templates: add gemini functions agent (#17141)

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

templates/gemini-functions-agent/LICENSE

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+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

templates/gemini-functions-agent/README.md

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+
+# gemini-functions-agent
+
+This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take. 
+
+This example creates an agent that can optionally look up information on the internet using Tavily's search engine.
+
+## Environment Setup
+
+The following environment variables need to be set:
+
+Set the `TAVILY_API_KEY` environment variable to access Tavily.
+
+You will also need to authenticate with Google:
+
+```shell
+gcloud auth application-default login
+```
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package gemini-functions-agent
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add gemini-functions-agent
+```
+
+And add the following code to your `server.py` file:
+```python
+from gemini_functions_agent import agent_executor as gemini_functions_agent_chain
+
+add_routes(app, gemini_functions_agent_chain, path="/openai-functions-agent")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/gemini-functions-agent/playground](http://127.0.0.1:8000/gemini-functions-agent/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/gemini-functions-agent")
+```

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

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+from gemini_functions_agent.agent import agent_executor
+
+__all__ = ["agent_executor"]

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

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+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
+)

templates/gemini-functions-agent/main.py

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+from openai_functions_agent.agent import agent_executor
+
+if __name__ == "__main__":
+    question = "who won the womens world cup in 2023?"
+    print(agent_executor.invoke({"input": question, "chat_history": []}))

templates/gemini-functions-agent/pyproject.toml

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+[tool.poetry]
+name = "gemini-functions-agent"
+version = "0.1.0"
+description = "Agent using Gemini function calling to execute functions, including search"
+authors = [
+    "Harrison Chase",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<4.0"
+langchain = "^0.1"
+tavily-python = "^0.1.9"
+langchain-google-genai = ">=0.0.7,<0.1"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.21"
+
+[tool.langserve]
+export_module = "gemini_functions_agent"
+export_attr = "agent_executor"
+
+[tool.templates-hub]
+use-case = "research"
+author = "LangChain"
+integrations = ["Google", "Tavily"]
+tags = ["search", "agents", "function-calling"]
+
+[build-system]
+requires = [
+    "poetry-core",
+]
+build-backend = "poetry.core.masonry.api"