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
Brazil, Indonesia, Japan, Germany, and India fueled a massive surge in 2025, adding nearly 36 million new developers to GitHub. 🌏 India alone added 5.2 million. 🇮🇳
Brazil, Indonesia, Japan, Germany, and India fueled a massive surge in 2025, adding nearly 36 million new developers to GitHub. 🌏 India alone added 5.2 million. 🇮🇳
View originalI'm wondering what other PPL codeburn stats look like , please share , here is mine from little while , how much do other people usually burn in a day? I am working on something to greatly reduce token burn , feedback is welcomed https://github.com/innov8ideas4u-alt/TKK
CodeBurn All Time │ │ $5440.35 cost 54,466 calls 1365 sessions 97.2% cache hit │ │ 927.9K in 26.4M out 7211.3M cached 206.6M written │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ╭──────────────────────────────────────────────────────────╮╭──────────────────────────────────────────────────────────╮ │ Daily Activity ││ By Project │ │ cost calls ││ cost avg/s sess overhead │ │ 05-09 ██░░░░░░░░ $178.82 1592 ││ ██████████ D/Dev/Proj$3515.60 $4.63 760 11.2K │ │ 05-10 █░░░░░░░░░ $54.10 529 ││ ███░░░░░░░ Projects/p$1213.97 $5.21 233 13.0K │ │ 05-11 █░░░░░░░░░ $76.48 587 ││ ██░░░░░░░░ D/Dev $532.68 $2.18 244 14.6K │ │ 05-12 █░░░░░░░░░ $49.36 364 ││ ░░░░░░░░░░ D/Dev/VikL $64.52 $1.11 58 11.2K │ │ 05-13 ░░░░░░░░░░ $38.20 260 ││ ░░░░░░░░░░ Dev/Projec $64.30 $1.65 39 11.2K │ │ 05-14 █░░░░░░░░░ $71.63 515 ││ ░░░░░░░░░░ D $40.02 $2.22 18 11.2K │ │ 05-15 ██████░░░░ $567.35 5040 ││ ░░░░░░░░░░ Projects/p $5.26 $5.26 1 11.2K │ │ 05-16 ███████░░░ $706.64 7164 ││ ░░░░░░░░░░ Projects/M $2.03 $2.03 1 11.2K │ │ 05-17 █████████░ $902.89 8124 ││ │ │ 05-18 ██████████ $956.94 10080 ││ │ │ 05-19 ░░░░░░░░░░ $38.59 315 ││ │ │ 05-20 ██░░░░░░░░ $188.58 1365 ││ │ │ 05-21 ██░░░░░░░░ $155.29 1576 ││ │ │ 05-22 █░░░░░░░░░ $108.74 690 ││ │ ╰──────────────────────────────────────────────────────────╯╰──────────────────────────────────────────────────────────╯ ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ Top Sessions │ │ cost calls │ │ ██████████ 2026-05-18 D/Dev/Projects $211.17 742 │ │ █████░░░░░ 2026-05-18 D/Dev/Projects $111.76 367 │ │ ████░░░░░░ 2026-05-16 D/Dev/Projects $90.56 261 │ │ ████░░░░░░ 2026-05-17 D/Dev/Projects $84.93 364 │ │ ████░░░░░░ 2026-05-05 Projects/pgvector/load $75.57 440 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ╭──────────────────────────────────────────────────────────╮╭──────────────────────────────────────────────────────────╮ │ By Activity ││ By Model │ │ cost turns 1-shot ││ cost cache calls │ │ ██████████ Coding $2394.35 461 60% ││ ██████████ Opus 4.7 $4938.00 97.2% 44184 │ │ ████░░░░░░ Debugging $938.97 445 85% ││ █░░░░░░░░░ Opus 4.6 $464.39 97.5% 6850 │ │ ███░░░░░░░ Exploration $713.74 684 - ││ ░░░░░░░░░░ Haiku 4.5 $28.17 94.9% 2995 │ │ ███░░░░░░░ Testing $650.08 276 - ││ ░░░░░░░░░░ Sonnet 4.6 $9.78 95.9% 386 │ │ █░░░░░░░░░ Feature Dev $241.21 106 72% ││ ░░░░░░░░░░ default $0.014 0.0% 1 │ │ █░░░░░░░░░ Build/Deploy $124.39 56 - ││ ░░░░░░░░░░ Sonnet 4.5 $0.0004 0.0% 1 │ │ ░░░░░░░░░░ Conversation $91.18 145 - ││ ░░░░░░░░░░ $0.0000 - 30 │ │ ░░░░░░░░░░ Delegation $72.41 21 44% ││ ░░░░░░░░░░ qwen35-opus-di $0.0000 0.0% 15 │ │ ░░░░░░░░░░ Planning $65.92 69 - ││ ░░░░░░░░░░ gemma4:26b $0.0000 0.0% 4 │ │ ░░░░░░░░░░ Refactoring $62.89 24 95% ││ │ │ ░░░░░░░░░░ Brainstorming $53.07 174 - ││ │ │ ░░░░░░░░░░ Git Ops $32.14 18 - ││ submitted by /u/Professional-Try6006 [link] [comments]
View originalHow does a Claude Code agent navigate hundreds of skills in a second?
