Merge pull request llmware-ai#691 from llmware-ai/readme-updates-and-cleanup

various readme updates and wheel archive cleanup

Author: doberst

Quest/README.md

@@ -0,0 +1,5 @@
+
+
+Quest: Are you ready for the adventure?   Coming soon ...
+===============
+

examples/Getting_Started/README.md

@@ -0,0 +1,45 @@
+ 🚀 Getting Set up - Key Items  🚀  
+===============
+
+**How to get started with LLMWare?**    
+
+In this repository, we have small code snippets with a lot of annotation that describe key setup items to get started quickly and successfully with LLMWare:
+
+1.  [**Configuring a DB**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Getting_Started/configure_db.py) - for a fast start, you don't have to do anything except set the .active_db parameter to "sqlite" and all libraries will be written on a local embedded sqlite instance with no installation required.  
+
+
+    from llmware.configs import LLMWareConfig
+    LLMWareConfig().set_active_db("sqlite")
+    
+
+   After writing content into your first library, you will find the sqlite db at the path defined here:  
+    
+    from llmware.configs import SQLiteConfig
+    SQLiteConfig().get_config("sqlite_db_folder_path")
+
+
+2.  [**Loading Sample Files**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Getting_Started/loading_sample_files.py) - for an overview of the sample files available and how to pull down (integrated into many examples too.)  
+
+
+    from llmware.setup import Setup
+    Setup().load_sample_files()
+
+
+3.  [**Using the Model Catalog**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Getting_Started/using_the_model_catalog.py) - this is the primary model discovery and loading class for LLMWare.  
+
+
+    from llmware.models import ModelCatalog
+    all_models = ModelCatalog().list_all_models()
+    for model in all_models: print("model: ", model)
+
+
+4.  [**Working with Libraries**](https://www.github.com/llmware-ai/llmware/tree/main/examples/Getting_Started/working_with_libraries.py) - Libraries are the main organizing construct for text collections in LLMWare.  
+
+
+As you are getting started, we would also recommend that you check out the [**Fast Start**](https://www.github.com/llmware-ai/llmware/tree/main/fast_start) examples and videos as well!  
+
+
+
+### **Let's get started!  🚀**
+
+

examples/Use_Cases/README.md

@@ -0,0 +1,64 @@
+ 🚀 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
+
+
+Check back often - we are updating these examples regularly - and many of these examples have companion videos as well.  
+
+
+### **Let's get started!  🚀**
+
+