mirror of
https://github.com/hwchase17/langchain.git
synced 2026-03-17 10:34:27 +00:00
feat(openai): support tool search (#35582)
This commit is contained in:
ccurme
GitHub
@@ -731,6 +731,11 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
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tool_call_block["extras"]["item_id"] = block["id"]
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if "index" in block:
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tool_call_block["index"] = f"lc_tc_{block['index']}"
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for extra_key in ("status", "namespace"):
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if extra_key in block:
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if "extras" not in tool_call_block:
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tool_call_block["extras"] = {}
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tool_call_block["extras"][extra_key] = block[extra_key]
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yield tool_call_block
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elif block_type == "web_search_call":
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@@ -979,6 +984,51 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
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mcp_list_tools_result["index"] = f"lc_mltr_{block['index'] + 1}"
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yield cast("types.ServerToolResult", mcp_list_tools_result)
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elif (
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block_type == "tool_search_call" and block.get("execution") == "server"
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):
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tool_search_call: dict[str, Any] = {
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"type": "server_tool_call",
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"name": "tool_search",
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"id": block["id"],
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"args": block.get("arguments", {}),
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}
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if "index" in block:
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tool_search_call["index"] = f"lc_tsc_{block['index']}"
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extras: dict[str, Any] = {}
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known = {"type", "id", "arguments", "index"}
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for key in block:
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if key not in known:
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extras[key] = block[key]
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if extras:
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tool_search_call["extras"] = extras
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yield cast("types.ServerToolCall", tool_search_call)
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elif (
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block_type == "tool_search_output"
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and block.get("execution") == "server"
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):
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tool_search_output: dict[str, Any] = {
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"type": "server_tool_result",
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"tool_call_id": block["id"],
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"output": {"tools": block.get("tools", [])},
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}
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status = block.get("status")
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if status == "failed":
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tool_search_output["status"] = "error"
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elif status == "completed":
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tool_search_output["status"] = "success"
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if "index" in block and isinstance(block["index"], int):
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tool_search_output["index"] = f"lc_tso_{block['index']}"
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extras_out: dict[str, Any] = {"name": "tool_search"}
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known_out = {"type", "id", "status", "tools", "index"}
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for key in block:
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if key not in known_out:
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extras_out[key] = block[key]
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if extras_out:
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tool_search_output["extras"] = extras_out
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yield cast("types.ServerToolResult", tool_search_output)
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elif block_type in types.KNOWN_BLOCK_TYPES:
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yield cast("types.ContentBlock", block)
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else:
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@@ -508,6 +508,8 @@ _WellKnownOpenAITools = (
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"image_generation",
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"web_search_preview",
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"web_search",
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"tool_search",
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"namespace",
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)
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@@ -103,6 +103,8 @@ def _convert_to_v03_ai_message(
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"mcp_list_tools",
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"mcp_approval_request",
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"image_generation_call",
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"tool_search_call",
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"tool_search_output",
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):
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# Store built-in tool calls in additional_kwargs
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if "tool_outputs" not in message.additional_kwargs:
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@@ -420,17 +422,58 @@ def _convert_from_v1_to_responses(
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new_block["name"] = block["name"]
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if "extras" in block and "arguments" in block["extras"]:
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new_block["arguments"] = block["extras"]["arguments"]
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if any(key not in block for key in ("name", "arguments")):
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if any(key not in new_block for key in ("name", "arguments")):
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matching_tool_calls = [
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call for call in tool_calls if call["id"] == block["id"]
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]
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if matching_tool_calls:
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tool_call = matching_tool_calls[0]
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if "name" not in block:
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if "name" not in new_block:
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new_block["name"] = tool_call["name"]
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if "arguments" not in block:
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new_block["arguments"] = json.dumps(tool_call["args"])
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if "arguments" not in new_block:
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new_block["arguments"] = json.dumps(
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tool_call["args"], separators=(",", ":")
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)
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if "extras" in block:
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for extra_key in ("status", "namespace"):
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if extra_key in block["extras"]:
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new_block[extra_key] = block["extras"][extra_key]
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new_content.append(new_block)
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elif block["type"] == "server_tool_call" and block.get("name") == "tool_search":
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extras = block.get("extras", {})
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new_block = {"id": block["id"]}
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status = extras.get("status")
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if status:
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new_block["status"] = status
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new_block["type"] = "tool_search_call"
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if "args" in block:
