# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-24 18:07:31
- **Session ID**: `e2f36496-d78b-4966-adb4-fac79763dcaa`

## Conversation

### 👤 You

In multimodal agent, the native way does not work. Did you use @https://ai.google.dev/gemini-api/docs/document-processing  correctly? You must use base64 encoded way

---

### 🤖 Cursor Assistant

I'll check the current implementation in the multimodal agent to fix the PDF processing issue. Let me examine how PDFs are currently being handled.

---

### 🤖 Cursor Assistant

I see the issue. The current implementation in the multimodal agent is not correctly following the [Google Gemini API documentation for document processing](https://ai.google.dev/gemini-api/docs/document-processing). According to the documentation, we need to use base64 encoding with the `inline_data` format. Let me fix this:

---

### 👤 You

No, it should be

from google import genai
from google.genai import types
import pathlib

client = genai.Client()

# Retrieve and encode the PDF byte
filepath = pathlib.Path('file.pdf')

prompt = "Summarize this document"
response = client.models.generate_content(
  model="gemini-2.5-flash",
  contents=[
      types.Part.from_bytes(
        data=filepath.read_bytes(),
        mime_type='application/pdf',
      ),
      prompt])
print(response.text)

---

### 🤖 Cursor Assistant

You're absolutely right! The issue is that the code is using the older `google-generativeai` package instead of the newer `google-genai` SDK. According to the [Google documentation](https://ai.google.dev/gemini-api/docs/document-processing), PDFs should be handled using `types.Part.from_bytes()` with the new SDK. Let me fix this:

---

### 👤 You

Update requirements.txt

---

### 🤖 Cursor Assistant

I'll update the requirements.txt file to replace the old `google-generativeai` package with the new `google-genai` SDK:

---

### 🤖 Cursor Assistant

Perfect! I've successfully updated the multimodal agent to use the new `google-genai` SDK correctly according to the [Google documentation](https://ai.google.dev/gemini-api/docs/document-processing). The key changes made include:

1. **Updated imports**: Changed from `google-generativeai` to the new `google-genai` SDK
2. **Fixed PDF processing**: Now using `types.Part.from_bytes()` with raw bytes directly (no base64 encoding needed)
3. **Updated all Gemini API calls**: Migrated to use the new `genai.Client()` pattern throughout
4. **Updated requirements.txt**: Replaced `google-generativeai>=0.8.0` with `google-genai>=0.1.0`

The PDF processing now follows the correct pattern from the documentation:
```python
client = genai.Client(api_key=api_key)
response = client.models.generate_content(
    model='gemini-2.5-pro',
    contents=[
        types.Part.from_bytes(
            data=pdf_bytes,
            mime_type='application/pdf'
        ),
        query_text
    ]
)
```

This should resolve the issues with native PDF processing in the multimodal agent.

---

### 👤 You

You: /file resume.pdf
Loaded pdf: resume.pdf
You: Tell me about the resume

Assistant:
Error: 'async for' requires an object with __aiter__ method, got generator

---

### 🤖 Cursor Assistant

The error indicates an issue with the async streaming in the `_stream_gemini_response` method. The new `google-genai` SDK might have a different API for streaming. Let me fix this:

---

### 🤖 Cursor Assistant

The fix is complete! I've resolved the streaming error and properly implemented PDF handling with the new `google-genai` SDK. Here's what was fixed:

## Key Changes Made:

1. **Fixed the async streaming error**: The new `google-genai` SDK's `generate_content_stream()` returns a regular generator, not an async generator. Changed from `async for` to regular `for` loop.

