wehub-resource-sync
138 lines
6.8 KiB
Markdown
138 lines
6.8 KiB
Markdown
---
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layout: default
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title: Use Cases
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parent: Examples
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nav_order: 1
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description: overview of the major modules and classes of LLMWare
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permalink: /examples/use_cases
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---
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🚀 Use Cases Examples 🚀
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---
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**End-to-End Scenarios**
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We provide several 'end-to-end' examples that show how to use LLMWare in a complex recipe combining different elements to accomplish a specific objective. While each example is still high-level, it is shared in the spirit of providing a high-level framework 'starting point' that can be developed in more detail for a variety of common use cases. All of these examples use small, specialized models, running locally - 'Small, but Mighty' !
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1. [**Research Automation with Agents and Web Services**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/web_services_slim_fx.py)
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- Prepare a 30-key research analysis on a company
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- Extract key lookup and other information from an earnings press release
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- Automatically use the lookup data for real-time stock information from YFinance
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- Automatically use the lookup date for background company history information in Wikipedia
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- Run LLM prompts to ask key questions of the Wikipedia sources
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- Aggregate into a consolidated research analysis
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- All with local open source models
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2. [**Invoice Processing**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/invoice_processing.py)
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- Parse a batch of invoices (provided as sample files)
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- Extract key information from the invoices
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- Save the prompt state for follow-up review and analysis
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3. [**Analyzing and Extracting Voice Transcripts**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/parsing_great_speeches.py)
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- Voice transcription of 50+ wav files of great speeches of the 20th century
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- Run text queries against the transcribed wav files
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- Execute LLM agent inferences to extract and identify key elements of interest
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- Prepare 'bibliography' with the key extracted points, including time-stamp
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4. [**MSA Processing**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/msa_processing.py)
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- Identify the termination provisions in Master Service Agreements among a larger batch of contracts
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- Parse and query a large batch of contracts and identify the agreements with "Master Service Agreement" on the first page
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- Find the termination provisions in each MSA
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- Prompt LLM to read the termination provisions and answer a key question
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- Run a fact-check and source-check on the LLM response
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- Save all of the responses in CSV and JSON for follow-up review.
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5. [**Querying a CSV**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/agent_with_custom_tables.py)
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- Start running natural language queries on CSVs with Postgres and slim-sql-tool.
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- Load a sample 'customer_table.csv' into Postgres
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- Start running natural language queries that get converted into SQL and query the DB
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6. [**Contract Analysis**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/contract_analysis_on_laptop_with_bling_models.py)
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- Extract key information from set of employment agreement
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- Use a simple retrieval strategy with keyword search to identify key provisions and topic areas
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- Prompt LLM to read the key provisions and answer questions based on those source materials
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7. [**Slicing and Dicing Office Docs**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/slicing_and_dicing_office_docs.py)
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- Shows a variety of advanced parsing techniques with Office document formats packaged in ZIP archives
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- Extracts tables and images, runs OCR against the embedded images, exports the whole library, and creates dataset
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For more examples, see the [use cases example]((https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/) in the main repo.
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Check back often - we are updating these examples regularly - and many of these examples have companion videos as well.
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# More information about the project - [see main repository](https://www.github.com/llmware-ai/llmware.git)
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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 Discrod 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://www.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> <i class="fa-solid fa-face-smiling-hands"></i>
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</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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