Simple experiment with Langchain library to quickly understand main functionality

Play with Langchain Camel Agents as well as with local Deeplake vectorDB

conda env create --name envname --file=environments.yml

pip install -r requirements.txt

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
data		data
deeplake		deeplake
utils		utils
.gitignore		.gitignore
1 - GPT call.ipynb		1 - GPT call.ipynb
1.5 LLM Chains.ipynb		1.5 LLM Chains.ipynb
2 - LLM + Vectorstore.ipynb		2 - LLM + Vectorstore.ipynb
3 - agents with solidity compiler tool.ipynb		3 - agents with solidity compiler tool.ipynb
4 - camel agents.ipynb		4 - camel agents.ipynb
5 - camel agent with solidity compiler tool.ipynb		5 - camel agent with solidity compiler tool.ipynb
6 - agents + tools.ipynb		6 - agents + tools.ipynb
6 two_agent_debate_tools.ipynb		6 two_agent_debate_tools.ipynb
6 two_agent_debate_tools2.ipynb		6 two_agent_debate_tools2.ipynb
README.MD		README.MD
camel_agents.py		camel_agents.py
environment.yml		environment.yml
paths.py		paths.py
requirements.txt		requirements.txt