docs: clean up documentation language and references (#1514)

* docs: clean up documentation language and references

Co-authored-by: Chris Parisien <64271260+cparisien@users.noreply.github.com>
Signed-off-by: Pouyan <13303554+Pouyanpi@users.noreply.github.com>

---------

Signed-off-by: Pouyan <13303554+Pouyanpi@users.noreply.github.com>
Co-authored-by: Chris Parisien <64271260+cparisien@users.noreply.github.com>

Author: Pouyanpi

README.md

@@ -2,16 +2,13 @@
 
 [![Tests](https://img.shields.io/badge/Tests-passing-green)](#)
 [![License](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](https://github.com/NVIDIA/NeMo-Guardrails/blob/main/LICENSE.md)
-[![Project Status](https://img.shields.io/badge/Status-beta-orange)](#)
 [![PyPI version](https://badge.fury.io/py/nemoguardrails.svg)](https://badge.fury.io/py/nemoguardrails)
 [![Python 3.8+](https://img.shields.io/badge/python-3.8%2B-green)](https://www.python.org/downloads/)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![arXiv](https://img.shields.io/badge/arXiv-2310.10501-b31b1b.svg)](https://arxiv.org/abs/2310.10501)
 
 > **LATEST RELEASE / DEVELOPMENT VERSION**: The [main](https://github.com/NVIDIA/NeMo-Guardrails/tree/main) branch tracks the latest released beta version: [0.18.0](https://github.com/NVIDIA/NeMo-Guardrails/tree/v0.18.0). For the latest development version, checkout the [develop](https://github.com/NVIDIA/NeMo-Guardrails/tree/develop) branch.
 
-> **DISCLAIMER**: The beta release is undergoing active development and may be subject to changes and improvements, which could cause instability and unexpected behavior. We currently do not recommend deploying this beta version in a production setting. We appreciate your understanding and contribution during this stage. Your support and feedback are invaluable as we advance toward creating a robust, ready-for-production LLM guardrails toolkit. The examples provided within the documentation are for educational purposes to get started with NeMo Guardrails, and are not meant for use in production applications.
-
 ✨✨✨
 
 📌 **The official NeMo Guardrails documentation has moved to [docs.nvidia.com/nemo/guardrails](https://docs.nvidia.com/nemo/guardrails).**
@@ -74,7 +71,6 @@ You can use programmable guardrails in different types of use cases:
 2. **Domain-specific Assistants** (a.k.a. chatbots): Ensure the assistant stays on topic and follows the designed conversational flows.
 3. **LLM Endpoints**: Add guardrails to your custom LLM for safer customer interaction.
 4. **LangChain Chains**: If you use LangChain for any use case, you can add a guardrails layer around your chains.
-5. **Agents (COMING SOON)**: Add guardrails to your LLM-based agent.
 
 ### Usage
 
@@ -214,7 +210,7 @@ define flow
 To configure and implement various types of guardrails, this toolkit introduces **Colang**, a modeling language specifically created for designing flexible, yet controllable, dialogue flows. Colang has a python-like syntax and is designed to be simple and intuitive, especially for developers.
 
 ```{note}
-Currently two versions of Colang, 1.0 and 2.0, are supported and Colang 1.0 is the default. Versions 0.1.0 up to 0.7.1 of NeMo Guardrails used Colang 1.0 exclusively. Versions 0.8.0 introduced Colang 2.0-alpha and version 0.9.0 introduced Colang 2.0-beta. We expect Colang 2.0 to go out of Beta and replace 1.0 as the default option in NeMo Guardrails version 0.12.0.
+Two versions of Colang, 1.0 and 2.0, are supported and Colang 1.0 is the default.
 ```
 
 For a brief introduction to the Colang 1.0 syntax, see the [Colang 1.0 Language Syntax Guide](https://docs.nvidia.com/nemo/guardrails/user-guides/colang-language-syntax-guide.html).
@@ -226,23 +222,10 @@ To get started with Colang 2.0, see the [Colang 2.0 Documentation](https://docs.
 NeMo Guardrails comes with a set of [built-in guardrails](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html).
 
 ```{note}
-The built-in guardrails are only intended to enable you to get started quickly with NeMo Guardrails. For production use cases, further development and testing of the rails are needed.
+The built-in guardrails may or may not be suitable for a given production use case. As always, developers should work with their internal application team to ensure guardrails meets requirements for the relevant industry and use case and address unforeseen product misuse.
 ```
 
