--- layout: default title: Use Cases parent: Examples nav_order: 1 description: overview of the major modules and classes of LLMWare permalink: /examples/use_cases --- 🚀 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/web_services_slim_fx.py) - 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/invoice_processing.py) - 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/parsing_great_speeches.py) - 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/msa_processing.py) - 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/agent_with_custom_tables.py) - 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/contract_analysis_on_laptop_with_bling_models.py) - 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**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/slicing_and_dicing_office_docs.py) - 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](https://www.github.com/llmware-ai/llmware.git) # About the project `llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home). ## Contributing 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). You can also write an email or start a discussion on our Discrod channel. Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md). ## Code of conduct We welcome everyone into the ``llmware`` community. [View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository. ## ``llmware`` and [AI Bloks](https://www.aibloks.com/home) ``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. [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. ## License `llmware` is distributed by an [Apache-2.0 license](https://www.github.com/llmware-ai/llmware/blob/main/LICENSE). ## Thank you to the contributors of ``llmware``! --- ---