Add Egnyte integration documentation

src/oss/python/integrations/providers/all_providers.mdx

@@ -945,6 +945,14 @@ Browse the complete collection of integrations available for Python. LangChain P
         Unified API for multiple AI services.
     </Card>
 
+    <Card
+        title="Egnyte"
+        href="/oss/integrations/providers/egnyte"
+        icon="link"
+    >
+        Cloud content collaboration and AI-powered search platform.
+    </Card>
+
     <Card
         title="Elasticsearch"
         href="/oss/integrations/providers/elasticsearch"

src/oss/python/integrations/providers/egnyte.mdx

@@ -0,0 +1,105 @@
+---
+title: Egnyte
+---
+
+[Egnyte](https://www.egnyte.com) is a cloud-based content collaboration and file sharing platform that enables organizations to securely store, share, and govern their files. Egnyte combines the power of cloud content management with advanced security, compliance, and AI-powered search capabilities.
+
+This package provides a LangChain integration for Egnyte's hybrid search API, which combines semantic and keyword search to help you find and retrieve documents from your Egnyte instance.
+
+## Installation and setup
+
+<CodeGroup>
+```bash pip
+pip install -U egnyte-langchain-connector
+```
+
+```bash uv
+uv add egnyte-langchain-connector
+```
+</CodeGroup>
+
+# egnyte-langchain-connector
+
+This package contains the LangChain integration with Egnyte. For more information about Egnyte, check out the [Egnyte developer documentation](https://developers.egnyte.com/).
+
+## Prerequisites
+
+In order to integrate with Egnyte, you 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.
+
+## Authentication
+
+The `egnyte-langchain-connector` package uses Bearer token authentication with Egnyte user tokens.
+
+### Generating a User Token
+
+To generate an Egnyte user token for authentication:
+
+1. **Register for a Developer Account**
+   - Visit [https://developers.egnyte.com/member/register](https://developers.egnyte.com/member/register)
+   - Create your free developer account
+
+2. **Generate User Token**
+   - Use your API key to generate a user token following the [Public API Authentication guide](https://developers.egnyte.com/docs/read/Public_API_Authentication)
+   - **Important**: Use the scope `Egnyte.ai` when generating the token to ensure proper access to AI-powered search features
+
+### Using the Token
+
+You can pass the token directly to the retriever:
+
+```python
+import getpass
+
+egnyte_user_token = getpass.getpass("Enter your Egnyte User Token: ")
+domain = "company.egnyte.com"  # Your Egnyte domain (without https://)
+```
+
+Or use environment variables:
+
+```python
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+egnyte_user_token = os.getenv("EGNYTE_USER_TOKEN")
+domain = os.getenv("EGNYTE_DOMAIN")
+```
+
+## Retrievers
+
+### EgnyteRetriever
+
+[See usage example](/oss/python/integrations/retrievers/egnyte)
+
+```python
+from langchain_egnyte import EgnyteRetriever
+
+retriever = EgnyteRetriever(domain="company.egnyte.com")
+```
+
+## Utilities
+
+### EgnyteSearchOptions
+
+Configure advanced search parameters:
+
+```python
+from langchain_egnyte import EgnyteSearchOptions
+
+search_options = EgnyteSearchOptions(
+    limit=50,
+    folderPath="/policies",
+    excludeFolderPaths=["/temp", "/archive"],
+    createdAfter=1640995200000,  # Unix timestamp in milliseconds
+    createdBefore=1672531200000
+)
+```
+
+## Help
+
+If you have questions, you can check out the [Egnyte developer documentation](https://developers.egnyte.com/) or reach out to us in our developer community.
+

src/oss/python/integrations/retrievers/egnyte.mdx

@@ -0,0 +1,198 @@
+---
+title: EgnyteRetriever
+---
+
+This will help you get started with the Egnyte [retriever](/oss/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
+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
+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
+pip install -qU egnyte-langchain-connector
+```
+
+```output
+Note: you may need to restart the kernel to use updated packages.
+```
+
+## Instantiation
+
+Now we can instantiate our retriever:
+
+```python
+from langchain_egnyte import EgnyteRetriever
+
+retriever = EgnyteRetriever(domain=domain, k=100)
+```
+
+## Usage
+
+### Basic Search
+
+```python
+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
+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
+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
+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
+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.
+

src/oss/python/integrations/retrievers/index.mdx

@@ -61,6 +61,7 @@ The below retrievers will search over an external index (e.g., constructed from
 <Card title="Dria" icon="link" href="/oss/integrations/retrievers/dria_index" arrow="true" cta="View guide" />
 <Card title="ElasticSearch BM25" icon="link" href="/oss/integrations/retrievers/elastic_search_bm25" arrow="true" cta="View guide" />
 <Card title="Elasticsearch" icon="link" href="/oss/integrations/retrievers/elasticsearch_retriever" arrow="true" cta="View guide" />
+<Card title="Egnyte" icon="link" href="/oss/integrations/retrievers/egnyte" arrow="true" cta="View guide" />
 <Card title="Embedchain" icon="link" href="/oss/integrations/retrievers/embedchain" arrow="true" cta="View guide" />
 <Card title="FlashRank reranker" icon="link" href="/oss/integrations/retrievers/flashrank-reranker" arrow="true" cta="View guide" />
 <Card title="Fleet AI Context" icon="link" href="/oss/integrations/retrievers/fleet_context" arrow="true" cta="View guide" />