-Currently, the NeMo Guardrails library includes guardrails for:
-
-- [Jailbreak Detection](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#jailbreak-detection-heuristics)
-- [Self-Check Input Moderation](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#self-input-output)
-- [Self-Check Output Moderation](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#self-check-output)
-- [Self-Check Fact-checking](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#fact-checking)
-- [Hallucination Detection](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#hallucination-detection)
-- [AlignScore-based Fact-checking](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#alignscore-based-fact-checking)
-- [LlamaGuard-based Content Moderation](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#llama-guard-based-content-moderation)
-- [RAG hallucination detection using Patronus Lynx](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#patronus-lynx-based-rag-hallucination-detection)
-- [Presidio-based Sensitive Data Detection](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#presidio-based-sensitive-data-detection)
-- [Input moderation using ActiveFence](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#activefence)
-- [RAG Hallucination detection using Got It AI's TruthChecker API](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#got-it-ai)
-- [AutoAlign-based guardrails](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html#autoalign)
+The library includes guardrails for LLM self-checking (input/output moderation, fact-checking, hallucination detection), NVIDIA safety models (content safety, topic safety), jailbreak and injection detection, and integrations with community models and third-party APIs. For the complete list, see the [Guardrails Library documentation](https://docs.nvidia.com/nemo/guardrails/user-guides/guardrails-library.html).
 
 ## CLI
 

docs/api/nemoguardrails.rails.llm.llmrails.md

@@ -108,8 +108,6 @@ The format for messages is the following:
 **Returns:**
  The completion (when a prompt is provided) or the next message.
 
-System messages are not yet supported.
-
 ---
 
 <a href="../../nemoguardrails/rails/llm/llmrails.py#L513"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square" /></a>

docs/user-guides/advanced/generation-options.md

@@ -141,8 +141,6 @@ res.log.print_summary()
 - [0.31s] OUTPUT (self check output): 1 actions (self_check_output), 1 llm calls [0.31s]
 ```
 
-**TODO**: add more details about the returned data.
-
 ## Disabling Rails
 
 You can choose which categories of rails you want to apply by using the `rails` generation option. The four supported categories are: `input`, `dialog`, `retrieval` and `output`. By default, all are enabled.

docs/user-guides/guardrails-library.md

@@ -14,7 +14,6 @@ NeMo Guardrails comes with a library of built-in guardrails that you can easily
    - [LlamaGuard-based Content Moderation](#llama-guard-based-content-moderation)
    - [Patronus Lynx-based RAG Hallucination Detection](#patronus-lynx-based-rag-hallucination-detection)
    - [Presidio-based Sensitive data detection](#presidio-based-sensitive-data-detection)
-   - BERT-score Hallucination Checking - *[COMING SOON]*
 
 3. Third-Party APIs
    - [ActiveFence Moderation](#activefence)
@@ -28,7 +27,6 @@ NeMo Guardrails comes with a library of built-in guardrails that you can easily
    - [Prompt Security Protection](#prompt-security-protection)
    - [Pangea AI Guard](#pangea-ai-guard)
    - [Trend Micro Vision One AI Application Security](#trend-micro-vision-one-ai-application-security)
-   - OpenAI Moderation API - *[COMING SOON]*
    - [Cisco AI Defense](#cisco-ai-defense)
 
 4. Other
@@ -428,7 +426,7 @@ Similar to the self-check fact-checking, we formulate the consistency checking s
 
 ## NVIDIA Models
 
-NeMo Guardrails provides out of the box connectivity for safety models trained by the NVIDIA for specialized use cases. These models shall be provided as both as HuggingFace checkpoints, and as NVIDIA NIM containers that will provide out of the box TRTLLM support with lower latency.
+NeMo Guardrails provides out of the box connectivity for safety models trained by NVIDIA for specialized use cases. These models are provided as both HuggingFace checkpoints and as NVIDIA NIM containers that provide out of the box TRTLLM support with lower latency.
 
 ### Content Safety
 
@@ -1036,8 +1034,6 @@ Using the default value allows for detection of 49/50 GCG-style attacks with a 0
 
 To compute the perplexity of a string, the current implementation uses the `gpt2-large` model.
 
-**NOTE**: in future versions, multiple options will be supported.
-
 #### Model-based Jailbreak Detections
 
 There is currently one available model-based detection, using a random forest-based detector trained on [`Snowflake/snowflake-arctic-embed-m-long`](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long) embeddings.