langchain[patch]: structured output chain nits (#17291)

libs/langchain/langchain/chains/__init__.py

@@ -82,6 +82,7 @@ from langchain.chains.router import (
 )
 from langchain.chains.sequential import SequentialChain, SimpleSequentialChain
 from langchain.chains.sql_database.query import create_sql_query_chain
+from langchain.chains.structured_output import create_structured_output_runnable
 from langchain.chains.summarize import load_summarize_chain
 from langchain.chains.transform import TransformChain
 
@@ -145,5 +146,6 @@ __all__ = [
     "create_sql_query_chain",
     "create_retrieval_chain",
     "create_history_aware_retriever",
+    "create_structured_output_runnable",
     "load_summarize_chain",
 ]

libs/langchain/langchain/chains/structured_output/base.py

@@ -22,7 +22,7 @@ from langchain.output_parsers.openai_functions import (
 def create_openai_fn_runnable(
     functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
     llm: Runnable,
-    prompt: BasePromptTemplate,
+    prompt: Optional[BasePromptTemplate] = None,
     *,
     enforce_single_function_usage: bool = True,
     output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
@@ -64,7 +64,6 @@ def create_openai_fn_runnable(
 
                 from langchain.chains.structured_output import create_openai_fn_runnable
                 from langchain_openai import ChatOpenAI
-                from langchain_core.prompts import ChatPromptTemplate
                 from langchain_core.pydantic_v1 import BaseModel, Field
 
 
@@ -85,15 +84,8 @@ def create_openai_fn_runnable(
 
 
                 llm = ChatOpenAI(model="gpt-4", temperature=0)
-                prompt = ChatPromptTemplate.from_messages(
+                structured_llm = create_openai_fn_runnable([RecordPerson, RecordDog], llm)
-                    [
+                structured_llm.invoke("Harry was a chubby brown beagle who loved chicken)
-                        ("system", "You are a world class algorithm for recording entities."),
-                        ("human", "Make calls to the relevant function to record the entities in the following input: {input}"),
-                        ("human", "Tip: Make sure to answer in the correct format"),
-                    ]
-                )
-                chain = create_openai_fn_runnable([RecordPerson, RecordDog], llm, prompt)
-                chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
                 # -> RecordDog(name="Harry", color="brown", fav_food="chicken")
     """  # noqa: E501
     if not functions:
@@ -103,14 +95,17 @@ def create_openai_fn_runnable(
     if len(openai_functions) == 1 and enforce_single_function_usage:
         llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]}
     output_parser = output_parser or get_openai_output_parser(functions)
-    return prompt | llm.bind(**llm_kwargs) | output_parser
+    if prompt:
+        return prompt | llm.bind(**llm_kwargs) | output_parser
+    else:
+        return llm.bind(**llm_kwargs) | output_parser
 
 
 # TODO: implement mode='openai-tools'.
 def create_structured_output_runnable(
     output_schema: Union[Dict[str, Any], Type[BaseModel]],
     llm: Runnable,
-    prompt: BasePromptTemplate,
+    prompt: Optional[BasePromptTemplate] = None,
     *,
     output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
     mode: Literal["openai-functions", "openai-json"] = "openai-functions",
@@ -152,7 +147,28 @@ def create_structured_output_runnable(
 
                 from typing import Optional
 
-                from langchain.chains.structured_output import create_structured_output_runnable
+                from langchain.chains import create_structured_output_runnable
+                from langchain_openai import ChatOpenAI
+                from langchain_core.pydantic_v1 import BaseModel, Field
+
+                class Dog(BaseModel):
+                    '''Identifying information about a dog.'''
+
+                    name: str = Field(..., description="The dog's name")
+                    color: str = Field(..., description="The dog's color")
+                    fav_food: Optional[str] = Field(None, description="The dog's favorite food")
+
+                llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
+                structured_llm = create_structured_output_runnable(Dog, llm, mode="openai-functions")
+                structured_llm.invoke("Harry was a chubby brown beagle who loved chicken")
+                # -> Dog(name="Harry", color="brown", fav_food="chicken")
+                
+    OpenAI functions with prompt example:
+        .. code-block:: python
+
+                from typing import Optional
+
+                from langchain.chains import create_structured_output_runnable
                 from langchain_openai import ChatOpenAI
                 from langchain_core.prompts import ChatPromptTemplate
                 from langchain_core.pydantic_v1 import BaseModel, Field
@@ -165,23 +181,20 @@ def create_structured_output_runnable(
                     fav_food: Optional[str] = Field(None, description="The dog's favorite food")
 
