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* update dataprep readme Signed-off-by: letonghan <letong.han@intel.com> * update vectorstore readme Signed-off-by: letonghan <letong.han@intel.com> * udpate retriever readme Signed-off-by: letonghan <letong.han@intel.com> * update retriever readme Signed-off-by: letonghan <letong.han@intel.com> * udpate embedding readme Signed-off-by: letonghan <letong.han@intel.com> * update guardrails readme Signed-off-by: letonghan <letong.han@intel.com> * update other readmes Signed-off-by: letonghan <letong.han@intel.com> * update reranks readme Signed-off-by: letonghan <letong.han@intel.com> * update llm&lvms readme Signed-off-by: letonghan <letong.han@intel.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: letonghan <letong.han@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Embeddings Microservice
The Embedding Microservice is designed to efficiently convert textual strings into vectorized embeddings, facilitating seamless integration into various machine learning and data processing workflows. This service utilizes advanced algorithms to generate high-quality embeddings that capture the semantic essence of the input text, making it ideal for applications in natural language processing, information retrieval, and similar fields.
Key Features:
High Performance: Optimized for quick and reliable conversion of textual data into vector embeddings.
Scalability: Built to handle high volumes of requests simultaneously, ensuring robust performance even under heavy loads.
Ease of Integration: Provides a simple and intuitive API, allowing for straightforward integration into existing systems and workflows.
Customizable: Supports configuration and customization to meet specific use case requirements, including different embedding models and preprocessing techniques.
Users are albe to configure and build embedding-related services according to their actual needs.
Embeddings Microservice with TEI
We support both langchain and llama_index for TEI serving.
For details, please refer to langchain readme or llama index readme.
Embeddings Microservice with Mosec
For details, please refer to this readme.
Embeddings Microservice with Multimodal
For details, please refer to this readme.
Embeddings Microservice with Multimodal Clip
For details, please refer to this readme.