mirror of
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partners: AI21 Labs Jamba Support (#20815)
Description: Added support for AI21 new model - Jamba Twitter handle: https://github.com/AI21Labs --------- Co-authored-by: Asaf Gardin <asafg@ai21.com> Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Asaf Joseph Gardin
GitHub
parent
7a39fe60da
commit
642975dd9f
0
libs/partners/ai21/langchain_ai21/chat/__init__.py
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0
libs/partners/ai21/langchain_ai21/chat/__init__.py
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130
libs/partners/ai21/langchain_ai21/chat/chat_adapter.py
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libs/partners/ai21/langchain_ai21/chat/chat_adapter.py
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@ -0,0 +1,130 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Union, cast
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from ai21.models import ChatMessage as J2ChatMessage
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from ai21.models import RoleType
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from ai21.models.chat import ChatMessage
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
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_ChatMessageTypes = Union[ChatMessage, J2ChatMessage]
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_SYSTEM_ERR_MESSAGE = "System message must be at beginning of message list."
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_ROLE_TYPE = Union[str, RoleType]
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class ChatAdapter(ABC):
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"""
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Provides a common interface for the different Chat models available in AI21.
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It converts LangChain messages to AI21 messages.
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Calls the appropriate AI21 model API with the converted messages.
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"""
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@abstractmethod
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def convert_messages(
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self,
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messages: List[BaseMessage],
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) -> Dict[str, Any]:
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pass
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def _convert_message_to_ai21_message(
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self,
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message: BaseMessage,
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) -> _ChatMessageTypes:
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content = cast(str, message.content)
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role = self._parse_role(message)
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return self._chat_message(role=role, content=content)
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def _parse_role(self, message: BaseMessage) -> _ROLE_TYPE:
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role = None
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if isinstance(message, HumanMessage):
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return RoleType.USER
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if isinstance(message, AIMessage):
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return RoleType.ASSISTANT
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if isinstance(self, J2ChatAdapter):
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if not role:
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raise ValueError(
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f"Could not resolve role type from message {message}. "
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f"Only support {HumanMessage.__name__} and {AIMessage.__name__}."
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)
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# if it gets here, we rely on the server to handle the role type
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return message.type
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@abstractmethod
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def _chat_message(
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self,
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role: _ROLE_TYPE,
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content: str,
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) -> _ChatMessageTypes:
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pass
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@abstractmethod
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def call(self, client: Any, **params: Any) -> List[BaseMessage]:
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pass
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def _get_system_message_from_message(self, message: BaseMessage) -> str:
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if not isinstance(message.content, str):
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raise ValueError(
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f"System Message must be of type str. Got {type(message.content)}"
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)
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return message.content
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class J2ChatAdapter(ChatAdapter):
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def convert_messages(self, messages: List[BaseMessage]) -> Dict[str, Any]:
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system_message = ""
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converted_messages = [] # type: ignore
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for i, message in enumerate(messages):
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if message.type == "system":
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if i != 0:
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raise ValueError(_SYSTEM_ERR_MESSAGE)
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else:
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system_message = self._get_system_message_from_message(message)
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else:
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converted_message = self._convert_message_to_ai21_message(message)
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converted_messages.append(converted_message)
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return {"system": system_message, "messages": converted_messages}
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def _chat_message(
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self,
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role: _ROLE_TYPE,
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content: str,
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) -> J2ChatMessage:
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return J2ChatMessage(role=RoleType(role), text=content)
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def call(self, client: Any, **params: Any) -> List[BaseMessage]:
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response = client.chat.create(**params)
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return [AIMessage(output.text) for output in response.outputs]
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class JambaChatCompletionsAdapter(ChatAdapter):
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def convert_messages(self, messages: List[BaseMessage]) -> Dict[str, Any]:
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return {
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"messages": [
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self._convert_message_to_ai21_message(message) for message in messages
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],
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}
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def _chat_message(
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self,
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role: _ROLE_TYPE,
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content: str,
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) -> ChatMessage:
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return ChatMessage(
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role=role.value if isinstance(role, RoleType) else role,
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content=content,
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)
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def call(self, client: Any, **params: Any) -> List[BaseMessage]:
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response = client.chat.completions.create(**params)
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return [AIMessage(choice.message.content) for choice in response.choices]
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15
libs/partners/ai21/langchain_ai21/chat/chat_factory.py
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libs/partners/ai21/langchain_ai21/chat/chat_factory.py
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@ -0,0 +1,15 @@
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from langchain_ai21.chat.chat_adapter import (
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ChatAdapter,
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J2ChatAdapter,
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JambaChatCompletionsAdapter,
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)
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def create_chat_adapter(model: str) -> ChatAdapter:
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if "j2" in model:
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return J2ChatAdapter()
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if "jamba" in model:
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return JambaChatCompletionsAdapter()
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raise ValueError(f"Model {model} not supported.")