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new_block["arguments"] = block["args"]
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execution = extras.get("execution")
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if execution:
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new_block["execution"] = execution
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new_content.append(new_block)
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elif (
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block["type"] == "server_tool_result"
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and block.get("extras", {}).get("name") == "tool_search"
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):
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extras = block.get("extras", {})
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new_block = {"id": block.get("tool_call_id", "")}
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status = block.get("status")
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if status == "success":
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new_block["status"] = "completed"
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elif status == "error":
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new_block["status"] = "failed"
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elif status:
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new_block["status"] = status
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new_block["type"] = "tool_search_output"
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new_block["execution"] = "server"
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output: dict = block.get("output", {})
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if isinstance(output, dict) and "tools" in output:
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new_block["tools"] = output["tools"]
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new_content.append(new_block)
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elif (
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is_data_content_block(cast(dict, block))
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and block["type"] == "image"
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@@ -441,7 +484,7 @@ def _convert_from_v1_to_responses(
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new_block = {"type": "image_generation_call", "result": block["base64"]}
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for extra_key in ("id", "status"):
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if extra_key in block:
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new_block[extra_key] = block[extra_key] # type: ignore[typeddict-item]
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new_block[extra_key] = block[extra_key] # type: ignore[literal-required]
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elif extra_key in block.get("extras", {}):
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new_block[extra_key] = block["extras"][extra_key]
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new_content.append(new_block)
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@@ -166,6 +166,7 @@ WellKnownTools = (
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"code_interpreter",
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"mcp",
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"image_generation",
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"tool_search",
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)
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@@ -1984,6 +1985,14 @@ class BaseChatOpenAI(BaseChatModel):
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formatted_tools = [
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convert_to_openai_tool(tool, strict=strict) for tool in tools
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]
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for original, formatted in zip(tools, formatted_tools, strict=False):
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if (
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isinstance(original, BaseTool)
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and hasattr(original, "extras")
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and isinstance(original.extras, dict)
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and "defer_loading" in original.extras
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):
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formatted["defer_loading"] = original.extras["defer_loading"]
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tool_names = []
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for tool in formatted_tools:
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if "function" in tool:
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@@ -3981,7 +3990,8 @@ def _construct_responses_api_payload(
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# chat api: {"type": "function", "function": {"name": "...", "description": "...", "parameters": {...}, "strict": ...}} # noqa: E501
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# responses api: {"type": "function", "name": "...", "description": "...", "parameters": {...}, "strict": ...} # noqa: E501
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if tool["type"] == "function" and "function" in tool:
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new_tools.append({"type": "function", **tool["function"]})
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extra = {k: v for k, v in tool.items() if k not in ("type", "function")}
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new_tools.append({"type": "function", **tool["function"], **extra})
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else:
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if tool["type"] == "image_generation":
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# Handle partial images (not yet supported)
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@@ -4308,6 +4318,8 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
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"mcp_call",
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"mcp_list_tools",
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"mcp_approval_request",
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"tool_search_call",
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"tool_search_output",
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):
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input_.append(_pop_index_and_sub_index(block))
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elif block_type == "image_generation_call":
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@@ -4353,7 +4365,7 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
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elif msg["role"] in ("user", "system", "developer"):
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if isinstance(msg["content"], list):
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new_blocks = []
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non_message_item_types = ("mcp_approval_response",)
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non_message_item_types = ("mcp_approval_response", "tool_search_output")
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for block in msg["content"]:
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if block["type"] in ("text", "image_url", "file"):
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new_blocks.append(
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@@ -4510,6 +4522,8 @@ def _construct_lc_result_from_responses_api(
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"mcp_list_tools",
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"mcp_approval_request",
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"image_generation_call",
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"tool_search_call",
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"tool_search_output",
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):
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content_blocks.append(output.model_dump(exclude_none=True, mode="json"))
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@@ -4719,6 +4733,8 @@ def _convert_responses_chunk_to_generation_chunk(
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"mcp_list_tools",
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"mcp_approval_request",
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"image_generation_call",
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"tool_search_call",
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"tool_search_output",
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):
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_advance(chunk.output_index)
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tool_output = chunk.item.model_dump(exclude_none=True, mode="json")
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BIN
libs/partners/openai/tests/cassettes/test_agent_loop.yaml.gz
Normal file
BIN
libs/partners/openai/tests/cassettes/test_agent_loop.yaml.gz
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
BIN
libs/partners/openai/tests/cassettes/test_tool_search.yaml.gz
Normal file
BIN
libs/partners/openai/tests/cassettes/test_tool_search.yaml.gz
Normal file
Binary file not shown.
