import json
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Literal,
Optional,
Sequence,
Type,
Union,
)
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.tools import BaseTool
from langchain_core.utils import (
convert_to_secret_str,
get_from_dict_or_env,
get_pydantic_field_names,
)
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import Field, SecretStr, model_validator
SUPPORTED_ROLES: List[str] = [
"system",
"user",
"assistant",
]
[docs]
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
[docs]
class ChatSnowflakeCortex(BaseChatModel):
"""Snowflake Cortex based Chat model
To use the chat model, 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()
"""
# test_tools: Dict[str, Any] = Field(default_factory=dict)
test_tools: Dict[str, Union[Dict[str, Any], Type, Callable, BaseTool]] = Field(
default_factory=dict
)
session: Any = None
"""Snowpark session object."""
model: str = "mistral-large"
"""Snowflake cortex hosted LLM model name, defaulted to `mistral-large`.
Refer to docs for more options. Also note, not all models support
agentic workflows."""
cortex_function: str = "complete"
"""Cortex function to use, defaulted to `complete`.
Refer to docs for more options."""
temperature: float = 0
"""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] = 0
"""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
@model_validator(mode="before")
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:
`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"],
"client_session_keep_alive": "True",
}
try:
values["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, "session", None) is not None:
self.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]
# Check for tool invocation in the messages and prepare for tool use
tool_output = None
for message in messages:
if (
isinstance(message.content, dict)
and isinstance(message, SystemMessage)
and "invoke_tool" in message.content
):
tool_info = json.loads(message.content.get("invoke_tool"))
tool_name = tool_info.get("tool_name")
if tool_name in self.test_tools:
tool_args = tool_info.get("args", {})
tool_output = self.test_tools[tool_name](**tool_args)
break
# Prepare messages for SQL query
if tool_output:
message_dicts.append(
{"tool_output": str(tool_output)}
) # Ensure tool_output is a string
# JSON dump the message_dicts and options without additional escaping
message_json = json.dumps(message_dicts)
options = {
"temperature": self.temperature,
"top_p": self.top_p if self.top_p is not None else 1.0,
"max_tokens": self.max_tokens if self.max_tokens is not None else 2048,
}
options_json = json.dumps(options) # JSON string of options
# Form the SQL statement using JSON literals
sql_stmt = f"""
select snowflake.cortex.{self.cortex_function}(
'{self.model}',
parse_json('{message_json}'),
parse_json('{options_json}')
) as llm_response;
"""
try:
# Use the Snowflake Cortex Complete function
self.session.sql(
f"USE WAREHOUSE {self.session.get_current_warehouse()};"
).collect()
l_rows = self.session.sql(sql_stmt).collect()
except Exception as e:
raise ChatSnowflakeCortexError(
f"Error while making request to Snowflake Cortex: {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])
def _stream_content(
self, content: str, stop: Optional[List[str]]
) -> Iterator[ChatGenerationChunk]:
"""
Stream the output of the model in chunks to return ChatGenerationChunk.
"""
chunk_size = 50 # Define a reasonable chunk size for streaming
truncated_content = _truncate_at_stop_tokens(content, stop)
for i in range(0, len(truncated_content), chunk_size):
chunk_content = truncated_content[i : i + chunk_size]
# Create and yield a ChatGenerationChunk with partial content
yield ChatGenerationChunk(message=AIMessageChunk(content=chunk_content))
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""Stream the output of the model in chunks to return ChatGenerationChunk."""
message_dicts = [_convert_message_to_dict(m) for m in messages]
# Check for and potentially use a tool before streaming
for message in messages:
if (
isinstance(message, str)
and isinstance(message, SystemMessage)
and "invoke_tool" in message.content
):
tool_info = json.loads(message.content)
tool_list = tool_info.get("invoke_tools", [])
for tool in tool_list:
tool_name = tool.get("tool_name")
tool_args = tool.get("args", {})
if tool_name in self.test_tools:
tool_args = tool_info.get("args", {})
tool_result = self.test_tools[tool_name](**tool_args)
additional_context = {"tool_output": tool_result}
message_dicts.append(
additional_context
) # Append tool result to message dicts
# JSON dump the message_dicts and options without additional escaping
message_json = json.dumps(message_dicts)
options = {
"temperature": self.temperature,
"top_p": self.top_p if self.top_p is not None else 1.0,
"max_tokens": self.max_tokens if self.max_tokens is not None else 2048,
# "stream": True,
}
options_json = json.dumps(options) # JSON string of options
# Form the SQL statement using JSON literals
sql_stmt = f"""
select snowflake.cortex.{self.cortex_function}(
'{self.model}',
parse_json('{message_json}'),
parse_json('{options_json}')
) as llm_stream_response;
"""
try:
# Use the Snowflake Cortex Complete function
self.session.sql(
f"USE WAREHOUSE {self.session.get_current_warehouse()};"
).collect()
result = self.session.sql(sql_stmt).collect()
# Iterate over the generator to yield streaming responses
for row in result:
response = json.loads(row["LLM_STREAM_RESPONSE"])
ai_message_content = response["choices"][0]["messages"]
# Stream response content in chunks
for chunk in self._stream_content(ai_message_content, stop):
yield chunk
except Exception as e:
raise ChatSnowflakeCortexError(
f"Error while making request to Snowflake Cortex stream: {e}"
)