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@ -1,79 +1,21 @@
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import asyncio
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from functools import partial
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from typing import Any, List, Mapping, Optional, Tuple, cast
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from typing import Any, Dict, List, Mapping, Optional
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from ai21.models import ChatMessage, RoleType
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatResult
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from langchain_core.pydantic_v1 import root_validator
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from langchain_ai21.ai21_base import AI21Base
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def _get_system_message_from_message(message: BaseMessage) -> str:
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if not isinstance(message.content, str):
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raise ValueError(
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f"System Message must be of type str. Got {type(message.content)}"
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)
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return message.content
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def _convert_messages_to_ai21_messages(
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messages: List[BaseMessage],
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) -> Tuple[Optional[str], List[ChatMessage]]:
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system_message = None
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converted_messages: List[ChatMessage] = []
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for i, message in enumerate(messages):
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if message.type == "system":
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if i != 0:
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raise ValueError("System message must be at beginning of message list.")
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else:
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system_message = _get_system_message_from_message(message)
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else:
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converted_message = _convert_message_to_ai21_message(message)
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converted_messages.append(converted_message)
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return system_message, converted_messages
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def _convert_message_to_ai21_message(
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message: BaseMessage,
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) -> ChatMessage:
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content = cast(str, message.content)
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role = None
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if isinstance(message, HumanMessage):
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role = RoleType.USER
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elif isinstance(message, AIMessage):
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role = RoleType.ASSISTANT
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if not role:
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raise ValueError(
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f"Could not resolve role type from message {message}. "
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f"Only support {HumanMessage.__name__} and {AIMessage.__name__}."
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)
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return ChatMessage(role=role, text=content)
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def _pop_system_messages(messages: List[BaseMessage]) -> List[SystemMessage]:
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system_message_indexes = [
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i for i, message in enumerate(messages) if isinstance(message, SystemMessage)
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]
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return [cast(SystemMessage, messages.pop(i)) for i in system_message_indexes]
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from langchain_ai21.chat.chat_adapter import ChatAdapter
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from langchain_ai21.chat.chat_factory import create_chat_adapter
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class ChatAI21(BaseChatModel, AI21Base):
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@ -119,6 +61,20 @@ class ChatAI21(BaseChatModel, AI21Base):
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"""A penalty applied to tokens based on their frequency
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in the generated responses."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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_chat_adapter: ChatAdapter
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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values = super().validate_environment(values)
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model = values.get("model")
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values["_chat_adapter"] = create_chat_adapter(model) # type: ignore
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return values
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class Config:
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"""Configuration for this pydantic object."""
