feat: rename may_execute_* to execute_* (#56)

Argcfile.sh

@@ -77,7 +77,7 @@ build() {
 # @option --declarations-file=functions.json <FILE> Path to a json file to save function declarations
 # Example:
 #   get_current_weather.sh
-#   may_execute_js_code.js
+#   execute_js_code.js
 # @arg tools*[`_choice_tool`] The tool filenames
 build@tool() {
     if [[ "${#argc_tools[@]}" -gt 0 ]]; then
@@ -347,9 +347,9 @@ test-execute-code-tools() {
         ext=".cmd"
     fi
     test_cases=( \
-        'sh#may_execute_command#{"command":"echo \"✓\""}' \
+        'sh#execute_command#{"command":"echo \"✓\""}' \
-        'js#may_execute_js_code#{"code":"console.log(\"✓\")"}' \
+        'js#execute_js_code#{"code":"console.log(\"✓\")"}' \
-        'py#may_execute_py_code#{"code":"print(\"✓\")"}' \
+        'py#execute_py_code#{"code":"print(\"✓\")"}' \
     )
 
     for test_case in "${test_cases[@]}"; do

README.md

@@ -45,23 +45,15 @@ ln -s "$(pwd)" "$(aichat --info | grep -w functions_dir | awk '{print $2}')"
 argc install
 ```
 
-Don't forget to add the following config to your AIChat `config.yaml` file:
-
-```yaml
-function_calling: true
-```
-
 AIChat will automatically load `functions.json` and execute commands located in the `./bin` directory based on your prompts.
 
 **4. Start using your functions:**
 
 Now you can interact with your LLM using natural language prompts that trigger your defined functions.
 
-## AIChat Showcases 
+## Showcases 
 
-![retrieve-type-showcase](https://github.com/sigoden/llm-functions/assets/4012553/7e628834-9863-444a-bad8-7b51bfb18dff)
+![tools-showcase](https://github.com/sigoden/aichat/assets/4012553/f9d7f827-9a2a-4ea1-8b8c-19e1c93178d4)
-
-![execute-type-showcase](https://github.com/sigoden/llm-functions/assets/4012553/1dbc345f-daf9-4d65-a49f-3df8c7df1727)
 
 ![agent-showcase](https://github.com/sigoden/llm-functions/assets/4012553/05e1e57e-3bcc-4504-b78f-c36b27d16bfd)