gpt-code-search
gorilla
gpt-code-search | gorilla | |
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6 | 52 | |
136 | 10,472 | |
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10.0 | 9.0 | |
11 months ago | 3 days ago | |
Python | 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.
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gpt-code-search
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Show HN: Open-source code search with OpenAI's function calling
Thanks for the clarification. I was confused since the About link at the top of the linked-to repo has the URL https://wolfia.com prominently displayed. Both projects are very interesting and cool. Thanks!
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Open-source code search tool with OpenAI's GPT-4 and function calling
Key Features: - Efficient: Code search, retrieval, and answering all performed with OpenAI's GPT-4 function calling. - Privacy-centric: Code snippets only leave your device when you ask a question and the LLM requires the relevant code. - Ready-to-use: No need for pre-processing, chunking, or indexing. Get started right away! - Universal: It works with any code on your device. Why is it important? This tool aids in leveraging the power of GPT-4 to scan your codebase, eliminating the need to manually copy and paste code snippets or share your code with another third-party service. The tool addresses these issues by letting GPT-4 identify the most relevant code snippets within your codebase, saving you the need to copy and paste or send your code elsewhere. Notably, it fits right into your terminal, sparing you the need for a new UI or window. Here are the types of questions you can ask: - Help with debugging errors and locating the relevant code and files - Document extensive files or functionalities formatted as markdown - Generate new code based on existing files and conventions - Ask general questions about any part of the codebase Despite a few limitations like the inability to load context across multiple files at once and limited search depth, this tool is a considerable step towards a more efficient coding experience. For those seeking an even more powerful tool that uses vector embeddings and a more robust search and retrieval system, check out Wolfia Codex, the cloud-based big brother to gpt-code-search. That's it!
If you want to get smarter in AI, look here first. All the information has been extracted on Reddit for your convenience but you can find the GitHub repo here.
gorilla
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Raft: Sailing Llama towards better domain-specific RAG
Retrieval-Augmented Fine-Tuning is a really promising technique.
FTA:
> Tianjun and Shishir were looking to improve these deficiencies of RAG. They hypothesized that a student who studies the textbooks before the open-book exam would be more likely to perform better than a student who references the textbook only during the exam. Translating that back to LLMs, if a model “studied” the documents beforehand, could that improve its RAG performance?
Incidentally, the team who wrote the paper released some nice code to generate domain-specific fine-tuning datasets: https://github.com/ShishirPatil/gorilla/tree/main/raft
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Launch HN: Nango (YC W23) – Open-Source Unified API
Do you leverage https://gorilla.cs.berkeley.edu/ at all? If not, perhaps consider if it would solve some pain for you.
- Autonomous LLM agents with human-out-of-loop
- Show HN: I made a script to scrape your Facebook group
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Pushing ChatGPT's Structured Data Support to Its Limits
* Gorilla [https://github.com/ShishirPatil/gorilla]
Could be interesting to try some of these exercises with these models.
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Guidance for selecting a function-calling library?
gorilla
- Gorilla: An API Store for LLMs
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Show HN: OpenAPI DevTools – Chrome ext. that generates an API spec as you browse
Nice this made me go back and check up on the Gorilla LLM project [1] to see whats they are doing with API and if they have applied their fine tuning to any of the newer foundation models but looks like things have slowed down since they launched (?) or maybe development is happening elsewhere on some invisible discord channel but I hope the intersection of API calling and LLM as a logic processing function keep getting focus it's an important direction for interop across the web.
[1] https://github.com/ShishirPatil/gorilla
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RestGPT
"Gorilla: Large Language Model Connected with Massive APIs" (2023) https://gorilla.cs.berkeley.edu/ :
> Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them!
eval/:
- Calling APIs with Natural Language