Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
This will help you get started with the Egnyte retriever. For detailed documentation of all EgnyteRetriever features and configurations head to the API reference.
Overview
The EgnyteRetriever class helps you search and retrieve documents from Egnyte using hybrid search capabilities that combine semantic and keyword search. This retriever is fully compliant with LangChain standards and supports both synchronous and asynchronous operations.
Integration details
Bring-your-own data (i.e., index and search a custom corpus of documents):
| Retriever | Self-host | Cloud offering | Package |
|---|
| EgnyteRetriever | ❌ | ✅ | egnyte-langchain-connector |
Setup
In order to use the Egnyte package, you will need:
- An Egnyte account — If you are not a current Egnyte customer or want to test outside of your production Egnyte instance, you can use a free developer account.
- An Egnyte app — This is configured in the developer console, and must have the appropriate scopes enabled.
- The app must be enabled by the administrator. For free developer accounts, this is whoever signed up for the account.
Credentials
For these examples, we will use Bearer token authentication with an Egnyte user token. To generate a user token:
- Register for a developer account at https://developers.egnyte.com/member/register
- Generate a user token following the Public API Authentication guide
- Important: Use the scope
Egnyte.ai when generating the token
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"
Installation
This retriever lives in the egnyte-langchain-connector package:
pip install -qU egnyte-langchain-connector
Note: you may need to restart the kernel to use updated packages.
Instantiation
Now we can instantiate our retriever:
from langchain_egnyte import EgnyteRetriever
retriever = EgnyteRetriever(domain=domain, k=100)
Usage
Basic search
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("---")
Advanced Search with Options
For more granular search, you can use EgnyteSearchOptions to filter results by folder path, date range, and more:
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
)
Async usage
The retriever supports asynchronous operations:
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())
Batch operations
You can process multiple queries in batch:
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'])
API reference
For detailed documentation of all EgnyteRetriever features and configurations, visit the GitHub repository.
Help
If you have questions, check out the Egnyte developer documentation or reach out to the Egnyte developer community.
