Module phc.services.agents

Classes

class Agents (session: phc.session.Session, run_async: bool = False, timeout: int = 30, trust_env: bool = False)

Provides access to the PHC agents API, which allows you to call LLM-based agents.

Expand source code

class Agents(BaseClient):
    """
    Provides access to the PHC agents API, which allows you to call LLM-based agents.
    """

    def invoke_basic(self, body: Union[str, List[Dict]]):
        """
        Invokes a basic agent, which supports either a basic prompt string or a list of
        messages which can include images as data urls. Requires the `invokeAgent` ABAC
        permission with `agent: "api-basic-agent"` in the policy, or the `accessAdmin`
        permission.
        """
        return self._api_call(
            "/v1/agents/basic/invoke", json={"input": body}, http_verb="POST"
        )

    def get_token(self):
        """
        Generates a temporary token for agent-based API authentication.

        This method creates a temporary authentication token for use with AI/ML agents
        like LangChain or other API integrations. The token includes AWS credentials
        that can be used to authenticate API calls.

        Permissions
        -----------
        Requires the ABAC policy rule `generateToken`.

        Returns
        -------
        phc.ApiResponse
            An API response object containing:
            - AccessKeyId: AWS access key for authentication
            - SecretAccessKey: AWS secret key for authentication
            - SessionToken: AWS session token for authentication
            - Expiration: Token expiration timestamp

        Example
        --------
        >>> from langchain_aws import ChatBedrock
        >>> from phc.services import Agents
        >>> agent = Agents(session)
        >>> token_resp = agent.get_token()
        >>> chat = ChatBedrock(
        >>>     aws_access_key_id=token_resp.data["AccessKeyId"],
        >>>     aws_secret_access_key=token_resp.data["SecretAccessKey"],
        >>>     aws_session_token=token_resp.data["SessionToken"],
        >>>     model_id="anthropic.claude-3-5-sonnet-20240620-v1:0",
        >>>     model_kwargs={
        >>>         "temperature": 0,
        >>>         "max_tokens": 500,
        >>>     }
        >>> )
        >>> ai_message = await llm.ainvoke([
                (
                    "system",
                    "You are a medical assistant, format below csv data into markdown".
                ),
                (
                    "human", "c,s,v,data"
                )
            ])
        """
        return self._api_call("/v1/agents/token", http_verb="GET")

Ancestors

• phc.base_client.BaseClient

Methods

def get_token(self)

Generates a temporary token for agent-based API authentication.

This method creates a temporary authentication token for use with AI/ML agents like LangChain or other API integrations. The token includes AWS credentials that can be used to authenticate API calls.

Permissions

Requires the ABAC policy rule generateToken.

Returns

ApiResponse

An API response object containing: - AccessKeyId: AWS access key for authentication - SecretAccessKey: AWS secret key for authentication - SessionToken: AWS session token for authentication - Expiration: Token expiration timestamp

Example

>>> from langchain_aws import ChatBedrock
>>> from phc.services import Agents
>>> agent = Agents(session)
>>> token_resp = agent.get_token()
>>> chat = ChatBedrock(
>>>     aws_access_key_id=token_resp.data["AccessKeyId"],
>>>     aws_secret_access_key=token_resp.data["SecretAccessKey"],
>>>     aws_session_token=token_resp.data["SessionToken"],
>>>     model_id="anthropic.claude-3-5-sonnet-20240620-v1:0",
>>>     model_kwargs={
>>>         "temperature": 0,
>>>         "max_tokens": 500,
>>>     }
>>> )
>>> ai_message = await llm.ainvoke([
        (
            "system",
            "You are a medical assistant, format below csv data into markdown".
        ),
        (
            "human", "c,s,v,data"
        )
    ])

Expand source code

def get_token(self):
    """
    Generates a temporary token for agent-based API authentication.

    This method creates a temporary authentication token for use with AI/ML agents
    like LangChain or other API integrations. The token includes AWS credentials
    that can be used to authenticate API calls.

    Permissions
    -----------
    Requires the ABAC policy rule `generateToken`.

    Returns
    -------
    phc.ApiResponse
        An API response object containing:
        - AccessKeyId: AWS access key for authentication
        - SecretAccessKey: AWS secret key for authentication
        - SessionToken: AWS session token for authentication
        - Expiration: Token expiration timestamp

    Example
    --------
    >>> from langchain_aws import ChatBedrock
    >>> from phc.services import Agents
    >>> agent = Agents(session)
    >>> token_resp = agent.get_token()
    >>> chat = ChatBedrock(
    >>>     aws_access_key_id=token_resp.data["AccessKeyId"],
    >>>     aws_secret_access_key=token_resp.data["SecretAccessKey"],
    >>>     aws_session_token=token_resp.data["SessionToken"],
    >>>     model_id="anthropic.claude-3-5-sonnet-20240620-v1:0",
    >>>     model_kwargs={
    >>>         "temperature": 0,
    >>>         "max_tokens": 500,
    >>>     }
    >>> )
    >>> ai_message = await llm.ainvoke([
            (
                "system",
                "You are a medical assistant, format below csv data into markdown".
            ),
            (
                "human", "c,s,v,data"
            )
        ])
    """
    return self._api_call("/v1/agents/token", http_verb="GET")

def invoke_basic(self, body: Union[str, List[Dict]])

Invokes a basic agent, which supports either a basic prompt string or a list of messages which can include images as data urls. Requires the invokeAgent ABAC permission with agent: "api-basic-agent" in the policy, or the accessAdmin permission.

Expand source code

def invoke_basic(self, body: Union[str, List[Dict]]):
    """
    Invokes a basic agent, which supports either a basic prompt string or a list of
    messages which can include images as data urls. Requires the `invokeAgent` ABAC
    permission with `agent: "api-basic-agent"` in the policy, or the `accessAdmin`
    permission.
    """
    return self._api_call(
        "/v1/agents/basic/invoke", json={"input": body}, http_verb="POST"
    )