> ## 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.

# EgnyteRetriever

This will help you get started with the Egnyte [retriever](/oss/python/langchain/retrieval). For detailed documentation of all `EgnyteRetriever` features and configurations head to the [API reference](https://github.com/egnyte/egnyte-langchain-connector).

# 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](https://github.com/egnyte/egnyte-langchain-connector) | ❌         |        ✅       | 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](https://developers.egnyte.com/member/register).
* An Egnyte app — This is configured in the [developer console](https://developers.egnyte.com/), 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:

1. Register for a developer account at [https://developers.egnyte.com/member/register](https://developers.egnyte.com/member/register)
2. Generate a user token following the [Public API Authentication guide](https://developers.egnyte.com/docs/read/Public_API_Authentication)
3. **Important**: Use the scope `Egnyte.ai` when generating the token

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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): ")
```

If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU egnyte-langchain-connector
```

```output theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Note: you may need to restart the kernel to use updated packages.
```

## Instantiation

Now we can instantiate our retriever:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_egnyte import EgnyteRetriever

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

## Usage

### Basic search

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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}}
)
```

## Use as an agent tool

Like other retrievers, EgnyteRetriever can be added to a LangGraph agent as a tool.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](https://github.com/egnyte/egnyte-langchain-connector).

## Help

If you have questions, check out the [Egnyte developer documentation](https://developers.egnyte.com/) or reach out to the Egnyte developer community.

***

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