AI Analytics for Natural Language Reasoning.
Note
🎉 We're excited to announce the release of Logikon 0.2.0
–– a major update to our analytics toolbox for natural-language reasoning.
Main changes:
- All LLM-based argument analysis pipelines are now built with LCEL/LangChain (and not with LMQL anymore).
- We're introducing Guided Reasoning™️ (abstract interface and simple implementations) for walking arbitrary conversational AI agents through complex reasoning processes.
- AGPL license.
Our short-term priorities are housekeeping, code cleaning, and documentation. Don't hesitate to reach out if you have any questions or feedback, or if you'd like to contribute to the project.
pip install git+https://github.com/logikon-ai/logikon@v0.2.0
import os
from logikon import ascore, ScoreConfig
# 🔧 config
config = ScoreConfig(
global_kwargs={
"expert_model": "meta-llama/Meta-Llama-3-70B-Instruct",
"inference_server_url": "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct",
"llm_backend": "HFChat",
"api_key": os.environ["HF_TOKEN"],
"classifier_kwargs": {
"model_id": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli",
"inference_server_url": "https://api-inference.huggingface.co/models/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli",
"api_key": os.environ["HF_TOKEN"],
"batch_size": 8,
},
}
)
# 📝 input to evaluate
issue = "Should I eat animals?",
reasoning = "No! Animals can suffer. Animal farming causes climate heating. Meat is not healthy.",
# 🏃♀️ run argument analysis
result = await ascore(
prompt = issue,
completion = reasoning,
config = config,
)
sequenceDiagram
autonumber
actor User
participant C as Client LLM
User->>+C: Problem statement
create participant G as Guide LLM
C->>+G: Problem statement
loop
G->>+C: Instructions...
C->>+G: Reasoning traces...
G->>+G: Evaluation
end
destroy G
G->>+C: Answer + Protocol
C->>+User: Answer (and Protocol)
User->>+C: Why?
C->>+User: Explanation (based on Protocol)