Templates (#12294)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>

templates/extraction-openai-functions/extraction_openai_functions/__init__.py

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+from extraction_openai_functions.chain import chain
+
+__all__ = ["chain"]

templates/extraction-openai-functions/extraction_openai_functions/chain.py

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+from langchain.pydantic_v1 import BaseModel
+from typing import List, Optional
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.utils.openai_functions import convert_pydantic_to_openai_function
+from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
+import json
+
+
+template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. 
+
+Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.
+
+Do not make up or guess ANY extra information. Only extract what exactly is in the text."""
+
+prompt = ChatPromptTemplate.from_messages([
+    ("system", template),
+    ("human", "{input}")
+])
+
+# Function output schema
+class Paper(BaseModel):
+    """Information about papers mentioned."""
+    title: str
+    author: Optional[str]
+
+
+class Info(BaseModel):
+    """Information to extract"""
+    papers: List[Paper]
+
+# Function definition
+model = ChatOpenAI()
+function = [convert_pydantic_to_openai_function(Info)]
+chain = prompt | model.bind(
+    functions=function, function_call={"name": "Info"}
+) | (lambda x: json.loads(x.additional_kwargs['function_call']['arguments'])['papers'])
+
+# chain = prompt | model.bind(
+#     functions=function, function_call={"name": "Info"}
+# ) | JsonKeyOutputFunctionsParser(key_name="papers")