<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
    <title>vector-search</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/1566/feed"/>
    <updated>2026-04-19T08:29:43+00:00</updated>
    <id>https://links.biapy.com/guest/tags/1566/feed</id>
            <entry>
            <id>https://links.biapy.com/links/12179</id>
            <title type="text"><![CDATA[Faiss]]></title>
            <link rel="alternate" href="https://faiss.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12179"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.

- [Faiss @ GitHub](https://github.com/facebookresearch/faiss).

Related contents:

- [How to Ship a Production-Ready RAG App with FAISS (Guardrails, Evals, and Fallbacks) @ freeCodeCamp](https://www.freecodecamp.org/news/build-rag-app-faiss-fastapi/).]]>
            </summary>
            <updated>2026-03-18T16:13:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11464</id>
            <title type="text"><![CDATA[WeKnora :cn:]]></title>
            <link rel="alternate" href="https://weknora.weixin.qq.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11464"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.

It adopts a modular architecture that combines multimodal preprocessing, semantic vector indexing, intelligent retrieval, and large language model inference. At its core, WeKnora follows the RAG (Retrieval-Augmented Generation) paradigm, enabling high-quality, context-aware answers by combining relevant document chunks with model reasoning.

- [WeKnora @ GitHub](https://github.com/Tencent/WeKnora).]]>
            </summary>
            <updated>2026-01-15T06:50:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10046</id>
            <title type="text"><![CDATA[LEANN]]></title>
            <link rel="alternate" href="https://github.com/yichuan-w/LEANN" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10046"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device. 

LEANN is an innovative vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using 97% less storage than traditional solutions without accuracy loss.

Related contents:

- [LEANN - L&amp;#039;IA personnelle qui écrase 97% de ses concurrents (en taille) @ Korben :fr:](https://korben.info/leann-rag.html).]]>
            </summary>
            <updated>2025-09-08T10:28:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2814</id>
            <title type="text"><![CDATA[Chroma]]></title>
            <link rel="alternate" href="https://www.trychroma.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2814"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[the AI-native open-source embedding database.  The fastest way to build Python or JavaScript LLM apps with memory! 
Chroma is the open-source AI application database. Batteries included.

Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. All in one place. Retrieval that just works. As it should be.

- [Chroma @ GitHub](https://github.com/chroma-core/chroma).

Related contents:

- [ChromaDB: An Open-source vector embedding database @ Futuresmart AI Blog](https://blog.futuresmart.ai/chromadb-an-open-source-vector-embedding-database).
- [How to Build a Local RAG App with Ollama and ChromaDB in the R Programming Language @ freeCodeCamp](https://www.freecodecamp.org/news/build-a-local-rag-app-with-ollama-and-chromadb-in-r/).]]>
            </summary>
            <updated>2026-02-09T16:46:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3855</id>
            <title type="text"><![CDATA[sqlite-vec]]></title>
            <link rel="alternate" href="https://github.com/asg017/sqlite-vec" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3855"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A vector search SQLite extension that runs anywhere!

- [Veille de la semaine du 5 août 2024 @ Veille de la semaine&amp;#039;s Substack :fr:](https://guikingone.substack.com/p/veille-de-la-semaine-du-5-aout-2024).
- [sqlite-vec now supports metadata columns and filtering @ Alex Garcia&amp;#039;s Blog](https://alexgarcia.xyz/blog/2024/sqlite-vec-metadata-release/index.html).]]>
            </summary>
            <updated>2025-08-29T02:39:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3930</id>
            <title type="text"><![CDATA[PGVecto.rs]]></title>
            <link rel="alternate" href="https://pgvecto.rs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3930"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. 

PGVecto.rs is a Postgres extension that enables scalable vector search, allowing you to build powerful similarity-based applications on top of your Postgres database.

- [PGvector.rs documentation](https://docs.pgvecto.rs/).
- [PGvector.rs @ GitHub](https://github.com/tensorchord/pgvecto.rs).]]>
            </summary>
            <updated>2025-08-29T02:51:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4827</id>
            <title type="text"><![CDATA[Lantern]]></title>
            <link rel="alternate" href="https://lantern.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4827"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The most powerful vector database for building AI applications. Open-source PostgreSQL database extension for vector data and vector search operations.

Lantern is an open-source PostgreSQL database extension to store vector data, generate embeddings, and handle vector search operations.

- [Lantern @ GitHub](https://github.com/lanterndata/lantern).]]>
            </summary>
            <updated>2025-08-29T05:21:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5910</id>
            <title type="text"><![CDATA[Qdrant - Vector Search Engine]]></title>
            <link rel="alternate" href="https://qdrant.tech/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5910"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Qdrant (read: quadrant ) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

- [Qdrant @ GitHub](https://github.com/qdrant/qdrant).

Related contents:

- [270 - DB Vectorielle - Noé Achache @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/db-vectorielle).
- [Episode 641: Qdrant&amp;#039;s Brian O&amp;#039;Grady @ Coder Radio](https://coder.show/641).]]>
            </summary>
            <updated>2026-03-12T19:36:11+00:00</updated>
        </entry>
    </feed>
