[TRTLLM-9242][doc] Add examples showcasing openai compatible APIs

[JunyiXu-nv]

Summary by CodeRabbit

• Documentation
  • Added comprehensive README guides for OpenAI API compatibility with examples covering chat completions, streaming responses, multi-turn conversations, JSON-mode outputs, tool calling, and advanced sampling parameters.
  • Included example scripts demonstrating practical usage patterns for the chat completions endpoint across various use cases.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

Description

Added more example codes for users to interact with trtllm-serve by openai sdk.

Test Coverage

PR Checklist

Please review the following before submitting your PR:

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• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

• Test cases are provided for new code paths (see test instructions)

• Any new dependencies have been scanned for license and vulnerabilities

• CODEOWNERS updated if ownership changes

• Documentation updated as needed

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[JunyiXu-nv]

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[coderabbitai]

📝 Walkthrough

Walkthrough

This PR adds documentation and example scripts demonstrating OpenAI API compatibility features for TensorRT-LLM's Chat Completions endpoint, including basic chat, streaming, multi-turn conversations, JSON mode, tool calling, and advanced sampling parameters.

Changes

Cohort / File(s)	Summary
Documentation examples/serve/compatibility/README.md, examples/serve/compatibility/chat_completions/README.md	Adds README files documenting the OpenAI API compatibility examples, prerequisites, server startup, client configuration, and a table of model requirements per feature (basic chat, streaming, JSON mode, tool calling).
Basic Chat Examples examples/serve/compatibility/chat_completions/example_01_basic_chat.py, examples/serve/compatibility/chat_completions/example_02_streaming_chat.py, examples/serve/compatibility/chat_completions/example_03_multi_turn_conversation.py	Example scripts demonstrating basic non-streaming chat, streaming chat with token-by-token output, and multi-turn conversation with context preservation.
Advanced Chat Examples examples/serve/compatibility/chat_completions/example_04_streaming_with_usage.py, examples/serve/compatibility/chat_completions/example_05_json_mode.py, examples/serve/compatibility/chat_completions/example_06_tool_calling.py, examples/serve/compatibility/chat_completions/example_07_advanced_sampling.py	Example scripts covering streaming with token usage statistics, structured JSON responses with schema validation, tool/function calling with simulated weather function, and advanced sampling parameters via extra\_body.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Client
    participant Server

    rect rgb(200, 220, 255)
    Note over User,Server: Non-Streaming / Multi-Turn Pattern
    User->>Client: Initialize with base_url, api_key
    Client->>Server: GET /v1/models
    Server-->>Client: Return model list
    Client->>User: Select model
    User->>Client: Call chat_completions.create()
    Client->>Server: POST /v1/chat/completions
    Server-->>Client: Return full response
    Client->>User: Display response & usage
    end

Loading

sequenceDiagram
    participant User
    participant Client
    participant Server

    rect rgb(220, 255, 220)
    Note over User,Server: Streaming Pattern
    User->>Client: Initialize with base_url, api_key
    Client->>Server: GET /v1/models
    Server-->>Client: Return model list
    User->>Client: Call chat_completions.create(stream=True)
    Client->>Server: POST /v1/chat/completions (stream=true)
    loop Each chunk arrives
        Server-->>Client: Streaming chunk
        Client->>User: Print token/delta
    end
    Client->>User: Display final stop_reason
    end

Loading

sequenceDiagram
    participant User
    participant Client
    participant Server
    participant ToolFunction

    rect rgb(255, 240, 200)
    Note over User,Server: Tool Calling Pattern
    User->>Client: Initialize & select model
    User->>Client: Call chat_completions.create(tools=[...])
    Client->>Server: POST /v1/chat/completions with tools
    Server-->>Client: Response with tool_calls
    Client->>User: Display tool call details
    User->>ToolFunction: Execute tool function locally
    ToolFunction-->>User: Return result
    User->>Client: Call chat_completions.create(messages=[...tool_result...])
    Client->>Server: POST /v1/chat/completions with tool result
    Server-->>Client: Final response
    Client->>User: Display final response
    end

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

• Documentation accuracy: Verify README content matches example implementations, especially model requirements table and feature descriptions
• Example script correctness: Each of the seven example scripts should be reviewed for:
  • Proper error handling (especially example_05_json_mode.py and example_06_tool_calling.py)
  • Correct API parameter usage (particularly example_04_streaming_with_usage.py with stream\_options and example_07_advanced_sampling.py with extra\_body)
• Tool calling implementation (example_06_tool_calling.py): Verify the tool specification format and function result message structure are correct
• Consistency: Ensure client initialization, model retrieval, and output formatting patterns are consistent across all scripts

Pre-merge checks and finishing touches

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description check	❓ Inconclusive	The description provides basic context but lacks critical sections required by the template: detailed explanation of the issue/solution, comprehensive test coverage information, and unchecked checklist items indicating incomplete review.	Expand the description section to explain the rationale and key examples added; document what testing approach was used (manual, automated, etc.) and add clarification on unchecked checklist items.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately summarizes the main change: adding example documentation and scripts showcasing OpenAI-compatible APIs for TensorRT-LLM.