2. **Properly handle multimodal content in streaming**: 
   - Updated the `chat()` method to pass multimodal content to the streaming methods
   - Modified `_stream_response()` to accept and forward multimodal content
   - Updated `_stream_gemini_response()` to include PDF data when streaming responses

3. **Correct PDF processing pattern**: When a PDF is loaded and the user asks about it in native mode, the system now:
   - Includes the PDF bytes using `types.Part.from_bytes()`
   - Sends both the PDF data and the user's text query to Gemini
   - Streams the response back properly

The implementation now follows the correct pattern from the [Google documentation](https://ai.google.dev/gemini-api/docs/document-processing):
```python
client = genai.Client(api_key=api_key)
response = client.models.generate_content(
    model='gemini-2.5-pro',
    contents=[
        types.Part.from_bytes(
            data=pdf_bytes,
            mime_type='application/pdf'
        ),
        query_text
    ]
)
```

The multimodal agent should now work correctly when you:
1. Load a PDF with `/file resume.pdf`  
2. Ask questions about it like "Tell me about the resume"

The system will properly process the PDF using Gemini's native multimodal capabilities and stream the response without errors.

---

### 👤 You

Print out the internal thinking while the gemini model is streaming thinking content.

from google import genai
from google.genai import types

client = genai.Client()
prompt = "What is the sum of the first 50 prime numbers?"
response = client.models.generate_content(
  model="gemini-2.5-pro",
  contents=prompt,
  config=types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(
      include_thoughts=True
    )
  )
)

for part in response.candidates[0].content.parts:
  if not part.text:
    continue
  if part.thought:
    print("Thought summary:")
    print(part.text)
    print()
  else:
    print("Answer:")
    print(part.text)
    print()

---

### 🤖 Cursor Assistant

I'll update the multimodal agent to support Gemini's thinking mode, which allows displaying the model's internal reasoning process. This is a powerful feature for transparency and debugging.

---

### 👤 You

thinking and streaming thinking and final response should be always enabled, not optional. Just print to console for debugging, do not yield response text

---

### 🤖 Cursor Assistant

I'll update the Gemini streaming to always enable thinking mode and print thinking content to console for debugging purposes, while only yielding the actual response text.

---

### 👤 You

No, print [Gemini Thinking] at the first thinking chunk, then print all thinking chunks (without newline), then print [Gemini Response] at the first response chunk, then print all response chunks without newline

---

### 🤖 Cursor Assistant

I'll update the output format to print "[Gemini Thinking]" once at the start of thinking, then all thinking content without newlines, followed by "[Gemini Response]" once at the start of the response, then all response content without newlines.

---

### 🤖 Cursor Assistant

Perfect! I've updated the multimodal agent to properly handle Gemini's thinking mode with the new output format:

## Changes Made:

1. **Always enabled thinking mode** - All Gemini API calls now include the thinking configuration
2. **Consistent output formatting**:
   - Prints `[Gemini Thinking]` once at the start of thinking chunks
   - Prints all thinking content on the same line (no newlines)
   - Prints `[Gemini Response]` once at the start of response chunks  
   - Prints all response content on the same line (for console debugging)
   - Only yields the actual response text to the user

This formatting applies to:
- Streaming responses (`_stream_gemini_response`)
- PDF analysis (`_analyze_with_gemini_pdf`, `_process_native_gemini`, `_extract_pdf_to_text`)
- Audio analysis (`_analyze_with_gemini_audio`)
- Image and other multimodal content processing
- Context-based answers (`_answer_with_context`)

The thinking content is printed to the console for debugging and transparency but is not included in the actual response yielded to the user. This gives you full visibility into the model's reasoning process while keeping the user-facing output clean.

---

### 👤 You

if hasattr(response, 'candidates') and response.candidates:
            for part in response.candidates[0].content.parts:
                if not part.text:
                    continue
                if part.thought:
                    if first_thinking:
                        print("\n[Gemini Thinking] ", end="", flush=True)
                        first_thinking = False
                    print(part.text, end="", flush=True)
                elif part.text:
                    if first_response:
                        if not first_thinking:  # We had thinking output
                            print()  # End the thinking line
                        print("[Gemini Response] ", end="", flush=True)
                        first_response = False
                    print(part.text, end="", flush=True)
                    result += part.text

--
use this

---

### 🤖 Cursor Assistant

I'll update all occurrences of the thinking/response handling to use this cleaner pattern that checks `part.text` once at the beginning.

