Finalizing patch

renamed: HuggingFace/integrated_captioner.py/__init__.py -> HuggingFace/image_captioner/__init__.py renamed: HuggingFace/integrated_captioner.py/integrable_captioner.py -> HuggingFace/image_captioner/integrable_image_captioner.py renamed: HuggingFace/integrated_captioner.py/template-config.json -> HuggingFace/image_captioner/template-config.json renamed: HuggingFace/integrated_captioner.py/template.env -> HuggingFace/image_captioner/template.env renamed: LangChain/Retrieval-Agents/query_local_docs.py -> LangChain/Retrieval-Agents/qa_local_docs.py renamed: LangChain/Retrieval-Agents/langchain_conv_agent.py -> LangChain/Retrieval-Agents/stateful_chatbot.py modified: README.md
Daethyra · Oct 8, 2023 · a676a45 · a676a45
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diff --git a/...gFace/integrated_captioner.py/__init__.py → HuggingFace/image_captioner/__init__.py b/...gFace/integrated_captioner.py/__init__.py → HuggingFace/image_captioner/__init__.py
diff --git a/...ated_captioner.py/integrable_captioner.py → ...e_captioner/integrable_image_captioner.py b/...ated_captioner.py/integrable_captioner.py → ...e_captioner/integrable_image_captioner.py
diff --git a/...egrated_captioner.py/template-config.json → ...Face/image_captioner/template-config.json b/...egrated_captioner.py/template-config.json → ...Face/image_captioner/template-config.json
diff --git a/...Face/integrated_captioner.py/template.env → HuggingFace/image_captioner/template.env b/...Face/integrated_captioner.py/template.env → HuggingFace/image_captioner/template.env
diff --git a/...hain/Retrieval-Agents/query_local_docs.py → LangChain/Retrieval-Agents/qa_local_docs.py b/...hain/Retrieval-Agents/query_local_docs.py → LangChain/Retrieval-Agents/qa_local_docs.py
@@ -1,4 +1,3 @@
-
 import os
 import glob
 from dotenv import load_dotenv

diff --git a/.../Retrieval-Agents/langchain_conv_agent.py → ...hain/Retrieval-Agents/stateful_chatbot.py b/.../Retrieval-Agents/langchain_conv_agent.py → ...hain/Retrieval-Agents/stateful_chatbot.py
diff --git a/README.md b/README.md
@@ -8,78 +8,82 @@
 
 #### 1. **[OpenAI: Utilikit](./OpenAI/)**
 
-   Contains `Auto-Embedder` and `GPT-Prompt-Examples`.
+---
+
+A. **[Auto-Embedder](./Auto-Embedder)**
 
-   A. **[Auto-Embedder](./Auto-Embedder)**
+Provides an automated pipeline for retrieving embeddings from[OpenAI&#39;s `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[OpenAI&#39;s `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](./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.
+---
 
-   B. **[GPT-Prompt-Examples](./GPT-Prompt-Examples)**
+B. **[GPT-Prompt-Examples](./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](GPT-Prompt-Examples/multi-shot), [system-role](GPT-Prompt-Examples/system-role), [user-role](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](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](./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.
 
 ---
 
 #### 2. **[LangChain: Pluggable Components](./LangChain/)**
 
-This directory contains pre-built `LangChain` modules that can be side-loaded for various functionalities.
+---
 
-  A. **`langchain_conv_agent.py`**
+A. **[`stateful_chatbot.py`](./LangChain/Retrieval-Agents/stateful_chatbot.py)**
 
-  This module offers a set of functionalities for conversational agents in LangChain. Specifically, it provides:
+This module offers a set of functionalities for conversational agents in LangChain. Specifically, it provides:
 
 - Argument parsing for configuring the agent
 - Document loading via `PyPDFDirectoryLoader`
 - 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.
+**Usage:**
+To use this module, simply import the functionalities you need and configure them accordingly.
 
 ---
 
-  B. **`query_local_docs.py`**
+B. **[`qa_local_docs.py`](./LangChain/Retrieval-Agents/qa_local_docs.py)**
 
-  This module focuses on querying local documents and employs the following features:
+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`
 
-  **Usage:**
-  Similar to `langchain_conv_agent.py`, you can import the functionalities you require.
+**Usage:**
+Similar to `langchain_conv_agent.py`, you can import the functionalities you require.
+
+---
 
 These modules are designed to be extensible and can be easily integrated into your LangChain projects.
 
 ---
 
 #### 3. **[HuggingFace: Pluggable Components](./HuggingFace/)**
 
-   A. **`integrable_captioner.py`**
+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:
+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
+- 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.
+**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.
 
 ---