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@ -139,6 +95,7 @@ class ChatAI21(BaseChatModel, AI21Base):
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"temperature": self.temperature,
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"top_p": self.top_p,
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"top_k_return": self.top_k_return,
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"n": self.n,
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}
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if self.count_penalty is not None:
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@ -159,7 +116,7 @@ class ChatAI21(BaseChatModel, AI21Base):
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**kwargs: Any,
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) -> Mapping[str, Any]:
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params = {}
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system, ai21_messages = _convert_messages_to_ai21_messages(messages)
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converted_messages = self._chat_adapter.convert_messages(messages)
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if stop is not None:
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if "stop" in kwargs:
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@ -167,8 +124,7 @@ class ChatAI21(BaseChatModel, AI21Base):
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params["stop_sequences"] = stop
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return {
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"system": system or "",
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"messages": ai21_messages,
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**converted_messages,
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**self._default_params,
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**params,
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**kwargs,
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@ -182,12 +138,10 @@ class ChatAI21(BaseChatModel, AI21Base):
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**kwargs: Any,
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) -> ChatResult:
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params = self._build_params_for_request(messages=messages, stop=stop, **kwargs)
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messages = self._chat_adapter.call(self.client, **params)
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generations = [ChatGeneration(message=message) for message in messages]
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response = self.client.chat.create(**params)
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outputs = response.outputs
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message = AIMessage(content=outputs[0].text)
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return ChatResult(generations=[ChatGeneration(message=message)])
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return ChatResult(generations=generations)
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async def _agenerate(
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self,
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@ -199,3 +153,11 @@ class ChatAI21(BaseChatModel, AI21Base):
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return await asyncio.get_running_loop().run_in_executor(
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None, partial(self._generate, **kwargs), messages, stop, run_manager
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)
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def _get_system_message_from_message(self, message: BaseMessage) -> str:
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if not isinstance(message.content, str):
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raise ValueError(
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f"System Message must be of type str. Got {type(message.content)}"
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)
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return message.content
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356
libs/partners/ai21/poetry.lock
generated
356
libs/partners/ai21/poetry.lock
generated
@ -2,17 +2,17 @@
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[[package]]
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name = "ai21"
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version = "2.1.3"
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version = "2.2.1"
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description = ""
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optional = false
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python-versions = "<4.0,>=3.8"
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files = [
|
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{file = "ai21-2.1.3-py3-none-any.whl", hash = "sha256:384f3b5769edfb7124e9288bc83daeeeee35f6f831be2f77425c061774d5ade3"},
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{file = "ai21-2.1.3.tar.gz", hash = "sha256:80014f54818453e87ced5c3d180e22a6cfb25911231ec834cae55f8769106afe"},
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{file = "ai21-2.2.1-py3-none-any.whl", hash = "sha256:c2727656b07a1284fc8f1f2fb1b065059f6fa5fcab8af2f45b0767086a044771"},
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{file = "ai21-2.2.1.tar.gz", hash = "sha256:69e659071e9bbadae7349465bc388312bcff30e22fb7d7a9de4319e2925acfff"},
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]
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[package.dependencies]
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ai21-tokenizer = ">=0.3.9,<0.4.0"
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ai21-tokenizer = ">=0.9.0,<0.10.0"
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dataclasses-json = ">=0.6.3,<0.7.0"
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requests = ">=2.31.0,<3.0.0"
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typing-extensions = ">=4.9.0,<5.0.0"
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@ -22,17 +22,18 @@ aws = ["boto3 (>=1.28.82,<2.0.0)"]
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[[package]]
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name = "ai21-tokenizer"
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version = "0.3.11"
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version = "0.9.0"
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description = ""
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optional = false
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python-versions = ">=3.7,<4.0"
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python-versions = "<4.0,>=3.7"
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files = [
|
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{file = "ai21_tokenizer-0.3.11-py3-none-any.whl", hash = "sha256:80d332c51cab3fa88f0fea7493240a6a5bc38fd24a3d0806d28731d8fc97691f"},
|
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{file = "ai21_tokenizer-0.3.11.tar.gz", hash = "sha256:ec11ce4e46d24f71f1c2756ad0de34e0adfd51b5bcd81b544aea13d6935ec905"},
|
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{file = "ai21_tokenizer-0.9.0-py3-none-any.whl", hash = "sha256:3e4927f0ef98923f53710405fb476b29102bc7c677c65243043c5671cb642d9e"},
|
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{file = "ai21_tokenizer-0.9.0.tar.gz", hash = "sha256:724d368eb74564950edfcd6590da7c3b702c4a8f59f60d36dbec5046e6856579"},
|
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]
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[package.dependencies]
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sentencepiece = ">=0.1.96,<0.2.0"
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sentencepiece = ">=0.2.0,<0.3.0"
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tokenizers = ">=0.15.2,<0.16.0"
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[[package]]
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name = "annotated-types"
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@ -215,6 +216,22 @@ files = [
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[package.extras]
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test = ["pytest (>=6)"]
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|
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[[package]]
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name = "filelock"
|
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version = "3.13.4"
|
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description = "A platform independent file lock."
