From 4953a29b067e0b14fc20f3eac6491ed202554155 Mon Sep 17 00:00:00 2001 From: Shreyas0410 <70795867+Shreyas0410@users.noreply.github.com> Date: Mon, 28 Oct 2024 19:28:47 +0530 Subject: [PATCH] Update examples.md --- docs/examples/examples.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/examples.md b/docs/examples/examples.md index 89f6a924..338d7016 100644 --- a/docs/examples/examples.md +++ b/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.