Why Vector Compression Matters

• jvector

  3 1,263 9.7 Java

  JVector: the most advanced embedded vector search engine

JVector builds on the ideas in DiskANN to provide state-of-the-art vector search for Java applications. I’ve used the JVector Bench driver to visualize how recall (search accuracy) degrades when searching for the top 100 neighbors in data sets created by different embedding models against a small sample of chunked Wikipedia articles. (The data sets are built using the open source Neighborhood Watch tool.) Perfect accuracy would be a recall of 1.0.

• trulens

  14 1,629 9.8 Jupyter Notebook

  Evaluation and Tracking for LLM Experiments

  

Retrieval using a single vector is called dense passage retrieval (DPR), because an entire passage (dozens to hundreds of tokens) is encoded as a single vector. ColBERT instead encodes a vector-per-token, where each vector is influenced by surrounding context. This leads to meaningfully better results; for example, here’s ColBERT running on Astra DB compared to DPR using openai-v3-small vectors, compared with TruLens for the Braintrust Coda Help Desk data set. ColBERT easily beats DPR at correctness, context relevance, and groundedness.

• InfluxDB

  www.influxdata.com featured

  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.

  

• ColBERT

  4 2,479 8.4 Python

  ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)

  

I’ll conclude by explaining how vector compression relates to ColBERT, a higher-level technique that Astra DB customers are starting to use successfully.

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