fix terminology

Author: neagualexa

docs/advanced/chat_functions/info.md

@@ -1,6 +1,6 @@
 # AI Chatbots - More information
 
-Chatbot agents are AI Assistants that students can chat with to ask for help or further explanations regarding the Question that they are working on. Each Agent has its own personality and approach to assisting the students.
+Chatbots are LLM assistants that students can chat with to ask for help or further explanations regarding the Question that they are working on. Each chatbot has its own personality and approach to assisting the students.
 
 The Chatbots have at their basis a [Large Language Model (LLM)](https://en.wikipedia.org/wiki/Large_language_model) which received information regarding:
 
@@ -30,4 +30,4 @@ Click on the links below for information on each chatbot:
 
 ## AI Chatbot Development
 
-Are you interested in developing your own chatbot? Then please check out the [Quickstart guide](quickstart.md) to develop and deploy your own AI chat agent for Lambda Feedback.
+Are you interested in developing your own chatbot? Then check out the [Quickstart guide](quickstart.md) to develop and deploy your own AI chat function for Lambda Feedback.

docs/advanced/chat_functions/local.md

@@ -1,19 +1,19 @@
-# Running and Testing Agents Locally
+# Running and Testing Chat function Locally
 
-You can run the Python function for your agent itself by writing a `main()` function, or you can call the [`testbench_prompts.py`](https://github.com/lambda-feedback/lambda-chat/blob/main/src/agents/utils/testbench_prompts.py) script that runs a similar pipeline to the `module.py`.
+You can run the Python function for your chat function itself by writing a `main()` function, or you can call the [`testbench_prompts.py`](https://github.com/lambda-feedback/lambda-chat/blob/main/src/agents/utils/testbench_prompts.py) script that runs a similar pipeline to the `module.py`.
 
 ```bash
 python src/agents/utils/testbench_prompts.py
 ```
 
-You can also use the `test_prompts.py` script to test the agents with example inputs from Lambda Feedback questions and synthetic conversations.
+You can also use the `test_prompts.py` script to test the chat function with example inputs from Lambda Feedback questions and synthetic conversations.
 ```bash
 python src/agents/utils/test_prompts.py
 ```
 
 ## Testing using the Docker Image [:material-docker:](https://www.docker.com/)
 
-You can also build and run the docker pipeline for the agents. The chatbot agents are deployed onto a AWS Lambda serverless cloud function using the docker image. Hence, for final testing of your chatbots, we recommend completing those steps.
+You can also build and run the docker pipeline for the chat function. The chatbot associated with the chat function is deployed onto a AWS Lambda serverless cloud function using the docker image. Hence, for final testing of your chatbot, we recommend completing those steps.
 
 #### Build the Docker Image
 

docs/advanced/chat_functions/quickstart.md

@@ -7,7 +7,7 @@ It's a function which calls Large Language Models (LLMs) to respond to the stude
 - question data
 - user data such as past responses to the problem
 
-Chat functions host a chatbot agent. Chatbot Agents capture and automate the process of assisting students during their learning process when outside of classroom.
+Chat functions host a chatbot. Chatbots capture and automate the process of assisting students during their learning process when outside of classroom.
 
 ## Getting Setup for Development
 
@@ -16,12 +16,12 @@ Chat functions host a chatbot agent. Chatbot Agents capture and automate the pro
 	- For new functions: clone the template repo for [chat-function-boilerplate](https://github.com/lambda-feedback/chat-function-boilerplate). **Make sure the new repository is set to public (it needs access to organisation secrets)**. 
 	- For existing functions: please make your changes on a new separate branch
 
-2. _If you are creating a new chatbot agent_, can either edit the `scr/agents/base_agent` or copy it and rename it based on your agent's name.
+2. _If you are creating a new chatbot_, you can either edit the `scr/agents/base_agent` or copy it and rename it based on your chatbot's name.
 3. You are now ready to start making changes and implementing features by editing each of the main function-logic files:
 
 	1. **`scr/agents/{base_agent}/{base}_agent.py`**: This file contains the main LLM pipeline using [LangGraph](https://langchain-ai.github.io/langgraph/) and [LangChain](https://python.langchain.com/docs/introduction/).
 
