minor updates readme

Author: neagualexa

.dockerignore

@@ -131,7 +131,7 @@ dmypy.json
 # VSCode configuration
 .vscode
 
-# Evaluation function config
+# Chat function config
 config.json
 
 # README

.gitignore

@@ -132,7 +132,6 @@ dmypy.json
 .vscode
 
 .DS_Store
-evaluation_function/db_analytics
 
 # Synthetic data conversations
 src/agents/utils/synthetic_conversations/*.json

README.md

@@ -110,7 +110,7 @@ docker run -e OPENAI_API_KEY={your key} -e OPENAI_MODEL={your LLM chosen model n
 docker run --env-file .env -it --name my-lambda-container -p 8080:8080 llm_chat
 ```
 
-This will start the evaluation function and expose it on port `8080` and it will be open to be curl:
+This will start the chat function and expose it on port `8080` and it will be open to be curl:
 
 ```bash
 curl --location 'http://localhost:8080/2015-03-31/functions/function/invocations' --header 'Content-Type: application/json' --data '{"message":"hi","params":{"conversation_id":"12345Test","conversation_history": [{"type":"user","content":"hi"}]}}'
@@ -156,19 +156,19 @@ Body with optional Params:
 
 Deploying the chat function to Lambda Feedback is simple and straightforward, as long as the repository is within the [Lambda Feedback organization](https://github.com/lambda-feedback).
 
-After configuring the repository, a [GitHub Actions workflow](.github/workflows/main.yml) will automatically build and deploy the evaluation function to Lambda Feedback as soon as changes are pushed to the main branch of the repository. For development, the [GitHub Actions Dev workflow](.github/workflows/dev.yml) also deploys a dev version of the function onto AWS.
+After configuring the repository, a [GitHub Actions workflow](.github/workflows/main.yml) will automatically build and deploy the chat function to Lambda Feedback as soon as changes are pushed to the main branch of the repository. For development, the [GitHub Actions Dev workflow](.github/workflows/dev.yml) also deploys a dev version of the function onto AWS.
 
 ## Troubleshooting
 
 ### Containerized Function Fails to Start
 
-If your evaluation function is working fine when run locally, but not when containerized, there is much more to consider. Here are some common issues and solution approaches:
+If your chat function is working fine when run locally, but not when containerized, there is much more to consider. Here are some common issues and solution approaches:
 
 **Run-time dependencies**
 
 Make sure that all run-time dependencies are installed in the Docker image.
 
 - Python packages: Make sure to add the dependency to the `requirements.txt` or `pyproject.toml` file, and run `pip install -r requirements.txt` or `poetry install` in the Dockerfile.
 - System packages: If you need to install system packages, add the installation command to the Dockerfile.
-- ML models: If your evaluation function depends on ML models, make sure to include them in the Docker image.
-- Data files: If your evaluation function depends on data files, make sure to include them in the Docker image.
+- ML models: If your chat function depends on ML models, make sure to include them in the Docker image.
+- Data files: If your chat function depends on data files, make sure to include them in the Docker image.