Binary file not shown.
@@ -1,3 +1,4 @@
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import json
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from typing import Any
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import pytest
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@@ -30,6 +31,9 @@ def remove_response_headers(response: dict) -> dict:
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def vcr_config() -> dict:
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"""Extend the default configuration coming from langchain_tests."""
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config = base_vcr_config()
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config["match_on"] = [
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m if m != "body" else "json_body" for m in config.get("match_on", [])
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]
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config.setdefault("filter_headers", []).extend(_EXTRA_HEADERS)
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config["before_record_request"] = remove_request_headers
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config["before_record_response"] = remove_response_headers
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@@ -38,6 +42,24 @@ def vcr_config() -> dict:
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return config
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def _json_body_matcher(r1: Any, r2: Any) -> None:
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"""Match request bodies as parsed JSON, ignoring key order."""
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b1 = r1.body or b""
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b2 = r2.body or b""
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if isinstance(b1, bytes):
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b1 = b1.decode("utf-8")
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if isinstance(b2, bytes):
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b2 = b2.decode("utf-8")
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try:
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j1 = json.loads(b1)
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j2 = json.loads(b2)
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except (json.JSONDecodeError, ValueError):
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assert b1 == b2, f"body mismatch (non-JSON):\n{b1}\n!=\n{b2}"
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return
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assert j1 == j2, f"body mismatch:\n{j1}\n!=\n{j2}"
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def pytest_recording_configure(config: dict, vcr: VCR) -> None:
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vcr.register_persister(CustomPersister())
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vcr.register_serializer("yaml.gz", CustomSerializer())
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vcr.register_matcher("json_body", _json_body_matcher)
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@@ -7,6 +7,13 @@ from typing import Annotated, Any, Literal, cast
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import openai
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import pytest
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from langchain.agents import create_agent
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from langchain.agents.middleware.types import (
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AgentMiddleware,
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AgentState,
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ToolCallRequest,
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hook_config,
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)
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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@@ -14,7 +21,10 @@ from langchain_core.messages import (
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BaseMessageChunk,
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HumanMessage,
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MessageLikeRepresentation,
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ToolMessage,
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)
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from langchain_core.tools import tool
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from pydantic import BaseModel
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from typing_extensions import TypedDict
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@@ -193,6 +203,74 @@ def test_function_calling(output_version: Literal["v0", "responses/v1", "v1"]) -
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_check_response(response)
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@pytest.mark.default_cassette("test_agent_loop.yaml.gz")
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@pytest.mark.vcr
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@pytest.mark.parametrize("output_version", ["responses/v1", "v1"])
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def test_agent_loop(output_version: Literal["responses/v1", "v1"]) -> None:
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@tool
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def get_weather(location: str) -> str:
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"""Get the weather for a location."""
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return "It's sunny."
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llm = ChatOpenAI(
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model="gpt-5.4",
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use_responses_api=True,
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output_version=output_version,
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)
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llm_with_tools = llm.bind_tools([get_weather])
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input_message = HumanMessage("What is the weather in San Francisco, CA?")
