Support more common tool variables in templates (tools, message.tool_calls) (#308)

* Add non-JSON version of `tools` and `functions` to `template_vars`.

Increase the compatibility with VLLM templates which use a non-JSON tools object.

* Add list of tool template variables to the documentation

* Use Jinja templates to provide `tools_json` and `functions_json`

This should be functionally equivelant, but the JSON won't be produced
unless it's needed.

* Make message.tool_calls match the JSON from ToolCallProcessor

* Log something when generating tool calls

* Add template for Qwen QwQ 32b

* Only log if tool calls have been detected

* API: Fix tool call variable assignments

Jinja functions do not run when variables are called. Use json.dumps
instead. In addition, log the request ID when stating that a tool
call was fired.

Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>

* Add `ToolCallProcessor.dump()` to get the list of processed dicts

* Remove qwen_qwq_32b.jinja

This will be added to the following repository at a later date:
https://github.com/theroyallab/llm-prompt-templates

---------

Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>
Co-authored-by: kingbri <8082010+kingbri1@users.noreply.github.com>

docs/10.-Tool-Calling.md

@@ -37,6 +37,11 @@ For example, if you are using a Llama 3.1 Family model you can simply modify you
 
 If loading via `/v1/model/load`, you would also need to specify a tool-supporting `prompt_template`.
 
+## Tool Template Variables
+
+- `tools`: Tools object.
+- `tools_json`: Tools object as a JSON string.
+
 ## Creating a Tool Calling Prompt Template
 
 Here's how to create a TabbyAPI tool calling prompt template:
@@ -142,4 +147,4 @@ When creating your own tool calling `prompt_template`, it's best to reference th
 
 ## Support and Bug Reporting
 
-For bugs, please create a detailed issue with the model, prompt template, and conversation that caused it. Alternatively, join our [Discord](https://discord.gg/sYQxnuD7Fj) and ask for Storm.
+For bugs, please create a detailed issue with the model, prompt template, and conversation that caused it. Alternatively, join our [Discord](https://discord.gg/sYQxnuD7Fj) and ask for Storm.

endpoints/OAI/utils/chat_completion.py

@@ -234,6 +234,10 @@ async def format_messages_with_template(
         if message.tool_calls:
             message.tool_calls_json = ToolCallProcessor.to_json(message.tool_calls)
 
+            # The tools variable is inspectable in the template, so
+            # store the list of dicts rather than the ToolCallProcessor object.
+            message.tool_calls = ToolCallProcessor.dump(message.tool_calls)
+
     special_tokens_dict = model.container.get_special_tokens(
         add_bos_token, ban_eos_token
     )
@@ -252,11 +256,16 @@ async def apply_chat_template(
     Template stop strings can be overriden by sampler overrides if force is true.
     """
 
+    # Locally store tools dict
+    tools = data.model_dump()["tools"]
+
     try:
         data.template_vars.update(
             {
                 "add_generation_prompt": data.add_generation_prompt,
-                "tools_json": json.dumps(data.model_dump()["tools"], indent=2),
+                "tools": tools,
+                "tools_json": json.dumps(tools, indent=2),
+                "functions": data.functions,
                 "functions_json": json.dumps(data.functions, indent=2),
                 "tool_precursor": tool_precursor,
             }
@@ -460,6 +469,10 @@ async def generate_tool_calls(
 
     for idx, gen in enumerate(generations):
         if gen["stop_str"] in tool_data.tool_call_start:
+            logger.info(
+                f"Detected tool call in chat completion request {request.state.id}"
+            )
+
             if "text" in gen:
                 # non streaming, all generations will have the text they generated
                 pre_tool_prompt, mm_embeddings = await apply_chat_template(

endpoints/OAI/utils/tools.py

@@ -18,6 +18,28 @@ class ToolCallProcessor:
 
         return [ToolCall(**tool_call) for tool_call in tool_calls]
 
+    @staticmethod
+    def dump(tool_calls: List[ToolCall]) -> List[dict]:
+        """
+        Convert ToolCall objects to a list of dictionaries.
+
+        Args:
+            tool_calls (List[ToolCall]): List of ToolCall objects to convert
+
+        Returns:
+            List[dict]: List of dictionaries representing the tool calls
+        """
+
+        # Don't use list comprehension here
+        # as that will fail rather than warn
+        dumped_tool_calls = []
+        for tool_call_obj in tool_calls:
+            try:
+                dumped_tool_calls.append(tool_call_obj.model_dump())
+            except (json.JSONDecodeError, AttributeError) as e:
+                logger.warning(f"Error processing tool call: {e}")
+        return dumped_tool_calls
+
     @staticmethod
     def to_json(tool_calls: List[ToolCall]) -> str:
         """
@@ -33,14 +55,8 @@ class ToolCallProcessor:
         if not tool_calls:
             return ""
 
-        # Don't use list comprehension here
-        # as that will fail rather than warn
-        dumped_tool_calls = []
-        for tool_call_obj in tool_calls:
-            try:
-                dumped_tool_calls.append(tool_call_obj.model_dump())
-            except (json.JSONDecodeError, AttributeError) as e:
-                logger.warning(f"Error processing tool call: {e}")
+        # Use the dump method to get the list of dictionaries
+        dumped_tool_calls = ToolCallProcessor.dump(tool_calls)
 
         # Serialize the dumped array
         return json.dumps(dumped_tool_calls, indent=2)