import json from typing import Any, Dict, List, Optional from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models import BaseChatModel from langchain_core.messages import ( AIMessage, BaseMessage, ChatMessage, HumanMessage, SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatResult from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, pre_init, ) from langchain_core.utils.utils import _build_model_kwargs from pydantic import Field, SecretStr, model_validator SUPPORTED_ROLES: List[str] = [ "system", "user", "assistant", ] class ChatSnowflakeCortexError(Exception): """Error with Snowpark client.""" def _convert_message_to_dict(message: BaseMessage) -> dict: """Convert a LangChain message to a dictionary. Args: message: The LangChain message. Returns: The dictionary. """ message_dict: Dict[str, Any] = { "content": message.content, } # populate role and additional message data if isinstance(message, ChatMessage) and message.role in SUPPORTED_ROLES: message_dict["role"] = message.role elif isinstance(message, SystemMessage): message_dict["role"] = "system" elif isinstance(message, HumanMessage): message_dict["role"] = "user" elif isinstance(message, AIMessage): message_dict["role"] = "assistant" else: raise TypeError(f"Got unknown type {message}") return message_dict def _truncate_at_stop_tokens( text: str, stop: Optional[List[str]], ) -> str: """Truncates text at the earliest stop token found.""" if stop is None: return text for stop_token in stop: stop_token_idx = text.find(stop_token) if stop_token_idx != -1: text = text[:stop_token_idx] return text class ChatSnowflakeCortex(BaseChatModel): """Snowflake Cortex based Chat model To use you must have the ``snowflake-snowpark-python`` Python package installed and either: 1. environment variables set with your snowflake credentials or 2. directly passed in as kwargs to the ChatSnowflakeCortex constructor. Example: .. code-block:: python from langchain_community.chat_models import ChatSnowflakeCortex chat = ChatSnowflakeCortex() """ _sp_session: Any = None """Snowpark session object.""" model: str = "snowflake-arctic" """Snowflake cortex hosted LLM model name, defaulted to `snowflake-arctic`. Refer to docs for more options.""" cortex_function: str = "complete" """Cortex function to use, defaulted to `complete`. Refer to docs for more options.""" temperature: float = 0.7 """Model temperature. Value should be >= 0 and <= 1.0""" max_tokens: Optional[int] = None """The maximum number of output tokens in the response.""" top_p: Optional[float] = None """top_p adjusts the number of choices for each predicted tokens based on cumulative probabilities. Value should be ranging between 0.0 and 1.0. """ snowflake_username: Optional[str] = Field(default=None, alias="username") """Automatically inferred from env var `SNOWFLAKE_USERNAME` if not provided.""" snowflake_password: Optional[SecretStr] = Field(default=None, alias="password") """Automatically inferred from env var `SNOWFLAKE_PASSWORD` if not provided.""" snowflake_account: Optional[str] = Field(default=None, alias="account") """Automatically inferred from env var `SNOWFLAKE_ACCOUNT` if not provided.""" snowflake_database: Optional[str] = Field(default=None, alias="database") """Automatically inferred from env var `SNOWFLAKE_DATABASE` if not provided.""" snowflake_schema: Optional[str] = Field(default=None, alias="schema") """Automatically inferred from env var `SNOWFLAKE_SCHEMA` if not provided.""" snowflake_warehouse: Optional[str] = Field(default=None, alias="warehouse") """Automatically inferred from env var `SNOWFLAKE_WAREHOUSE` if not provided.""" snowflake_role: Optional[str] = Field(default=None, alias="role") """Automatically inferred from env var `SNOWFLAKE_ROLE` if not provided.""" @model_validator(mode="before") @classmethod def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) values = _build_model_kwargs(values, all_required_field_names) return values @pre_init def validate_environment(cls, values: Dict) -> Dict: try: from snowflake.snowpark import Session except ImportError: raise ImportError( "`snowflake-snowpark-python` package not found, please install it with " "`pip install snowflake-snowpark-python`" ) values["snowflake_username"] = get_from_dict_or_env( values, "snowflake_username", "SNOWFLAKE_USERNAME" ) values["snowflake_password"] = convert_to_secret_str( get_from_dict_or_env(values, "snowflake_password", "SNOWFLAKE_PASSWORD") ) values["snowflake_account"] = get_from_dict_or_env( values, "snowflake_account", "SNOWFLAKE_ACCOUNT" ) values["snowflake_database"] = get_from_dict_or_env( values, "snowflake_database", "SNOWFLAKE_DATABASE" ) values["snowflake_schema"] = get_from_dict_or_env( values, "snowflake_schema", "SNOWFLAKE_SCHEMA" ) values["snowflake_warehouse"] = get_from_dict_or_env( values, "snowflake_warehouse", "SNOWFLAKE_WAREHOUSE" ) values["snowflake_role"] = get_from_dict_or_env( values, "snowflake_role", "SNOWFLAKE_ROLE" ) connection_params = { "account": values["snowflake_account"], "user": values["snowflake_username"], "password": values["snowflake_password"].get_secret_value(), "database": values["snowflake_database"], "schema": values["snowflake_schema"], "warehouse": values["snowflake_warehouse"], "role": values["snowflake_role"], } try: values["_sp_session"] = Session.builder.configs(connection_params).create() except Exception as e: raise ChatSnowflakeCortexError(f"Failed to create session: {e}") return values def __del__(self) -> None: if getattr(self, "_sp_session", None) is not None: self._sp_session.close() @property def _llm_type(self) -> str: """Get the type of language model used by this chat model.""" return f"snowflake-cortex-{self.model}" def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: message_dicts = [_convert_message_to_dict(m) for m in messages] message_str = str(message_dicts) options = {"temperature": self.temperature} if self.top_p is not None: options["top_p"] = self.top_p if self.max_tokens is not None: options["max_tokens"] = self.max_tokens options_str = str(options) sql_stmt = f""" select snowflake.cortex.{self.cortex_function}( '{self.model}' ,{message_str},{options_str}) as llm_response;""" try: l_rows = self._sp_session.sql(sql_stmt).collect() except Exception as e: raise ChatSnowflakeCortexError( f"Error while making request to Snowflake Cortex via Snowpark: {e}" ) response = json.loads(l_rows[0]["LLM_RESPONSE"]) ai_message_content = response["choices"][0]["messages"] content = _truncate_at_stop_tokens(ai_message_content, stop) message = AIMessage( content=content, response_metadata=response["usage"], ) generation = ChatGeneration(message=message) return ChatResult(generations=[generation])