fix(main.py): handle tool being a pydantic object (#13274)
* fix(main.py): handle tool being a pydantic object Fixes https://github.com/BerriAI/litellm/issues/13064 * fix(prompt_templates/common_utils.py): fix unpack defs deepcopy issue Fixes https://github.com/BerriAI/litellm/issues/13151 * fix(utils.py): handle tools is none
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Krish Dholakia
GitHub
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eb49f987de
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416da066eb
@ -519,25 +519,25 @@ def unpack_defs(schema: dict, defs: dict) -> None:
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}
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# Use iterative approach with queue to avoid recursion
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# Each item in queue is (node, parent_container, key/index, active_defs, seen_ids)
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# Each item in queue is (node, parent_container, key/index, active_defs, ref_chain)
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queue: deque[
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tuple[Any, Union[dict, list, None], Union[str, int, None], dict, set]
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] = deque([(schema, None, None, root_defs, set())])
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while queue:
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node, parent, key, active_defs, seen = queue.popleft()
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# Avoid infinite loops on self-referential schemas
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if id(node) in seen:
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continue
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seen = seen.copy() # Create new set for this branch
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seen.add(id(node))
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node, parent, key, active_defs, ref_chain = queue.popleft()
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# ----------------------------- dict -----------------------------
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if isinstance(node, dict):
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# --- Case 1: this node *is* a reference ---
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if "$ref" in node:
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ref_name = node["$ref"].split("/")[-1]
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# Check for circular reference in the resolution chain
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if ref_name in ref_chain:
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# Circular reference detected - leave as-is to prevent infinite recursion
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continue
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target_schema = active_defs.get(ref_name)
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# Unknown reference – leave untouched
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if target_schema is None:
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@ -563,8 +563,12 @@ def unpack_defs(schema: dict, defs: dict) -> None:
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schema.update(resolved)
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resolved = schema
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# Add to ref chain to track circular references
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new_ref_chain = ref_chain.copy()
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new_ref_chain.add(ref_name)
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# Add resolved node to queue for further processing
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queue.append((resolved, parent, key, child_defs, seen))
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queue.append((resolved, parent, key, child_defs, new_ref_chain))
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continue
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# --- Case 2: regular dict – process its values ---
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@ -577,13 +581,13 @@ def unpack_defs(schema: dict, defs: dict) -> None:
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# Add all dict values to queue
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for k, v in node.items():
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queue.append((v, node, k, current_defs, seen))
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queue.append((v, node, k, current_defs, ref_chain))
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# ---------------------------- list ------------------------------
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elif isinstance(node, list):
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# Add all list items to queue
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for idx, item in enumerate(node):
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queue.append((item, node, idx, active_defs, seen))
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queue.append((item, node, idx, active_defs, ref_chain))
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def _get_image_mime_type_from_url(url: str) -> Optional[str]:
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@ -107,6 +107,7 @@ from litellm.utils import (
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supports_httpx_timeout,
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token_counter,
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validate_and_fix_openai_messages,
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validate_and_fix_openai_tools,
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validate_chat_completion_tool_choice,
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)
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@ -965,6 +966,7 @@ def completion( # type: ignore # noqa: PLR0915
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raise ValueError("model param not passed in.")
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# validate messages
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messages = validate_and_fix_openai_messages(messages=messages)
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tools = validate_and_fix_openai_tools(tools=tools)
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# validate tool_choice
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tool_choice = validate_chat_completion_tool_choice(tool_choice=tool_choice)
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######### unpacking kwargs #####################
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@ -6604,6 +6604,19 @@ def validate_and_fix_openai_messages(messages: List):
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new_messages.append(cleaned_message)
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return validate_chat_completion_user_messages(messages=new_messages)
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def validate_and_fix_openai_tools(tools: Optional[List]) -> Optional[List[dict]]:
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"""
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Ensure tools is List[dict] and not List[BaseModel]
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"""
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new_tools = []
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if tools is None:
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return tools
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for tool in tools:
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if isinstance(tool, BaseModel):
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new_tools.append(tool.model_dump())
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elif isinstance(tool, dict):
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new_tools.append(tool)
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return new_tools
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def cleanup_none_field_in_message(message: AllMessageValues):
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"""
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@ -603,3 +603,54 @@ def test_openai_deepresearch_model_bridge():
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)
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print("response: ", response)
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def test_openai_tool_calling():
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from pydantic import BaseModel
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from typing import Any, Literal
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class OpenAIFunction(BaseModel):
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description: Optional[str] = None
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name: str
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parameters: Optional[dict[str, Any]] = None
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class OpenAITool(BaseModel):
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type: Literal["function"]
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function: OpenAIFunction
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completion_params = {
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"model": "openai/gpt-4.1",
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is TSLA stock price at today?"}
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],
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}
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],
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"stream": False,
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"temperature": 0.5,
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"stop": None,
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"max_tokens": 1600,
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"tools": [
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OpenAITool(
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type="function",
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function=OpenAIFunction(
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description="Get the current stock price for a given ticker symbol.",
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name="get_stock_price",
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parameters={
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"type": "object",
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"properties": {
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"ticker": {
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"type": "string",
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"description": "The stock ticker symbol, e.g. AAPL for Apple Inc.",
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}
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},
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"required": ["ticker"],
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},
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),
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)
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],
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}
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response = litellm.completion(**completion_params)
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@ -435,3 +435,73 @@ def test_convert_gemini_messages():
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message=message,
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last_message_with_tool_calls=last_message_with_tool_calls,
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)
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def test_bedrock_tools_unpack_defs():
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"""
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Test that the unpack_defs method handles nested $ref inside anyOf items correctly
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"""
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from litellm.litellm_core_utils.prompt_templates.factory import _bedrock_tools_pt
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circularRefSchema = {
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"type": "object",
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"properties": {
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"type": {"type": "string", "enum": ["doc"]},
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"content": {"type": "array", "items": {"$ref": "#/$defs/node"}},
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},
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"required": ["type", "content"],
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"additionalProperties": False,
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"$defs": {
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"node": {
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"type": "object",
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"anyOf": [
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{
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"type": "object",
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"properties": {
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"type": {"type": "string", "enum": ["bulletList"]},
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"content": {
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"type": "array",
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"items": {"$ref": "#/$defs/listItem"},
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},
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},
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"required": ["type"],
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"additionalProperties": True,
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},
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{
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"type": "object",
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"properties": {
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"type": {"type": "string", "enum": ["orderedList"]},
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"content": {
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"type": "array",
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"items": {"$ref": "#/$defs/listItem"},
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},
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},
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"required": ["type"],
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"additionalProperties": True,
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},
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],
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},
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"listItem": {
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"type": "object",
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"properties": {
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"type": {"type": "string", "enum": ["listItem"]},
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"content": {"type": "array", "items": {"$ref": "#/$defs/node"}},
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},
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"required": ["type"],
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"additionalProperties": True,
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},
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},
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}
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tools = [
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{
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"type": "function",
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"function": {
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"name": "json_schema",
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"description": "Process the content using json schema validation",
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"parameters": circularRefSchema,
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},
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}
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]
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_bedrock_tools_pt(tools=tools)
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