🚀 Use Cases Examples 🚀

End-to-End Scenarios

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’ !

  1. Research Automation with Agents and Web Services

    • Prepare a 30-key research analysis on a company
    • Extract key lookup and other information from an earnings press release
    • Automatically use the lookup data for real-time stock information from YFinance
    • Automatically use the lookup date for background company history information in Wikipedia
    • Run LLM prompts to ask key questions of the Wikipedia sources
    • Aggregate into a consolidated research analysis
    • All with local open source models

  2. Invoice Processing

    • Parse a batch of invoices (provided as sample files)
    • Extract key information from the invoices
    • Save the prompt state for follow-up review and analysis

  3. Analyzing and Extracting Voice Transcripts

    • Voice transcription of 50+ wav files of great speeches of the 20th century
    • Run text queries against the transcribed wav files
    • Execute LLM agent inferences to extract and identify key elements of interest
    • Prepare ‘bibliography’ with the key extracted points, including time-stamp

  4. MSA Processing

    • Identify the termination provisions in Master Service Agreements among a larger batch of contracts
    • Parse and query a large batch of contracts and identify the agreements with “Master Service Agreement” on the first page
    • Find the termination provisions in each MSA
    • Prompt LLM to read the termination provisions and answer a key question
    • Run a fact-check and source-check on the LLM response
    • Save all of the responses in CSV and JSON for follow-up review.

  5. Querying a CSV

    • Start running natural language queries on CSVs with Postgres and slim-sql-tool.
    • Load a sample ‘customer_table.csv’ into Postgres
    • Start running natural language queries that get converted into SQL and query the DB

  6. Contract Analysis

    • Extract key information from set of employment agreement
    • Use a simple retrieval strategy with keyword search to identify key provisions and topic areas
    • Prompt LLM to read the key provisions and answer questions based on those source materials

  7. Slicing and Dicing Office Docs

    • Shows a variety of advanced parsing techniques with Office document formats packaged in ZIP archives
    • Extracts tables and images, runs OCR against the embedded images, exports the whole library, and creates dataset

For more examples, see the [use cases example]((https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/) in the main repo.

Check back often - we are updating these examples regularly - and many of these examples have companion videos as well.

More information about the project - see main repository

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 Discrod 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!