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# vectordatabase

Vector databases are purpose-built databases that are specialized to tackle the problems that arise when managing vector embeddings in production scenarios.

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My binary vector search is better than your FP32 vectors
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My binary vector search is better than your FP32 vectors

10
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5 min read
Couchbase Server 7.6 Top New Features For Developers

Couchbase Server 7.6 Top New Features For Developers

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3 min read
How ColBERT Helps Developers Overcome the Limits of Retrieval-Augmented Generation
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How ColBERT Helps Developers Overcome the Limits of Retrieval-Augmented Generation

17
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6 min read
Using pgvector To Locate Similarities In Enterprise Data

Using pgvector To Locate Similarities In Enterprise Data

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Comments 1
11 min read
Unlock Advanced Search Capabilities with Milvus and Read about RAG
Cover image for Unlock Advanced Search Capabilities with Milvus and Read about RAG

Unlock Advanced Search Capabilities with Milvus and Read about RAG

10
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5 min read
Mastering LLM Challenges: An Exploration of Retrieval Augmented Generation

Mastering LLM Challenges: An Exploration of Retrieval Augmented Generation

6
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5 min read
Advanced Querying Techniques in Vector Databases

Advanced Querying Techniques in Vector Databases

19
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7 min read
pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL

pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL

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7 min read
How to Evaluate RAG Applications

How to Evaluate RAG Applications

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Comments 1
10 min read
We Built an Open Source Drop-In for OpenAI's Assistants API Powered by Astra DB Vector Search
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We Built an Open Source Drop-In for OpenAI's Assistants API Powered by Astra DB Vector Search

2
Comments
3 min read
pgvecto.rs 0.2: Unifying Relational Queries and Vector Search in PostgreSQL

pgvecto.rs 0.2: Unifying Relational Queries and Vector Search in PostgreSQL

6
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5 min read
Information Retrieval Metrics

Information Retrieval Metrics

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5 min read
Qdrant 1.8.0 - Major Performance Enhancements
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Qdrant 1.8.0 - Major Performance Enhancements

28
Comments
5 min read
Beyond Vector Search: How Metadata Indexing Helps to Find Dating App Matches
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Beyond Vector Search: How Metadata Indexing Helps to Find Dating App Matches

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4 min read
The Subtleties of Vector Similarity Scales (part 4)
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The Subtleties of Vector Similarity Scales (part 4)

10
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10 min read
Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant

5
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8 min read
Using Vectors with Apache Cassandra and DataStax Astra DB (part 3)
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Using Vectors with Apache Cassandra and DataStax Astra DB (part 3)

10
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8 min read
Build knowledge graphs with LLM-driven entity extraction

Build knowledge graphs with LLM-driven entity extraction

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3 min read
Advanced RAG with graph path traversal

Advanced RAG with graph path traversal

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6 min read
💡 What's new in txtai 7.0

💡 What's new in txtai 7.0

1
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6 min read
Vector Similarities: How Similar Are They? (part 2)
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Vector Similarities: How Similar Are They? (part 2)

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Comments 2
9 min read
Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search
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Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search

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Comments 2
15 min read
Navigating Hybrid Search with MongoDB: Ugly Approach
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Navigating Hybrid Search with MongoDB: Ugly Approach

5
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3 min read
Using LLM, Postgres VectorDB, and OpenAI to Perform Semantic Search on PDF Documents

Using LLM, Postgres VectorDB, and OpenAI to Perform Semantic Search on PDF Documents

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2 min read
Best Practices for Massive-Scale Deployments: Multitenancy and Custom Sharding
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Best Practices for Massive-Scale Deployments: Multitenancy and Custom Sharding

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6 min read
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