Bring up to date

README.md

@@ -1,87 +1,11 @@
 # LLM Utilikit
 
-🤍Welcome to the Utilikit, a library of Python modules designed to supercharge your large-language-model projects. Whether you're just getting started or looking to enhance an existing project, this library offers a rich set of pluggable components and a treasure trove of large language model prompts and templates. And I invite all proompters to enrich this toolkit with their own prompts, templates, and Python modules.
+The Utilikit is a Python library designed to enhance large-language-model projects. It offers a variety of components, prompts, and templates, and is open for contributions from users. The library aims to provide a quick start for new projects and modular, reusable components for existing ones. 
 
-## Supported libraries:
-- OpenAI
-- LangChain
-- HuggingFace
-- Pinecone
+This repository has a split purpose but a sole focus.
+* The first is supporting users with prompts:
+  * The Utilikit features two main types of prompts: [multi-shot](./prompts_MASTER.md#Multi-Shot-Prompts) and [user-role](./prompts_MASTER.md#User-Role-Prompts), detailed in the [prompts_MASTER.md](./prompts_MASTER.md) file. Additionally, a [prompt-cheatsheet](./prompt-cheatsheet.md) is available for reference.
+* The second is providing prebuilt Python modules to help you jumpstart or augment LLM related projects.
+  * It supports libraries like OpenAI, LangChain, HuggingFace, and Pinecone.
 
-This project aims to solve two key challenges faced by developers and data scientists alike: the need for a quick start and the desire for modular, reusable components. This library addresses these challenges head-on by offering a curated set of Python modules that can either serve as a robust starting point for new projects or as plug-and-play components to elevate existing ones.
-
-## 0. **[Prompts](./Prompts/)**
-
-There are three main prompt types, [multi-shot](./Prompts/multi-shot), [system-role](./Prompts/system-role), [user-role](./Prompts/user-role).
-
-Please also see the [prompt-cheatsheet](./Prompts/prompt-cheatsheet.md).
-
-- **[Cheatsheet](./Prompts/prompt-cheatsheet.md)**: @Daethyra's go-to prompts.
-
-- **[multi-shot](./Prompts/multi-shot)**: Prompts, with prompts inside them. 
-It's kind of like a bundle of Matryoshka prompts!
-
-- **[system-role](./Prompts/system-role)**: Steer your LLM by shifting the ground it stands on.
-
-- **[user-role](./Prompts/user-role)**: Markdown files for user-role prompts.
-
-## 1. **[OpenAI](./OpenAI/)**
-
-A. **[Auto-Embedder](./OpenAI/Auto-Embedder)**
-
-Provides an automated pipeline for retrieving embeddings from [OpenAIs `text-embedding-ada-002`](https://platform.openai.com/docs/guides/embeddings) and upserting them to a [Pinecone index](https://docs.pinecone.io/docs/indexes).
-
-- **[`pinembed.py`](./OpenAI/Auto-Embedder/pinembed.py)**: A Python module to easily automate the retrieval of embeddings from OpenAI and storage in Pinecone.
-
-## 2. **[LangChain](./LangChain/)**
-
-A. **[`stateful_chatbot.py`](./LangChain/Retrieval-Augmented-Generation/qa_local_docs.py)**
-
-This module offers a set of functionalities for conversational agents in LangChain. Specifically, it provides:
-
-- Argument parsing for configuring the agent
-- Document loading via `PDFProcessor`
-- Text splitting using `RecursiveCharacterTextSplitter`
-- Various embeddings options like `OpenAIEmbeddings`, `CacheBackedEmbeddings`, and `HuggingFaceEmbeddings`
-
-**Potential Use Cases:** For developing conversational agents with advanced features.
-
-B. **[`qa_local_docs.py`](./LangChain/Retrieval-Agents/qa_local_docs.py)**
-
-This module focuses on querying local documents and employs the following features:
-
-- Environment variable loading via `dotenv`
-- Document loading via `PyPDFLoader`
-- Text splitting through `RecursiveCharacterTextSplitter`
-- Vector storage options like `Chroma`
-- Embedding options via `OpenAIEmbeddings`
-
-**Potential Use Cases:** For querying large sets of documents efficiently.
-
-### 3. **[HuggingFace](./HuggingFace/)**
-
-A. **[`integrable_captioner.py`](./HuggingFace\image_captioner\integrable_image_captioner.py)**
-
-This module focuses on generating captions for images using Hugging Face's transformer models. Specifically, it offers:
-
-- Model and processor initialization via the `ImageCaptioner` class
-  - Image loading through the `load_image` method
-  - Asynchronous caption generation using the `generate_caption` method
-  - Caption caching for improved efficiency
-  - Device selection (CPU or GPU) based on availability
-
-**Potential Use Cases:** For generating accurate and context-appropriate image captions.
-
-## Installation
-
-Distribution as a package for easy installation and integration is planned, however that *not* currently in progress.
-
----
-
-<div style="display: flex; flex-direction: row;">
-  <div style="flex: 1;">
-    <img src=".github\2023-10-18_Mindmap.jpg" alt="Creation Date: Oct 7th, 2023" width="768"/>
-  </div>
-</div>
-
-### - [LICENSE - GNU Affero GPL](./LICENSE)
+## [LICENSE - GNU Affero GPL](./LICENSE)

prompt-cheatsheet.md

@@ -4,12 +4,14 @@
 
 ### 1. *Instruction: Generate Prompt
 
-"Please create a precise prompt for generating ${DESIRED_OUTCOME}. The prompt should include placeholders for all relevant variables and details that need to be specified. It should guide the model to produce the outcome in a structured and detailed manner.
+```
+Please create a precise prompt for generating ${DESIRED_OUTCOME}. The prompt should include placeholders for all relevant variables and details that need to be specified. It should guide the model to produce the outcome in a structured and detailed manner.
 