|
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optional = false
|
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python-versions = ">=3.8"
|
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files = [
|
||||
{file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
|
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{file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
|
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]
|
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|
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[package.extras]
|
||||
docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
|
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testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
|
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typing = ["typing-extensions (>=4.8)"]
|
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[[package]]
|
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name = "freezegun"
|
||||
version = "1.4.0"
|
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@ -229,6 +246,75 @@ files = [
|
||||
[package.dependencies]
|
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python-dateutil = ">=2.7"
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[[package]]
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name = "fsspec"
|
||||
version = "2024.3.1"
|
||||
description = "File-system specification"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
|
||||
{file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
abfs = ["adlfs"]
|
||||
adl = ["adlfs"]
|
||||
arrow = ["pyarrow (>=1)"]
|
||||
dask = ["dask", "distributed"]
|
||||
devel = ["pytest", "pytest-cov"]
|
||||
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
|
||||
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
|
||||
fuse = ["fusepy"]
|
||||
gcs = ["gcsfs"]
|
||||
git = ["pygit2"]
|
||||
github = ["requests"]
|
||||
gs = ["gcsfs"]
|
||||
gui = ["panel"]
|
||||
hdfs = ["pyarrow (>=1)"]
|
||||
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
|
||||
libarchive = ["libarchive-c"]
|
||||
oci = ["ocifs"]
|
||||
s3 = ["s3fs"]
|
||||
sftp = ["paramiko"]
|
||||
smb = ["smbprotocol"]
|
||||
ssh = ["paramiko"]
|
||||
tqdm = ["tqdm"]
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.22.2"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
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files = [
|
||||
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||||
{file = "huggingface_hub-0.22.2.tar.gz", hash = "sha256:32e9a9a6843c92f253ff9ca16b9985def4d80a93fb357af5353f770ef74a81be"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
fsspec = ">=2023.5.0"
|
||||
packaging = ">=20.9"
|
||||
pyyaml = ">=5.1"
|
||||
requests = "*"
|
||||
tqdm = ">=4.42.1"
|
||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[package.extras]
|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
cli = ["InquirerPy (==0.3.4)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
|
||||
hf-transfer = ["hf-transfer (>=0.1.4)"]
|
||||
inference = ["aiohttp", "minijinja (>=1.0)"]
|
||||
quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
|
||||
torch = ["safetensors", "torch"]
|
||||
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.6"
|
||||
@ -278,7 +364,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.1.42"
|
||||
version = "0.1.45"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -737,7 +823,6 @@ files = [
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||||
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||||
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|
||||
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|
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|
||||
@ -821,56 +906,64 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "sentencepiece"
|
||||
version = "0.1.99"
|
||||
version = "0.2.0"
|
||||
description = "SentencePiece python wrapper"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
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||||
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[[package]]
