Pin python version

haystack/components/agents/state/state.py

@@ -0,0 +1,180 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from copy import deepcopy
+from typing import Any, Callable, Dict, List, Optional
+
+from haystack.dataclasses import ChatMessage
+from haystack.utils import deserialize_value, serialize_value
+from haystack.utils.callable_serialization import deserialize_callable, serialize_callable
+from haystack.utils.type_serialization import deserialize_type, serialize_type
+
+from .state_utils import _is_list_type, _is_valid_type, merge_lists, replace_values
+
+
+def _schema_to_dict(schema: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Convert a schema dictionary to a serializable format.
+
+    Converts each parameter's type and optional handler function into a serializable
+    format using type and callable serialization utilities.
+
+    :param schema: Dictionary mapping parameter names to their type and handler configs
+    :returns: Dictionary with serialized type and handler information
+    """
+    serialized_schema = {}
+    for param, config in schema.items():
+        serialized_schema[param] = {"type": serialize_type(config["type"])}
+        if config.get("handler"):
+            serialized_schema[param]["handler"] = serialize_callable(config["handler"])
+
+    return serialized_schema
+
+
+def _schema_from_dict(schema: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Convert a serialized schema dictionary back to its original format.
+
+    Deserializes the type and optional handler function for each parameter from their
+    serialized format back into Python types and callables.
+
+    :param schema: Dictionary containing serialized schema information
+    :returns: Dictionary with deserialized type and handler configurations
+    """
+    deserialized_schema = {}
+    for param, config in schema.items():
+        deserialized_schema[param] = {"type": deserialize_type(config["type"])}
+
+        if config.get("handler"):
+            deserialized_schema[param]["handler"] = deserialize_callable(config["handler"])
+
+    return deserialized_schema
+
+
+def _validate_schema(schema: Dict[str, Any]) -> None:
+    """
+    Validate that a schema dictionary meets all required constraints.
+
+    Checks that each parameter definition has a valid type field and that any handler
+    specified is a callable function.
+
+    :param schema: Dictionary mapping parameter names to their type and handler configs
+    :raises ValueError: If schema validation fails due to missing or invalid fields
+    """
+    for param, definition in schema.items():
+        if "type" not in definition:
+            raise ValueError(f"StateSchema: Key '{param}' is missing a 'type' entry.")
+        if not _is_valid_type(definition["type"]):
+            raise ValueError(f"StateSchema: 'type' for key '{param}' must be a Python type, got {definition['type']}")
+        if definition.get("handler") is not None and not callable(definition["handler"]):
+            raise ValueError(f"StateSchema: 'handler' for key '{param}' must be callable or None")
+        if param == "messages" and definition["type"] is not List[ChatMessage]:
+            raise ValueError(f"StateSchema: 'messages' must be of type List[ChatMessage], got {definition['type']}")
+
+
+class State:
+    """
+    A class that wraps a StateSchema and maintains an internal _data dictionary.
+
+    Each schema entry has:
+      "parameter_name": {
+        "type": SomeType,
+        "handler": Optional[Callable[[Any, Any], Any]]
+      }
+    """
+
+    def __init__(self, schema: Dict[str, Any], data: Optional[Dict[str, Any]] = None):
+        """
+        Initialize a State object with a schema and optional data.
+
+        :param schema: Dictionary mapping parameter names to their type and handler configs.
+            Type must be a valid Python type, and handler must be a callable function or None.
+            If handler is None, the default handler for the type will be used. The default handlers are:
+                - For list types: `haystack.dataclasses.state_utils.merge_lists`
+                - For all other types: `haystack.dataclasses.state_utils.replace_values`
+        :param data: Optional dictionary of initial data to populate the state
+        """
+        _validate_schema(schema)
+        self.schema = deepcopy(schema)
+        if self.schema.get("messages") is None:
+            self.schema["messages"] = {"type": List[ChatMessage], "handler": merge_lists}
+        self._data = data or {}
+
+        # Set default handlers if not provided in schema
+        for definition in self.schema.values():
+            # Skip if handler is already defined and not None
+            if definition.get("handler") is not None:
+                continue
+            # Set default handler based on type
+            if _is_list_type(definition["type"]):
+                definition["handler"] = merge_lists
+            else:
+                definition["handler"] = replace_values
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """
+        Retrieve a value from the state by key.
+
+        :param key: Key to look up in the state
+        :param default: Value to return if key is not found
+        :returns: Value associated with key or default if not found
+        """
+        return deepcopy(self._data.get(key, default))
+
+    def set(self, key: str, value: Any, handler_override: Optional[Callable[[Any, Any], Any]] = None) -> None:
+        """
+        Set or merge a value in the state according to schema rules.
+
+        Value is merged or overwritten according to these rules:
+          - if handler_override is given, use that
+          - else use the handler defined in the schema for 'key'
+
+        :param key: Key to store the value under
+        :param value: Value to store or merge
+        :param handler_override: Optional function to override the default merge behavior
+        """
+        # If key not in schema, we throw an error
+        definition = self.schema.get(key, None)
+        if definition is None:
+            raise ValueError(f"State: Key '{key}' not found in schema. Schema: {self.schema}")
+
+        # Get current value from state and apply handler
+        current_value = self._data.get(key, None)
+        handler = handler_override or definition["handler"]
+        self._data[key] = handler(current_value, value)
+
+    @property
+    def data(self):
+        """
+        All current data of the state.
+        """
+        return self._data
+
+    def has(self, key: str) -> bool:
+        """
+        Check if a key exists in the state.
+
+        :param key: Key to check for existence
+        :returns: True if key exists in state, False otherwise
+        """
+        return key in self._data
+
+    def to_dict(self):
+        """
+        Convert the State object to a dictionary.
+        """
+        serialized = {}
+        serialized["schema"] = _schema_to_dict(self.schema)
+
+        serialized["data"] = serialize_value(self._data)
+        return serialized
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]):
+        """
+        Convert a dictionary back to a State object.
+        """
+        schema = _schema_from_dict(data.get("schema", {}))
+        deserialized_data = deserialize_value(data.get("data", {}))
+        return State(schema, deserialized_data)

