141 lines
6.8 KiB
Markdown
141 lines
6.8 KiB
Markdown
# Chat Tool - General Development Chat & Collaborative Thinking
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**Your thinking partner - bounce ideas, get second opinions, brainstorm collaboratively**
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The `chat` tool is your collaborative thinking partner for development conversations. It's designed to help you brainstorm, validate ideas, get second opinions, and explore alternatives in a conversational format.
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## Thinking Mode
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**Default is `medium` (8,192 tokens).** Use `low` for quick questions to save tokens, or `high` for complex discussions when thoroughness matters.
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## Example Prompt
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```
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I need to pick between Redis and Memcached for session storage and I need an expert opinion for the project
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I'm working on. Take a look at the code and get an idea of what this project does, pick one of the two options
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and then chat with gemini pro and continue discussing pros and cons to come to a final conclusion. I need a one
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word verdict in the end.
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```
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<div style="center">
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[Chat Redis or Memcached_web.webm](https://github.com/user-attachments/assets/41076cfe-dd49-4dfc-82f5-d7461b34705d)
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</div>
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**Another Example**:
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* We ask Claude code to pick one of two frameworks, then `chat` with `gemini` to make a final decision
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* Gemini responds, confirming choice. We use `continuation` to ask another question using the same conversation thread
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* Gemini responds with explanation. We use continuation again, using `/pal:continue (MCP)` command the second time
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<div style="center">
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[Chat With Gemini_web.webm](https://github.com/user-attachments/assets/37bd57ca-e8a6-42f7-b5fb-11de271e95db)
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</div>
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## Key Features
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- **Collaborative thinking partner** for your analysis and planning
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- **Get second opinions** on your designs and approaches
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- **Brainstorm solutions** and explore alternatives together
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- **Structured code generation**: When using GPT-5.2 or Gemini 3.0 / 2.5 Pro, get complete, production-ready implementations saved to `pal_generated.code` for your CLI to review and apply
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- **Validate your checklists** and implementation plans
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- **General development questions** and explanations
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- **Technology comparisons** and best practices
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- **Architecture and design discussions**
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- **File reference support**: `"Use gemini to explain this algorithm with context from algorithm.py"`
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- **Image support**: Include screenshots, diagrams, UI mockups for visual analysis: `"Chat with gemini about this error dialog screenshot to understand the user experience issue"`
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- **Dynamic collaboration**: Models can request additional files or context during the conversation if needed for a more thorough response
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- **Web search awareness**: Automatically identifies when online research would help and instructs Claude to perform targeted searches using continuation IDs
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## Tool Parameters
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- `prompt`: Your question or discussion topic (required)
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- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
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- `absolute_file_paths`: Optional absolute file or directory paths for additional context
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- `images`: Optional images for visual context (absolute paths)
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- `working_directory_absolute_path`: **Required** - Absolute path to an existing directory where generated code artifacts will be saved
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- `temperature`: Response creativity (0-1, default 0.5)
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- `thinking_mode`: minimal|low|medium|high|max (default: medium, Gemini only)
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- `continuation_id`: Continue previous conversations
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## Structured Code Generation
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When using advanced reasoning models like **GPT-5.2 Pro** or **Gemini 3.0 Pro**, the chat tool can generate complete, production-ready code implementations in a structured format.
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### How It Works
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1. You ask your AI agent to implement a complex new feature using `chat` with a higher-reasoning model such as **GPT-5.2 Pro** or **Gemini 3.0 Pro**
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2. The model generates structured implementation and shares the complete implementation with PAL
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3. PAL saves the code to `pal_generated.code` and asks AI agent to implement the plan
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4. AI agent continues from the previous context, reads the file, applies the implementation
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### When Code Generation Activates
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The structured format activates for **substantial implementation work**:
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- Creating new features from scratch with multiple files or significant code
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- Major refactoring across multiple files or large sections
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- Implementing new modules, components, or subsystems
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- Large-scale updates affecting substantial portions of the codebase
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- Complete rewrites of functions, algorithms, or approaches
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For minor changes (small tweaks, bug fixes, algorithm improvements), the model responds normally with inline code blocks.
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### Example Usage
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```
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chat with gpt-5.2-pro and ask it to make me a standalone, classic version of the
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Pacman game using pygame that I can run from the commandline. Give me a single
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script to execute in the end with any / all dependencies setup for me.
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Do everything using pygame, we have no external resources / images / audio at
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hand. Instead of ghosts, it'll be different geometric shapes moving around
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in the maze that Pacman can eat (so there are no baddies). Pacman gets to eat
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everything including bread-crumbs and large geometric shapes but make me the
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classic maze / walls that it navigates within using keyboard arrow keys.
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```
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See the [Configuration Guide](../configuration.md#code-generation-capability) for details on the `allow_code_generation` flag.
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## Usage Examples
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**Basic Development Chat:**
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```
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"Chat with pal about the best approach for user authentication in my React app"
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```
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**Technology Comparison:**
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```
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"Use flash to discuss whether PostgreSQL or MongoDB would be better for my e-commerce platform"
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```
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**Architecture Discussion:**
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```
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"Chat with pro about microservices vs monolith architecture for my project, consider scalability and team size"
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```
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**File Context Analysis:**
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```
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"Use gemini to chat about the current authentication implementation in auth.py and suggest improvements"
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```
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**Visual Analysis:**
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```
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"Chat with gemini about this UI mockup screenshot - is the user flow intuitive?"
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```
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## Best Practices
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- **Be specific about context**: Include relevant files or describe your project scope
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- **Ask for trade-offs**: Request pros/cons for better decision-making
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- **Use conversation continuation**: Build on previous discussions with `continuation_id`
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- **Leverage visual context**: Include diagrams, mockups, or screenshots when discussing UI/UX
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- **Encourage research**: When you suspect documentation has changed, explicitly ask the assistant to confirm by requesting a web search
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## When to Use Chat vs Other Tools
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- **Use `chat`** for: Open-ended discussions, brainstorming, getting second opinions, technology comparisons
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- **Use `thinkdeep`** for: Extending specific analysis, challenging assumptions, deeper reasoning
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- **Use `analyze`** for: Understanding existing code structure and patterns
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- **Use `debug`** for: Specific error diagnosis and troubleshooting
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