Notebook HTML Repository

This repository contains HTML versions of various Jupyter notebooks. These files are accessible directly in a web browser, allowing for easy viewing and sharing of notebook content without requiring a Jupyter Notebook environment.

Overview

The notebooks in this repository cover a range of topics, including interactions with different language model APIs (e.g., OpenAI, Anthropic, Mistral), text generation, retrieval-augmented generation (RAG), summarization, translation, and multi-step prompt workflows. This repository serves as a reference for various NLP tasks and language model functionalities.

Repository Structure

Below is a list of each notebook with a brief description of its content. Click the links to view each notebook.

Anthropic Notebooks

• ANTHROPIC-CHATBOT: Demonstrates how to use the Anthropic API for chatbot interactions.
• ANTHROPIC-RAG: Shows Retrieval-Augmented Generation with Anthropic models.
• ANTHROPIC-TEXTGEN: Explores text generation capabilities with Anthropic models.

Cohere Notebooks

• COHERE-CHATBOT: Implements a chatbot using the Cohere API.
• COHERE-RAG: Demonstrates Retrieval-Augmented Generation with Cohere models.

Julia API Notebooks

• JULIA_ANTHROPIC_API: Interacts with the Anthropic API using Julia.
• JULIA_COHERE_API: Shows Cohere API usage in Julia.
• JULIA_MISTRAL_API: Demonstrates Mistral API interaction in Julia.
• JULIA_OPENAI_API: Provides OpenAI API interaction examples in Julia.

Multi-Step Flow Notebooks

• LLM_FLOW_1: Introduces a modular framework for interacting with various LLM APIs, covering text generation and embedding retrieval.
• LLM_FLOW_2: Expands on the multi-step framework, demonstrating semantic search and advanced workflows.

Mistral Notebooks

• MISTRAL-CHATBOT: Builds a chatbot using the Mistral API.
• MISTRAL-RAG: Implements Retrieval-Augmented Generation with Mistral models.
• MISTRAL-TEXTGEN: Explores text generation capabilities with Mistral models.

OpenAI Notebooks

• OPENAI-CHAT: Creates a chatbot using the OpenAI API.
• OPENAI-RAG: Demonstrates Retrieval-Augmented Generation with OpenAI models.
• OPENAI-TEXTGEN: Showcases text generation with OpenAI models.
• OPENAI_PROMPTING: Covers techniques for effective prompt engineering with OpenAI models.
• OPENAI_REFERENCE_RAG: Uses reference materials for enhanced Retrieval-Augmented Generation.
• OPENAI_VECTOR_EMB: Explores vector embedding retrieval and similarity search with OpenAI models.

PDF Summarization

• PDF_CHUNK_SUMMARIZATION: Summarizes PDF content by chunking and analyzing text segments.

Python API Notebooks

• PYTHON_ANTHROPIC_API: Interacts with the Anthropic API in Python.
• PYTHON_COHERE_API: Shows usage of the Cohere API in Python.
• PYTHON_MISTRAL_API: Demonstrates Mistral API interaction in Python.
• PYTHON_OPENAI_API: Covers OpenAI API calls and parameters in Python.

Question Answering

• QA_llm_function: Demonstrates question-answering using language models, providing answers based on context or a document.

R API Notebooks

• R_ANTHROPIC_API: Uses the Anthropic API in R.
• R_COHERE_API: Interacts with the Cohere API in R.
• R_MISTRAL_API: Demonstrates Mistral API interaction in R.
• R_OPENAI_API: Uses the OpenAI API in R.

LLM Function Notebooks

• fewshot_llm_function: Demonstrates few-shot learning with LLMs, providing a limited number of examples to guide model responses.
• generate_llm_function: Explores text generation tasks using various LLMs.
• summarize_llm_function: Implements summarization of text input.
• translate_llm_function: Provides language translation capabilities using LLMs.

License

This repository is licensed under the MIT License.