codingjaguar

6 ms·

1.
Memory Plugin for Claude Code(github.com)
2 points by codingjaguar 7mo ago|0 comments
2.
Semantic highlight model to cut token cost for RAG(huggingface.co)
2 points by codingjaguar 8mo ago|0 comments
3.
by codingjaguar 9mo ago
well it's apples and oranges. Why do people buy F150 instead of fitting things into the trunk of a Corolla? cuz they got a lot of stuff. For people who run thousands of QPS on billions of vectors, Milvus is a solid choice. For someone
4.
by codingjaguar 11mo ago
This quite aligns with our observation at Milvus. Recently, we helped several users migrate from pgvector as the workload grew substantially. It’s worth recognising the strengths of pgvector: • For small-to-medium scale workloads (e.g., up
5.
by codingjaguar 1y ago
Curious is that colbert-like ones?
6.
Building Code Retrieval for Claude Code from Scratch(zc277584121.github.io)
1 points by codingjaguar 1y ago|0 comments
7.
Benchmarking the latency of 20 embedding services(milvus.io)
1 points by codingjaguar 1y ago|0 comments
8.
RaBitQ is the best binary quantization(milvus.io)
2 points by codingjaguar 1y ago|0 comments
9.
Avoid manual sharding in vector db or any db(milvus.io)
2 points by codingjaguar 1y ago|2 comments
10.
Trends of information retrieval in 2024: how RAG moves to production(zilliz.com)
1 points by codingjaguar 2y ago|0 comments
11.
Why quality eval is the key to search and RAG(medium.com)
1 points by codingjaguar 2y ago|1 comments
12.
A look back to 10 popular open source LLM frameworks in 2024(zilliz.com)
1 points by codingjaguar 2y ago|0 comments
13.
by codingjaguar 2y ago
The complex aggregations are indeed an important feature for sophisticated search products like e-commerce search with interactive filtering. There is probably no easy way for a vector db to catch up quick. But, - Most RAG and enterprise ap
14.
by codingjaguar 2y ago
This article depicts a perfect world and links it to a solution which is fairly distant from that. I understand the wishful thinking of having a "magic box" for search infrastructure but as someone worked on web-scale search at Go
15.
by codingjaguar 2y ago
The developer community of milvus vector database benefits a lot from the inkeep ask-ai-button in discord and milvus.io website. As a user we are happy with the rich feature set of inkeep, like integration with github/discord, admin to
16.
Show HN: Live Demo of Composite Image Retrieval (CIR)(multimodal-demo.milvus.io)
2 points by codingjaguar 2y ago|0 comments
17.
by codingjaguar 3y ago
"By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s." This AI game
18.
by codingjaguar 3y ago
Interesting that what comes with disassembly is standardization, and performance is no longer a main differentiator. In addition to the UI/UX features emphasized in the post, ecosystem integration will also be a main differentiating fa
19.
by codingjaguar 3y ago
SQL was introduced in 1970s. Considering vector search was only adopted in the last 5 years, I’m not surprised by the lack of standards on vector API. At Google embedding as retrieval became popular in 2019-2020. This is the new kid in town
20.
by codingjaguar 3y ago
The dataset probably ranges from hundreds to tens of thousands of queries. The exact number is confidential and frankly, it changes over time and differs from product to product, so the order of magnitude is more indicative. This also match
21.
A front end/UI focused RAG framework(github.com)
1 points by codingjaguar 3y ago|0 comments
22.
by codingjaguar 3y ago
Didn't their copy of Snap story feature successfully enough? In my personal experience with Instagram, I rarely look at any post anymore. My viewing typically finishes after watching a few story videos. Their failure is probably on con
23.
by codingjaguar 3y ago
Here is an article that systematically discusses how vector retrieval and BM25 affects the search quality, in another word, what kind of systems are the past, now and future: https://thenewstack.io/the-transformative-fusion-
24.
Why vector search doesn't always beat BM25 but eventually it will(thenewstack.io)
1 points by codingjaguar 3y ago|0 comments
25.
Fully-managed and scalable service for unstructured data indexing and retrieval(github.com)
1 points by codingjaguar 3y ago|0 comments
26.
by codingjaguar 3y ago
Partially agree. Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important. As