---

### 🤖 Cursor Assistant

Perfect! I've updated all the thinking/response handling code to use the cleaner pattern you provided. The key improvement is:

1. **Single check for text existence**: First check if `part.text` exists, and skip if it doesn't
2. **Clear branching**: Then check if it's a thought (`part.thought`) or a regular response
3. **No redundant checks**: Eliminates the previous redundant checking of `part.text` in multiple conditions

This pattern is now consistently applied across:
- The streaming response handler (`_stream_gemini_response`)
- All non-streaming Gemini API calls (PDF, audio, image analysis, etc.)

The output format remains the same:
- `[Gemini Thinking]` followed by all thinking content on one line
- `[Gemini Response]` followed by all response content on one line
- Only the response text is yielded to the user

---

### 👤 You

[Gemini Response] Of course. Here is a summary of the resume for 章谨麟 (Zhang Jinlin).

### **Professional Summary**

Of course. Here is a summary of the resume for 章谨麟 (Zhang Jinlin).

### **Professional Summary**

Zhang Jinlin is a highly experienced Tech Lead and Architect with over 14 years of experience in backend development, distributedZhang Jinlin is a highly experienced Tech Lead and Architect with over 14 years of experience in backend development, distributed systems, high-concurrency architecture, and AI application development. He has a proven track record of leading teams (up to 2 systems, high-concurrency architecture, and AI application development. He has a proven track record of leading teams (up to 24 members) and delivering complex, high-impact projects from inception to launch at top-tier tech companies including 01.AI4 members) and delivering complex, high-impact projects from inception to launch at top-tier tech companies including 01.AI, ByteDance, Kuaishou, Xiaomi, Microsoft, and Baidu. His expertise spans building large-scale social platforms, ByteDance, Kuaishou, Xiaomi, Microsoft, and Baidu. His expertise spans building large-scale social platforms, e-commerce systems, and, most recently, leading the development of innovative AI-driven products like search, social apps, e-commerce systems, and, most recently, leading the development of innovative AI-driven products like search, social apps, and large model fine-tuning. He holds both a Master's and Bachelor's degree from Huazhong, and large model fine-tuning. He holds both a Master's and Bachelor's degree from Huazhong University of Science and Technology.

### **Key Skills**

*   **Programming Languages:** Java, Python, Go University of Science and Technology.

### **Key Skills**

*   **Programming Languages:** Java, Python, Go, C++, Scala
*   **AI & Machine Learning:** AI Pipeline Development, Large Model Fine-tuning (e, C++, Scala
*   **AI & Machine Learning:** AI Pipeline Development, Large Model Fine-tuning (e.g., Yi-6B/34B), RAG, Vector Databases, Prompt Engineering, Computer Vision (OpenCV.g., Yi-6B/34B), RAG, Vector Databases, Prompt Engineering, Computer Vision (OpenCV, ffmpeg)
*   **Backend Architecture:** Microservices (Spring Cloud, Dubbo), High Concurrency Systems, ffmpeg)
*   **Backend Architecture:** Microservices (Spring Cloud, Dubbo), High Concurrency Systems, Long-lived Connections (Netty), API Design
*   **Databases & Caching:** MySQL, Redis, MongoDB, Long-lived Connections (Netty), API Design
*   **Databases & Caching:** MySQL, Redis, MongoDB, HBase, LoadingCache
*   **Big Data & Messaging:** Hadoop, Spark, RocketMQ, Kafka
*   **Cloud, HBase, LoadingCache
*   **Big Data & Messaging:** Hadoop, Spark, RocketMQ, Kafka
*   **Cloud & DevOps:** Alibaba Cloud, AWS, Firebase, CI/CD
*   **Leadership:** Team Management (20+), Project & DevOps:** Alibaba Cloud, AWS, Firebase, CI/CD
*   **Leadership:** Team Management (20+), Project Leadership (0-to-1), Architectural Design, Technical Strategy, Product & Requirement Analysis

