Feature/cleanup docs (#121)

* tweak local logic for sciphi

* fix

* cleanup docs

README.md

@@ -8,7 +8,7 @@ With SciPhi, users can:
 
 - **Custom Data Creation**: Generate datasets via LLMs that are tailored to your needs.
    - Anthropic, OpenAI, vLLM, and SciPhi API are supported.
-- **Retriever-Augmented Generation (RAG) on Demand**: Built-in RAG Provider Interface to anchor generated data to real-world sources. 
+- **Retrieval-Augmented Generation (RAG) on Demand**: Built-in RAG Provider Interface to anchor generated data to real-world sources. 
    - _Coming Soon_ - End-to-end cloud and local RAG knowledge engine API for seamless use. 
 - **Customize Data Creation**: Generate datasets via LLMs that are tailored to your needs, for LLM training, RAG, and more.
    - _Included Example_ - A dedicated textbook module which writes RAG-enhanced textbooks directly from a provided table of contents.
@@ -235,7 +235,7 @@ from sciphi.llm import GenerationConfig
   # ... Continue ...
 ```
 
-Supported LLM providers include OpenAI, Anthropic, HuggingFace, and vLLM. For RAG database access, configure your own or use the SciPhi **World Databasef API**.
+Supported LLM providers include OpenAI, Anthropic, HuggingFace, and vLLM. For RAG database access, configure your own or use the SciPhi **World DatabasefAPI**.
 
 ### Setting Up Locally
 

docs/source/index.rst

@@ -26,7 +26,7 @@ SciPhi is a powerful knowledge engine tailored for LLM-based inference, data gen
 
 With SciPhi, you can:
 
-* Leverage **Retriever-Augmented Generation (RAG)** for better accuracy and efficiency.
+* Leverage **Retrieval-Augmented Generation (RAG)** for better accuracy and efficiency.
    - Features like end-to-end cloud and local RAG knowledge engine APIs are underway!
 * Generate truthful datasets using various LLMs, supporting **Anthropic**, **OpenAI**, **vLLM**, and **SciPhi**.
 * Custom tailor your data creation for applications such as LLM training, RAG, and beyond.
@@ -44,7 +44,7 @@ Diverse Features:
 * Evaluate your RAG systems effectively with the SciPhi evaluation harness.
 * Engage with the community on platforms like `Discord <https://discord.gg/j9GxfbxqAe>`_.
 
-Developers can also instantiate their own LLM and RAG providers using the SciPhi framework. The supported LLM providers include popular choices like OpenAI, Anthropic, HuggingFace, and vLLM. For specialized RAG capabilities, SciPhi offers the `World Databasef API <https://sciphi.readthedocs.io/en/latest/api/main.html>`_ for comprehensive knowledge access.
+Developers can also instantiate their own LLM and RAG providers using the SciPhi framework. The supported LLM providers include popular choices like OpenAI, Anthropic, HuggingFace, and vLLM. For specialized RAG capabilities, SciPhi offers the `World Database API <https://sciphi.readthedocs.io/en/latest/api/main.html>`_ for comprehensive knowledge access.
 
 You can use this format in your reStructuredText documentation, and it should render as a clickable link.
 For a detailed setup guide, deeper feature exploration, and developer insights, refer to:

docs/source/setup/quickstart.rst

@@ -3,7 +3,7 @@
 SciPhi Quickstart
 =================
 
-Welcome to the SciPhi quickstart guide! SciPhi, or ΨΦ, is your portal to using large language models (LLMs) like OpenAI's models, Anthropic, HuggingFace, and vLLM, combined with the power of Retriever-Augmented Generation (RAG).
+Welcome to the SciPhi quickstart guide! SciPhi, or ΨΦ, is your portal to using large language models (LLMs) like OpenAI's models, Anthropic, HuggingFace, and vLLM, combined with the power of Retrieval-Augmented Generation (RAG).
 
 This guide will introduce you to:
 - Generating data tailored to your needs.