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tool_call_message = llm_with_tools.invoke([input_message])
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assert isinstance(tool_call_message, AIMessage)
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tool_calls = tool_call_message.tool_calls
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assert len(tool_calls) == 1
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tool_call = tool_calls[0]
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tool_message = get_weather.invoke(tool_call)
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assert isinstance(tool_message, ToolMessage)
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response = llm_with_tools.invoke(
|
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[
|
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input_message,
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tool_call_message,
|
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tool_message,
|
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]
|
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)
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assert isinstance(response, AIMessage)
|
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|
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|
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@pytest.mark.default_cassette("test_agent_loop_streaming.yaml.gz")
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@pytest.mark.vcr
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@pytest.mark.parametrize("output_version", ["responses/v1", "v1"])
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def test_agent_loop_streaming(output_version: Literal["responses/v1", "v1"]) -> None:
|
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@tool
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def get_weather(location: str) -> str:
|
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"""Get the weather for a location."""
|
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return "It's sunny."
|
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|
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llm = ChatOpenAI(
|
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model="gpt-5.2",
|
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use_responses_api=True,
|
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reasoning={"effort": "medium", "summary": "auto"},
|
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streaming=True,
|
||||
output_version=output_version,
|
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)
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llm_with_tools = llm.bind_tools([get_weather])
|
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input_message = HumanMessage("What is the weather in San Francisco, CA?")
|
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tool_call_message = llm_with_tools.invoke([input_message])
|
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assert isinstance(tool_call_message, AIMessage)
|
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tool_calls = tool_call_message.tool_calls
|
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assert len(tool_calls) == 1
|
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tool_call = tool_calls[0]
|
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tool_message = get_weather.invoke(tool_call)
|
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assert isinstance(tool_message, ToolMessage)
|
||||
response = llm_with_tools.invoke(
|
||||
[
|
||||
input_message,
|
||||
tool_call_message,
|
||||
tool_message,
|
||||
]
|
||||
)
|
||||
assert isinstance(response, AIMessage)
|
||||
|
||||
|
||||
class Foo(BaseModel):
|
||||
response: str
|
||||
|
||||
@@ -1267,3 +1345,183 @@ def test_csv_input() -> None:
|
||||
"3" in str(response2.content).lower()
|
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or "three" in str(response2.content).lower()
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.default_cassette("test_tool_search.yaml.gz")
|
||||
@pytest.mark.vcr
|
||||
@pytest.mark.parametrize("output_version", ["responses/v1", "v1"])
|
||||
def test_tool_search(output_version: str) -> None:
|
||||
@tool(extras={"defer_loading": True})
|
||||
def get_weather(location: str) -> str:
|
||||
"""Get the current weather for a location."""
|
||||
return f"The weather in {location} is sunny and 72°F"
|
||||
|
||||
@tool(extras={"defer_loading": True})
|
||||
def get_recipe(query: str) -> None:
|
||||
"""Get a recipe for chicken soup."""
|
||||
|
||||
model = ChatOpenAI(
|
||||
model="gpt-5.4",
|
||||
use_responses_api=True,
|
||||
output_version=output_version,
|
||||
)
|
||||
|
||||
agent = create_agent(
|
||||
model=model,
|
||||
tools=[get_weather, get_recipe, {"type": "tool_search"}],
|
||||
)
|
||||
input_message = {"role": "user", "content": "What's the weather in San Francisco?"}
|
||||
result = agent.invoke({"messages": [input_message]})
|
||||
assert len(result["messages"]) == 4
|
||||
tool_call_message = result["messages"][1]
|
||||
assert isinstance(tool_call_message, AIMessage)
|
||||
assert tool_call_message.tool_calls
|
||||
if output_version == "v1":
|
||||
assert [block["type"] for block in tool_call_message.content] == [ # type: ignore[index]
|
||||
"server_tool_call",
|
||||
"server_tool_result",
|
||||
"tool_call",
|
||||
]
|
||||
else:
|
||||
assert [block["type"] for block in tool_call_message.content] == [ # type: ignore[index]
|
||||
"tool_search_call",
|
||||
"tool_search_output",
|
||||
"function_call",
|
||||
]
|
||||
|
||||
assert isinstance(result["messages"][2], ToolMessage)
|
||||
|
||||
assert result["messages"][3].text
|
||||
|
||||
|
||||
@pytest.mark.default_cassette("test_tool_search_streaming.yaml.gz")
|
||||
@pytest.mark.vcr
|
||||
@pytest.mark.parametrize("output_version", ["responses/v1", "v1"])
|
||||
def test_tool_search_streaming(output_version: str) -> None:
|
||||
@tool(extras={"defer_loading": True})
|
||||
def get_weather(location: str) -> str:
|
||||
"""Get the current weather for a location."""
