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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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txtai
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
EVA supports many other NLP pipelines, including summarization and text2text generation.
[2] is an illustrative notebook that presents an HF-based object segmentation pipeline (not NLP-based though). We would love to jointly explore how to best support your NLP pipeline. Please consider opening an issue with more details on your use case.
[1] https://github.com/georgia-tech-db/eva/blob/4fa52f893e7661d4...
[2] https://evadb.readthedocs.io/en/latest/source/tutorials/07-o...
https://evadb.readthedocs.io/en/stable/source/tutorials/01-m...
* Recognizing license plates: https://github.com/georgia-tech-db/license-plate-recognition
* Analysing toxicity of social media memes: https://github.com/georgia-tech-db/toxicity-classification
https://evadb.readthedocs.io/en/stable/source/tutorials/01-m...
* Recognizing license plates: https://github.com/georgia-tech-db/license-plate-recognition
* Analysing toxicity of social media memes: https://github.com/georgia-tech-db/toxicity-classification
By reusing the results of the first query and reordering the predicates based on the available cached inference results, EVA runs the second query 10 times faster!
More generally, EVA's query optimizer factors the dollar cost of running models for a given AI task (like a question-answering LLM). It picks the appropriate model pipeline with the lowest price that satisfies the user's accuracy requirement.
Query optimization with a declarative query language is the crucial difference between EVA and inspiring AI pipeline frameworks like LangChain and TxtAI [1]. We would love to hear the community's thoughts on the pros and cons of these two approaches.
[1] https://github.com/neuml/txtai