Merge pull request #1081 from Shreyas0410/Shreyas0410-broken_link

Update examples.md

docs/examples/examples.md

@@ -14,12 +14,12 @@ small language models:
    - [Embedding examples](https://www.github.com/llmware-ai/llmware/tree/main/examples/Embedding) - ~15 stand-alone embedding examples to show how to use ~10 different vector databases and wide range of leading open source embedding models (including sentence transformers).  
    - [Retrieval examples](https://www.github.com/llmware-ai/llmware/tree/main/examples/Retrieval) - ~10 stand-alone examples illustrating different query and retrieval techniques - semantic queries, text queries, document filters, page filters, 'hybrid' queries, author search, using query state, and generating bibliographies.  
    - [Dataset examples](https://www.github.com/llmware-ai/llmware/tree/main/examples/Datasets) - ~5 stand-alone examples to show 'next steps' of how to leverage a Library to re-package content into various datasets and automated NLP analytics.  
-   - [Fast start example #1-Parsing](https://www.github.com/llmware-ai/llmware/tree/main/fast_start/example-1-create_first_library.py) - shows the basics of parsing.  
-   - [Fast start example #2-Embedding](https://www.github.com/llmware-ai/llmware/tree/main/fast_start/example-2-build_embeddings.py) - shows the basics of building embeddings.  
-   - [CustomTable examples](https://www.github.com/llmware-ai/llmware/tree/main/Structured_Tables) - ~5 examples to start building structured tables that can be used in conjunction with LLM-based workflows.  
+   - [Fast start example #1-Parsing](https://github.com/llmware-ai/llmware/blob/main/fast_start/rag/example-1-create_first_library.py) - shows the basics of parsing.  
+   - [Fast start example #2-Embedding](https://github.com/llmware-ai/llmware/blob/main/fast_start/rag/example-2-build_embeddings.py) - shows the basics of building embeddings.  
+   - [CustomTable examples](https://github.com/llmware-ai/llmware/tree/main/examples/Structured_Tables) - ~5 examples to start building structured tables that can be used in conjunction with LLM-based workflows.  
 
    - [Models examples](https://www.github.com/llmware-ai/llmware/tree/main/examples/Models) - ~20 examples showing a wide range of different model inferences and use cases, including the ability to integrate Ollama models, OpenChat (e.g., LMStudio) models, using LLama-3 and Phi-3, bringing your own models into the ModelCatalog, and configuring sampling settings.  
    - [Prompts examples](https://www.github.com/llmware-ai/llmware/tree/main/examples/Prompts) - ~5 examples that illustrate how to use Prompt as an integrated workflow for integrating knowledge sources, managing prompt history, and applying fact-checking.  
    - [SLIM-Agents examples](https://www.github.com/llmware-ai/llmware/tree/main/examples/SLIM-Agents) - ~20 examples showing how to build multi-model, multi-step Agent processes using locally-running SLIM function calling models.  
-   - [Fast start example #3-Prompts and Models](https://www.github.com/llmware-ai/llmware/tree/main/fast_start/example-3-prompts_and_models.py) - getting started with model inference. 
+   - [Fast start example #3-Prompts and Models](https://github.com/llmware-ai/llmware/blob/main/fast_start/rag/example-3-prompts_and_models.py) - getting started with model inference.