rfc: stream tools pydantic

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -135,7 +135,8 @@ class PydanticToolsParser(JsonOutputToolsParser):
                         f"Tool arguments must be specified as a dict, received: "
                         f"{res['args']}"
                     )
-                pydantic_objects.append(name_dict[res["type"]](**res["args"]))
+                pydantic_cls = name_dict[res["type"]]
+                pydantic_objects.append(pydantic_cls.construct(**res["args"]))
             except (ValidationError, ValueError) as e:
                 if partial:
                     continue

libs/core/tests/unit_tests/output_parsers/test_openai_tools.py

@@ -6,7 +6,7 @@ from langchain_core.output_parsers.openai_tools import (
     JsonOutputToolsParser,
     PydanticToolsParser,
 )
-from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel
 
 STREAMED_MESSAGES: list = [
     AIMessageChunk(content=""),
@@ -434,14 +434,27 @@ class Person(BaseModel):
 
 
 class NameCollector(BaseModel):
-    """record names of all people mentioned"""
-
-    names: List[str] = Field(..., description="all names mentioned")
-    person: Person = Field(..., description="info about the main subject")
+    names: List[str]
+    person: Person
 
 
 # Expected to change when we support more granular pydantic streaming.
 EXPECTED_STREAMED_PYDANTIC = [
+    NameCollector.construct(),
+    NameCollector.construct(names=["suz"]),
+    NameCollector.construct(names=["suzy"]),
+    NameCollector.construct(names=["suzy", "jerm"]),
+    NameCollector.construct(names=["suzy", "jermaine"]),
+    NameCollector.construct(names=["suzy", "jermaine", "al"]),
+    NameCollector.construct(names=["suzy", "jermaine", "alex"]),
+    NameCollector.construct(names=["suzy", "jermaine", "alex"], person={}),
+    NameCollector.construct(names=["suzy", "jermaine", "alex"], person={"age": 39}),
+    NameCollector.construct(
+        names=["suzy", "jermaine", "alex"], person={"age": 39, "hair_color": "br"}
+    ),
+    NameCollector.construct(
+        names=["suzy", "jermaine", "alex"], person={"age": 39, "hair_color": "brown"}
+    ),
     NameCollector(
         names=["suzy", "jermaine", "alex"],
         person=Person(age=39, hair_color="brown", job="c"),