haystack/components/agents/state/state_utils.py

@@ -0,0 +1,77 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import inspect
+from typing import Any, List, TypeVar, Union, get_origin
+
+T = TypeVar("T")
+
+
+def _is_valid_type(obj: Any) -> bool:
+    """
+    Check if an object is a valid type annotation.
+
+    Valid types include:
+    - Normal classes (str, dict, CustomClass)
+    - Generic types (List[str], Dict[str, int])
+    - Union types (Union[str, int], Optional[str])
+
+    :param obj: The object to check
+    :return: True if the object is a valid type annotation, False otherwise
+
+    Example usage:
+        >>> _is_valid_type(str)
+        True
+        >>> _is_valid_type(List[int])
+        True
+        >>> _is_valid_type(Union[str, int])
+        True
+        >>> _is_valid_type(42)
+        False
+    """
+    # Handle Union types (including Optional)
+    if hasattr(obj, "__origin__") and obj.__origin__ is Union:
+        return True
+
+    # Handle normal classes and generic types
+    return inspect.isclass(obj) or type(obj).__name__ in {"_GenericAlias", "GenericAlias"}
+
+
+def _is_list_type(type_hint: Any) -> bool:
+    """
+    Check if a type hint represents a list type.
+
+    :param type_hint: The type hint to check
+    :return: True if the type hint represents a list, False otherwise
+    """
+    return type_hint is list or (hasattr(type_hint, "__origin__") and get_origin(type_hint) is list)
+
+
+def merge_lists(current: Union[List[T], T, None], new: Union[List[T], T]) -> List[T]:
+    """
+    Merges two values into a single list.
+
+    If either `current` or `new` is not already a list, it is converted into one.
+    The function ensures that both inputs are treated as lists and concatenates them.
+
+    If `current` is None, it is treated as an empty list.
+
+    :param current: The existing value(s), either a single item or a list.
+    :param new: The new value(s) to merge, either a single item or a list.
+    :return: A list containing elements from both `current` and `new`.
+    """
+    current_list = [] if current is None else current if isinstance(current, list) else [current]
+    new_list = new if isinstance(new, list) else [new]
+    return current_list + new_list
+
+
+def replace_values(current: Any, new: Any) -> Any:
+    """
+    Replace the `current` value with the `new` value.
+
+    :param current: The existing value
+    :param new: The new value to replace
+    :return: The new value
+    """
+    return new

pyproject.toml

@@ -8,7 +8,7 @@ dynamic = ["version"]
 description = "LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data."
 readme = "README.md"
 license = "Apache-2.0"
-requires-python = ">=3.9"
+requires-python = ">=3.9, <3.13"
 authors = [{ name = "deepset.ai", email = "malte.pietsch@deepset.ai" }]
 keywords = [
   "BERT",