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Zhongwei Li
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skills/langgraph-master/06_llm_model_ids_openai_advanced.md
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skills/langgraph-master/06_llm_model_ids_openai_advanced.md
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# OpenAI GPT-5 Advanced Features
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Advanced settings and multimodal features for GPT-5 models.
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## Parameter Settings
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```python
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from langchain_openai import ChatOpenAI
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llm = ChatOpenAI(
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model="gpt-5",
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temperature=0.7, # Creativity (0.0-2.0)
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max_tokens=128000, # Max output (GPT-5: 128K)
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top_p=0.9, # Diversity
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frequency_penalty=0.0, # Repetition penalty
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presence_penalty=0.0, # Topic diversity
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)
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# GPT-5 Pro (larger max output)
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llm_pro = ChatOpenAI(
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model="gpt-5-pro",
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max_tokens=272000, # GPT-5 Pro: 272K
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)
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```
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## Context Window and Output Limits
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| Model | Context Window | Max Output Tokens |
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|--------|-------------------|---------------|
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| `gpt-5` | 400,000 (API) | 128,000 |
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| `gpt-5-mini` | 400,000 (API) | 128,000 |
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| `gpt-5-nano` | 400,000 (API) | 128,000 |
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| `gpt-5-pro` | 400,000 | 272,000 |
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| `gpt-5.1` | 128,000 (ChatGPT) / 400,000 (API) | 128,000 |
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| `gpt-5.1-codex` | 400,000 | 128,000 |
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**Note**: Context window is the combined length of input + output.
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## Vision (Image Processing)
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```python
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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llm = ChatOpenAI(model="gpt-5")
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message = HumanMessage(
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content=[
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{"type": "text", "text": "What is shown in this image?"},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://example.com/image.jpg",
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"detail": "high" # "low", "high", "auto"
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}
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}
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]
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)
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response = llm.invoke([message])
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```
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## Tool Use (Function Calling)
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```python
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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@tool
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def get_weather(location: str) -> str:
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"""Get weather"""
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return f"The weather in {location} is sunny"
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@tool
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def calculate(expression: str) -> float:
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"""Calculate"""
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return eval(expression)
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llm = ChatOpenAI(model="gpt-5")
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llm_with_tools = llm.bind_tools([get_weather, calculate])
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response = llm_with_tools.invoke("Tell me the weather in Tokyo and 2+2")
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print(response.tool_calls)
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```
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## Parallel Tool Calling
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```python
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@tool
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def get_stock_price(symbol: str) -> float:
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"""Get stock price"""
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return 150.25
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@tool
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def get_company_info(symbol: str) -> dict:
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"""Get company information"""
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return {"name": "Apple Inc.", "industry": "Technology"}
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llm = ChatOpenAI(model="gpt-5")
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llm_with_tools = llm.bind_tools([get_stock_price, get_company_info])
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# Call multiple tools in parallel
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response = llm_with_tools.invoke("Tell me the stock price and company info for AAPL")
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```
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## Streaming
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```python
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llm = ChatOpenAI(
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model="gpt-5",
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streaming=True
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)
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for chunk in llm.stream("Question"):
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print(chunk.content, end="", flush=True)
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```
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## JSON Mode
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```python
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llm = ChatOpenAI(
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model="gpt-5",
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model_kwargs={"response_format": {"type": "json_object"}}
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)
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response = llm.invoke("Return user information in JSON format")
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```
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## Using GPT-5.1 Adaptive Reasoning
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### Instant Mode
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Balance between speed and accuracy:
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```python
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llm = ChatOpenAI(model="gpt-5.1-instant")
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# Adaptively adjusts reasoning time
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response = llm.invoke("Solve this problem...")
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```
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### Thinking Mode
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Deep thought for complex problems:
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```python
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llm = ChatOpenAI(model="gpt-5.1-thinking")
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# Improves accuracy with longer thinking time
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response = llm.invoke("Complex math problem...")
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```
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## Leveraging GPT-5 Pro
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Extended reasoning for enterprise and research environments:
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```python
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llm = ChatOpenAI(
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model="gpt-5-pro",
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temperature=0.3, # Precision-focused
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max_tokens=272000 # Large output possible
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)
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# More detailed and reliable responses
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response = llm.invoke("Detailed analysis of...")
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```
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## Code Generation Specialized Models
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```python
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# Codex used in GitHub Copilot
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llm = ChatOpenAI(model="gpt-5.1-codex")
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response = llm.invoke("Implement quicksort in Python")
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# Compact version (fast)
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llm_mini = ChatOpenAI(model="gpt-5.1-codex-mini")
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```
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## Tracking Token Usage
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```python
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from langchain.callbacks import get_openai_callback
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llm = ChatOpenAI(model="gpt-5")
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with get_openai_callback() as cb:
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response = llm.invoke("Question")
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print(f"Total Tokens: {cb.total_tokens}")
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print(f"Prompt Tokens: {cb.prompt_tokens}")
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print(f"Completion Tokens: {cb.completion_tokens}")
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print(f"Total Cost (USD): ${cb.total_cost}")
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```
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## Azure OpenAI Service
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GPT-5 is also available on Azure:
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```python
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from langchain_openai import AzureChatOpenAI
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llm = AzureChatOpenAI(
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azure_endpoint="https://your-resource.openai.azure.com/",
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api_key="your-azure-api-key",
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api_version="2024-12-01-preview",
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deployment_name="gpt-5",
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model="gpt-5"
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)
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```
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### Environment Variables (Azure)
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```bash
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export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
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export AZURE_OPENAI_API_KEY="your-azure-api-key"
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export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5"
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```
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## Error Handling
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```python
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from langchain_openai import ChatOpenAI
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from openai import OpenAIError, RateLimitError
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try:
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llm = ChatOpenAI(model="gpt-5")
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response = llm.invoke("Question")
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except RateLimitError:
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print("Rate limit reached")
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except OpenAIError as e:
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print(f"OpenAI error: {e}")
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```
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## Handling Rate Limits
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```python
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from tenacity import retry, wait_exponential, stop_after_attempt
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from openai import RateLimitError
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@retry(
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wait=wait_exponential(multiplier=1, min=4, max=60),
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stop=stop_after_attempt(5),
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retry=lambda e: isinstance(e, RateLimitError)
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)
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def invoke_with_retry(llm, messages):
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return llm.invoke(messages)
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llm = ChatOpenAI(model="gpt-5")
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response = invoke_with_retry(llm, ["Question"])
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```
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## Leveraging Large Context
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Utilizing GPT-5's 400K context window:
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```python
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llm = ChatOpenAI(model="gpt-5")
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# Process large amounts of documents at once
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long_document = "..." * 100000 # Long document
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response = llm.invoke(f"""
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Please analyze the following document:
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{long_document}
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Provide a summary and key points.
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""")
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```
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## Compaction Technology
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GPT-5.1 introduces technology that effectively handles longer contexts:
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```python
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# Processing very long conversation histories or documents
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llm = ChatOpenAI(model="gpt-5.1")
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# Efficiently processed through Compaction
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response = llm.invoke(very_long_context)
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```
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## Reference Links
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- [OpenAI GPT-5 Documentation](https://openai.com/gpt-5/)
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- [OpenAI GPT-5.1 Documentation](https://openai.com/index/gpt-5-1/)
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- [OpenAI API Reference](https://platform.openai.com/docs/api-reference)
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- [OpenAI Platform Models](https://platform.openai.com/docs/models)
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- [Azure OpenAI Documentation](https://learn.microsoft.com/azure/ai-services/openai/)
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