Open-source vector similarity search for Postgres. Contribute to pgvector/pgvector development by creating an account on GitHub.
While specific user reviews and mentions of "pgvector" are not directly visible in the provided data, pgvector is generally appreciated for its abilities in managing and querying vector data types, which is highly beneficial in AI applications and machine learning workflows. Users have highlighted its strengths in integrating with PostgreSQL, offering seamless data handling capabilities. There aren't specific criticisms or pricing concerns mentioned, but such tools often attract users who value effective data integration over cost. Overall, pgvector maintains a positive reputation, especially amongst developers needing robust vector support within traditional databases.
Mentions (30d)
51
10 this week
Reviews
0
Platforms
4
GitHub Stars
20,528
1,122 forks
While specific user reviews and mentions of "pgvector" are not directly visible in the provided data, pgvector is generally appreciated for its abilities in managing and querying vector data types, which is highly beneficial in AI applications and machine learning workflows. Users have highlighted its strengths in integrating with PostgreSQL, offering seamless data handling capabilities. There aren't specific criticisms or pricing concerns mentioned, but such tools often attract users who value effective data integration over cost. Overall, pgvector maintains a positive reputation, especially amongst developers needing robust vector support within traditional databases.
Features
Use Cases
Industry
information technology & services
Employees
6,200
Funding Stage
Other
Total Funding
$7.9B
20,528
GitHub stars
20
npm packages
2
HuggingFace models
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View originalRepository Audit Available
Deep analysis of pgvector/pgvector — architecture, costs, security, dependencies & more
pgvector uses a tiered pricing model. Visit their website for current pricing details.
Key features include: exact and approximate nearest neighbor search, single-precision, half-precision, binary, and sparse vectors, L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance, Write, clarify, or fix documentation, Suggest or add new features, Linux and Mac, Windows, Distances.
pgvector is commonly used for: Semantic search in databases, Recommendation systems, Image similarity search, Natural language processing tasks, Anomaly detection in data sets, Real-time data retrieval for AI applications.
pgvector integrates with: PostgreSQL, Docker, Spring Boot, Kubernetes, Apache Kafka, TensorFlow, PyTorch, FastAPI, Flask, Node.js.
pgvector has a public GitHub repository with 20,528 stars.
Based on user reviews and social mentions, the most common pain points are: down, breaking, right now, API costs.
Based on 131 social mentions analyzed, 8% of sentiment is positive, 92% neutral, and 0% negative.