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@ -912,6 +1005,133 @@ files = [
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[package.extras]
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[[package]]
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name = "tokenizers"
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version = "0.15.2"
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description = ""
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optional = false
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||||
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|
||||
{file = "tokenizers-0.15.2.tar.gz", hash = "sha256:e6e9c6e019dd5484be5beafc775ae6c925f4c69a3487040ed09b45e13df2cb91"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
huggingface_hub = ">=0.16.4,<1.0"
|
||||
|
||||
[package.extras]
|
||||
dev = ["tokenizers[testing]"]
|
||||
docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
|
||||
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
@ -923,6 +1143,26 @@ files = [
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.2"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
|
||||
{file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[package.extras]
|
||||
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
|
||||
notebook = ["ipywidgets (>=6)"]
|
||||
slack = ["slack-sdk"]
|
||||
telegram = ["requests"]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.10.0"
|
||||
@ -1010,4 +1250,4 @@ watchmedo = ["PyYAML (>=3.10)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "446d53423f89a3a378db9cff7ce8cb392e146e294d31e9d1bbfc23108a571097"
|
||||
content-hash = "892d39e8c2a50b049e3c2bb8f39b8a2126273d9644944456bd9900f6d15c5cb2"
|
||||
|
||||
@ -9,7 +9,7 @@ readme = "README.md"
|
||||
python = ">=3.8.1,<4.0"
|
||||
langchain-core = "^0.1.28"
|
||||
langchain-text-splitters = "^0.0.1"
|
||||
ai21 = "^2.1.2"
|
||||
ai21 = "^2.2.1"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
@ -1,39 +1,80 @@
|
||||
"""Test ChatAI21 chat model."""
|
||||
|
||||
import pytest
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langchain_core.outputs import ChatGeneration
|
||||
|
||||
from langchain_ai21.chat_models import ChatAI21
|
||||
|
||||
_MODEL_NAME = "j2-ultra"
|
||||
from tests.unit_tests.conftest import J2_CHAT_MODEL_NAME, JAMBA_CHAT_MODEL_NAME
|
||||
|
||||
|
||||
def test_invoke() -> None:
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_j2_model",
|
||||
"when_jamba_model",
|
||||
],
|
||||
argnames=["model"],
|
||||
argvalues=[
|
||||
(J2_CHAT_MODEL_NAME,),
|
||||
(JAMBA_CHAT_MODEL_NAME,),
|
||||
],
|
||||
)
|
||||
def test_invoke(model: str) -> None:
|
||||
"""Test invoke tokens from AI21."""
|
||||
llm = ChatAI21(model=_MODEL_NAME)
|
||||
llm = ChatAI21(model=model)
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
def test_generation() -> None:
|
||||
"""Test invoke tokens from AI21."""
|
||||
llm = ChatAI21(model=_MODEL_NAME)
|
||||
message = HumanMessage(content="Hello")
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_j2_model_num_results_is_1",
|
||||
"when_j2_model_num_results_is_3",
|
||||
"when_jamba_model_n_is_1",
|
||||
"when_jamba_model_n_is_3",
|
||||
],
|
||||
argnames=["model", "num_results"],
|
||||
argvalues=[
|
||||
(J2_CHAT_MODEL_NAME, 1),
|
||||
(J2_CHAT_MODEL_NAME, 3),
|
||||
(JAMBA_CHAT_MODEL_NAME, 1),
|
||||
(JAMBA_CHAT_MODEL_NAME, 3),
|
||||
],
|
||||
)
|
||||
def test_generation(model: str, num_results: int) -> None:
|
||||
"""Test generation with multiple models and different result counts."""
|
||||
# Determine the configuration key based on the model type
|
||||
config_key = "n" if model == JAMBA_CHAT_MODEL_NAME else "num_results"
|
||||
|
||||
result = llm.generate([[message], [message]], config=dict(tags=["foo"]))
|
||||
# Create the model instance using the appropriate key for the result count
|
||||
llm = ChatAI21(model=model, **{config_key: num_results})
|
||||
|
||||
message = HumanMessage(content="Hello, this is a test. Can you help me please?")
