] chore: add JAX to README Signed-off-by: Joan Fontanals Martinez --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 277496d305..f75e08559c 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ DocArray is a Python library expertly crafted for the [representation](#represen -- :fire: Offers native support for **[NumPy](https://github.com/numpy/numpy)**, **[PyTorch](https://github.com/pytorch/pytorch)**, and **[TensorFlow](https://github.com/tensorflow/tensorflow)**, catering specifically to **model training scenarios**. +- :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**. - :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/)**. - :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)**. - :chains: Allows data transmission as JSON over **HTTP** or as **[Protobuf](https://protobuf.dev/)** over **[gRPC](https://grpc.io/)**. @@ -309,7 +309,7 @@ doc_4 = MyDocument.from_bytes(bytes_) doc_5 = MyDocument.parse_raw(json) ``` -Of course, serialization is not all you need. So check out how DocArray integrates with FastAPI and Jina. +Of course, serialization is not all you need. So check out how DocArray integrates with **[Jina](https://github.com/jina-ai/jina/)** and **[FastAPI](https://github.com/tiangolo/fastapi/)**. ## Store @@ -421,7 +421,7 @@ They are now called **Document Indexes** and offer the following improvements (s - **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 - **Increased flexibility:** We strive to support any configuration or setting that you could perform through the DB's first-party client -For now, 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. +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. @@ -525,7 +525,6 @@ To see the effect of this, let's first observe a vanilla PyTorch implementation ```python import torch from torch import nn -import torch def encoder(x): @@ -776,11 +775,12 @@ Currently, DocArray supports the following vector databases: - [Qdrant](https://qdrant.tech/) - [Elasticsearch](https://www.elastic.co/elasticsearch/) v8 and v7 - [Redis](https://redis.io/) -- [HNSWlib](https://github.com/nmslib/hnswlib) as a local-first alternative +- ExactNNMemorySearch as a local alternative with exact kNN search. +- [HNSWlib](https://github.com/nmslib/hnswlib) as a local-first ANN alternative An integration of [OpenSearch](https://opensearch.org/) is currently in progress. -DocArray <=0.21 also support [Redis](https://redis.io/) and [Milvus](https://milvus.io/), but these are not yet supported in the current version. +DocArray <=0.21 also support [Milvus](https://milvus.io/), but this is not yet supported in the current version. Of course this is only one of the things that DocArray can do, so we encourage you to check out the rest of this readme!