I asked my agent: "do an SEO audit on my Shopify store." It searched its skill library, 686 skills sitting in a vector database, in under a second and returned its top candidates. Five of the top seven were exactly what you'd want: seo-content (on-page strategy) seo-images (image optimization) seo-aeo-content-quality-auditor (answer-engine optimization) seo-content-auditor (content quality) indexing-issue-auditor (crawl/index issues) The other two were false matches, unrelated skills that triggered on the word "audit." Easy to filter. I never specified which skills to use. The agent picked them on its own. How this is wired Claude Code's default loading strategy is what Anthropic calls "progressive disclosure". At startup it reads only the name and short description of every skill into the system prompt, then reads the full body on demand when it decides to invoke a skill. That handles the body problem nicely. But it does not handle the index problem. The names and descriptions are loaded for every skill, every session, before any work starts. At 100 skills that costs ~5K tokens. At 1,000 it's 50K. The full 4,556-skill public community catalog overflows a 200K context window entirely. The semantic router pattern removes both costs. Each skill's name + description is embedded once into a vector store (mesh-memory in my case, Postgres + pgvector, MIT). At task time the agent runs ONE search against the indexed skills, pulls the top 5 candidates, and only reads the full SKILL.md body for the one it actually wants to use. Constant cost per task regardless of catalog size. Benchmark To check whether the picking is actually any good, I ran 8 diverse task queries (deploy docker, security audit, optimize SQL, build React TS, debug memory leak C++, CI/CD pipeline, stock market analysis, marketing email): Correct skill as TOP-1 result: 5/8 (62.5%) Right skill present in TOP-5: 7/8 (87.5%) Cosine similarity for top-1: 0.83-0.88 Latency: under 1 second per query The one consistent failure was the SQL-optimization query. The relevant skill (sql-optimization-patterns) existed in the corpus but did not land in the random 1,000-skill sample I indexed. Router accuracy is bounded by corpus depth, not by the search algorithm. Convergence curve (cumulative indexed -> top-1 / top-5): Indexed Strict top-1 Top-5 cluster 91 25% ~70% 177 43% ~85% 500 ~57% ~85% 686 62.5% 87.5% Top-5 saturates fast. Top-1 keeps climbing as exact-match skills surface. Full writeup with methodology, raw results, and a 70-line Python reproducer on the blog. Curious if anyone else has tried different embedders, I only tested intfloat/multilingual-e5-base. submitted by /u/Hungry_Management_10 [link] [comments]
View originalI built a Laravel package that turns your app into a database-backed personal knowledge vault (Obsidian style) with a 16-tool MCP server
Hey! I'm the author. laravel-commonplace is a database-backed personal knowledge vault you install into an existing Laravel app. Adjacent to Obsidian, Logseq, and Notion as personal-knowledge tooling, except the storage layer is your existing Laravel app's database instead of files on disk or a third-party SaaS. Notes are Eloquent models in your DB, gated by your app's auth, shareable per-user via an owner plus Share model. It ships a browser UI (editor, graph view, search, journal) and an MCP server with 16 tools. If you have a Laravel app, the MCP server lets Claude Desktop, Claude Code, Cursor, Zed, Continue, Cline, Pi, or any other MCP client read and write your notes as the host app's user. Default middleware is auth:sanctum (Bearer PAT), and every tool resolves to $request->user(). There's no synthetic agent identity to provision, scope, or revoke separately. The agent gets exactly what the user gets, evaluated against the same Policies the controllers already use. Session, Passport, and OAuth-DCR are all configurable if PAT isn't what you want. The 16 tools, grouped: CRUD: create-note-tool, read-note-tool, update-note-tool, edit-note-tool (surgical