|
||||
|
||||
result = llm.generate([[message]], config=dict(tags=["foo"]))
|
||||
|
||||
for generations in result.generations:
|
||||
assert len(generations) == 1
|
||||
assert len(generations) == num_results
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
async def test_ageneration() -> None:
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_j2_model",
|
||||
"when_jamba_model",
|
||||
],
|
||||
argnames=["model"],
|
||||
argvalues=[
|
||||
(J2_CHAT_MODEL_NAME,),
|
||||
(JAMBA_CHAT_MODEL_NAME,),
|
||||
],
|
||||
)
|
||||
async def test_ageneration(model: str) -> None:
|
||||
"""Test invoke tokens from AI21."""
|
||||
llm = ChatAI21(model=_MODEL_NAME)
|
||||
llm = ChatAI21(model=model)
|
||||
message = HumanMessage(content="Hello")
|
||||
|
||||
result = await llm.agenerate([[message], [message]], config=dict(tags=["foo"]))
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
"""Standard LangChain interface tests"""
|
||||
|
||||
import time
|
||||
from typing import Type
|
||||
|
||||
import pytest
|
||||
@ -9,17 +10,15 @@ from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
|
||||
from langchain_ai21 import ChatAI21
|
||||
|
||||
|
||||
class TestAI21Standard(ChatModelIntegrationTests):
|
||||
class TestAI21J2(ChatModelIntegrationTests):
|
||||
def teardown(self) -> None:
|
||||
# avoid getting rate limited
|
||||
time.sleep(1)
|
||||
|
||||
@pytest.fixture
|
||||
def chat_model_class(self) -> Type[BaseChatModel]:
|
||||
return ChatAI21
|
||||
|
||||
@pytest.fixture
|
||||
def chat_model_params(self) -> dict:
|
||||
return {
|
||||
"model": "j2-ultra",
|
||||
}
|
||||
|
||||
@pytest.mark.xfail(reason="Emits AIMessage instead of AIMessageChunk.")
|
||||
def test_stream(
|
||||
self,
|
||||
@ -41,3 +40,47 @@ class TestAI21Standard(ChatModelIntegrationTests):
|
||||
chat_model_class,
|
||||
chat_model_params,
|
||||
)
|
||||
|
||||
@pytest.fixture
|
||||
def chat_model_params(self) -> dict:
|
||||
return {
|
||||
"model": "j2-ultra",
|
||||
}
|
||||
|
||||
|
||||
class TestAI21Jamba(ChatModelIntegrationTests):
|
||||
def teardown(self) -> None:
|
||||
# avoid getting rate limited
|
||||
time.sleep(1)
|
||||
|
||||
@pytest.fixture
|
||||
def chat_model_class(self) -> Type[BaseChatModel]:
|
||||
return ChatAI21
|
||||
|
||||
@pytest.mark.xfail(reason="Emits AIMessage instead of AIMessageChunk.")
|
||||
def test_stream(
|
||||
self,
|
||||
chat_model_class: Type[BaseChatModel],
|
||||
chat_model_params: dict,
|
||||
) -> None:
|
||||
super().test_stream(
|
||||
chat_model_class,
|
||||
chat_model_params,
|
||||
)
|
||||
|
||||
@pytest.mark.xfail(reason="Emits AIMessage instead of AIMessageChunk.")
