autopilot
marqo
autopilot | marqo | |
---|---|---|
13 | 114 | |
560 | 4,124 | |
- | 1.6% | |
3.7 | 9.3 | |
9 days ago | 3 days ago | |
JavaScript | Python | |
- | Apache License 2.0 |
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autopilot
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Lessons from Creating a VSCode Extension with GPT-4
Thanks for sharing your work! Let’s connect cause it seems we are exploring a similar space. Here’s the project I have been working on: https://github.com/fjrdomingues/autopilot
- Best way to work with a bigger code base?
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How to work with larger Code bases?
- https://github.com/fjrdomingues/autopilot/issues/178 (the code is mostly correct, minus a minor change in the package that was updated and gpt doesn't know. It was almost a copy-paste).
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Code Autopilot AI can work on entire codebases
Code Autopilot is an AI-powered tool designed to tackle software engineering tasks. It shares similarities with Github Copilot, but with some key distinctions: - It derives context from your entire codebase, enabling it to handle complex tasks spanning multiple files. - It integrates with your Github account to obtain context and respond to issues you open. - The core technology is open-source, from https://github.com/fjrdomingues/autopilot
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Is GPT-4 going to give me a better code?
Consider using autopilot for coding https://github.com/fjrdomingues/autopilot It’s open source In my tests it works with gpt-3.5 but gpt-4 gives much better/reliable answers. Problem is the price difference
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Codebase Context Code Generation
The solution I found was to create an agent that summarises all the files in a project and then a sequence of agents that picks the necessary code/folders for a task. That way I was able to provide the required context and then let GPT do its magic. The project is public at: https://github.com/fjrdomingues/autopilot
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Has anyone here had success creating parseable JSON with GPT-3.5-Turbo?
Check what we are doing in https://github.com/fjrdomingues/autopilot we have multiple use cases were we ask gpt 3.5 to reply in JSON
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Autopilot: GPT to work on larger databases
Link to project: https://github.com/fjrdomingues/autopilot
- Introducing Autopilot: GPT to work on larger databases
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I built a chatbot that lets you talk to any Github repository
I developed a too for the same problem but with a slightly different approach: https://github.com/fjrdomingues/autopilot It uses multiple GPT calls to get context on the codebase and when you give it a task it uses the context to understand where to implement the code changes and suggests the code to implement. It would be great to merge the best of both project
marqo
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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
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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)
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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).
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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.
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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.
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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
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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
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[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
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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?
VectorTileExporter - Just some scripts to export vector tiles to geojson.
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.
app.cofounder - Find your perfect co-founder with our AI-powered matching platform! Built on Node.js/Express and React, it's the ultimate tool to launch your startup journey. 🚀🤖
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
askai - Command Line Interface for OpenAi ChatGPT
Milvus - A cloud-native vector database, storage for next generation AI applications
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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.
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
awesome-vector-search - Collections of vector search related libraries, service and research papers