find-and-replace), delete-note-tool (history preserved), move-tool (rewrites referring wikilinks). Discovery: list-tool (folder/tag/visibility filters), search-tool (substring), semantic-search-tool (embedding search), suggested-links-tool (embedding-similar notes not yet linked). Graph: backlinks-tool, neighborhood-tool (N-hop traversal), shortest-path-tool (chain between two notes), hub-notes-tool (most-connected), orphan-notes-tool (no inbound or outbound links). History: history-tool (version snapshots, survives deletion). On the semantic tools: the vector driver defaults to in_php_cosine for portability across SQLite, MySQL, and Postgres. If you're on Postgres, switching to the pgvector driver gets you indexed similarity and removes the in-PHP candidate cap. You swap it with a published migration and an env flag, and the docs recommend it once you're past a couple thousand notes. The tools live in src/Mcp/ if you want to see how a multi-tool MCP server is wired into a Laravel app. Caveats: Pre-1.0 (v0.2.0). APIs may shift before 1.0. Laravel-only by design. The whole point is reusing the host app's DB and auth. MCP is off by default. One env flag turns it on. Operator decision. Prompt injection through note content is the unsolved hard part. Notes are untrusted text, and notes other users share with you can carry instructions an agent might follow. The package doesn't pretend to solve this. The threat model at docs/threat-model.md says what's mitigated and what isn't. No per-tool capability gating yet. Enabling MCP enables all 16 tools the user is otherwise allowed to invoke. It's named as a limitation in the threat model. Feedback I'd actually use: Laravel folks who install it and tell me where it breaks, and anyone who reads the threat model and finds a hole I missed. Repo: https://github.com/non-convex-labs/laravel-commonplace submitted by /u/aaddrick [link] [comments]
View originalNeed help picking the right emoji (like we did for this post)? 🤔 @cassidoo made an emoji list generator with Copilot CLI. Learn how she did it and pick up tools and tricks for your next project. 👇
Need help picking the right emoji (like we did for this post)? 🤔 @cassidoo made an emoji list generator with Copilot CLI. Learn how she did it and pick up tools and tricks for your next project. 👇 https://t.co/13xwmu6tE9 https://t.co/pCy8PGfUIE
View originalCooking up something new 🧑🍳 Join the waitlist for early access to technical preview of the GitHub Copilot app 👇 https://t.co/ODODKdvzOA https://t.co/1h7AJPAhiH
Cooking up something new 🧑🍳 Join the waitlist for early access to technical preview of the GitHub Copilot app 👇 https://t.co/ODODKdvzOA https://t.co/1h7AJPAhiH
View originalNew to open source? Learn how to find a good first issue, open a pull request, and make your first contribution with GitHub for Beginners. 👇 https://t.co/PNRb746zCh
New to open source? Learn how to find a good first issue, open a pull request, and make your first contribution with GitHub for Beginners. 👇 https://t.co/PNRb746zCh
View originalRT @cinnamon_msft: GitHub Copilot CLI now has a statusline feature! Here's how to set it up with Oh My Posh ❤️🔥 https://t.co/DpNR8Bjt7G
RT @cinnamon_msft: GitHub Copilot CLI now has a statusline feature! Here's how to set it up with Oh My Posh ❤️🔥 https://t.co/DpNR8Bjt7G
View originalFind out what vulnerabilities are lurking in your code. 👀 GitHub's new Code Security Risk Assessment scans your organization's code and delivers a vulnerability dashboard broken down by severity, la
Find out what vulnerabilities are lurking in your code. 👀 GitHub's new Code Security Risk Assessment scans your organization's code and delivers a vulnerability dashboard broken down by severity, language, and repo. No config, no commitment. Run your free assessment now.
