email inbox agent

.gitignore

@@ -0,0 +1,7 @@
+
+.env
+__pycache__/custom_tools.cpython-310.pyc
+email_pairs.csv
+faq.csv
+past_email_mbox.csv
+Sent.mbox

app.py

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+from dotenv import find_dotenv, load_dotenv
+
+from langchain.agents import initialize_agent
+from langchain.agents import AgentType
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import MessagesPlaceholder
+from langchain.memory import ConversationSummaryBufferMemory
+from langchain.chains.summarize import load_summarize_chain
+from langchain.schema import SystemMessage
+from custom_tools import CreateEmailDraftTool, GenerateEmailResponseTool, ReplyEmailTool, EscalateTool, ProspectResearchTool, CategoriseEmailTool
+
+load_dotenv()
+llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
+
+system_message = SystemMessage(
+    content="""
+    You are an email inbox assistant of an AI youtube channel called "AI Jason", 
+    who is creating AI educational content, 
+    Your goal is to handle all the incoming emails by categorising them based on 
+    guideline and decide on next steps
+    """
+)
+
+tools = [
+    CategoriseEmailTool(),
+    ProspectResearchTool(),
+    EscalateTool(),
+    ReplyEmailTool(),
+    CreateEmailDraftTool(),
+    GenerateEmailResponseTool(),
+]
+
+agent_kwargs = {
+    "extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
+    "system_message": system_message,
+}
+memory = ConversationSummaryBufferMemory(
+    memory_key="memory", return_messages=True, llm=llm, max_token_limit=1000)
+
+agent = initialize_agent(
+    tools,
+    llm,
+    agent=AgentType.OPENAI_FUNCTIONS,
+    verbose=True,
+    agent_kwargs=agent_kwargs,
+    memory=memory,
+)
+
+
+test_email = """
+xxxxxxx
+"""
+
+agent({"input": test_email})