Agents

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Agents with Function Calls and SLIM Models 🔥

llmware has been designed to enable Agent and LLM-based function calls using small language models designed for local and private deployment and the ability to leverage open source models to conduct complex RAG and knowledge-based workflow automation.

The key elements in llmware:

• SLIM models - 18 function-calling small language models, optimized for a specific extraction, classification, generation, or summarization activity, and generate python dictionaries and lists as output.

• LLMfx class - enables a wide range of agent-based processes.

Here is an example to get started:

from llmware.agents import LLMfx

text = ("Tesla stock fell 8% in premarket trading after reporting fourth-quarter revenue and profit that "
        "missed analysts’ estimates. The electric vehicle company also warned that vehicle volume growth in "
        "2024 'may be notably lower' than last year’s growth rate. Automotive revenue, meanwhile, increased "
        "just 1% from a year earlier, partly because the EVs were selling for less than they had in the past. "
        "Tesla implemented steep price cuts in the second half of the year around the world. In a Wednesday "
        "presentation, the company warned investors that it’s 'currently between two major growth waves.'")

#   create an agent using LLMfx class
agent = LLMfx()

#   load text to process
agent.load_work(text)

#   load 'models' as 'tools' to be used in analysis process
agent.load_tool("sentiment")
agent.load_tool("extract")
agent.load_tool("topics")
agent.load_tool("boolean")

#   run function calls using different tools
agent.sentiment()
agent.topics()
agent.extract(params=["company"])
agent.extract(params=["automotive revenue growth"])
agent.xsum()
agent.boolean(params=["is 2024 growth expected to be strong? (explain)"])

#   at end of processing, show the report that was automatically aggregated by key
report = agent.show_report()

#   displays a summary of the activity in the process
activity_summary = agent.activity_summary()

#   list of the responses gathered
for i, entries in enumerate(agent.response_list):
    print("update: response analysis: ", i, entries)

output = {"report": report, "activity_summary": activity_summary, "journal": agent.journal}  

Need help or have questions?

Check out the llmware videos and GitHub repository.

Reach out to us on GitHub Discussions.

About the project

llmware is © 2023-2024 by AI Bloks.

Contributing

Please first discuss any change you want to make publicly, for example on GitHub via raising an issue or starting a new discussion. You can also write an email or start a discussion on our Discord channel. Read more about becoming a contributor in the GitHub repo.

Code of conduct

We welcome everyone into the llmware community. View our Code of Conduct in our GitHub repository.

llmware and AI Bloks

llmware is an open source project from AI Bloks - the company behind llmware. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. AI Bloks was founded by Namee Oberst and Darren Oberst in October 2022.

License

llmware is distributed by an Apache-2.0 license.

Thank you to the contributors of llmware!

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