                 llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
+                structured_llm = create_structured_output_runnable(Dog, llm, mode="openai-functions")
+                system = '''Extract information about any dogs mentioned in the user input.'''
                 prompt = ChatPromptTemplate.from_messages(
-                    [
+                    [("system", system), ("human", "{input}"),]
-                        ("system", "You are a world class algorithm for extracting information in structured formats."),
-                        ("human", "Use the given format to extract information from the following input: {input}"),
-                        ("human", "Tip: Make sure to answer in the correct format"),
-                    ]
                 )
-                chain = create_structured_output_runnable(Dog, llm, prompt, mode="openai-functions")
+                chain = prompt | structured_llm
                 chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
                 # -> Dog(name="Harry", color="brown", fav_food="chicken")
-                
     OpenAI json response format example:
         .. code-block:: python
         
                 from typing import Optional
 
-                from langchain.chains.structured_output import create_structured_output_runnable
+                from langchain.chains import create_structured_output_runnable
                 from langchain_openai import ChatOpenAI
                 from langchain_core.prompts import ChatPromptTemplate
                 from langchain_core.pydantic_v1 import BaseModel, Field
@@ -194,32 +207,30 @@ def create_structured_output_runnable(
                     fav_food: Optional[str] = Field(None, description="The dog's favorite food")
 
                 llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
+                structured_llm = create_structured_output_runnable(Dog, llm, mode="openai-json")
                 system = '''You are a world class assistant for extracting information in structured JSON formats. \
                 
                 Extract a valid JSON blob from the user input that matches the following JSON Schema:
                 
                 {output_schema}'''
                 prompt = ChatPromptTemplate.from_messages(
-                    [
+                    [("system", system), ("human", "{input}"),]
-                        ("system", system),
-                        ("human", "{input}"),
-                    ]
                 )
-                chain = create_structured_output_runnable(Dog, llm, prompt, mode="openai-json")
+                chain = prompt | structured_llm
                 chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
     """  # noqa: E501
     if mode == "openai-functions":
         return _create_openai_functions_structured_output_runnable(
             output_schema,
             llm,
-            prompt,
+            prompt=prompt,
             output_parser=output_parser,
             enforce_single_function_usage=enforce_single_function_usage,
             **kwargs,
         )
     elif mode == "openai-json":
         return _create_openai_json_runnable(
-            output_schema, llm, prompt, output_parser=output_parser, **kwargs
+            output_schema, llm, prompt=prompt, output_parser=output_parser, **kwargs
         )
     else:
         raise ValueError(
@@ -263,7 +274,7 @@ def get_openai_output_parser(
 def _create_openai_json_runnable(
     output_schema: Union[Dict[str, Any], Type[BaseModel]],
     llm: Runnable,
-    prompt: BasePromptTemplate,
+    prompt: Optional[BasePromptTemplate] = None,
     *,
     output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
 ) -> Runnable:
@@ -277,17 +288,20 @@ def _create_openai_json_runnable(
         output_parser = output_parser or JsonOutputParser()
         schema_as_dict = output_schema
 
-    if "output_schema" in prompt.input_variables:
-        prompt = prompt.partial(output_schema=json.dumps(schema_as_dict, indent=2))
-
     llm = llm.bind(response_format={"type": "json_object"})
-    return prompt | llm | output_parser
+    if prompt:
+        if "output_schema" in prompt.input_variables:
+            prompt = prompt.partial(output_schema=json.dumps(schema_as_dict, indent=2))
+
+        return prompt | llm | output_parser
+    else:
+        return llm | output_parser
 
 
 def _create_openai_functions_structured_output_runnable(
     output_schema: Union[Dict[str, Any], Type[BaseModel]],
     llm: Runnable,
-    prompt: BasePromptTemplate,
+    prompt: Optional[BasePromptTemplate] = None,
     *,
     output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
     **kwargs: Any,
@@ -315,7 +329,7 @@ def _create_openai_functions_structured_output_runnable(
     return create_openai_fn_runnable(
         [function],
         llm,
-        prompt,
+        prompt=prompt,
         output_parser=output_parser,
         **kwargs,
     )

libs/langchain/tests/unit_tests/chains/test_imports.py

@@ -62,6 +62,7 @@ EXPECTED_ALL = [
     "create_history_aware_retriever",
     "create_retrieval_chain",
     "load_summarize_chain",
+    "create_structured_output_runnable",
 ]