refactor: several improvements (#856)

assets/roles/%create-prompt%.md

@@ -18,6 +18,8 @@ Your output format should include:
 - **Examples**: Show a case of input and output that fits the scenario.
 
 Your workflow should be:
-1. **Analyze User Input**: Extract key information from user requests to determine design objectives.
-2. **Conceive New Prompts**: Based on user needs, create prompts that meet requirements, with each part being professional and detailed.
-3. **Generate Output**: Must only output the newly generated and optimized prompts, without explanation, and without wrapping it in markdown code block.
+1. Extract key information from user requests to determine design objectives.
+2. Based on user needs, create prompts that meet requirements, with each part being professional and detailed.
+3. Must only output the newly generated and optimized prompts, without explanation, without wrapping it in markdown code block.
+
+My first request is: __INPUT__

models.yaml

@@ -607,8 +607,8 @@
       supports_function_calling: true
     - name: ernie-4.0-8k-preview
       max_input_tokens: 8192
-      input_price: 16.8
-      output_price: 16.8
+      input_price: 5.6
+      output_price: 11.2
       supports_function_calling: true
     - name: ernie-3.5-8k-preview
       max_input_tokens: 8192
@@ -736,6 +736,11 @@
       input_price: 7
       output_price: 7
       supports_function_calling: true
+    - name: glm-4-alltools
+      max_input_tokens: 2048
+      input_price: 14
+      output_price: 14
+      supports_function_calling: true
     - name: glm-4-0520
       max_input_tokens: 128000
       input_price: 14
@@ -746,11 +751,6 @@
       input_price: 0.14
       output_price: 0.14
       supports_function_calling: true
-    - name: glm-4-alltools
-      max_input_tokens: 2048
-      input_price: 14
-      output_price: 14
-      supports_function_calling: true
     - name: glm-4-flash
       max_input_tokens: 128000
       input_price: 0
@@ -761,11 +761,6 @@
       input_price: 1.4
       output_price: 1.4
       supports_vision: true
-    - name: glm-4v
-      max_input_tokens: 2048
-      input_price: 7
-      output_price: 7
-      supports_vision: true
     - name: embedding-3
       type: embedding
       max_input_tokens: 8192
@@ -855,10 +850,6 @@
     - name: cohere-command-r
       max_input_tokens: 128000
       supports_function_calling: true
-    - name: phi-3-medium-128k-instruct
-      max_input_tokens: 128000
-    - name: phi-3-mini-128k-instruct
-      max_input_tokens: 128000
     - name: cohere-embed-v3-english
       type: embedding
       max_tokens_per_chunk: 512
@@ -869,6 +860,16 @@
       max_tokens_per_chunk: 512
       default_chunk_size: 1000
       max_batch_size: 96
+    - name: ai21-jamba-1.5-large
+      max_input_tokens: 256000
+      supports_function_calling: true
+    - name: ai21-jamba-1.5-mini
+      max_input_tokens: 256000
+      supports_function_calling: true
+    - name: phi-3.5-mini-instruct
+      max_input_tokens: 128000
+    - name: phi-3-medium-128k-instruct
+      max_input_tokens: 128000
 
 # Links:
 #  - https://deepinfra.com/models
@@ -1313,7 +1314,7 @@
       max_input_tokens: 8192
       input_price: 0
       output_price: 0
-    - name: deepseek-ai/DeepSeek-V2-Chat
+    - name: deepseek-ai/DeepSeek-V2.5
       max_input_tokens: 32768
       input_price: 0.186
       output_price: 0.186
@@ -1394,44 +1395,44 @@
   models:
     - name: jina-clip-v1
       type: embedding
-      input_price: 0.02
+      input_price: 0
       max_tokens_per_chunk: 8192
       default_chunk_size: 1500
       max_batch_size: 100
     - name: jina-embeddings-v2-base-en
       type: embedding
-      input_price: 0.02
+      input_price: 0
       max_tokens_per_chunk: 8192
       default_chunk_size: 1500
       max_batch_size: 100
     - name: jina-embeddings-v2-base-zh
       type: embedding
-      input_price: 0.02
+      input_price: 0
       max_tokens_per_chunk: 8192
       default_chunk_size: 1500
       max_batch_size: 100
     - name: jina-colbert-v2
       type: embedding
-      input_price: 0.02
+      input_price: 0
       max_tokens_per_chunk: 8192
       default_chunk_size: 1500
       max_batch_size: 100
     - name: jina-reranker-v2-base-multilingual
       type: reranker
       max_input_tokens: 1024
-      input_price: 0.02
+      input_price: 0
     - name: jina-reranker-v1-turbo-en
       type: reranker
       max_input_tokens: 8192
-      input_price: 0.02
+      input_price: 0
     - name: jina-reranker-v1-base-en
       type: reranker
       max_input_tokens: 8192
-      input_price: 0.02
+      input_price: 0
     - name: jina-colbert-v2
       type: reranker
       max_input_tokens: 8192
-      input_price: 0.02
+      input_price: 0
 
 # Links:
 #  - https://docs.voyageai.com/docs/embeddings

src/main.rs

@@ -241,25 +241,25 @@ async fn shell_execute(config: &GlobalConfig, shell: &Shell, mut input: Input) -
         loop {
             let answer = Select::new(
                 eval_str.trim(),
-                vec!["Execute", "Revise", "Explain", "Cancel"],
+                vec!["✅ Execute", "🔄 Revise", "📖 Explain", "❌ Cancel"],
             )
             .prompt()?;
 
             match answer {
-                "Execute" => {
+                "✅ Execute" => {
                     debug!("{} {:?}", shell.cmd, &[&shell.arg, &eval_str]);
                     let code = run_command(&shell.cmd, &[&shell.arg, &eval_str], None)?;
                     if code != 0 {
                         process::exit(code);
                     }
                 }
-                "Revise" => {
+                "🔄 Revise" => {
                     let revision = Text::new("Enter your revision:").prompt()?;
                     let text = format!("{}\n{revision}", input.text());
                     input.set_text(text);
                     return shell_execute(config, shell, input).await;
                 }
-                "Explain" => {
+                "📖 Explain" => {
                     let role = config.read().retrieve_role(EXPLAIN_SHELL_ROLE)?;
                     let input = Input::from_str(config, &eval_str, Some(role));
                     let abort = create_abort_signal();