import getpass
import os

egnyte_user_token = getpass.getpass("Enter your Egnyte User Token: ")
domain = input("Enter your Egnyte domain (e.g., company.egnyte.com): ")

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

pip install -qU egnyte-langchain-connector

Note: you may need to restart the kernel to use updated packages.

from langchain_egnyte import EgnyteRetriever

retriever = EgnyteRetriever(domain=domain, k=100)

query = "machine learning policies"

documents = retriever.invoke(query, egnyte_user_token=egnyte_user_token)

for doc in documents:
    print(f"Title: {doc.metadata.get('title', 'N/A')}")
    print(f"Content: {doc.page_content[:200]}...")
    print("---")

from langchain_egnyte import EgnyteRetriever, EgnyteSearchOptions

search_options = EgnyteSearchOptions(
    limit=50,
    folderPath="/policies",
    excludeFolderPaths=["/temp", "/archive"],
    createdAfter=1640995200000,  # Unix timestamp in milliseconds (Jan 1, 2022)
    createdBefore=1672531200000  # Unix timestamp in milliseconds (Jan 1, 2023)
)

retriever = EgnyteRetriever(
    domain=domain,
    k=50,
    search_options=search_options
)

documents = retriever.invoke(
    "compliance requirements",
    egnyte_user_token=egnyte_user_token
)

import asyncio

async def search_async():
    documents = await retriever.ainvoke(
        "data privacy guidelines",
        egnyte_user_token=egnyte_user_token
    )
    return documents

# Run async search
documents = asyncio.run(search_async())

queries = [
    "security policies",
    "employee handbook",
    "compliance guidelines"
]

# Synchronous batch
results = retriever.batch(
    queries,
    config={"configurable": {"egnyte_user_token": egnyte_user_token}}
)

# Asynchronous batch
results = await retriever.abatch(
    queries,
    config={"configurable": {"egnyte_user_token": egnyte_user_token}}
)

from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.tools.retriever import create_retriever_tool
from langchain_openai import ChatOpenAI

retriever = EgnyteRetriever(domain=domain, k=50)

egnyte_search_tool = create_retriever_tool(
    retriever,
    "egnyte_search_tool",
    "This tool searches Egnyte and retrieves documents that match the search criteria using hybrid search"
)

tools = [egnyte_search_tool]

prompt = hub.pull("hwchase17/openai-tools-agent")
llm = ChatOpenAI(temperature=0)

agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

result = agent_executor.invoke({
    "input": "Find all documents related to data privacy policies"
})

print(result['output'])