Harrison/openai functions (#6261)

Co-authored-by: Francisco Ingham <24279597+fpingham@users.noreply.github.com>

langchain/chains/openai_functions.py

@@ -2,7 +2,7 @@ import json
 from functools import partial
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
@@ -15,6 +15,9 @@ from langchain.chains.transform import TransformChain
 from langchain.prompts.base import BasePromptTemplate
 from langchain.prompts.chat import ChatPromptTemplate
 
+EXTRACTION_NAME = "information_extraction"
+EXTRACTION_KWARGS = {"function_call": {"name": "information_extraction"}}
+
 
 def _resolve_schema_references(schema: Any, definitions: Dict[str, Any]) -> Any:
     """
@@ -70,6 +73,7 @@ class OpenAIFunctionsChain(Chain):
     prompt: BasePromptTemplate
     llm: BaseLanguageModel
     functions: List[Dict]
+    kwargs: Dict = Field(default_factory=dict)
 
     @property
     def input_keys(self) -> List[str]:
@@ -90,7 +94,7 @@ class OpenAIFunctionsChain(Chain):
         _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
         callbacks = _run_manager.get_child()
         predicted_message = self.llm.predict_messages(
-            messages, functions=self.functions, callbacks=callbacks
+            messages, functions=self.functions, callbacks=callbacks, **self.kwargs
         )
         return {"output": predicted_message}
 
@@ -105,7 +109,7 @@ class OpenAIFunctionsChain(Chain):
         _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
         callbacks = _run_manager.get_child()
         predicted_message = await self.llm.apredict_messages(
-            messages, functions=self.functions, callbacks=callbacks
+            messages, functions=self.functions, callbacks=callbacks, **self.kwargs
         )
         return {"output": predicted_message}
 
@@ -122,7 +126,7 @@ def _convert_schema(schema: dict) -> dict:
 def _get_extraction_functions(entity_schema: dict) -> List[dict]:
     return [
         {
-            "name": "information_extraction",
+            "name": EXTRACTION_NAME,
             "description": "Extracts the relevant information from the passage.",
             "parameters": {
                 "type": "object",
@@ -138,7 +142,7 @@ def _get_extraction_functions(entity_schema: dict) -> List[dict]:
 def _get_tagging_functions(schema: dict) -> List[dict]:
     return [
         {
-            "name": "information_extraction",
+            "name": EXTRACTION_NAME,
             "description": "Extracts the relevant information from the passage.",
             "parameters": _convert_schema(schema),
         }
@@ -156,7 +160,9 @@ Passage:
 def create_extraction_chain(schema: dict, llm: BaseLanguageModel) -> Chain:
     functions = _get_extraction_functions(schema)
     prompt = ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
-    chain = OpenAIFunctionsChain(llm=llm, prompt=prompt, functions=functions)
+    chain = OpenAIFunctionsChain(
+        llm=llm, prompt=prompt, functions=functions, kwargs=EXTRACTION_KWARGS
+    )
     parsing_chain = TransformChain(
         transform=_parse_entities,
         input_variables=["input"],
@@ -178,7 +184,9 @@ def create_extraction_chain_pydantic(
 
     functions = _get_extraction_functions(openai_schema)
     prompt = ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
-    chain = OpenAIFunctionsChain(llm=llm, prompt=prompt, functions=functions)
+    chain = OpenAIFunctionsChain(
+        llm=llm, prompt=prompt, functions=functions, kwargs=EXTRACTION_KWARGS
+    )
     pydantic_parsing_chain = TransformChain(
         transform=partial(_parse_entities_pydantic, pydantic_schema=PydanticSchema),
         input_variables=["input"],
@@ -197,7 +205,9 @@ Passage:
 def create_tagging_chain(schema: dict, llm: BaseLanguageModel) -> Chain:
     functions = _get_tagging_functions(schema)
     prompt = ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
-    chain = OpenAIFunctionsChain(llm=llm, prompt=prompt, functions=functions)
+    chain = OpenAIFunctionsChain(
+        llm=llm, prompt=prompt, functions=functions, kwargs=EXTRACTION_KWARGS
+    )
     parsing_chain = TransformChain(
         transform=_parse_tag, input_variables=["input"], output_variables=["output"]
     )
@@ -211,7 +221,9 @@ def create_tagging_chain_pydantic(
 
     functions = _get_tagging_functions(openai_schema)
     prompt = ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
-    chain = OpenAIFunctionsChain(llm=llm, prompt=prompt, functions=functions)
+    chain = OpenAIFunctionsChain(
+        llm=llm, prompt=prompt, functions=functions, kwargs=EXTRACTION_KWARGS
+    )
     pydantic_parsing_chain = TransformChain(
         transform=partial(_parse_tag_pydantic, pydantic_schema=pydantic_schema),
         input_variables=["input"],