rasa
lambdaprompt
rasa | lambdaprompt | |
---|---|---|
16 | 8 | |
17,984 | 368 | |
1.0% | 0.8% | |
9.6 | 5.6 | |
7 days ago | 4 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
rasa
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
Support Rasa on GitHub ⭐
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
It itself is not a GPT. It is a a framework of a framework project built on top of Rasa (https://github.com/RasaHQ/rasa) and Langchain which by default uses gpt3.5-turbo (change it in the .env file) or any foundation model you wish.
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Problem with RASA Open Source "The client is using an unsupported version of the Socket.IO or Engine.IO"
There was already a GitHub issue https://github.com/RasaHQ/rasa/issues/11084 but it seems like there was no solution provided.
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I plan to build my own AI powered search engine for my portfolio. Do you know ones that are open-source?
Rasa - also an open source platform. You can use it to build chatbots that are more in line with customer service and sales. Reading that, it’s kind of a big yikes for people under this kind of job knowing you can automate a part of their job via a chatbot. I think they also have a Pro version which is an extension that has additional analytics and probably more security features. I probably will check out the Open source one first and maybe ask about what the Pro version is.
- How to Installing Rasa & Building Rasa Chatbot on an M1 Macbook.
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Show HN: Flex – transpile natural language to a programming language
At the moment it can recognise the type of statements in the training data set [1] and transpile them to Python, Java or C++ using the mappings defined here [2].
This is very different from how Codex/Autopilot work as it is trained using an NLU framework [3] which is usually used for training chatbots.
[1]: https://github.com/Flex-lang/transpiler/tree/master/transpil...
[2]: https://github.com/Flex-lang/transpiler/tree/master/transpil...
[3]: https://github.com/RasaHQ/rasa
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Seek alternative to Wix chatbox.
Check out Rasa
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Meet Jeanie - Your AI Powered Virtual Girlfriend - Beta
It looks like it is built upon a Chatbot platform called “messengerx.io”, which itself seems to be built upon an open-source/machine-learning framework for voice/text conversations called “Rasa”
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What are some good languages if you want to code AI?
Use Python and use the Rasa framework to make a chat bot. https://rasa.com/docs/
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How to Create the Perfect README for Your Open Source Project
This example is sourced from RasaHQ
lambdaprompt
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Ask HN: What have you built with LLMs?
We're using all sorts of different stacks and tooling. We made our own tooling at one point (https://github.com/approximatelabs/lambdaprompt/), but have more recently switched to just using the raw requests ourselves and writing out the logic ourselves in the product. For our main product, the code just lives in our next app, and deploys on vercel.
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
https://github.com/approximatelabs/lambdaprompt It has served all of my personal use-cases since making it, including powering `sketch` (copilot for pandas) https://github.com/approximatelabs/sketch
Core things it does: Uses jinja templates, does sync and async, and most importantly treats LLM completion endpoints as "function calls", which you can compose and build structures around just with simple python. I also combined it with fastapi so you can just serve up any templates you want directly as rest endpoints. It also offers callback hooks so you can log & trace execution graphs.
All together its only ~600 lines of python.
I haven't had a chance to really push all the different examples out there, but most "complex behaviors", so there aren't many patterns to copy. But if you're comfortable in python, then I think it offers a pretty good interface.
I hope to get back to it sometime in the next week to introduce local-mode (eg. all the open source smaller models are now available, I want to make those first-class)
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Replacing a SQL analyst with 26 recursive GPT prompts
This is great~ There's been some really rapid progress on Text2SQL in the last 6 months, and I really thinking this will have a real impact on the modern data stack ecosystem!
I had similar success with lambdaprompt for solving Text2SQL (https://github.com/approximatelabs/lambdaprompt/)
- λprompt - Composing Ai prompts with python in a functional style
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LangChain: Build AI apps with LLMs through composability
This is great! I love seeing how rapidly in the past 6 months these ideas are evolving. I've been internally calling these systems "prompt machines". I'm a strong believer that chaining together language model prompts is core to extracting real, and reproducible value from language models. I sometimes even wonder if systems like this are the path to AGI as well, and spent a full month 'stuck' on that hypothesis in October.
Specific to prompt-chaining: I've spent a lot of time ideating about where "prompts live" (are they best as API endpoint, as cli programs, as machines with internal state, treated as a single 'assembly instruction' -- where do "prompts" live naturally) and eventually decided on them being the most synonymous with functions (and api endpoints via the RPC concept)
mental model I've developed (sharing in case it resonates with anyone else)
a "chain" is `a = 'text'; b = p1(a); c = p2(b)` where p1 and p2 are LLM prompts.
What comes next (in my opinion) is other programming constructs: loops, conditionals, variables (memory), etc. (I think LangChain represents some of these concepts as their "areas" -> chain (function chaining), agents (loops), memory (variables))
To offer this code-style interface on top of LLMs, I made something similar to LangChain, but scoped what i made to only focus on the bare functional interface and the concept of a "prompt function", and leave the power of the "execution flow" up to the language interpreter itself (in this case python) so the user can make anything with it.
https://github.com/approximatelabs/lambdaprompt
I've had so much fun recently just playing with prompt chaining in general, it feels like the "new toy" in the AI space (orders of magnitude more fun than dall-e or chat-gpt for me). (I built sketch (posted the other day on HN) based on lambdaprompt)
My favorites have been things to test the inherent behaviors of language models using iterated prompts. I spent some time looking for "fractal" like behavior inside the functions, hoping that if I got the right starting point, an iterated function would avoid fixed points --> this has eluded me so far, so if anyone finds non-fixed points in LLMs, please let me know!
I'm a believer that the "next revolution" in machine-written code and behavior from LLMs will come when someone can tame LLM prompting to self-write prompt chains themselves (whether that is on lambdaprompt, langchain, or something else!)
All in all, I'm super hyped about LangChain, love the space they are in and the rapid attention they are getting~
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Show HN: Sketch – AI code-writing assistant that understands data content
From https://github.com/approximatelabs/sketch/blob/main/sketch/p... it appears that this library is calling a remote API, which obviates the utility of the demonstrated use case.
Upon closer inspection, it looks like https://github.com/approximatelabs/sketch interfaces with the model via https://github.com/approximatelabs/lambdaprompt, which is made by the same organization. This suggests to me that the former may be a toy demonstration of the latter.
- Show HN: Prompt – Build, compose and call templated LLM prompts
What are some alternatives?
ChatterBot - ChatterBot is a machine learning, conversational dialog engine for creating chat bots
datasloth - Natural language Pandas queries and data generation powered by GPT-3
opendialog - OpenDialog is a Conversational Experience Platform - the starting point for your own conversational applications.
lmql - A language for constraint-guided and efficient LLM programming.
chatwoot - Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. 🔥💬
LiteratureReviewBot - Experiment to use GPT-3 to help write grant proposals.
tock - Tock, the open source conversational AI toolkit.
kor - LLM(😽)
botman - A framework agnostic PHP library to build chat bots
olympe - Query your database in plain english
botkit - Botkit is an open source developer tool for building chat bots, apps and custom integrations for major messaging platforms.
com2fun - Transform document into function.