feat(unified_config): keep global config and use throughout the appli… (

#1467)

* feat(Unified_config): keep global config and use throughout the application unless changed explicit

* remove extra comments

* feat: improve deprecation warning

Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>

---------

Co-authored-by: Gabriele Venturi <[email protected]>
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>

pandasai/__init__.py

@@ -10,18 +10,24 @@
 
 import pandas as pd
 
+from pandasai.config import ConfigManager
 from pandasai.exceptions import DatasetNotFound, PandasAIApiKeyError
 from pandasai.helpers.path import find_project_root
 from pandasai.helpers.request import get_pandaai_session
 from .agent import Agent
 from .helpers.cache import Cache
 from .dataframe.base import DataFrame
 from .data_loader.loader import DatasetLoader
+from .smart_dataframe import SmartDataframe
+from .smart_datalake import SmartDatalake
 
 # Global variable to store the current agent
 _current_agent = None
 
 
+config = ConfigManager()
+
+
 def clear_cache(filename: str = None):
     """Clear the cache"""
     cache = Cache(filename) if filename else Cache()
@@ -119,4 +125,6 @@ def read_csv(filepath: str) -> DataFrame:
     "chat",
     "follow_up",
     "load",
+    "SmartDataframe",
+    "SmartDatalake",
 ]

pandasai/agent/base.py

@@ -36,6 +36,7 @@
 from ..llm.base import LLM
 from importlib.util import find_spec
 from ..config import Config
+import warnings
 
 
 class Agent:
@@ -59,6 +60,13 @@ def __init__(
             memory_size (int, optional): Conversation history to use during chat.
             Defaults to 1.
         """
+        if config is not None:
+            warnings.warn(
+                "The 'config' parameter is deprecated and will be removed in a future version. "
+                "Please use the global configuration instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
 
         self._state = AgentState()
 
@@ -72,9 +80,6 @@ def __init__(
         # Instantiate the config
         self._state.config = self._get_config(config)
 
-        # Set llm in state
-        self._state.llm = self._get_llm(self._state.config.llm)
-
         # Validate df input with configurations
         self._validate_input()
 
@@ -86,6 +91,10 @@ def __init__(
             save_logs=self._state.config.save_logs, verbose=self._state.config.verbose
         )
 
+        # If user provided config but not llm but have setup the env for BambooLLM, will be deprecated in future
+        if config:
+            self._state.config.llm = self._get_llm(self._state.config.llm)
+
         # Initiate VectorStore
         self._state.vectorstore = vectorstore
 
@@ -298,6 +307,10 @@ def _process_query(self, query: str, output_type: Optional[str] = None):
         try:
             self._assign_prompt_id()
 
+            # To ensure the cache is set properly if config is changed in between
+            if self._state.config.enable_cache and self._state.cache is None:
+                self._state.cache = Cache()
+
             # Generate code
             code, additional_dependencies = self.generate_code(query)
 
@@ -354,7 +367,7 @@ def _get_config(self, config: Union[Config, dict]):
         """
 
         config = load_config_from_json(config)
-        return Config(**config)
+        return Config(**config) if config else None
 
     def _get_llm(self, llm: Optional[LLM] = None) -> LLM:
         """

pandasai/agent/state.py

@@ -12,7 +12,6 @@
 if TYPE_CHECKING:
     from pandasai.dataframe import DataFrame
     from pandasai.dataframe import VirtualDataFrame
-    from pandasai.llm.base import LLM
 
 
 @dataclass
@@ -22,10 +21,9 @@ class AgentState:
     """
 
     dfs: List[Union[DataFrame, VirtualDataFrame]] = field(default_factory=list)
-    config: Union[Config, dict] = field(default_factory=dict)
+    _config: Union[Config, dict] = field(default_factory=dict)
     memory: Memory = field(default_factory=Memory)
     cache: Optional[Cache] = None
-    llm: LLM = None
     vectorstore: Optional[VectorStore] = None
     intermediate_values: Dict[str, Any] = field(default_factory=dict)
     logger: Optional[Logger] = None
@@ -58,3 +56,22 @@ def add_many(self, values: Dict[str, Any]):
     def get(self, key: str, default: Any = "") -> Any:
         """Fetches a value from intermediate values or returns a default."""
         return self.intermediate_values.get(key, default)
+
+    @property
+    def config(self):
+        """
+        Returns the local config if set, otherwise fetches the global config.
+        """
+        if self._config is not None:
+            return self._config
+
+        import pandasai as pai
+
+        return pai.config.get()
+
+    @config.setter
+    def config(self, value: Union[Config, dict, None]):
+        """
+        Allows setting a new config value.
+        """
+        self._config = Config(**value) if isinstance(value, dict) else value

pandasai/config.py

@@ -1,4 +1,6 @@
+from importlib.util import find_spec
 import json
+import os
 
 import pandasai.llm as llm
 from pandasai.llm.base import LLM
@@ -33,6 +35,53 @@ def from_dict(cls, config: Dict[str, Any]) -> "Config":
         return cls(**config)
 
 
+class ConfigManager:
+    """A singleton class to manage the global configuration."""
+
+    _config: Config = Config()
+
+    @classmethod
+    def set(cls, config_dict: Dict[str, Any]) -> None:
+        """Set the global configuration."""
+        cls._config = Config.from_dict(config_dict)
+        cls.validate_llm()
+
+    @classmethod
+    def get(cls) -> Config:
+        """Get the global configuration."""
+        if cls._config.llm is None and os.environ.get("PANDASAI_API_KEY"):
+            from pandasai.llm.bamboo_llm import BambooLLM
+
+            cls._config.llm = BambooLLM()
+
+        return cls._config
+
+    @classmethod
+    def update(cls, config_dict: Dict[str, Any]) -> None:
+        """Update the existing configuration with new values."""
+        current_config = cls._config.model_dump()
+        current_config.update(config_dict)
+        cls._config = Config.from_dict(current_config)
+
+    @classmethod
+    def validate_llm(cls):
+        """
+        Initializes a default LLM if not provided.
+        """
+        if cls._config.llm is None and os.environ.get("PANDASAI_API_KEY"):
+            from pandasai.llm.bamboo_llm import BambooLLM
+
+            cls._config.llm = BambooLLM()
+            return
+
+        # Check if pandasai_langchain is installed
+        if find_spec("pandasai_langchain") is not None:
+            from pandasai_langchain.langchain import LangchainLLM, is_langchain_llm
+
+            if is_langchain_llm(cls._config.llm):
+                cls._config.llm = LangchainLLM(cls._config.llm)
+
+
 def load_config_from_json(
     override_config: Optional[Union[Config, dict]] = None,
 ):