obsidian-copilot
qdrant
obsidian-copilot | qdrant | |
---|---|---|
5 | 142 | |
445 | 18,219 | |
- | 4.8% | |
7.3 | 9.9 | |
3 months ago | 1 day ago | |
Python | Rust | |
Apache License 2.0 | Apache License 2.0 |
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obsidian-copilot
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
hadn't seen your repo yet [1] - adding it to my list right now.
Your blog post is really neat on top - thanks for sharing
https://github.com/eugeneyan/obsidian-copilot
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Obsidian-Copilot: A Prototype Assistant for Writing and Thinking
Um... can someone explain what this actually does?
In the video the user chooses the 'Copilot: Draft' action, and wow, it generates code...
...but, the 'draft' action [1] calls `/get_chunks` and then runs 'queryLLM' [2] which then just invokes 'https://api.openai.com/v1/chat/completions' directly.
So, generating text this way is 100% not interesting or relevant.
What's interesting here is how it's building the prompt to send to the openai-api.
So... can anyone shed some light on what the actual code [3] in get_chunks() does, and why you would... hm... I guess, do a lookup and pass the results to the openai api, instead of just the raw text?
The repo says: "You write a section header and the copilot retrieves relevant notes & docs to draft that section for you.", and you can see in the linked post [4], this is basically what the OP is trying to implement here; you write 'I want X', and the plugin (a bit like copilot) does a lookup of related documents, crafts a meta-prompt and passes the prompt to the openai api.
...but, it doesn't seem to do that. It seems to ignore your actual prompt, lookup related documents by embedding similarity... and then... pass those documents in as the prompt?
I'm pretty confused as to why you would want that.
It basically requires that you write your prompt separately before hand, so you can invoke it magically with a one-line prompt later. Did I misunderstand how this works?
[1] - https://github.com/eugeneyan/obsidian-copilot/blob/bdabdc422...
[2] - https://github.com/eugeneyan/obsidian-copilot/blob/bdabdc422...
[3] - https://github.com/eugeneyan/obsidian-copilot/blob/main/src/...
[4] - https://eugeneyan.com/writing/llm-experiments/#shortcomings-...
qdrant
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.
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Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
I'm currently looking to implement locally, using QDrant [1] for instance.
I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].
[1]. https://qdrant.tech/
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Show HN: A fast HNSW implementation in Rust
Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Open-source Rust-based RAG
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb
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Qdrant 1.8.0 - Major Performance Enhancements
For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.
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7 Vector Databases Every Developer Should Know!
Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.
- Ask HN: Who is hiring? (February 2024)
What are some alternatives?
obsidian-smart-connections - Chat with your notes & see links to related content with AI embeddings. Use local models or 100+ via APIs like Claude, Gemini, ChatGPT & Llama 3
Milvus - A cloud-native vector database, storage for next generation AI applications
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
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​.
tonic_validate - Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.
faiss - A library for efficient similarity search and clustering of dense vectors.
chroma-langchain
pgvector - Open-source vector similarity search for Postgres
ResuLLMe - Enhance your résumé with Large Language Models
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
markdown-embeddings-search - Obisidan notes to pinecone embeddings plus other files in effor to learn llama_index
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.