✨ Finishing touches

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• Create PR with unit tests
• Post copyable unit tests in a comment

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[tensorrt-cicd]

PR_Github #26005 [ run ] triggered by Bot. Commit: f934896

[coderabbitai]

Actionable comments posted: 8

🧹 Nitpick comments (7)

examples/serve/compatibility/chat_completions/example_02_streaming_chat.py (1)

40-61: Potential NameError if stream is empty.

The chunk variable is used on line 61 after the for loop. If the stream yields no chunks, chunk would be undefined, causing a NameError. While unlikely in practice, consider guarding against this edge case.

 current_state = "none"
+finish_reason = None
 for chunk in stream:
     has_content = hasattr(chunk.choices[0].delta, "content") and chunk.choices[0].delta.content
     has_reasoning_content = (
         hasattr(chunk.choices[0].delta, "reasoning_content")
         and chunk.choices[0].delta.reasoning_content
     )
     if has_content:
         if current_state != "content":
             print("Content: ", end="", flush=True)
             current_state = "content"

         print(chunk.choices[0].delta.content, end="", flush=True)

     if has_reasoning_content:
         if current_state != "reasoning_content":
             print("Reasoning: ", end="", flush=True)
             current_state = "reasoning_content"

         print(chunk.choices[0].delta.reasoning_content, end="", flush=True)
+
+    if chunk.choices[0].finish_reason:
+        finish_reason = chunk.choices[0].finish_reason
 print("\n")

-print("Stop reason: ", chunk.choices[0].finish_reason)
+print("Stop reason: ", finish_reason)

examples/serve/compatibility/chat_completions/example_05_json_mode.py (1)

48-65: Consider narrowing the exception handling scope.

The broad Exception catch (flagged by Ruff BLE001) obscures different failure modes. For an example script, this is acceptable since it provides a clear message about requirements. However, you could improve clarity by catching specific exceptions.

+from openai import APIError
+
 try:
     # Create chat completion with JSON schema
     response = client.chat.completions.create(
         model=model,
         messages=[
             {"role": "system", "content": "You are a helpful assistant that outputs JSON."},
             {"role": "user", "content": "Give me information about Tokyo."},
         ],
         response_format={"type": "json_schema", "json_schema": schema},
         max_tokens=4096,
     )

     print("JSON Response:")
     result = json.loads(response.choices[0].message.content)
     print(json.dumps(result, indent=2))
-except Exception as e:
+except (APIError, json.JSONDecodeError) as e:
     print("JSON schema support requires xgrammar and proper configuration.")
     print(f"Error: {e}")

examples/serve/compatibility/chat_completions/example_04_streaming_with_usage.py (2)

15-17: Optionally guard against an empty model list from the server.

models.data[0] on Lines 16–17 will raise IndexError if the server returns no models. In practice this should not happen, but a small guard would make the example more robust and error-message friendly.

For example:

-# Get the model name from the server
-models = client.models.list()
-model = models.data[0].id
+# Get the model name from the server
+models = client.models.list()
+if not models.data:
+    raise RuntimeError("No models available from the server; please verify trtllm-serve is running with a model.")
+model = models.data[0].id

---

36-66: Guard access to chunk.usage in case no chunks or no usage info are returned.

Lines 62–66 assume that:

• At least one chunk was received, and
• The last chunk has a .usage field.

If the stream fails early or the server doesn’t send usage (misconfig, older server, etc.), this will raise an AttributeError. You can keep behavior identical for the happy path but add a safe fallback:

-chunk = None
+chunk = None
 current_state = "none"
 for chunk in stream:
@@
 print()
 
-print(
-    f"Tokens used: {chunk.usage.total_tokens} "
-    f"(prompt: {chunk.usage.prompt_tokens}, "
-    f"completion: {chunk.usage.completion_tokens})"
-)
+if chunk is not None and getattr(chunk, "usage", None) is not None:
+    print(
+        f"Tokens used: {chunk.usage.total_tokens} "
+        f"(prompt: {chunk.usage.prompt_tokens}, "
+        f"completion: {chunk.usage.completion_tokens})"
+    )
+else:
+    print("Tokens used: (usage information not available)")

examples/serve/compatibility/chat_completions/example_06_tool_calling.py (1)

100-105: Narrow or document the broad except Exception handler.

The except Exception as e: on Lines 103–105 is intentionally user‑friendly but very broad and suppresses all unexpected errors, which can make debugging harder and conflicts with the guideline of catching the smallest relevant set of exceptions.

