🚀 Use Cases Examples  🚀  
===============

**End-to-End Scenarios**    

In this repository, we feature 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  
    
8. **LLMWare Private Inference Server**

    - Set up server in minutes on CPU, GPU or local - [server](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/llmware_inference_server.py)  
    
    - Run 3 different modes of client access to the API - [client](https://www.github.com/llmware-ai/llmware/tree/main/examples/Use_Cases/llmware_inference_api_client.py)  
   
    - Supports rapid development, testing and prototyping and flexibility of deployment models for wide range of RAG and Agent use cases  


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


### **Let's get started!  🚀**