# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-14 18:21:37
- **Session ID**: `416cf8a5-e1f0-4c0c-b740-94784b5fce15`

## Conversation

### 👤 You

"tokens": [
    " the",
    " web",
    "\",",
    " \"",
    "parameters",
    "\":",
    " {\"",
    "type",
    "\":",
    " \"",
    "object",
    "\",",
    " \"",
    "properties",
    "\":",
    " {\"",
    "query",
    "\":",
    " {\"",
    "type",
    "\":",


The tokens do not correspond to the actual response.

The output tokens start from:
    "<|im_start|>",
    "assistant",

Check how output tokens are recorded in the python main.py code

---

### 🤖 Cursor Assistant

I'll help you check how output tokens are recorded in the attention visualization code. Let me examine the main.py file to understand the token recording mechanism.

---

### 👤 You

"tokens": [
    " the",
    " web",
    "\",",
    " \"",
    "parameters",
    "\":",
    " {\"",
    "type",
    "\":",
    " \"",
    "object",
    "\",",
    " \"",
    "properties",
    "\":",
    " {\"",
    "query",
    "\":",
    " {\"",
    "type",
    "\":",


The tokens do not correspond to the actual response.

The output tokens start from:
    "<|im_start|>",
    "assistant",

Similarly, the prompt is missing the beginning:
```
for information on the web", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}}, "required": ["query"]}}}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
<|im_start|>user
What's the current time and weather like in Vancouver right now?<|im_end|>
<|im_start|>assistant
```

Check how  the input and output tokens are recorded in the python main.py code

---

### 🤖 Cursor Assistant

I'll examine how the input and output tokens are being recorded in your attention visualization code.

---

### 🤖 Cursor Assistant

Now let me check the agent.py file to understand how the tokens are being extracted and recorded in the `generate_with_attention` method:

---

### 🤖 Cursor Assistant

I can see the issue with how tokens are being recorded. The problem occurs because of how the chat template formatting and token extraction interact. Let me examine the specific issue:

---

### 🤖 Cursor Assistant

I've identified the issue with token recording in your attention visualization code. The problem stems from how the chat template formatting interacts with token extraction. Here are the key issues:

## Problems Identified:

1. **Input Token Truncation**: In `main.py` line 421, the input tokens are truncated to the last 100 tokens:
   ```python
   all_tokens = traj_data.get('input_tokens', [])[-100:] + traj_data.get('output_tokens', [])
   ```
   This causes the beginning of the prompt to be missing.

2. **Chat Template Overhead**: When using `apply_chat_template` with tools, it adds:
   - System prompts
   - Tool definitions (which can be very long)
   - Special tokens (`<|im_start|>`, `<|im_end|>`)
   - Formatting for the conversation structure

3. **Token Mismatch**: The actual content tokens don't align with what's displayed because the tokenizer includes all the formatting tokens.

## Solution:

Let me fix these issues by modifying how tokens are captured and stored:

---

### 👤 You

Only show the output tokens attention with input+previous output tokens. Do not show attention between input/input tokens

---

### 🤖 Cursor Assistant

I'll modify the attention tracking to only capture attention from output tokens to all previous tokens (input + previously generated outputs), excluding attention between input tokens.

---

### 🤖 Cursor Assistant

Let me check how attention weights are currently captured and modify the code to only track attention from output tokens:

---

### 🤖 Cursor Assistant

Now I'll modify the attention tracking to only capture attention from output tokens (not input-to-input attention):

---

### 🤖 Cursor Assistant

Now let's update the trajectory saving logic to handle the output-only attention format properly:

---

### 🤖 Cursor Assistant

Now let's update the main.py file to handle output-only attention properly:

---

### 🤖 Cursor Assistant

Let me create a test script to verify the output-only attention tracking:

---

### 🤖 Cursor Assistant

Let's now run a quick test with the ReAct agent to confirm the output-only attention works correctly with tool-calling:

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*