anything-llm
marqo
anything-llm | marqo | |
---|---|---|
21 | 114 | |
12,782 | 4,152 | |
24.4% | 2.3% | |
9.8 | 9.3 | |
5 days ago | 6 days ago | |
JavaScript | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
anything-llm
- AnythingLLM: Chat with your documents using any LLM
-
Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
anything-llm looks pretty interesting and easy to use https://github.com/Mintplex-Labs/anything-llm
-
local/private llm based chatbot using free/open source tools.
You can just fork AnythingLLM for a very advanced starting point or just straight rip the code ive already written to build yours 🚀
-
Some solutions that work on older intel macs
AnythingLLM also works on an Intel Mac (i develop it on an intel mac) and can use any GGUF model to do local inferencing. Includes document embedding + local vector database so i can do chatting with documents and even coding inside of it. Pretty much a ChatGPT equilivent i can run locally via the repo or docker.
-
What tools or programs have you made or are working on?
If you want a UI you can leverage https://github.com/Mintplex-Labs/anything-llm and do all your coding in localhost with a locally running model.
- Web interface for Azure Open Ai
- DIY custom AI chatbot trained on your company data
marqo
-
Are we at peak vector database?
We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo
-
Qdrant, the Vector Search Database, raised $28M in a Series A round
Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)
-
Ask HN: Is there any good semantic search GUI for images or documents?
Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).
-
90x Faster Than Pgvector – Lantern's HNSW Index Creation Time
That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.
-
Embeddings: What they are and why they matter
Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.
-
Choosing vector database: a side-by-side comparison
As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.
- Show HN: Marqo – Vectorless Vector Search
-
AI for AWS Documentation
Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo
-
[N] Open-source search engine Meilisearch launches vector search
Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo
-
Ask HN: Which Vector Database do you recommend for LLM applications?
Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo
What are some alternatives?
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
privateGPT - Interact with your documents using the power of GPT, 100% privately, no data leaks [Moved to: https://github.com/zylon-ai/private-gpt]
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
LLMStack - No-code platform to build LLM Agents, workflows and applications with your data
Milvus - A cloud-native vector database, storage for next generation AI applications
gpt4all - gpt4all: run open-source LLMs anywhere
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
awesome-ml - Curated list of useful LLM / Analytics / Datascience resources
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
CSharp-ChatBot-GPT - This repository contains a simple C# chatbot powered by OpenAI’s ChatGPT. The chatbot utilizes the RestSharp and Newtonsoft.Json libraries to interact with the ChatGPT API and process user input.
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]