GPTCache
guardrails
| GPTCache | guardrails | |
|---|---|---|
| 43 | 13 | |
| 6,446 | 3,361 | |
| 2.1% | 6.4% | |
| 7.7 | 9.9 | |
| about 1 month ago | 6 days ago | |
| Python | 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.
GPTCache
-
Ask HN: What are the drawbacks of caching LLM responses?
Just found this: https://github.com/zilliztech/GPTCache which seems to address this idea/issue.
-
Open Source Advent Fun Wraps Up!
21. GPTCache | Github | tutorial
- Semantic Cache
-
Show HN: Danswer – open-source question answering across all your docs
Check this out. Built on a vector database (https://github.com/milvus-io/milvus) and a semantic cache (https://github.com/zilliztech/GPTCache)
https://osschat.io/
- GPTCache
-
Ask HN: Is LLM Caching Necessary?
With the proliferation of large models, an increasing number of enterprises and individual developers are now developing applications based on these models. As such, it is worth considering whether large model caching is necessary during the development process.
Our project: https://github.com/zilliztech/GPTCache
-
Gorilla-CLI: LLMs for CLI including K8s/AWS/GCP/Azure/sed and 1500 APIs
Maybe [GPTCache](https://github.com/zilliztech/GPTCache) can make it more attractive, because similar problems can be less expensive, and can also be responded to faster. Of course, the specific configuration needs to be based on real usage scenarios.
- Limited budget or machine resources, how to achieve a decent LLM experience?
guardrails
- Guardrails AI
- Does anyone have an example of a langchain based customer facing agent like a cashier/waitress?
- Is there a UI that can limit LLM tokens to a preset list?
-
A minimal design pattern for LLM-powered microservices with FastAPI & LangChain
You're absolutely correct, and I agree that there's potentially a risk of quality loss. But likewise, since these are all intrinsically linked, it may be possible to leverage strength by combining these tasks. I'm unaware of a paper reviewing the reliability and/or performance of LLMs in this specific scenario. If you find any, do share :) With regards to generating JSON responses - there are simple ways to nudge the model and even validate it, using libraries such as https://github.com/promptslab/Promptify, https://github.com/eyurtsev/kor and https://github.com/ShreyaR/guardrails
- Ask HN: People who were laid off or quit recently, how are you doing?
-
Ask HN: AI to study my DSL and then output it?
There are a couple different approaches:
- Use multi-shot prompting with something like guardrails to try prompting a commercial model until it works. [1]
- Use a local model with something with a final layer that steers token selection towards syntactically valid tokens [2]
[1] https://github.com/ShreyaR/guardrails
[2] "Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema" @ https://github.com/newhouseb/clownfish.
-
Introducing :🤖 Megabots - State-of-the-art, production ready full-stack LLM apps made mega-easy with LangChain and FastAPI
👍 validate and correct the outputs of LLMs using guardrails
- For consistent output from vicuna 13b
-
[D] Is all the talk about what GPT can do on Twitter and Reddit exaggerated or fairly accurate?
not vouching for it, but I know this is at least a thing that exists and I like the general idea: https://github.com/shreyar/guardrails
- Introducing Agents in Haystack: Make LLMs resolve complex tasks
What are some alternatives?
gorilla-cli - LLMs for your CLI
lmql - A language for constraint-guided and efficient LLM programming.
danswer - Gen-AI Chat for Teams - Think ChatGPT if it had access to your team's unique knowledge.
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
DB-GPT - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
truss - Assertions micro-library for Clojure/Script
gpt4free - The official gpt4free repository | various collection of powerful language models
dynamic-gpt-ui - Dynamic UI generation with GPT-3 (OpenAI)
sheetgpt - ChatGPT integration with Google Sheets
ghostwheel - Hassle-free inline clojure.spec with semi-automatic generative testing and side effect detection
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
empirical-philosophy - A collection of empirical experiments using large language models and other neural network architectures to test the usefulness of metaphysical constructs.