Metadata-Version: 2.1
Name: deepllm
Version: 0.6.0
Summary: Deep, recursive, goal-driven LLM explorer
Home-page: https://github.com/ptarau/recursors.git
Author: Paul Tarau
Author-email: paul.tarau@gmail.com
License: GPL-3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: openai
Requires-Dist: tiktoken

# deepllm: Full Automation of Goal-driven LLM Dialog Threads with And-Or Recursors and Refiner Oracles


### Overview
We automate deep step-by step reasoning in an LLM dialog thread by recursively exploring alternatives (OR-nodes) and expanding details (AND-nodes) up to a given depth. Starting from a single succinct task-specific initiator we steer the automated dialog thread to stay focussed on the task by synthesizing a prompt that summarizes the depth-first steps taken so far. 

Our algorithm is derived from a simple recursive descent implementation
of a Horn Clause interpreter, except that we accommodate our logic engine to fit the natural language reasoning patterns LLMs have been trained on. Semantic similarity to ground-truth facts or oracle advice from another LLM instance is used to restrict the search space and validate the traces of justification steps returned as answers. At the end, the unique minimal model of a generated Horn Clause program collects the results of the reasoning process.

As applications, we sketch implementations of consequence predictions, causal explanations,  recommendation systems and topic-focussed exploration of scientific literature.


### INSTALLATION and USAGE:

#### Getting the OpenAI key:

As the code will use it, make sure you acquire it and have 

```
export OPENAI_KEY=<your_key>
```

added to your shell environment.

#### Downloading

Cloning with:

```
git clone git@github.com:ptarau/recursors.git
```

#### Installing

If you have cloned this repo, you can install the package ```deepllm``` by typing in folder ```recursors```

```
pip3 install -e .
```

You can also install it from [pypi](https://pypi.org/search/?q=deepllm) with

```
pip3 install deepllm
```

#### API

The DeepLLM [API](https://github.com/ptarau/recursors/blob/main/deepllm/api.py) exposes its high-level functions ready to embed in your application with something as simple as (assuming the your OPENAI_KEY is exported by your environment):

```
for result in run_recursor(initiator='Using tactical nukes', prompter=conseq_prompter, lim=2):
    print(result)
```

Also, you can explore questions with less gruesome results like in:

```
for result in run_rater(initiator='Artificial General Intelligence', prompter=sci_prompter, lim=2, threshold=0.5):
    print(result)
```

#### Tests and demos 

* Take a look at folder [deepllm/tests](https://github.com/ptarau/recursors/tree/main/deepllm/tests) for typical uses.

* There are more extensive demos in folder  [deepllm/demos](https://github.com/ptarau/recursors/tree/main/deepllm/demos) .

* There will be soon ```streamlit``` apps at https://github.com/ptarau/recursors/tree/main/deepllm/apps showing typical use cases.

* If you install  [fastchat](https://github.com/lm-sys/FastChat), there are examples of using Vicuna models with it in folder [deepllm/local_llms](https://github.com/ptarau/recursors/tree/main/deepllm/local_llms).

#### Streamlit web app

After installing streamlit, try it with:

```
streamlit run deepllm/apps/app.py
```

#### Paper describing this work

If you find this software useful please cite it as:

```
@ARTICLE{tarau2023automation,
       author = {{Tarau}, Paul},
        title = "{Full Automation of Goal-driven LLM Dialog Threads with And-Or Recursors and Refiner Oracles}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science},
         year = 2023,
        month = jun,
          eid = {arXiv:2306.14077},
        pages = {arXiv:2306.14077},
          doi = {10.48550/arXiv.2306.14077},
archivePrefix = {arXiv},
       eprint = {2306.14077},
 primaryClass = {cs.AI},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230614077T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```

You can also find the paper (and future related work) in folder  [docs](https://github.com/ptarau/recursors/tree/main/docs).

Enjoy,

Paul Tarau