View originalNew to GitHub Copilot CLI? Our beginner series makes it easy to get started. Bring agentic AI right to your terminal and speed up your workflow. 💻✨ Get the tutorial here. 👇 https://t.co/bNLnpdgTxr
New to GitHub Copilot CLI? Our beginner series makes it easy to get started. Bring agentic AI right to your terminal and speed up your workflow. 💻✨ Get the tutorial here. 👇 https://t.co/bNLnpdgTxr
View originalTanStack now has TanStack AI. 👀 Here's what to expect from this new, fully open-source toolkit. ▶️ https://t.co/AjmutvBYve
TanStack now has TanStack AI. 👀 Here's what to expect from this new, fully open-source toolkit. ▶️ https://t.co/AjmutvBYve
View originalOf course GitHub will be at Microsoft Build. 🎉 Dive into real code, real systems, and real workflows with the teams building and scaling AI. Join us for exclusive events like: • Lots of GitHub sessi
Of course GitHub will be at Microsoft Build. 🎉 Dive into real code, real systems, and real workflows with the teams building and scaling AI. Join us for exclusive events like: • Lots of GitHub sessions • GitHub Social Club • OpenClaw meetup at GitHub HQ Not registered for https://t.co/SRz9hfizRr
View originalTomorrow on Open Source Friday 👇 We're breaking down Spec Kit: what it is, the problems it solves, and how clear specs make collaboration actually work. Principal Software Engineer Manfred Riem exp
Tomorrow on Open Source Friday 👇 We're breaking down Spec Kit: what it is, the problems it solves, and how clear specs make collaboration actually work. Principal Software Engineer Manfred Riem explains live. Set a reminder. 🔔 https://t.co/g0xrLf3Hb5 https://t.co/8dg3gvLFXf
View originalHappy World Password Day! Consider updating your password from ******** to *********. https://t.co/Ofx6j0d074
Happy World Password Day! Consider updating your password from ******** to *********. https://t.co/Ofx6j0d074
View originalMichael Babcock (@PayOwn) of @acbnational wanted to cut down time-consuming weekly tasks. Even though he’s not a developer, he built the solution himself. Meet ACB Community Builder, made with GitHub
Michael Babcock (@PayOwn) of @acbnational wanted to cut down time-consuming weekly tasks. Even though he’s not a developer, he built the solution himself. Meet ACB Community Builder, made with GitHub Copilot and JAWS. ▶️ https://t.co/JmUJ34U076
View originalMaintainer Month is here, with better tools, helpful resources, and community events for the people behind the code. 💻 Check out what’s new. 👇 https://t.co/CvPO32H7d8
Maintainer Month is here, with better tools, helpful resources, and community events for the people behind the code. 💻 Check out what’s new. 👇 https://t.co/CvPO32H7d8
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 134 social mentions analyzed, 8% of sentiment is positive, 92% neutral, and 0% negative.