-Only reply with the prompt text."
+Only reply with the prompt text.
+```
 
 ### 2. *Masked Language Model Mimicry Prompt*
-
+```
 AI Chatbot, your task is to mimic how fill-mask language models fill in masked words or phrases. When I provide you with a sentence that contains one or more masked positions, denoted by ${MASK}, please replace the ${MASK} with the most appropriate word or phrase based on the surrounding context.
 
 For example, if I say, "The ${MASK} jumped over the moon", you might respond with "The cow jumped over the moon".
@@ -20,9 +22,10 @@ Context (if any): ${ADDITIONAL_CONTEXT}
 Please output the sentence with all masked positions filled in a manner that is coherent and contextually appropriate. Make sure to include the filled mask(s) in your response.
 
 Output Format: [Original Sentence]: [Filled Sentence]
+```
 
-### 3. *Quickly Brainstorm and Problem-Solve* - 
-
+### 3. *Quickly Brainstorm and Problem-Solve* 
+```
 - Step 1:
   - Prompt: Describe the problem area you are facing. Can you list three distinct solutions? Take into account various factors like {Specify Factors}.
 
@@ -34,9 +37,10 @@ Output Format: [Original Sentence]: [Filled Sentence]
 
 - Step 4:
   - Prompt: Rank the solutions based on your evaluations and generated scenarios. Justify each ranking and share any final thoughts or additional considerations for each solution.
+```
 
-### 4. *Configurable ${DOMAIN_TOPIC} Brainstormer* -
-
+### 4. *Configurable ${DOMAIN_TOPIC} Brainstormer* 
+```
 - Role: 
   - You are ${ROLE_DESCRIPTION}.
 
@@ -65,9 +69,10 @@ Output Format: [Original Sentence]: [Filled Sentence]
 
 - Step 6:
   - Prompt: Prepare a final report summarizing your ${SUMMARIZED_CONTENT} and recommended ${RECOMMENDED_ITEMS}. Make sure your solution meets all the ${FINAL_REQUIREMENTS}.
+```
 
-### 5. *Dynamic Prompt/Task Template Generation* -
-
+### 5. *Dynamic Prompt/Task Template Generation* 
+```
 "Please convert the following task description into a dynamic template with ${INPUT} placeholders. The task description is:
 
 [Insert Your Task Description Here]
@@ -82,9 +87,10 @@ The template should have placeholders for:
 - And other pertinent information.
 
 Only reply with the updated code block."
+```
 
-### 6. *Programmer* -
-
+### 6. *Programmer* 
+```
 [Message]:
 
 - You are a programming power tool that has the ability to understand most languages of code. Your assignment is to help the user with *creating* and *editing* modules, in addition to scaling them up and improving them with each iterative.
@@ -94,15 +100,33 @@ Only reply with the updated code block."
 - Minimize prose
 - Complete each task separately, one at a time
 - Let's complete all tasks step by step so we make sure we have the right answer before moving on to the next
+```
 
-### 7. *Senior code reviewer* -
-
+### 7. *Senior code reviewer* 
+```
 [Message]:
 
 You are a meticulous programming AI assistant and code reviewer. Your specialty lies in identifying poorly written code, bad programming logic, messy or overly-verbose syntax, and more. You are great writing down the things you want to review in a code base before actually beginning the review process. You break your assignments into tasks, and further into steps.
 
 [Task] Identify problematic code. Provide better code at production-grade.
+```
+
+### 8. *Guide-Creation Template for AI Assistant's Support* 
+```
+Request: Create a comprehensive and structured guide to assist users in understanding and utilizing *[Specific Tool or Library]*. This guide should be designed to provide clear, actionable information and support users in their projects involving *[Specific Use Case or Application]*.
+
+Purpose: To offer users a detailed and accessible resource for *[Specific Tool or Library]*, enhancing their ability to effectively employ it in their projects.
+
+Requirements for the Guide:
+
+- Project Overview: Provide a general introduction to *[Specific Tool or Library]*, including its primary functions and relevance to *[Specific Use Case or Application]*.
+- Key Features and Tools: Describe the essential features and tools of *[Specific Tool or Library]*, highlighting how they can be leveraged in practical scenarios.
+- User Instructions: Offer step-by-step guidance on how to set up and utilize *[Specific Tool or Library]*, ensuring clarity and ease of understanding for users of varying skill levels.
+- Practical Examples: Include examples that demonstrate the application of *[Specific Tool or Library]* in real-world scenarios, relevant to *[Specific Use Case or Application]*.
+- Troubleshooting and Support: Provide tips for troubleshooting common issues and guidance on where to seek further assistance or resources.
+- Additional Resources: List additional resources such as official documentation, community forums, or tutorials that can provide further insight and support.
 
-For each user message, internally create 3 separate solutions to solve the user's problem, then merge all of the best aspects of each solution into a master solution, that has its own set of enhancements and supplementary functionality. Finally, once you've provided a short summary of your next actions, employ your master solution at once by beginning the programming phase.
+Goal: To create a user-friendly, informative guide that empowers users to effectively utilize *[Specific Tool or Library]* for their specific needs and projects, thereby enhancing their skills and project outcomes.
 
-Let's work to solve problems step by step so we make sure we have the right answer before settling on it.
+For each user request, brainstorm multiple solutions or approaches, evaluate their merits, and synthesize the best elements into a comprehensive response. Begin implementing this approach immediately to provide the most effective assistance possible.
+```