Update to support str initialization of llms

pyproject.toml

@@ -10,7 +10,7 @@ dependencies = [
     "jsonpatch<2.0,>=1.33",
 ]
 name = "trustcall"
-version = "0.0.26"
+version = "0.0.27"
 description = "Tenacious & trustworthy tool calling built on LangGraph."
 readme = "README.md"
 
@@ -31,6 +31,7 @@ dev = [
     "anyio>=4.7.0",
     "pytest-asyncio-cooperative>=0.37.0",
 ]
+standard = ["langchain>=0.3"]
 
 [tool.setuptools]
 packages = ["trustcall"]

tests/evals/test_evals.py

@@ -5,7 +5,6 @@
 import langsmith as ls
 import pytest
 from dydantic import create_model_from_schema
-from langchain.chat_models import init_chat_model
 from langsmith import aevaluate, expect, traceable
 from langsmith.evaluation import EvaluationResults
 from langsmith.schemas import Example, Run
@@ -61,8 +60,7 @@ async def predict_with_model(
         ),
         ("user", inputs["input_str"]),
     ]
-    llm = init_chat_model(model_name, temperature=0.8)
-    extractor = create_extractor(llm, tools=[tool_def], tool_choice=tool_def["name"])
+    extractor = create_extractor(model_name, tools=[tool_def], tool_choice=tool_def["name"])
     existing = inputs.get("current_value", {})
     extractor_inputs: dict = {"messages": messages}
     if existing:
@@ -226,7 +224,7 @@ def query_docs(query: str) -> str:
         return "I am a document."
 
     extractor = create_extractor(
-        init_chat_model("gpt-4o"), tools=[query_docs], tool_choice="query_docs"
+        "gpt-4o", tools=[query_docs], tool_choice="query_docs"
     )
     extractor.invoke({"messages": [("user", "What are the docs about?")]})
 
@@ -246,8 +244,9 @@ def validate_query_length(cls, v: str) -> str:
                 )
             return v
 
-    llm = init_chat_model("gpt-4o-mini")
-    extractor = create_extractor(llm, tools=[query_docs], tool_choice="any")
+    extractor = create_extractor(
+        "gpt-4o", tools=[query_docs], tool_choice="any"
+    )
     extractor.invoke(
         {
             "messages": [

trustcall/_base.py

@@ -119,7 +119,7 @@ class ExtractionOutputs(TypedDict):
 
 
 def create_extractor(
-    llm: BaseChatModel,
+    llm: str | BaseChatModel,
     *,
     tools: Sequence[TOOL_T],
     tool_choice: Optional[str] = None,
@@ -258,6 +258,17 @@ def create_extractor(
         ...     }
         ... )
     """  # noqa
+    if isinstance(llm, str):
+        try:
+            from langchain.chat_models import init_chat_model
+        except ImportError:
+            raise ImportError(
+                "Creating extractors from a string requires langchain>=0.3.0,"
+                " as well as the provider-specific package"
+                " (like langchain-openai, langchain-anthropic, etc.)"
+                " Please install langchain to continue."
+            )
+        llm = init_chat_model(llm)
     builder = StateGraph(ExtractionState)
 
     def format_exception(error: BaseException, call: ToolCall, schema: Type[BaseModel]):