Merge pull request #787 from llmware-ai/documentation-nav-order-update

updating docs nav order and other minor updates
llmware-ai · May 22, 2024 · f442ee7 · f442ee7
2 parents ad21eed + 5685170
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diff --git a/docs/architecture.md b/docs/architecture.md
@@ -1,12 +1,12 @@
 ---
 layout: default
 title: Architecture  
-nav_order: 1
+nav_order: 5
 description: overview of the major modules and classes of LLMWare  
 permalink: /architecture
 ---
 # LLMWare Architecture
-===============
+---
 
 llmware is characterized by a logically integrated set of data pipelines involved in building LLM-based workflows, centered on two main sub-pipelines with high-level interfaces intended to provide an abstraction layer over individual 'end point' components to promote code re-use and the ability to easily 'swap' different components with minimal, if any, code change:  
 

diff --git a/docs/contributing/code.md b/docs/contributing/code.md
@@ -5,7 +5,7 @@ parent: Contributing
 nav_order: 1
 permalink: /contributing/code
 ---
-# Contiributing code
+# Contributing code
 One way to contribute to ``llmware`` is by contributing to the code base.
 
 We briefly describe some of the important modules of ``llmware`` next, so you can more easily navigate the code base.

diff --git a/docs/contributing/contributing.md b/docs/contributing/contributing.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Contributing
-nav_order: 3
+nav_order: 10
 has_children: true
 description: llmware contributions.
 permalink: /contributing

diff --git a/docs/examples.md b/docs/examples.md
@@ -1,7 +1,7 @@
 ---
 layout: default
-title: Introduction by Examples
-nav_order: 2
+title: Introduction by Examples 
+nav_order: 9
 permalink: /examples
 ---
 # Introduction by Examples

diff --git a/docs/fast_start.md b/docs/fast_start.md
@@ -1,12 +1,12 @@
 ---
 layout: default
-title: Fast Start Series | llmware
-nav_order: 1
+title: Fast Start Series
+nav_order: 3
 description: llmware is an integrated framework with over 50+ models for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows.
 permalink: /fast_start
 ---
 Fast Start: Learning RAG with llmware through 6 examples 
-===============
+---
 
 **Welcome to llmware!**    
 
@@ -20,7 +20,7 @@ Platforms:
 - Mac M1/M2/M3, Windows, Linux (Ubuntu 20 or Ubuntu 22 preferred)  
 - RAM: 16 GB minimum  
 - Python 3.9, 3.10, 3.11, 3.12
-- Pull the latest version of llmware == 0.2.13 (as of mid-May 2024)  
+- Pull the latest version of llmware == 0.2.14 (as of mid-May 2024)  
 - Please note that we have updated the examples from the original versions, to use new features in llmware, so there may be minor differences with the videos, which are annotated in the comments in each example.    
 
 There are 6 examples, designed to be used step-by-step, but each is self-contained,  

diff --git a/docs/index.md b/docs/index.md
@@ -12,7 +12,7 @@ permalink: /
     </li>
 </ul>  
 
-## 🧰🛠️🔩The Ultimate Toolkit for Building LLM Apps
+## 🧰🛠️🔩The Ultimate Toolkit for Enterprise RAG Pipelines with Small, Specialized Models   
 
 From quickly building POCs to scalable LLM Apps for the enterprise, LLMWare is packed with all the tools you need. 
 
@@ -90,6 +90,17 @@ git clone [email protected]:llmware-ai/llmware.git
 
 - Please ensure that you are capturing and updating the /llmware/lib folder, which includes required compiled shared libraries.  If you prefer, you can keep only those libs required for your OS platform.  
 
+- After cloning the repo, we provide a short 'welcome to llmware' automation script, which can be used to install the projects requirements (from llmware/requirements.txt), install several optional dependencies that are commonly used in examples, copy several good 'getting started' examples into the root folder, and then run a 'welcome_example.py' script to get started using our models.  To use the "welcome to llmware" script:  
+
+Windows:  
+```bash
+.\welcome_to_llmware_windows.sh
+```
+
+Mac/Linux:
+```bash
+sh ./welcome_to_llmware.sh
+```
 