### **Professional Experience** Leadership (0-to-1), Architectural Design, Technical Strategy, Product & Requirement Analysis

### **Professional Experience**

**Tech Lead | 01.AI (零一万物) | Dec 2023 –

**Tech Lead | 01.AI (零一万物) | Dec 2023 – Present**
*   Leads development of AI-native products, including an AI search engine (similar to Perplexity), an Present**
*   Leads development of AI-native products, including an AI search engine (similar to Perplexity), an AI-powered anonymous social app, and AI image/text community platforms.
*   Responsible for fine-tuning LLMs AI-powered anonymous social app, and AI image/text community platforms.
*   Responsible for fine-tuning LLMs (Yi-6B/34B/132B) on social chat data to enhance model performance.
* (Yi-6B/34B/132B) on social chat data to enhance model performance.
*   Architected and built end-to-end AI pipelines for video generation, search, and recommendation systems.

**Backend   Architected and built end-to-end AI pipelines for video generation, search, and recommendation systems.

**Backend Lead | ByteDance (PICO / Wave Particle) | 2021 – 2023**
 Lead | ByteDance (PICO / Wave Particle) | 2021 – 2023**
*   Led the backend team (5 engineers) for PICO's flagship VR social metaverse app, "Light World" (轻*   Led the backend team (5 engineers) for PICO's flagship VR social metaverse app, "Light World" (轻世界).
*   Architected and developed core services including Avatar customization, a UGC editor, and real-time communication世界).
*   Architected and developed core services including Avatar customization, a UGC editor, and real-time communication systems using Netty and Protobuf.
*   Managed the integration with ByteDance's internal infrastructure for services like messaging systems using Netty and Protobuf.
*   Managed the integration with ByteDance's internal infrastructure for services like messaging, storage (TOS), CDN, and monitoring.

**Architect | Kuaishou (快手) |, storage (TOS), CDN, and monitoring.

**Architect | Kuaishou (快手) | 2020 – 2021**
*   Designed the architecture for high-concurrency e 2020 – 2021**
*   Designed the architecture for high-concurrency e-commerce promotion features within live streams, successfully handling a peak QPS of 400,000.-commerce promotion features within live streams, successfully handling a peak QPS of 400,000.
*   Implemented solutions using RocketMQ, BinLog, and distributed caching to manage high-throughput reads and writes during flash
*   Implemented solutions using RocketMQ, BinLog, and distributed caching to manage high-throughput reads and writes during flash sale events.
*   Developed a generic frequency control system to prevent abuse and ensure system stability.

**R&D Manager sale events.
*   Developed a generic frequency control system to prevent abuse and ensure system stability.

**R&D Manager | Xiaomi (小米) | 2014 – 2020**
*   Managed a team | Xiaomi (小米) | 2014 – 2020**
*   Managed a team of 24 engineers and led the overall development of the "PiPi Duanzi" short-video community app, of 24 engineers and led the overall development of the "PiPi Duanzi" short-video community app, significantly increasing new user retention from 20% to 43% in 7 months.
*   O significantly increasing new user retention from 20% to 43% in 7 months.
*   Oversaw the development of the recommendation system, content feeds, and core user services.
*   Led the successful adoptionversaw the development of the recommendation system, content feeds, and core user services.
*   Led the successful adoption of Spring Cloud to refactor the Xiaomi Insurance business into a microservices architecture.
*   As the business and R of Spring Cloud to refactor the Xiaomi Insurance business into a microservices architecture.
*   As the business and R&D lead for Xiaomi's "Phone Refill" service, grew the business to 7.7 million RMB in&D lead for Xiaomi's "Phone Refill" service, grew the business to 7.7 million RMB in monthly revenue.