|
||||
return f"The weather in {location} is sunny and 72°F"
|
||||
|
||||
@tool(extras={"defer_loading": True})
|
||||
def get_recipe(query: str) -> None:
|
||||
"""Get a recipe for chicken soup."""
|
||||
|
||||
model = ChatOpenAI(
|
||||
model="gpt-5.4",
|
||||
use_responses_api=True,
|
||||
streaming=True,
|
||||
output_version=output_version,
|
||||
)
|
||||
|
||||
agent = create_agent(
|
||||
model=model,
|
||||
tools=[get_weather, get_recipe, {"type": "tool_search"}],
|
||||
)
|
||||
input_message = {"role": "user", "content": "What's the weather in San Francisco?"}
|
||||
result = agent.invoke({"messages": [input_message]})
|
||||
assert len(result["messages"]) == 4
|
||||
tool_call_message = result["messages"][1]
|
||||
assert isinstance(tool_call_message, AIMessage)
|
||||
assert tool_call_message.tool_calls
|
||||
if output_version == "v1":
|
||||
assert [block["type"] for block in tool_call_message.content] == [ # type: ignore[index]
|
||||
"server_tool_call",
|
||||
"server_tool_result",
|
||||
"tool_call",
|
||||
]
|
||||
else:
|
||||
assert [block["type"] for block in tool_call_message.content] == [ # type: ignore[index]
|
||||
"tool_search_call",
|
||||
"tool_search_output",
|
||||
"function_call",
|
||||
]
|
||||
|
||||
assert isinstance(result["messages"][2], ToolMessage)
|
||||
|
||||
assert result["messages"][3].text
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
def test_client_executed_tool_search() -> None:
|
||||
@tool
|
||||
def get_weather(location: str) -> str:
|
||||
"""Get the current weather for a location."""
|
||||
return f"The weather in {location} is sunny and 72°F"
|
||||
|
||||
def search_tools(goal: str) -> list[dict]:
|
||||
"""Search for available tools to help answer the question."""
|
||||
return [
|
||||
{
|
||||
"type": "function",
|
||||
"defer_loading": True,
|
||||
**convert_to_openai_tool(get_weather)["function"],
|
||||
}
|
||||
]
|
||||
|
||||
tool_search_schema = convert_to_openai_tool(search_tools, strict=True)
|
||||
tool_search_config: dict = {
|
||||
"type": "tool_search",
|
||||
"execution": "client",
|
||||
"description": tool_search_schema["function"]["description"],
|
||||
"parameters": tool_search_schema["function"]["parameters"],
|
||||
}
|
||||
|
||||
class ClientToolSearchMiddleware(AgentMiddleware):
|
||||
@hook_config(can_jump_to=["model"])
|
||||
def after_model(self, state: AgentState, runtime: Any) -> dict[str, Any] | None:
|
||||
last_message = state["messages"][-1]
|
||||
if not isinstance(last_message, AIMessage):
|
||||
return None
|
||||
for block in last_message.content:
|
||||
if isinstance(block, dict) and block.get("type") == "tool_search_call":
|
||||
call_id = block.get("call_id")
|
||||
args = block.get("arguments", {})
|
||||
goal = args.get("goal", "") if isinstance(args, dict) else ""
|
||||
loaded_tools = search_tools(goal)
|
||||
tool_search_output = {
|
||||
"type": "tool_search_output",
|
||||
"execution": "client",
|
||||
"call_id": call_id,
|
||||
"status": "completed",
|
||||
"tools": loaded_tools,
|
||||
}
|
||||
return {
|
||||
"messages": [HumanMessage(content=[tool_search_output])],
|
||||
"jump_to": "model",
|
||||
}
|
||||
return None
|
||||
|
||||
def wrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Any,
|
||||
) -> Any:
|
||||
if request.tool_call["name"] == "get_weather":