 # More information about the project - [see main repository](https://www.github.com/llmware-ai/llmware.git)
 

diff --git a/docs/platforms.md b/docs/platforms.md
@@ -1,7 +1,7 @@
 ---
 layout: default
-title: Platform Support| llmware
-nav_order: 1
+title: Platform Support
+nav_order: 2
 description: llmware is an integrated framework with over 50+ models for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows.
 permalink: /platform_support
 ---
@@ -73,7 +73,6 @@ LLMWareConfig().set_llmware_path_name("llmware_data2")
 llmware_fp = LLMWareConfig().get_llmware_path()
 print("updated llmware path: ", llmware_fp)
 
-
 ```
 
 ___

diff --git a/docs/release_history.md b/docs/release_history.md
@@ -1,13 +1,12 @@
 ---
 layout: default
-title: Release History | llmware
-nav_order: 1
+title: Release History 
+nav_order: 7
 description: llmware is an integrated framework with over 50+ models for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows.
 permalink: /release_history
 ---
-
 Release History
-===============
+---
 
 - For Specific Wheels:  [Wheel Archives](https://www.github.com/llmware-ai/llmware/tree/main/wheel_archives)  
 - For Features Details: [Main README-'Release notes and Change Log'](https://www.github.com/llmware-ai/llmware/tree/main/)  

diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md
@@ -1,7 +1,7 @@
 ---
 layout: default
-title: Common Troubleshooting Tips | llmware
-nav_order: 1
+title: Troubleshooting
+nav_order: 8
 description: llmware is an integrated framework with over 50+ models for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows.
 permalink: /troubleshooting
 ---
@@ -22,14 +22,14 @@ ___
     -- If you cloned the repository, please confirm that the /lib folder has been copied into your local path.  
 
 
-3.  **Pytorch Model not loading**
+3. **Pytorch Model not loading**
 
    -- Confirm the obvious stuff - correct model name, model exists in Huggingface repository, connected to the Internet with open ports for HTTPS connection, etc.  
 
    -- Check Pytorch version - update Pytorch to >2.0, which is required for many recent models released in the last 6 months, and in some cases, may require other dependencies not included in the llmware package.  
         --note: we have seen some compatibility issues with Pytorch==2.3 on Wintel platforms - if you run into these issues, we recommend using a back-level Pytorch==2.1, which we have seen fixing the issue.  
 
-4.  **GGUF Model not loading**
+4. **GGUF Model not loading**
 
    -- Confirm that you are using llmware>=0.2.11 for the latest GGUF support.  
 
@@ -54,10 +54,10 @@ If you have any trouble, feel free to raise an Issue and we can provide you with
    -- Specific GGUF model - if you are successfully using other GGUF models, and only having problems with a specific model, then please raise an Issue, and share the specific model and architecture.  
 
 
-5.  **Example not working as expected** - please raise an issue, so we can evaluate and fix any bugs in the example code.  Also, pull requests are always especially welcomed with a fix or improvement in an example.  
+5. **Example not working as expected** - please raise an issue, so we can evaluate and fix any bugs in the example code.  Also, pull requests are always especially welcomed with a fix or improvement in an example.  
 
 
-6.  **Model not leveraging CUDA available in environment.**  
+6. **Model not leveraging CUDA available in environment.**  
 
     -- **Check CUDA drivers installed correctly** - easy check of the NVIDIA CUDA drivers is to use `nvidia-smi` and `nvcc --version` from the command line.  Both commands should respond positively with details on the versions and implementations.  Any errors indicates that either the driver or CUDA toolkit are not installed or recognized.  It can be complicated at times to debug the environment, usually with some trial and error.   See extensive [Nvidia Developer documentation](https://docs.nvidia.com) for trouble-shooting steps, specific to your environment.  
 

diff --git a/docs/use_cases.md b/docs/use_cases.md
@@ -1,12 +1,12 @@
 ---
 layout: default
-title: Use Cases | llmware
-nav_order: 1
+title: Use Cases  
+nav_order: 4
 description: llmware is an integrated framework with over 50+ models for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows.
 permalink: /use_cases
 ---
 🚀 Use Cases Examples  🚀  
-===============
+---
 
 **End-to-End Scenarios**    
 

diff --git a/docs/videos.md b/docs/videos.md
@@ -1,12 +1,12 @@
 ---
 layout: default
-title: Videos | llmware
-nav_order: 1
+title: Videos
+nav_order: 6
 description: llmware is an integrated framework with over 50+ models for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows.
 permalink: /videos
 ---
 llmware Youtube Video Channel
-===============
+---
 
 **Tutorial Videos** - check out our Youtube channel for high-impact 5-10 minute tutorials on the latest examples.