Moved my GPT4 Custom Instructions, updated docs

renamed:    OpenAI/GPT-Prompt-Examples/Daethyra_Custom-Instruction_GPT4.md -> OpenAI/GPT-Prompt-Examples/MS-6_Daethyra_Custom-Instruction_GPT4.md
modified:   Project TODO List.md
modified:   README.md

Project TODO List.md

@@ -1,5 +1,13 @@
 ### Todo list 
 
+[README]
+
+- Add intro 
+  - Clearly define: [Utilikit, Pluggable/Components, multi-shot, zero-shot, ]
+    - create summarization of prompt reusability, and component extendability 
+  - Then, clearly state the intention of the repository
+  - Finally, provide one to two brief statements to close out and resummarize 
+
 ---
 
 [GitHub]
@@ -12,6 +20,8 @@
     we can use LangChain to query the top_k results for functions to serve
     contextual needs."
 
+---
+
 [LangChain]
 
 - langchain_conv_agent.py
@@ -28,6 +38,8 @@
         (HF model is cached after first download. Therefore, all runs after the first,
         are entirely local since we're using ChromaDB)
 
+---
+
 [OpenAI]
 
 - Auto-Embedder

README.md

@@ -4,26 +4,26 @@
 
 ---
 
-A. **[Auto-Embedder](./Auto-Embedder)**
+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).
+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`](./Auto-Embedder/pinembed.py)**: A Python module to easily automate the retrieval of embeddings from OpenAI and storage in Pinecone.
-  - **[.env.template](./Auto-Embedder/.env.template)**: Template for environment variables.
+- **[`pinembed.py`](./OpenAI/Auto-Embedder/pinembed.py)**: A Python module to easily automate the retrieval of embeddings from OpenAI and storage in Pinecone.
+  - **[.env.template](./OpenAI/Auto-Embedder/.env.template)**: Template for environment variables.
 
 ---
 
-B. **[GPT-Prompt-Examples](./GPT-Prompt-Examples)**
+B. **[GPT-Prompt-Examples](./OpenAI/GPT-Prompt-Examples)**
 
-There are three main prompt types,[multi-shot](GPT-Prompt-Examples/multi-shot), [system-role](GPT-Prompt-Examples/system-role), [user-role](GPT-Prompt-Examples/user-role).
+There are three main prompt types, [multi-shot](./OpenAI/GPT-Prompt-Examples/multi-shot), [system-role](./OpenAI/GPT-Prompt-Examples/system-role), [user-role](./OpenAI/GPT-Prompt-Examples/user-role).
 
-Please also see the[OUT-prompt-cheatsheet](GPT-Prompt-Examples/OUT-prompt-cheatsheet.md).
+Please also see the [OUT-prompt-cheatsheet](./OpenAI/GPT-Prompt-Examples/OUT-prompt-cheatsheet.md).
 
-- **[Cheatsheet for quick power-prompts](./GPT-Prompt-Examples/OUT-prompt-cheatsheet.md)**: A cheatsheet for GPT prompts.
-  - **[multi-shot](./GPT-Prompt-Examples/multi-shot)**: Various markdown and text files for multi-shot prompts.
-  - **[system-role](./GPT-Prompt-Examples/system-role)**: Various markdown files for system-role prompts.
-  - **[user-role](./GPT-Prompt-Examples/user-role)**: Markdown files for user-role prompts.
-  - **[Reference Chatlogs with GPT4](./GPT-Prompt-Examples/ChatGPT_reference_chatlogs)**: Contains chat logs and shorthand prompts.
+- **[Cheatsheet for quick power-prompts](./OpenAI/GPT-Prompt-Examples/OUT-prompt-cheatsheet.md)**: @Daethyra's go-to prompts.
+  - **[multi-shot](./OpenAI/GPT-Prompt-Examples/multi-shot)**: Prompts with prompts inside them! It's kind of like a bundle of Matryoshka prompts.
+  - **[system-role](./OpenAI/GPT-Prompt-Examples/system-role)**: Steer your LLM by shifting the ground it stands on.
+  - **[user-role](./OpenAI/GPT-Prompt-Examples/user-role)**: Markdown files for user-role prompts.
+  - **[Reference Chatlogs with GPT4](./OpenAI/GPT-Prompt-Examples/ChatGPT_reference_chatlogs)**: Contains chat logs and shorthand prompts.
 
 ---
 
@@ -40,8 +40,8 @@ This module offers a set of functionalities for conversational agents in LangCha
 - Text splitting using `RecursiveCharacterTextSplitter`
 - Various embeddings options like `OpenAIEmbeddings`, `CacheBackedEmbeddings`, and `HuggingFaceEmbeddings`
 
-**Usage:**
-To use this module, simply import the functionalities you need and configure them accordingly.
+**Potential Use Cases:**
+${MASK}
 
 ---
 
@@ -55,8 +55,8 @@ This module focuses on querying local documents and employs the following featur
 - Vector storage options like `Chroma`
 - Embedding options via `OpenAIEmbeddings`
 
-**Usage:**
-Similar to `langchain_conv_agent.py`, you can import the functionalities you require.
+**Potential Use Cases:**
+${MASK}
 
 ---
 
@@ -70,14 +70,14 @@ A. **[`integrable_captioner.py`](./HuggingFace\image_captioner\integrable_image_
 
 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
+- 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
 
-**Usage:**
-  To utilize this module, import the `ImageCaptioner` class and initialize it with a model of your choice. You can then use its methods to load images and generate captions.
+**Potential Use Cases:**
+${MASK}
 
 ---
 
@@ -91,7 +91,6 @@ This module focuses on generating captions for images using Hugging Face's trans
   </div>
 </div>
 
+# - [LICENSE - GNU Affero GPL](./LICENSE)
 
----
-
-- [LICENSE - GNU Affero GPL](./LICENSE)
+# - [Please see the contribuutting file](./CONTRIBUTING.md)