For an example script, two lightweight options:

• Narrow to the specific client/network exceptions you expect (e.g., connection errors, API errors), or
• Keep the broad handler but re‑raise unknown errors after logging, or at least add a comment/# noqa: BLE001 to acknowledge the trade‑off.

For example:

-except Exception as e:
-    print("Note: Tool calling requires model support (e.g., Llama 3.1+ models)")
-    print(f"Error: {e}")
+except Exception as e:
+    # Broad catch for demo purposes; in production, catch specific client/network errors instead.
+    print("Note: Tool calling requires model support (e.g., Llama 3.1+ models)")
+    print(f"Error: {e}")

examples/serve/compatibility/chat_completions/README.md (1)

43-47: Align tool‑calling model examples and clean up wording.

The tool‑calling requirements are described slightly differently in a few places:

• Here in the Examples Overview and Model Requirements table you mention “Qwen3, gpt_oss”.
• In example_06_tool_calling.py you mention “Llama 3.1+, Mistral Instruct”.
• In the top‑level compatibility README you refer generically to “tool‑capable” models.

To avoid confusing users, consider standardizing the wording across these docs, e.g., something like:

Requires a tool‑capable model (e.g., Qwen3, Llama 3.1+, Mistral Instruct, gpt_oss).

Also, in the Model Requirements table row:

| Tool calling | Compatible model (Qwen3 and gpt_oss.) |

you can drop the final period and/or make the phrasing parallel with the JSON mode row, e.g.:

| Tool calling | Compatible model (Qwen3, `gpt_oss`, etc.) |

Also applies to: 94-100

examples/serve/compatibility/README.md (1)

12-16: Tighten wording around reasoning/tool‑calling models.

The sentence around Lines 12–16 reads a bit awkwardly:

for reasoning model or model with tool calling ability. Specify --tool_parser and --reasoning_parser, e.g.

Consider something like:

For a reasoning model or a model with tool‑calling ability, specify `--tool_parser` and `--reasoning_parser`, for example:

This both fixes the grammar and adds the recommended hyphenation.

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📥 Commits

Reviewing files that changed from the base of the PR and between 3ada0bf and f934896.

📒 Files selected for processing (9)

• examples/serve/compatibility/README.md (1 hunks)
• examples/serve/compatibility/chat_completions/README.md (1 hunks)
• examples/serve/compatibility/chat_completions/example_01_basic_chat.py (1 hunks)
• examples/serve/compatibility/chat_completions/example_02_streaming_chat.py (1 hunks)
• examples/serve/compatibility/chat_completions/example_03_multi_turn_conversation.py (1 hunks)
• examples/serve/compatibility/chat_completions/example_04_streaming_with_usage.py (1 hunks)
• examples/serve/compatibility/chat_completions/example_05_json_mode.py (1 hunks)
• examples/serve/compatibility/chat_completions/example_06_tool_calling.py (1 hunks)
• examples/serve/compatibility/chat_completions/example_07_advanced_sampling.py (1 hunks)

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces; do not use tabs
Always maintain the namespace when importing in Python, even if only one class or function from a module is used (e.g., use from package.subpackage import foo and then foo.SomeClass() instead of from package.subpackage.foo import SomeClass)
Python filenames should use snake_case (e.g., some_file.py)
Python class names should use PascalCase (e.g., class SomeClass)
Python function and method names should use snake_case (e.g., def my_awesome_function():)
Python local variable names should use snake_case, with prefix k for variable names that start with a number (e.g., k_99th_percentile = ...)
Python global variables should use upper snake_case with prefix G (e.g., G_MY_GLOBAL = ...)
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...)
Avoid shadowing variables declared in an outer scope in Python
Initialize all externally visible members of a Python class in the constructor
For Python interfaces that may be used outside a file, prefer docstrings over comments
Python comments should be reserved for code within a function, or interfaces that are local to a file
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx
Python attributes and variables can be documented inline with type and description (e.g., self.x = 5 followed by """<type>: Description of 'x'""" )
Avoid using reflection in Python when functionality can be easily achieved without reflection
When using try-except blocks in Python, limit the except clause to the smallest set of specific errors possible instead of catching all exceptions
When using try-except blocks in Python to handle multiple possible variable types (duck-typing), keep the body of the try as small as possible and use the else block to implement the logic

Files:

• examples/serve/compatibility/chat_completions/example_02_streaming_chat.py
• examples/serve/compatibility/chat_completions/example_06_tool_calling.py
• examples/serve/compatibility/chat_completions/example_04_streaming_with_usage.py
• examples/serve/compatibility/chat_completions/example_05_json_mode.py
• examples/serve/compatibility/chat_completions/example_03_multi_turn_conversation.py
• examples/serve/compatibility/chat_completions/example_01_basic_chat.py
• examples/serve/compatibility/chat_completions/example_07_advanced_sampling.py