-   	- the agent expects the following inputs when it being called:
+   	- the chatbot expects the following inputs when it being called:
 
    	Body with necessary Params:
 
@@ -54,17 +54,17 @@ Chat functions host a chatbot agent. Chatbot Agents capture and automate the pro
 
    2. **`scr/agents/{base_agent}/{base}_prompts.py`**: This is where you can write the system prompts that describe how your AI Assistant should behave and respond to the user.
 
-   3. _If you edited the agent file name_, make sure to add your agent `invoke()` function to the `module.py` file.
+   3. _If you edited the chatbot agent file name_, make sure to add your chatbot `invoke()` function to the `module.py` file.
 
 	 4. Update the `config.json` file with the chatbot's name.
 
-   5. Please add a `README.md` file to describe the use and behaviour of your agent.
+   5. Please add a `README.md` file to describe the use and behaviour of your chatbot.
 
 4. Changes can be tested locally by running the pipeline tests using:
 	```bash
 	pytest src/module_test.py
 	```
-   [Running and Testing Agents Locally](local.md){ .md-button }
+   [Running and Testing Chat Functions Locally](local.md){ .md-button }
 
 
 5. Merge commits into dev branch will trigger the `dev.yml` workflow, which will build the docker image, push it to a shared `dev` ECR repository and deploy an AWS Lambda function for the `dev` client app to call. In order to make your new chatbot available on the `dev` environment of the Lambda Feedback platform, you will have to get in contact with the ADMINS on the platform.
@@ -90,6 +90,6 @@ Chat functions host a chatbot agent. Chatbot Agents capture and automate the pro
 					}
 			}'
 
-6. Once the `dev` chatbot is fully tested, you can merge the code to the default branch (`main`). This will trigger the `main.yml` workflow, which will deploy the `staging` and `prod` versions of your chatbot. Please contact the ADMIN to provide the URLS for the `staging` and `prod` versions of your agent.
+6. Once the `dev` chat function is fully tested, you can merge the code to the default branch (`main`). This will trigger the `main.yml` workflow, which will deploy the `staging` and `prod` versions of your chat function. Please contact the ADMIN to provide the URLS for the `staging` and `prod` versions of your chat function.
 
-6. In order to make your new chatbot available on any of the environments of the Lambda Feedback platform, you will have to get in contact with the ADMINS on the platform.
+6. In order to make your new chat function available on any of the environments of the Lambda Feedback platform, you will have to get in contact with the ADMINS on the platform.

docs/advanced/index.md

@@ -8,8 +8,8 @@ The fundamental idea of Lambda Feedback is that it calls external microservices
 Evaluate a student response and provide feedback: 
 [Evaluation functions - Quickstart Guide](evaluation_functions/quickstart.md){ .md-button .md-button--primary style="width: 400px;"}
 
-Dialogic conversations with students:<br>
-[Chat functions - Quickstart guide ](chatbot_agents/quickstart.md){ .md-button .md-button--primary style="width: 400px;"}
+AI Chatbots to converse with students:<br>
+[Chat functions - Quickstart guide ](chat_functions/quickstart.md){ .md-button .md-button--primary style="width: 400px;"}
 
 All microservices are called over http. There is complete freedom in their implementation. Lambda Feedback also provides families of deployed microservices, using open source code available in our public GitHub repositories.
 

docs/student/getting_started_student.md

@@ -45,7 +45,7 @@ A pane where you can write down your thought process and notes for the previewed
 ![Canvas Interface](images/canvas_interface.png)
 
 #### Chat: 
-A chat interface connecting you with helpful Chatbots. Chatbot agents are AI Assistants that you can chat with to ask for help or further explanations regarding the Question that you are working on. 
+A chat interface connecting you with helpful Chatbots. The Chatbots are AI Assistants that you can chat with to ask for help or further explanations regarding the Question that you are working on. 
 
 ![Chat Interface](images/chat_interface.png)