|
||||
return handler(request.override(tool=get_weather))
|
||||
return handler(request)
|
||||
|
||||
llm = ChatOpenAI(model="gpt-5.4", use_responses_api=True)
|
||||
|
||||
agent = create_agent(
|
||||
model=llm,
|
||||
tools=[tool_search_config],
|
||||
middleware=[ClientToolSearchMiddleware()],
|
||||
)
|
||||
|
||||
result = agent.invoke(
|
||||
{"messages": [HumanMessage("What's the weather in San Francisco?")]}
|
||||
)
|
||||
messages = result["messages"]
|
||||
search_tool_call = messages[1]
|
||||
assert search_tool_call.content[0]["type"] == "tool_search_call"
|
||||
|
||||
search_tool_output = messages[2]
|
||||
assert search_tool_output.content[0]["type"] == "tool_search_output"
|
||||
|
||||
tool_call = messages[3]
|
||||
assert tool_call.tool_calls
|
||||
|
||||
assert isinstance(messages[4], ToolMessage)
|
||||
|
||||
assert messages[5].text
|
||||
|
||||
@@ -2787,13 +2787,13 @@ def test_convert_from_v1_to_chat_completions(
|
||||
"type": "function_call",
|
||||
"call_id": "call_123",
|
||||
"name": "get_weather",
|
||||
"arguments": '{"location": "San Francisco"}',
|
||||
"arguments": '{"location":"San Francisco"}',
|
||||
},
|
||||
{
|
||||
"type": "function_call",
|
||||
"call_id": "call_234",
|
||||
"name": "get_weather_2",
|
||||
"arguments": '{"location": "New York"}',
|
||||
"arguments": '{"location":"New York"}',
|
||||
"id": "fc_123",
|
||||
},
|
||||
{"type": "text", "text": "Hello "},
|
||||
@@ -3474,3 +3474,113 @@ def test_context_overflow_error_backwards_compatibility() -> None:
|
||||
# Verify it's both types (multiple inheritance)
|
||||
assert isinstance(exc_info.value, openai.BadRequestError)
|
||||
assert isinstance(exc_info.value, ContextOverflowError)
|
||||
|
||||
|
||||
def test_tool_search_passthrough() -> None:
|
||||
"""Test that tool_search dict is passed through as a built-in tool."""
|
||||
llm = ChatOpenAI(model="gpt-4o")
|
||||
tool_search = {"type": "tool_search"}
|
||||
bound = llm.bind_tools([tool_search])
|
||||
payload = bound._get_request_payload( # type: ignore[attr-defined]
|
||||
"test",
|
||||
**bound.kwargs, # type: ignore[attr-defined]
|
||||
)
|
||||
assert {"type": "tool_search"} in payload["tools"]
|
||||
assert "input" in payload
|
||||
|
||||
|
||||
def test_tool_search_with_defer_loading_extras() -> None:
|
||||
"""Test that defer_loading from BaseTool extras is merged into tool defs."""
|
||||
from langchain_core.tools import tool
|
||||
|
||||
@tool(extras={"defer_loading": True})
|
||||
def get_weather(location: str) -> str:
|
||||
"""Get weather for a location."""
|
||||
return f"Weather in {location}"
|
||||
|
||||
llm = ChatOpenAI(model="gpt-4o")
|
||||
bound = llm.bind_tools([get_weather, {"type": "tool_search"}])
|
||||
payload = bound._get_request_payload( # type: ignore[attr-defined]
|
||||
"test",
|
||||
**bound.kwargs, # type: ignore[attr-defined]
|
||||
)
|
||||
weather_tool = None
|
||||
for t in payload["tools"]:
|
||||
if t.get("type") == "function" and t.get("name") == "get_weather":
|
||||
weather_tool = t
|
||||
break
|
||||
assert weather_tool is not None
|
||||
assert weather_tool["defer_loading"] is True
|
||||
assert {"type": "tool_search"} in payload["tools"]
|
||||
|
||||
|
||||
def test_namespace_passthrough() -> None:
|
||||
"""Test that namespace tool dicts are passed through unchanged."""
