sqlite-vss
simpleaichat
sqlite-vss | simpleaichat | |
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17 | 22 | |
1,455 | 3,386 | |
- | - | |
8.0 | 8.7 | |
about 2 months ago | 4 months ago | |
C++ | Python | |
MIT License | MIT License |
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sqlite-vss
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I'm writing a new vector search SQLite Extension
I guess this is an answer to the GitHub issue I opened against SQLite-vss a couple of months ago?
https://github.com/asg017/sqlite-vss/issues/124
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Embeddings are a good starting point for the AI curious app developer
Perhaps sqlite-vss? It adds vector searches to sqlite.
https://github.com/asg017/sqlite-vss
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How to Enhance Content with Semantify
Utilizing sqlite-vss to store and query vector embeddings managed by a local SQLite database, Semantify conducts fast, precise vector searches within these embeddings to find and recommend relevant content, ensuring readers are presented with articles that truly match their interests.
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SQLite vs. Chroma: A Comparative Analysis for Managing Vector Embeddings
Whether you’re navigating through well-known options like SQLite, enriched with the sqlite-vss extension, or exploring other avenues like Chroma, an open-source vector database, selecting the right tool is paramount. This article compares these two choices, guiding you through the pros and cons of each, helping you choose the right tool for storing and querying vector embeddings for your project.
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Vector database is not a separate database category
Here is a SQLite extension that uses Faiss under the hood.
https://github.com/asg017/sqlite-vss
Not associated with the project, just love SQLite and find it very useful.
- SQLite-Vss: A SQLite Extension for Vector Search
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Introduction to Vector Search and Embeddings
Vector Databases: As your data grows, efficiently searching through millions of vectors can become a challenge. Specialized vector databases like FAISS, Annoy, or Elasticsearch's vector search capabilities can be explored to manage and search through large-scale vector data. Your sentence is grammatically correct. In addition, databases like SQLite and PostgreSQL have extensions, such as sqlite-vss and pgvector, that can be used to store and query vector embeddings, respectively.
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The Problem with LangChain
I had a go at one of those a few months ago: https://datasette.io/plugins/datasette-faiss
Alex Garcia built a better one here as a SQLite Rust extension: https://github.com/asg017/sqlite-vss
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Every request, every microsecond: scalable machine learning at Cloudflare
Since the problem domain is that of anomaly detection from constructed request feature embeddings, I wonder if an ANN-search methodology using an embedded database (such as https://github.com/asg017/sqlite-vss or similar) was explored.
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Disrupting the AI Scene with Open Source and Open Innovation
As I searched for "sqlite vector plugin" I didn't find any results, before a couple of weeks ago. Two weeks ago I found Alex' SQLite VSS plugin for SQLite. The library was an amazing piece of engineering from an "idea perspective". However, as I started playing around with it, I realised it was ipso facto like "Titanic". Beautiful and amazing, but destined to leak water and sink to the bottom of the ocean because of what we software engineers refers to as "memory leaks".
simpleaichat
- Efficient Coding Assistant with Simpleaichat
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Please Don't Ask If an Open Source Project Is Dead
I checked both the issues mentioned, people have been respectful and showing empathy to author's situation
https://github.com/minimaxir/simpleaichat/issues/91
https://github.com/minimaxir/simpleaichat/issues/92
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We Built an AI-Powered Magic the Gathering Card Generator
ChatGPT's June updated added support for "function calling", which in practice is structured data I/O marketed very poorly: https://openai.com/blog/function-calling-and-other-api-updat...
Here's an example of using structured data for better output control (lightly leveraging my Python package to reduce LoC: https://github.com/minimaxir/simpleaichat/blob/main/examples... )
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LangChain Agent Simulation – Multi-Player Dungeons and Dragons
So what are the alternatives to LangChain that the HN crowd uses?
I see two contenders:
https://github.com/minimaxir/simpleaichat/tree/main/simpleai...
https://github.com/griptape-ai/griptape
There is also the llm command line utility that has a very thin underlying library, but which might grow eventually:
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Custom Instructions for ChatGPT
A fun note is that even with system prompt engineering it may not give the most efficient solution: ChatGPT still outputs the avergage case.
I tested around it and doing two passes (generate code and "make it more efficient") works best, with system prompt engineering to result in less code output: https://github.com/minimaxir/simpleaichat/blob/main/examples...
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The Problem with LangChain
I played around with simpleaichat for a few minutes just now, and I really like it. Unlike LangChain, I can understand what it does in minutes, and it looks like its primitives are fairly powerful. It looks like it's going to replace the `openai` library for me, it seems like a nice wrapper.
I'm especially looking forward to playing with the structured data models bit: https://github.com/minimaxir/simpleaichat/blob/main/examples...
Well done, Max!
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How is Langchain's dev experience? Any alternatives?
https://github.com/minimaxir/simpleaichat bills itself as a simpler alternative to langchain. I have not tried it, but it looks interesting.
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Stanford A.I. Courses
I think you are asking specifically about practical LLM engineering and not the underlying science.
Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!).
https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack.
Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates
Simon W has some good writeups too: https://simonwillison.net/
I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you gourde the LLM, etc.
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Where is the engineering part in "prompt engineer"?
This notebook from the repo I linked to is a concise example, and the reason you would want to optimize prompts.
- Show HN: Python package for interfacing with ChatGPT with minimized complexity
What are some alternatives?
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
lmql - A language for constraint-guided and efficient LLM programming.
chroma - the AI-native open-source embedding database
langroid - Harness LLMs with Multi-Agent Programming
pgvector-go - pgvector support for Go
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
milvus-lite - A lightweight version of Milvus wrapped with Python.
typesense-instantsearch-semantic-search-demo - A demo that shows how to build a semantic search experience with Typesense's vector search feature and Instantsearch.js
gchain - Composable LLM Application framework inspired by langchain
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.