templates: add gemini functions agent (#17141)

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/gemini-functions-agent/gemini_functions_agent/agent.py

@@ -0,0 +1,81 @@
+from typing import List, Tuple
+
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_function_messages
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.utilities.tavily_search import TavilySearchAPIWrapper
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+# Create the tool
+search = TavilySearchAPIWrapper()
+description = """"A search engine optimized for comprehensive, accurate, \
+and trusted results. Useful for when you need to answer questions \
+about current events or about recent information. \
+Input should be a search query. \
+If the user is asking about something that you don't know about, \
+you should probably use this tool to see if that can provide any information."""
+tavily_tool = TavilySearchResults(api_wrapper=search, description=description)
+
+tools = [tavily_tool]
+
+llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro")
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+llm_with_tools = llm.bind(
+    functions=[
+        {
+            "name": tavily_tool.name,
+            "description": tavily_tool.description,
+            "parameters": {
+                "type": "object",
+                "properties": {"query": {"type": "string"}},
+                "required": ["query"],
+            },
+        }
+    ]
+)
+
+
+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_function_messages(
+            x["intermediate_steps"]
+        ),
+    }
+    | prompt
+    | llm_with_tools
+    | OpenAIFunctionsAgentOutputParser()
+)
+
+
+class AgentInput(BaseModel):
+    input: str
+    chat_history: List[Tuple[str, str]] = Field(
+        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
+    )
+
+
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
+    input_type=AgentInput
+)