|
||||
async def test_astream(
|
||||
self,
|
||||
chat_model_class: Type[BaseChatModel],
|
||||
chat_model_params: dict,
|
||||
) -> None:
|
||||
await super().test_astream(
|
||||
chat_model_class,
|
||||
chat_model_params,
|
||||
)
|
||||
|
||||
@pytest.fixture
|
||||
def chat_model_params(self) -> dict:
|
||||
return {
|
||||
"model": "jamba-instruct-preview",
|
||||
}
|
||||
|
||||
9
libs/partners/ai21/tests/unit_tests/chat/conftest.py
Normal file
9
libs/partners/ai21/tests/unit_tests/chat/conftest.py
Normal file
@ -0,0 +1,9 @@
|
||||
import pytest
|
||||
|
||||
from langchain_ai21.chat.chat_adapter import ChatAdapter
|
||||
from langchain_ai21.chat.chat_factory import create_chat_adapter
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def chat_adapter(model: str) -> ChatAdapter:
|
||||
return create_chat_adapter(model)
|
||||
195
libs/partners/ai21/tests/unit_tests/chat/test_chat_adapter.py
Normal file
195
libs/partners/ai21/tests/unit_tests/chat/test_chat_adapter.py
Normal file
@ -0,0 +1,195 @@
|
||||
from typing import List
|
||||
|
||||
import pytest
|
||||
from ai21.models import ChatMessage as J2ChatMessage
|
||||
from ai21.models import RoleType
|
||||
from ai21.models.chat import ChatMessage
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
BaseMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.messages import (
|
||||
ChatMessage as LangChainChatMessage,
|
||||
)
|
||||
|
||||
from langchain_ai21.chat.chat_adapter import ChatAdapter
|
||||
|
||||
_J2_MODEL_NAME = "j2-ultra"
|
||||
_JAMBA_MODEL_NAME = "jamba-instruct-preview"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_human_message_j2_model",
|
||||
"when_ai_message_j2_model",
|
||||
"when_human_message_jamba_model",
|
||||
"when_ai_message_jamba_model",
|
||||
],
|
||||
argnames=["model", "message", "expected_ai21_message"],
|
||||
argvalues=[
|
||||
(
|
||||
_J2_MODEL_NAME,
|
||||
HumanMessage(content="Human Message Content"),
|
||||
J2ChatMessage(role=RoleType.USER, text="Human Message Content"),
|
||||
),
|
||||
(
|
||||
_J2_MODEL_NAME,
|
||||
AIMessage(content="AI Message Content"),
|
||||
J2ChatMessage(role=RoleType.ASSISTANT, text="AI Message Content"),
|
||||
),
|
||||
(
|
||||
_JAMBA_MODEL_NAME,
|
||||
HumanMessage(content="Human Message Content"),
|
||||
ChatMessage(role=RoleType.USER, content="Human Message Content"),
|
||||
),
|
||||
(
|
||||
_JAMBA_MODEL_NAME,
|
||||
AIMessage(content="AI Message Content"),
|
||||
ChatMessage(role=RoleType.ASSISTANT, content="AI Message Content"),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_convert_message_to_ai21_message(
|
||||
message: BaseMessage,
|
||||
expected_ai21_message: ChatMessage,
|
||||
chat_adapter: ChatAdapter,
|
||||
) -> None:
|
||||
ai21_message = chat_adapter._convert_message_to_ai21_message(message)
|
||||
assert ai21_message == expected_ai21_message
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_system_message_j2_model",
|
||||
"when_langchain_chat_message_j2_model",
|
||||
],
|
||||
argnames=["model", "message"],
|
||||
argvalues=[
|
||||
(
|
||||
_J2_MODEL_NAME,
|
||||
SystemMessage(content="System Message Content"),
|
||||
),
|
||||
(
|
||||
_J2_MODEL_NAME,
|
||||
LangChainChatMessage(content="Chat Message Content", role="human"),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_convert_message_to_ai21_message__when_invalid_role__should_raise_exception(
|
||||
message: BaseMessage,
|
||||
chat_adapter: ChatAdapter,
|
||||
) -> None:
|
||||
with pytest.raises(ValueError) as e:
|
||||
chat_adapter._convert_message_to_ai21_message(message)
|
||||
assert e.value.args[0] == (
|
||||
f"Could not resolve role type from message {message}. "
|
||||
f"Only support {HumanMessage.__name__} and {AIMessage.__name__}."