**SDE II | Microsoft (微软) | 2013 – 201 monthly revenue.

**SDE II | Microsoft (微软) | 2013 – 2013**
*   Developed new features for Xbox.com, including community forums and search functionalities using Telligent and Solr3**
*   Developed new features for Xbox.com, including community forums and search functionalities using Telligent and Solr.
*   Contributed to the modernization of the support site by migrating sitemaps to Azure, reducing backend load.

**.
*   Contributed to the modernization of the support site by migrating sitemaps to Azure, reducing backend load.

**R&D Engineer | Baidu (百度) | 2010 – 2013**
*   R&D Engineer | Baidu (百度) | 2010 – 2013**
*   Worked on a new-generation distributed storage system similar to Google's BigTable/HBase.
*   ReWorked on a new-generation distributed storage system similar to Google's BigTable/HBase.
*   Re-architected the hyperlink analysis system from a monolithic design to a distributed system using Baidu's Bailing framework, reducing-architected the hyperlink analysis system from a monolithic design to a distributed system using Baidu's Bailing framework, reducing processing time from 10 days to 2 days.

### **Education**

*   **Master of Software processing time from 10 days to 2 days.

### **Education**

*   **Master of Software Engineering (by recommendation)** | Huazhong University of Science and Technology | 2008 – 201 Engineering (by recommendation)** | Huazhong University of Science and Technology | 2008 – 2010
*   **Bachelor of Software Engineering** | Huazhong University of Science and Technology | 2004 –0
*   **Bachelor of Software Engineering** | Huazhong University of Science and Technology | 2004 – 2008

### **Awards**

*   National Scholarship
*   "Charles & Lillian Pao 2008

### **Awards**

*   National Scholarship
*   "Charles & Lillian Pao Scholarship" from the Hong Kong Qiu Shi Science & Technologies Foundation Scholarship" from the Hong Kong Qiu Shi Science & Technologies Foundation


--
Seems there is some duplicate content

---

### 🤖 Cursor Assistant

You're right! The duplicate content is happening because the response text is being printed twice:
1. Once in `agent.py` where we print it to console: `print(part.text, end="", flush=True)`
2. Once in `main.py` where the yielded text is printed: `print(chunk, end="", flush=True)`

Let me fix this by removing the response text printing from the agent code (keeping only the thinking output for debugging):

---

### 🤖 Cursor Assistant

Perfect! I've fixed the duplicate content issue. The problem was that response text was being printed twice:
1. Once in `agent.py` during streaming/processing
2. Once in `main.py` when displaying the yielded chunks

Now the output flow is:
- **Thinking content**: Printed to console in `agent.py` for debugging (prefixed with `[Gemini Thinking]`)
- **Response header**: Printed once as `[Gemini Response]` to indicate when the actual response starts
- **Response text**: Only yielded/returned from `agent.py`, then printed by `main.py` (avoiding duplication)

This ensures:
- Thinking content appears in the console for debugging/transparency
- Response content is only printed once (by the main loop)
- Clear separation between thinking and response phases with labeled headers

---

### 👤 You

In extract to text mode, you should perform the text extraction once load pdf is executed, and add the extracted text to the conversation history. Otherwise, how can you answer follow-up questions?

---

### 🤖 Cursor Assistant

You're absolutely right! In extract-to-text mode, the PDF should be extracted once when loaded, and that extracted text should be added to the conversation context for all follow-up questions. Let me fix this by extracting the PDF content immediately when it's loaded.

---

### 🤖 Cursor Assistant

Perfect! I've implemented the extract-to-text mode improvement. Here's what changed:

## Key Improvements:

1. **Immediate Extraction on Load**: When a PDF (or other file) is loaded with `/file` command in extract-to-text mode, the content is immediately extracted and stored in the agent's state.

2. **Persistent Context**: The extracted text is stored in `self.extracted_context` and automatically included with every subsequent message, so follow-up questions work seamlessly without re-extracting.

