Format Templates (#12396)

templates/openai-functions-agent/openai_functions_agent/agent.py

@@ -1,15 +1,15 @@
 from typing import List, Tuple
-from langchain.schema.messages import HumanMessage, AIMessage
-from langchain.chat_models import ChatOpenAI
+
 from langchain.agents import AgentExecutor
-from langchain.utilities.tavily_search import TavilySearchAPIWrapper
-from langchain.tools.tavily_search import TavilySearchResults
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.tools.render import format_tool_to_openai_function
 from langchain.agents.format_scratchpad import format_to_openai_functions
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.pydantic_v1 import BaseModel
-
+from langchain.schema.messages import AIMessage, HumanMessage
+from langchain.tools.render import format_tool_to_openai_function
+from langchain.tools.tavily_search import TavilySearchResults
+from langchain.utilities.tavily_search import TavilySearchAPIWrapper
 
 # Fake Tool
 search = TavilySearchAPIWrapper()
@@ -18,17 +18,21 @@ tavily_tool = TavilySearchResults(api_wrapper=search)
 tools = [tavily_tool]
 
 llm = ChatOpenAI(temperature=0)
-prompt = ChatPromptTemplate.from_messages([
-    ("system", "You are very powerful assistant, but bad at calculating lengths of words."),
-    MessagesPlaceholder(variable_name="chat_history"),
-    ("user", "{input}"),
-    MessagesPlaceholder(variable_name="agent_scratchpad"),
-])
-
-llm_with_tools = llm.bind(
-    functions=[format_tool_to_openai_function(t) for t in tools]
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are very powerful assistant, but bad at calculating lengths of words.",
+        ),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
 )
 
+llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
+
+
 def _format_chat_history(chat_history: List[Tuple[str, str]]):
     buffer = []
     for human, ai in chat_history:
@@ -37,16 +41,25 @@ def _format_chat_history(chat_history: List[Tuple[str, str]]):
     return buffer
 
 
-agent = {
-    "input": lambda x: x["input"],
-    "chat_history": lambda x: _format_chat_history(x['chat_history']),
-    "agent_scratchpad": lambda x: format_to_openai_functions(x['intermediate_steps']),
-} | prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser()
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "chat_history": lambda x: _format_chat_history(x["chat_history"]),
+        "agent_scratchpad": lambda x: format_to_openai_functions(
+            x["intermediate_steps"]
+        ),
+    }
+    | prompt
+    | llm_with_tools
+    | OpenAIFunctionsAgentOutputParser()
+)
+
 
 class AgentInput(BaseModel):
     input: str
     chat_history: List[Tuple[str, str]]
 
+
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
     input_type=AgentInput
 )