spellcheck

docs/guides/python/llama-rag.mdx

@@ -76,7 +76,7 @@ We'll organize our project structure like so:
 
 ## Setting up our LLM
 
-We'll define a `ModelParameters` class which will have parameters used throughout our application. By putting it in a class, it means it will lazily load the llm, [embed model](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings/), and tokenizer so that it doesn't slow down other modules that don't require everything to be initialised. At this point we can also create a prompt template for prompts with our query engine. It will just sanitize some of the hallucinations so that if the model does not know an answer it won't pretend like it does. We'll also define two functions that will convert a prompt or message into the required Llama 3.1 format.
+We'll define a `ModelParameters` class which will have parameters used throughout our application. By putting it in a class, it means it will lazily load the LLM, [embed model](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings/), and tokenizer so that it doesn't slow down other modules that don't require everything to be initialized. At this point we can also create a prompt template for prompts with our query engine. It will just sanitize some of the hallucinations so that if the model does not know an answer it won't pretend like it does. We'll also define two functions that will convert a prompt or message into the required Llama 3.1 format.
 
 ```python title:common/model_parameters.py
 import os
@@ -192,7 +192,7 @@ The next step is where we embed our context into the LLM. For this example we wi
 git clone https://github.com/nitrictech/docs.git nitric-docs
 ```
 
-We'll create a script which will download the [LLM](https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF), the embed model (using a recommended [model](https://huggingface.co/BAAI/bge-large-en-v1.5) from Hugging Face), and create the vectorised documentation using the embed model.
+We'll create a script which will download the [LLM](https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF), the embed model (using a recommended [model](https://huggingface.co/BAAI/bge-large-en-v1.5) from Hugging Face), and convert the documentation into a vector model using the embed model.
 
 ```python title:model_utilities.py
 import os
@@ -262,7 +262,7 @@ download_llm()
 build_query_engine()
 ```
 
-You can then run this using the following command. This should output the models and the vectorised documentation into the `./models` folder.
+You can then run the script using the following command. This should output the models and the vector model into the `./models` folder.
 
 ```bash
 uv run model_utilities.py
@@ -456,7 +456,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
   uv sync --extra ml --frozen --no-dev --no-python-downloads
 ```
 
-To ensure an optimised docker image, update the `python.dockerfile.dockerignore` to include the models folder.
+To ensure an optimized docker image, update the `python.dockerfile.dockerignore` to include the models folder.
 
 ```text
 .mypy_cache/