examples
gorilla-cli
Our great sponsors
examples | gorilla-cli | |
---|---|---|
5 | 11 | |
376 | 1,149 | |
10.9% | 6.7% | |
6.8 | 5.5 | |
3 months ago | 2 months ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | 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.
examples
- FLaNK Stack Weekly for 07August2023
-
Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
-
Ask HN: Any good self-hosted image recognition software?
Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings.
The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For this step the data you would need is all the images of the object you have with a bounding box created around it, the version of the object doesnt matter here.
The second step involves using a generalized image classification model thats been pretrained on generalized data (VGG, etc.) and a vector search engine/vector database. You would start by using the image classification model to generate vector embeddings (https://frankzliu.com/blog/understanding-neural-network-embe...) of all the different versions of the object. The more ground truth images you have, the better, but it doesn't require the same amount as training a classifier model. Once you have your versions of the object as embeddings, you would store them in a vector database (for example Milvus: https://github.com/milvus-io/milvus).
Now whenever you want to detect the object in an image you can run the image through the detection model to find the object in the image, then run the sliced out image of the object through the vector embedding model. With this vector embedding you can then perform a search in the vector database, and the closest results will most likely be the version of the object.
Hopefully this helps with the general rundown of how it would look like. Here is an example using Milvus and Towhee https://github.com/towhee-io/examples/tree/3a2207d67b10a246f....
Disclaimer: I am a part of those two open source projects.
-
Deep Dive into Real-World Image Search Engine with Python
I have shown how to Build an Image Search Engine in Minutes in the previous tutorial. Here is another one for how to optimize the algorithm, feed it with large-scale image datasets, and deploy it as a micro-service.
-
Build an Image Search Engine in Minutes
The full tutorial is at https://github.com/towhee-io/examples/blob/main/image/reverse_image_search/build_image_search_engine.ipynb
gorilla-cli
- FLaNK 15 Jan 2024
-
Show HN: Shell-AI, run shell commands with natural language
Hello HN! I know this project is a super simple wrapper around LangChain/OpenAI but I just found myself wanting this badly myself: a super simple `pip install` package that I can use to get command suggestions within the terminal as I'm being productive doing other things.
The implementation is literally one short glue of LangChain and InquirerPy for interactive CLI.
I'm curious which ideas you all have to make this smarter/better. MIT licensed, if you're keen on contributing please feel free to do so. It's a pure hobby project for me.
Some key objectives: never automatically run shell code, I want to see what I run before I run it, present me with some alternatives, a simple path to using local models in the future (Llama 2 Code soon?).
Will add I was inspired by the great https://github.com/gorilla-llm/gorilla-cli project, but didn't like that it sent the prompt to some IP based endpoint.
-
Show HN: Poozle – open-source Plaid for LLMs
Very cool product! Have you consider relying on Gorilla for integrations?
https://github.com/gorilla-llm/gorilla-cli
- FLaNK Stack Weekly for 07August2023
- Show HN: Lemon AI – open-source Zapier NLA to empower agents
- GitHub - gorilla-llm/gorilla-cli: LLMs for your CLI (cum să faci operations doar în limba engleză)
-
30-Jun-2023
gorilla-cli: LLMs for your CLI (https://github.com/gorilla-llm/gorilla-cli)
- Gorilla-CLI: LLMs for CLI including K8s/AWS/GCP/Azure/sed and 1500 APIs
What are some alternatives?
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
GPTCache - Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
milvus-lite - A lightweight version of Milvus wrapped with Python.
FLiPStackWeekly - FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
shell_gpt - A command-line productivity tool powered by AI large language models like GPT-4, will help you accomplish your tasks faster and more efficiently.
EverythingApacheNiFi - EverythingApacheNiFi
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.
harlequin - The SQL IDE for Your Terminal.
CallCMLModel - An example on calling models deployed in CML
OpenBuddy - Open Multilingual Chatbot for Everyone