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

@@ -0,0 +1,54 @@
+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.pydantic_v1 import BaseModel
+
+
+# Fake Tool
+search = TavilySearchAPIWrapper()
+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]
+)
+
+def _format_chat_history(chat_history: List[Tuple[str, str]]):
+    buffer = []
+    for human, ai in chat_history:
+        buffer.append(HumanMessage(content=human))
+        buffer.append(AIMessage(content=ai))
+    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()
+
+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
+)
+
+agent_executor = agent_executor | (lambda x: x["output"])