**/*.{cpp,h,cu,py}

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

All TensorRT-LLM Open Source Software code files should contain an NVIDIA copyright header that includes the current year at the top

Files:

• examples/serve/compatibility/chat_completions/example_02_streaming_chat.py
• examples/serve/compatibility/chat_completions/example_06_tool_calling.py
• examples/serve/compatibility/chat_completions/example_04_streaming_with_usage.py
• examples/serve/compatibility/chat_completions/example_05_json_mode.py
• examples/serve/compatibility/chat_completions/example_03_multi_turn_conversation.py
• examples/serve/compatibility/chat_completions/example_01_basic_chat.py
• examples/serve/compatibility/chat_completions/example_07_advanced_sampling.py

🧠 Learnings (3)

📓 Common learnings

Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.

Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

📚 Learning: 2025-08-01T15:14:45.673Z

Learnt from: yibinl-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.

Applied to files:

• examples/serve/compatibility/README.md

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

Applied to files:

• examples/serve/compatibility/README.md

🧬 Code graph analysis (1)

examples/serve/compatibility/chat_completions/example_02_streaming_chat.py (1)

tensorrt_llm/inputs/multimodal.py (1)

• has_content (519-521)

🪛 LanguageTool

examples/serve/compatibility/README.md

[grammar] ~12-~12: Use a hyphen to join words.
Context: ... for reasoning model or model with tool calling ability. Specify --tool_parser...

(QB_NEW_EN_HYPHEN)

🪛 Ruff (0.14.6)

examples/serve/compatibility/chat_completions/example_02_streaming_chat.py

1-1: Shebang is present but file is not executable

(EXE001)

examples/serve/compatibility/chat_completions/example_06_tool_calling.py

1-1: Shebang is present but file is not executable

(EXE001)

---

103-103: Do not catch blind exception: Exception

(BLE001)

examples/serve/compatibility/chat_completions/example_04_streaming_with_usage.py

1-1: Shebang is present but file is not executable

(EXE001)

examples/serve/compatibility/chat_completions/example_05_json_mode.py

1-1: Shebang is present but file is not executable

(EXE001)

---

63-63: Do not catch blind exception: Exception

(BLE001)

examples/serve/compatibility/chat_completions/example_03_multi_turn_conversation.py

1-1: Shebang is present but file is not executable

(EXE001)

examples/serve/compatibility/chat_completions/example_01_basic_chat.py

1-1: Shebang is present but file is not executable

(EXE001)

examples/serve/compatibility/chat_completions/example_07_advanced_sampling.py

1-1: Shebang is present but file is not executable

(EXE001)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (3)

examples/serve/compatibility/chat_completions/example_01_basic_chat.py (1)

7-43: LGTM - Clean example implementation.

The example clearly demonstrates the basic chat completion workflow: client initialization, model discovery, request creation, and response handling. The code follows Python conventions and is well-structured for educational purposes.

examples/serve/compatibility/chat_completions/example_07_advanced_sampling.py (1)

38-44: Good demonstration of extra_body for TensorRT-LLM specific parameters.

The use of extra_body to pass TensorRT-LLM specific sampling parameters is the correct pattern for extending the OpenAI client API.

examples/serve/compatibility/chat_completions/example_03_multi_turn_conversation.py (1)

24-58: LGTM - Clear multi-turn conversation pattern.

The example effectively demonstrates how to maintain conversation context by appending messages to the history. Using temperature=0 for the math questions ensures deterministic outputs, which is appropriate for this demonstration.

[tensorrt-cicd]

PR_Github #26005 [ run ] completed with state SUCCESS. Commit: f934896
/LLM/main/L0_MergeRequest_PR pipeline #19728 completed with status: 'SUCCESS'
Pipeline passed with automatic retried tests. Check the rerun report for details.

[JunyiXu-nv]

/bot reuse-pipeline

[tensorrt-cicd]

PR_Github #26101 [ reuse-pipeline ] triggered by Bot. Commit: 607a236

[tensorrt-cicd]

PR_Github #26101 [ reuse-pipeline ] completed with state SUCCESS. Commit: 607a236
Reusing PR_Github #26005 for commit 607a236

[LinPoly]

LGTM, THX for enhancing examples.

[JunyiXu-nv]

/bot reuse-pipeline

[tensorrt-cicd]

PR_Github #26693 [ reuse-pipeline ] triggered by Bot. Commit: 4ad2217

[tensorrt-cicd]

PR_Github #26693 [ reuse-pipeline ] completed with state SUCCESS. Commit: 4ad2217
Reusing PR_Github #26005 for commit 4ad2217