|
||||
llm = ChatOpenAI(model="gpt-4o")
|
||||
namespace_tool = {
|
||||
"type": "namespace",
|
||||
"name": "crm",
|
||||
"description": "CRM tools.",
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "list_orders",
|
||||
"description": "List orders.",
|
||||
"defer_loading": True,
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"customer_id": {"type": "string"}},
|
||||
"required": ["customer_id"],
|
||||
},
|
||||
}
|
||||
],
|
||||
}
|
||||
bound = llm.bind_tools([namespace_tool, {"type": "tool_search"}])
|
||||
payload = bound._get_request_payload( # type: ignore[attr-defined]
|
||||
"test",
|
||||
**bound.kwargs, # type: ignore[attr-defined]
|
||||
)
|
||||
ns = None
|
||||
for t in payload["tools"]:
|
||||
if t.get("type") == "namespace":
|
||||
ns = t
|
||||
break
|
||||
assert ns is not None
|
||||
assert ns["name"] == "crm"
|
||||
assert ns["tools"][0]["defer_loading"] is True
|
||||
assert {"type": "tool_search"} in payload["tools"]
|
||||
|
||||
|
||||
def test_defer_loading_in_responses_api_payload() -> None:
|
||||
"""Test that defer_loading is preserved in Responses API tool format."""
|
||||
from langchain_openai.chat_models.base import _construct_responses_api_payload
|
||||
|
||||
messages: list = []
|
||||
payload = {
|
||||
"model": "gpt-4o",
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get weather.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"location": {"type": "string"}},
|
||||
},
|
||||
},
|
||||
"defer_loading": True,
|
||||
},
|
||||
{"type": "tool_search"},
|
||||
],
|
||||
}
|
||||
result = _construct_responses_api_payload(messages, payload)
|
||||
weather_tool = None
|
||||
for t in result["tools"]:
|
||||
if t.get("name") == "get_weather":
|
||||
weather_tool = t
|
||||
break
|
||||
assert weather_tool is not None
|
||||
assert weather_tool["defer_loading"] is True
|
||||
assert weather_tool["type"] == "function"
|
||||
assert {"type": "tool_search"} in result["tools"]
|
||||
|
||||
8
libs/partners/openai/uv.lock
generated
8
libs/partners/openai/uv.lock
generated
@@ -1,5 +1,5 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
revision = 2
|
||||
requires-python = ">=3.10.0, <4.0.0"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
|
||||
@@ -1105,7 +1105,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "2.21.0"
|
||||
version = "2.26.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
@@ -1117,9 +1117,9 @@ dependencies = [
|
||||
{ name = "tqdm" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/92/e5/3d197a0947a166649f566706d7a4c8f7fe38f1fa7b24c9bcffe4c7591d44/openai-2.21.0.tar.gz", hash = "sha256:81b48ce4b8bbb2cc3af02047ceb19561f7b1dc0d4e52d1de7f02abfd15aa59b7", size = 644374, upload-time = "2026-02-14T00:12:01.577Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d7/91/2a06c4e9597c338cac1e5e5a8dd6f29e1836fc229c4c523529dca387fda8/openai-2.26.0.tar.gz", hash = "sha256:b41f37c140ae0034a6e92b0c509376d907f3a66109935fba2c1b471a7c05a8fb", size = 666702, upload-time = "2026-03-05T23:17:35.874Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/cc/56/0a89092a453bb2c676d66abee44f863e742b2110d4dbb1dbcca3f7e5fc33/openai-2.21.0-py3-none-any.whl", hash = "sha256:0bc1c775e5b1536c294eded39ee08f8407656537ccc71b1004104fe1602e267c", size = 1103065, upload-time = "2026-02-14T00:11:59.603Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c6/2e/3f73e8ca53718952222cacd0cf7eecc9db439d020f0c1fe7ae717e4e199a/openai-2.26.0-py3-none-any.whl", hash = "sha256:6151bf8f83802f036117f06cc8a57b3a4da60da9926826cc96747888b57f394f", size = 1136409, upload-time = "2026-03-05T23:17:34.072Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
Reference in New Issue
Block a user