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

   • 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

8. LLMWare Private Inference Server

   • Set up server in minutes on CPU, GPU or local - server

   • Run 3 different modes of client access to the API - client

   • 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.