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Turbocharge LangChain: guide to 20x faster embedding

Anyscale · published 3 May 2023

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Building an LLM open source search engine in 100 lines using LangChain and Ray Read at the source ↗

https://github.com/langchain-ai/langchain

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https://docs.ray.io/en/latest/data/data.html

docs.ray.io

https://docs.ray.io/en/master/data/api/input_output.html

docs.ray.io

https://github.com/ray-project/langchain-ray/blob/main/embedding_pdf_documents/arxiv_dataset_generation.py

github.com

https://github.com/ray-project/langchain-ray/blob/main/embedding_pdf_documents/embedding_ray.py

github.com

https://github.com/ray-project/langchain-ray/blob/main/embedding_pdf_documents/README.md

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https://docs.ray.io/en/latest/cluster/getting-started.html

docs.ray.io

https://docs.anyscale.com/develop/workspaces/get-started

docs.anyscale.com

https://docs.ray.io/en/latest/ray-core/handling-dependencies.html

docs.ray.io

https://github.com/langchain-ai/langchain/pull/3915

github.com

https://github.com/langchain-ai/langchain/pull/3914

github.com

Building a Self Hosted Question Answering Service using LangChain + Ray in 20 minutes Read at the source ↗

https://raysummit.anyscale.com/

raysummit.anyscale.com

https://github.com/ray-project/ray-educational-materials/blob/main/NLP_workloads/Text_generation/LLM_finetuning_and_batch_inference.ipynb

github.com