wehub-resource-sync
137 lines
5.4 KiB
Markdown
137 lines
5.4 KiB
Markdown
---
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layout: default
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title: Agents
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parent: Components
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nav_order: 4
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description: overview of the major modules and classes of LLMWare
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permalink: /components/agents
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---
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# Agents
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---
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Agents with Function Calls and SLIM Models 🔥
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llmware has been designed to enable Agent and LLM-based function calls using small language models designed for local and private
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deployment and the ability to leverage open source models to conduct complex RAG and knowledge-based workflow automation.
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The key elements in llmware:
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- **SLIM models** - 18 function-calling small language models, optimized for a specific extraction, classification, generation, or
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summarization activity, and generate python dictionaries and lists as output.
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- **LLMfx class** - enables a wide range of agent-based processes.
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Here is an example to get started:
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```python
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from llmware.agents import LLMfx
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text = ("Tesla stock fell 8% in premarket trading after reporting fourth-quarter revenue and profit that "
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"missed analysts’ estimates. The electric vehicle company also warned that vehicle volume growth in "
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"2024 'may be notably lower' than last year’s growth rate. Automotive revenue, meanwhile, increased "
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"just 1% from a year earlier, partly because the EVs were selling for less than they had in the past. "
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"Tesla implemented steep price cuts in the second half of the year around the world. In a Wednesday "
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"presentation, the company warned investors that it’s 'currently between two major growth waves.'")
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# create an agent using LLMfx class
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agent = LLMfx()
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# load text to process
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agent.load_work(text)
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# load 'models' as 'tools' to be used in analysis process
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agent.load_tool("sentiment")
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agent.load_tool("extract")
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agent.load_tool("topics")
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agent.load_tool("boolean")
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# run function calls using different tools
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agent.sentiment()
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agent.topics()
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agent.extract(params=["company"])
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agent.extract(params=["automotive revenue growth"])
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agent.xsum()
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agent.boolean(params=["is 2024 growth expected to be strong? (explain)"])
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# at end of processing, show the report that was automatically aggregated by key
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report = agent.show_report()
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# displays a summary of the activity in the process
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activity_summary = agent.activity_summary()
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# list of the responses gathered
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for i, entries in enumerate(agent.response_list):
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print("update: response analysis: ", i, entries)
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output = {"report": report, "activity_summary": activity_summary, "journal": agent.journal}
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```
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Need help or have questions?
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============================
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Check out the [llmware videos](https://www.youtube.com/@llmware) and [GitHub repository](https://github.com/llmware-ai/llmware).
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Reach out to us on [GitHub Discussions](https://github.com/llmware-ai/llmware/discussions).
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# About the project
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`llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home).
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## Contributing
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Please first discuss any change you want to make publicly, for example on GitHub via raising an [issue](https://github.com/llmware-ai/llmware/issues) or starting a [new discussion](https://github.com/llmware-ai/llmware/discussions).
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You can also write an email or start a discussion on our Discord channel.
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Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md).
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## Code of conduct
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We welcome everyone into the ``llmware`` community.
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[View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository.
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## ``llmware`` and [AI Bloks](https://www.aibloks.com/home)
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``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``.
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The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service.
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[AI Bloks](https://www.aibloks.com/home) was founded by [Namee Oberst](https://www.linkedin.com/in/nameeoberst/) and [Darren Oberst](https://www.linkedin.com/in/darren-oberst-34a4b54/) in October 2022.
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## License
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`llmware` is distributed by an [Apache-2.0 license](https://github.com/llmware-ai/llmware/blob/main/LICENSE).
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## Thank you to the contributors of ``llmware``!
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<ul class="list-style-none">
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{% for contributor in site.github.contributors %}
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<li class="d-inline-block mr-1">
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<a href="{{ contributor.html_url }}">
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<img src="{{ contributor.avatar_url }}" width="32" height="32" alt="{{ contributor.login }}">
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</a>
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</li>
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{% endfor %}
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</ul>
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---
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<ul class="list-style-none">
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<li class="d-inline-block mr-1">
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<a href="https://discord.gg/MhZn5Nc39h"><span><i class="fa-brands fa-discord"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.youtube.com/@llmware"><span><i class="fa-brands fa-youtube"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://huggingface.co/llmware"><span> <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" class="hugging-face-logo"/> </span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.linkedin.com/company/aibloks/"><span><i class="fa-brands fa-linkedin"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://twitter.com/AiBloks"><span><i class="fa-brands fa-square-x-twitter"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.instagram.com/aibloks/"><span><i class="fa-brands fa-instagram"></i></span></a>
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</li>
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</ul>
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---
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