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client_secret*.json | ||
credentials.json | ||
token.json | ||
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MIT License | ||
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Copyright (c) 2023 LangChain, Inc. | ||
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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: | ||
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The above copyright notice and this permission notice shall be included in all | ||
copies or substantial portions of the Software. | ||
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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. |
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# OpenAI Functions Agent - Gmail | ||
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This template implements a simple agent using OpenAI function calling imports directly from [langchain-core](https://pypi.org/project/langchain-core/) and [`langchain-community`](https://pypi.org/project/langchain-community/). | ||
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This template creates an agent that uses OpenAI function calling to communicate its decisions on what actions to take. | ||
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This example creates an agent that can optionally look up information on the internet using Tavily's search engine. | ||
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## Environment Setup | ||
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The following environment variables need to be set: | ||
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. | ||
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Create a [`credentials.json`](https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application) file containing your OAuth client ID from Gmail. To customize authentication, see the [Customize Auth](#customize-auth) section below. | ||
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_*Note:* The first time you run this app, it will force you to go through a user authentication flow._ | ||
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## Usage | ||
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To use this package, you should first have the LangChain CLI installed: | ||
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```shell | ||
pip install -U langchain-cli | ||
``` | ||
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To create a new LangChain project and install this as the only package, you can do: | ||
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```shell | ||
langchain app new my-app --package openai-functions-agent-gmail | ||
``` | ||
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If you want to add this to an existing project, you can just run: | ||
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```shell | ||
langchain app add openai-functions-agent-gmail | ||
``` | ||
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And add the following code to your `server.py` file: | ||
```python | ||
from openai_functions_agent import agent_executor as openai_functions_agent_chain | ||
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add_routes(app, openai_functions_agent_chain, path="/openai-functions-agent-gmail") | ||
``` | ||
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(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 | ||
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```shell | ||
export LANGCHAIN_TRACING_V2=true | ||
export LANGCHAIN_API_KEY=<your-api-key> | ||
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default" | ||
``` | ||
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If you are inside this directory, then you can spin up a LangServe instance directly by: | ||
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```shell | ||
langchain serve | ||
``` | ||
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This will start the FastAPI app with a server is running locally at | ||
[http://localhost:8000](http://localhost:8000) | ||
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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/openai-functions-agent-gmail/playground](http://127.0.0.1:8000/openai-functions-agent/playground) | ||
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We can access the template from code with: | ||
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```python | ||
from langserve.client import RemoteRunnable | ||
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runnable = RemoteRunnable("http://localhost:8000/openai-functions-agent-gmail") | ||
``` | ||
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## Customize Auth | ||
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``` | ||
from langchain.tools.gmail.utils import build_resource_service, get_gmail_credentials | ||
# Can review scopes here https://developers.google.com/gmail/api/auth/scopes | ||
# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly' | ||
credentials = get_gmail_credentials( | ||
token_file="token.json", | ||
scopes=["https://mail.google.com/"], | ||
client_secrets_file="credentials.json", | ||
) | ||
api_resource = build_resource_service(credentials=credentials) | ||
toolkit = GmailToolkit(api_resource=api_resource) | ||
``` |
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from openai_functions_agent.agent import agent_executor | ||
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if __name__ == "__main__": | ||
question = ( | ||
"Write a draft response to LangChain's last email. " | ||
"First do background research on the sender and topics to make sure you" | ||
" understand the context, then write the draft." | ||
) | ||
print(agent_executor.invoke({"input": question, "chat_history": []})) |
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templates/openai-functions-agent-gmail/openai_functions_agent/__init__.py
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from openai_functions_agent.agent import agent_executor | ||
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__all__ = ["agent_executor"] |
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templates/openai-functions-agent-gmail/openai_functions_agent/agent.py
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from typing import List, Tuple | ||
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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.tools.render import format_tool_to_openai_function | ||
from langchain_community.chat_models import ChatOpenAI | ||
from langchain_community.tools.gmail import ( | ||
GmailCreateDraft, | ||
GmailGetMessage, | ||
GmailGetThread, | ||
GmailSearch, | ||
GmailSendMessage, | ||
) | ||
from langchain_community.tools.gmail.utils import build_resource_service | ||
from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper | ||
from langchain_core.messages import AIMessage, HumanMessage | ||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder | ||
from langchain_core.pydantic_v1 import BaseModel, Field | ||
from langchain_core.tools import tool | ||
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@tool | ||
def search_engine(query: str, max_results: int = 5) -> str: | ||
""""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.""" | ||
return TavilySearchAPIWrapper().results(query, max_results=max_results) | ||
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# Create the tools | ||
tools = [ | ||
GmailCreateDraft(), | ||
GmailGetMessage(), | ||
GmailGetThread(), | ||
GmailSearch(), | ||
GmailSendMessage(), | ||
search_engine, | ||
] | ||
current_user = ( | ||
build_resource_service().users().getProfile(userId="me").execute()["emailAddress"] | ||
) | ||
assistant_system_message = """You are a helpful assistant aiding a user with their \ | ||
emails. Use tools (only if necessary) to best answer \ | ||
the users questions.\n\nCurrent user: {user}""" | ||
prompt = ChatPromptTemplate.from_messages( | ||
[ | ||
("system", assistant_system_message), | ||
MessagesPlaceholder(variable_name="chat_history"), | ||
("user", "{input}"), | ||
MessagesPlaceholder(variable_name="agent_scratchpad"), | ||
] | ||
).partial(user=current_user) | ||
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llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0) | ||
llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools]) | ||
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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 | ||
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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() | ||
) | ||
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class AgentInput(BaseModel): | ||
input: str | ||
chat_history: List[Tuple[str, str]] = Field( | ||
..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}} | ||
) | ||
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types( | ||
input_type=AgentInput | ||
) |
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