Security for internal chatbot

[kientv]

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Example Code

mychain = MyChain(
    chatbot_name="gemini",
    api_key="..."
).configurable_fields(
    chatbot_name=ConfigurableField(
        id="chatbot_name",
        name="Chatbot name",
        description="The name of the chatbot to use for the retriever.",
    ),
    api_key=ConfigurableField(
        id="api_key",
        name="API KEY",
        description="API KEY for internal chatbot",
    )
)

Description

we have many chatbots in the same server
Now I want to security for internal chatbot (not public), so I can add api_key in the configurable (each api_key for one chatbot)
Do you have any better solution?

System Info

langchain==0.2.1
langchain-community==0.0.38
langchain-core==0.2.1
langchain-google-genai==0.0.4
langchain-google-vertexai==0.0.1.post1
langchain-openai==0.0.2
langchain-text-splitters==0.2.0

Python 3.11.0rc1

Platform: linux

[dosubot[bot]]

Yes, there is a better solution for securing the internal chatbot on your server than using an API key in the configurable fields. You can use Azure Active Directory (AD) tokens for authentication instead of API keys. This method is more secure as it leverages Azure's identity and access management capabilities.

Here is an example of how you can configure the Azure AD token in the LangChain framework:

client_params = {
    "api_version": values["openai_api_version"],
    "azure_endpoint": values["azure_endpoint"],
    "azure_deployment": values["deployment_name"],
    "api_key": None,  # Do not use API key
    "azure_ad_token": values["azure_ad_token"].get_secret_value()
    if values["azure_ad_token"]
    else None,
    "azure_ad_token_provider": values["azure_ad_token_provider"],
    "organization": values["openai_organization"],
    "base_url": values["openai_api_base"],
    "timeout": values["request_timeout"],
    "max_retries": values["max_retries"],
    "default_headers": values["default_headers"],
    "default_query": values["default_query"],
}

Ensure that you have the azure_ad_token and azure_ad_token_provider properly configured and retrieved from a secure source, such as environment variables or a secrets management service ^[1].

To continue talking to Dosu, mention @dosu.

[kientv]

@dosu We don't have Azure AD,
Our solution is SaaS, but I want to support both public chatbot and internal chatbot.
Do you have any other suggestions?

[dosubot[bot]]

For securing both public and internal chatbots in a SaaS environment, you can use OAuth or JWT for authentication. This approach is more secure and flexible than using API keys. Here’s how you can implement it:

1. OAuth Authentication:

   • Use OAuth tokens to authenticate users and grant access to the chatbots.
   • Store and manage tokens securely using environment variables or a secret management service.

2. JWT Authentication:

   • Use JWTs to securely transmit information between parties.
   • JWTs can be used to verify the identity of the user and ensure they have the necessary permissions.

Here’s an example of how you can implement OAuth authentication in LangChain:

from typing import Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator

class OAuthWrapper(BaseModel):
    """Wrapper for OAuth authentication."""

    oauth_access_token: str
    api_key: Optional[str] = None
    api_base: str = "https://api.example.com/"

    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid

    def _format_headers(self) -> Dict[str, str]:
        """Format headers for requests."""
        headers = {
            "Accept": "application/json",
            "Content-Type": "application/json",
        }

        if self.oauth_access_token:
            headers.update(
                {"Authorization": f"Bearer {self.oauth_access_token}"}
            )
        elif self.api_key:
            headers.update({"X-API-Key": self.api_key})

        return headers

    def _get_session(self) -> requests.Session:
        session = requests.Session()
        session.headers.update(self._format_headers())
        return session

    async def _arequest(self, method: str, url: str, **kwargs: Any) -> Dict[str, Any]:
        """Make an async request."""
        async with aiohttp.ClientSession(headers=self._format_headers()) as session:
            async with session.request(method, url, **kwargs) as response:
                response.raise_for_status()
                return await response.json()

    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that at least one auth method exists."""
        if not values.get("oauth_access_token") and not values.get("api_key"):
            raise ValueError("Either oauth_access_token or api_key must be provided.")
        return values

    def make_request(self, endpoint: str, method: str = "GET", **kwargs: Any) -> Dict:
        """Make a synchronous request."""
        session = self._get_session()
        url = self.api_base + endpoint
        response = session.request(method, url, **kwargs)
        response.raise_for_status()
        return response.json()

    async def make_async_request(self, endpoint: str, method: str = "GET", **kwargs: Any) -> Dict:
        """Make an asynchronous request."""
        url = self.api_base + endpoint
        return await self._arequest(method, url, **kwargs)

This class handles both OAuth and API Key authentication methods, allowing you to securely manage access to your chatbots.

Additionally, you can use LangChain's built-in support for environment variables to securely store and access sensitive information like tokens and API keys ^[1][2].

For more advanced security, consider integrating with secret management services like AWS Secrets Manager or HashiCorp Vault to manage your secrets securely.

To continue talking to Dosu, mention @dosu.