emerging-trajectories VS lambdaprompt

I think LLMs can do a lot more than people assume, but they need to be given the proper frameworks.

When was the last time a researcher, economist, etc. was given 10,000 papers and simply told "do some original work"? That's not how it works. Daniel (the author) provides some good examples where _streamlined_ work can happen, but again, this is pretty basic stuff.

To push this further, though, imagine LLMs that fill in frameworks... A few steps here: (1) do a lit review, (2) fill in the framework, (3) discuss what might be missing, and maybe even try and fill in the missing information.

I'm doing something like this with politics and economics (see: https://emergingtrajectories.com/) and it works generally well. I think with a ton more engineering, curating of knowledge bases, etc., one can get these LLMs to actually find some new "nuggets" of information.

Admittedly, it's very hard, but I think there's something there.

RAG will have a place in the LLM world, since it's a way to obtain data/facts/info for relevant queries.

Since you asked about alternatives...

(a) "World models" where LLMs structure information into code, structured data, etc. and query those models will likely be a thing. AlphaGeometry uses this[1], and people have tried to abstract this in different ways[2].

(b) Depending on how you define RAG, knowledge graphs could be a form of RAG or alternatively an alternative to them. Companies like Elemental Cognition[3] are building distinct alternatives to RAG that use such graphs and give LLMs the ability to run queries on said graphs. Another approach here is to build "fact databases" where, you structure observations about the world into standalone concepts/ideas/observations and reference those[4]. Again, similar to RAG but not quite RAG as we know it today.

[1] https://deepmind.google/discover/blog/alphageometry-an-olymp...

[2] https://arxiv.org/abs/2306.12672

[3] https://ec.ai/

[4] https://emergingtrajectories.com/

For those interested in using search-augmented "reasoning", I implemented something similar in Emerging Trajectories[1], an open source package that forecasts geopolitical and economic events. We extract facts[2] from various websites (Google searches, news articles, RSS feeds) and have the LLM generate a hypothesis on a metric.

We're tracking the info forecasts to see how well this does for future events. For example, we're pitting the LLMs against each other to predict March 2024 CPI[3].

[1] https://emergingtrajectories.com/

[2] Sample code: https://github.com/wgryc/emerging-trajectories/blob/main/eme...

[3] https://emergingtrajectories.com/a/statement/28

I'm working on research agents to help with economic, financial, and political research. These agents are open source (see: https://github.com/wgryc/emerging-trajectories).

The use cases are pretty straight forward and low risk:

1. Run a Google web search.

2. Query a news API.

3. Write a document based on the above, while citing sources.

Here's an example of something written yesterday, where I'm forecasting whether July 2024 will be the hottest on record: https://emergingtrajectories.com/a/forecast/74

This is working well in that the writeups are great and there are some "aha" moments, like the agent finding and referencing the The National Snow and Ice Data Center (NSIDC)... Very cool! I wouldn't have thought of it.

Then there's the part where the agent also tells me that the Oregon Department of Transportation has holidays during the summer, which doesn't matter at all.

So, YMMV, as they say... But I am more productive with these agents. I wouldn't publish anything formally without confirming and reviewing the content, though.

LLM agents to forecast geopolitical and economic events.

- Site: https://emergingtrajectories.com/

- GitHub repo: https://github.com/wgryc/emerging-trajectories

I've helped a number of companies build various sorts of LLM-powered apps (chatbots mainly) and found it interesting but not incredibly inspiring. The above is my attempt to build something no one else is working on.

It's been a lot of fun. Not sure if it'll be a "thing" ever, but I enjoy it.

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.

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)

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/)

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~

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.