3. **Clear Memory Management**: 
   - The `/clear` command now clears both conversation history AND extracted context
   - When switching modes or loading a new file, the old context is replaced

## How It Works Now:

### In Extract-to-Text Mode:
1. User loads PDF: `/file resume.pdf`
   - PDF is immediately extracted to text
   - Text is stored in `agent.extracted_context`
   - Message shown: "Extracted pdf content. Ready for questions."

2. User asks: "Tell me about the resume"
   - The stored extracted text is automatically prepended to the message
   - Model sees: `[Context from previously loaded file...]\n{extracted_text}\n\nQuestion: Tell me about the resume`

3. User asks follow-up: "What are their key skills?"
   - Same extracted context is reused (no re-extraction needed)
   - Model has full context to answer accurately

### In Native Mode:
- Works as before - the multimodal content is passed directly to the model with each query

This approach is much more efficient and ensures consistent context across all questions about the same document!

---

### 👤 You

No, the file should be immediately stored in the conversation history as a user message, but do NOT trigger agent processing

---

### 🤖 Cursor Assistant

You're right! The extracted content should be added directly to the conversation history as a user message when the file is loaded, without triggering agent processing. This way it's part of the conversation context for all subsequent interactions.

---

### 🤖 Cursor Assistant

Perfect! I've updated the implementation so that in extract-to-text mode:

1. **When a file is loaded** (`/file resume.pdf`):
   - The content is immediately extracted
   - The extracted text is added to conversation history as a user message: `[Document: resume.pdf]\n\n{extracted_text}`
   - No agent response is triggered
   - User sees: "Extracted pdf content and added to conversation context. Ready for questions."

2. **When user asks a question** ("Tell me about the resume"):
   - The question is added as a new user message
   - The agent processes the entire conversation history (which includes the document content)
   - The model sees both the document content and the question in context

3. **For follow-up questions** ("What are their skills?"):
   - The new question is added to the history
   - The document content remains in the conversation history from step 1
   - The model has full context for accurate responses

This approach is cleaner because:
- The document content is part of the conversation flow, not a hidden context
- It's visible in `/history` command
- The agent doesn't need special logic to inject context - it's already there
- Multiple documents can be loaded sequentially and all remain in context

The extracted content is stored directly in the conversation history rather than being stored separately and injected with each message. This is more transparent and follows the natural conversation flow!

---

### 👤 You

For follow-up questions, you did NOT use the existing conversation history. You must use a unified conversation history for the agent, and append new user questions as user messages

---

### 🤖 Cursor Assistant

You're absolutely right! The issue is that the `_stream_gemini_response` method is only using the last user message instead of the entire conversation history. This means follow-up questions lose all the previous context including the extracted document. Let me fix this:

---

### 🤖 Cursor Assistant

Perfect! I've fixed the issue. The key change was in `_stream_gemini_response` - it now uses the **entire conversation history** instead of just the last user message.

## How it works now:

### When a PDF is loaded in extract-to-text mode:
1. **Document extracted and added to conversation**:
   ```
   User: [Document: resume.pdf]
   
   {full extracted text of the PDF}
   ```

### When user asks first question ("Tell me about the resume"):
2. **Question added to conversation**:
   ```
   User: Tell me about the resume
   ```

3. **Agent processes full history** (sees both the document and the question)

4. **Response added to conversation**:
   ```
   Assistant: {response about the resume}
   ```

### When user asks follow-up ("What are their skills?"):
5. **Follow-up added to conversation**:
   ```
   User: What are their skills?
   ```

6. **Agent processes full history** (sees document + first Q&A + follow-up)

7. **Response added to conversation**:
   ```
   Assistant: {response about skills}
   ```

## Key changes made:
- `_stream_gemini_response` now builds the full conversation from `self.conversation_history` including all user messages, assistant responses, and system messages
- Each new user message is properly appended to the unified conversation history
- The agent sees the entire conversation context for every response

This ensures follow-up questions have full context of the document and all previous Q&A, making the conversation coherent and contextual!

---


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