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_all_messages_are_human_messages__should_return_system_none_j2_model",
|
||||
"when_first_message_is_system__should_return_system_j2_model",
|
||||
"when_all_messages_are_human_messages__should_return_system_none_jamba_model",
|
||||
"when_first_message_is_system__should_return_system_jamba_model",
|
||||
],
|
||||
argnames=["model", "messages", "expected_messages"],
|
||||
argvalues=[
|
||||
(
|
||||
_J2_MODEL_NAME,
|
||||
[
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
HumanMessage(content="Human Message Content 2"),
|
||||
],
|
||||
{
|
||||
"system": "",
|
||||
"messages": [
|
||||
J2ChatMessage(
|
||||
role=RoleType.USER,
|
||||
text="Human Message Content 1",
|
||||
),
|
||||
J2ChatMessage(
|
||||
role=RoleType.USER,
|
||||
text="Human Message Content 2",
|
||||
),
|
||||
],
|
||||
},
|
||||
),
|
||||
(
|
||||
_J2_MODEL_NAME,
|
||||
[
|
||||
SystemMessage(content="System Message Content 1"),
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
],
|
||||
{
|
||||
"system": "System Message Content 1",
|
||||
"messages": [
|
||||
J2ChatMessage(
|
||||
role=RoleType.USER,
|
||||
text="Human Message Content 1",
|
||||
),
|
||||
],
|
||||
},
|
||||
),
|
||||
(
|
||||
_JAMBA_MODEL_NAME,
|
||||
[
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
HumanMessage(content="Human Message Content 2"),
|
||||
],
|
||||
{
|
||||
"messages": [
|
||||
ChatMessage(
|
||||
role=RoleType.USER,
|
||||
content="Human Message Content 1",
|
||||
),
|
||||
ChatMessage(
|
||||
role=RoleType.USER,
|
||||
content="Human Message Content 2",
|
||||
),
|
||||
]
|
||||
},
|
||||
),
|
||||
(
|
||||
_JAMBA_MODEL_NAME,
|
||||
[
|
||||
SystemMessage(content="System Message Content 1"),
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
],
|
||||
{
|
||||
"messages": [
|
||||
ChatMessage(role="system", content="System Message Content 1"),
|
||||
ChatMessage(role="user", content="Human Message Content 1"),
|
||||
],
|
||||
},
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_convert_messages(
|
||||
chat_adapter: ChatAdapter,
|
||||
messages: List[BaseMessage],
|
||||
expected_messages: List[ChatMessage],
|
||||
) -> None:
|
||||
converted_messages = chat_adapter.convert_messages(messages)
|
||||
assert converted_messages == expected_messages
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_j2_model",
|
||||
],
|
||||
argnames=["model"],
|
||||
argvalues=[
|
||||
(_J2_MODEL_NAME,),
|
||||
],
|
||||
)
|
||||
def test_convert_messages__when_system_is_not_first(chat_adapter: ChatAdapter) -> None:
|
||||
messages = [
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
SystemMessage(content="System Message Content 1"),
|
||||
]
|
||||
with pytest.raises(ValueError):
|
||||
chat_adapter.convert_messages(messages)
|
||||
@ -0,0 +1,34 @@
|
||||
from typing import Type
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_ai21.chat.chat_adapter import (
|
||||
ChatAdapter,
|
||||
J2ChatAdapter,
|
||||
JambaChatCompletionsAdapter,
|
||||
)
|
||||
from langchain_ai21.chat.chat_factory import create_chat_adapter
|
||||
from tests.unit_tests.conftest import J2_CHAT_MODEL_NAME, JAMBA_CHAT_MODEL_NAME
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_j2_model",
|
||||
"when_jamba_model",
|
||||
],
|
||||
argnames=["model", "expected_chat_type"],
|
||||
argvalues=[
|
||||
(J2_CHAT_MODEL_NAME, J2ChatAdapter),
|
||||
(JAMBA_CHAT_MODEL_NAME, JambaChatCompletionsAdapter),
|
||||
],
|
||||
)
|
||||
def test_create_chat_adapter_with_supported_models(
|
||||
model: str, expected_chat_type: Type[ChatAdapter]
|
||||
) -> None:
|
||||
adapter = create_chat_adapter(model)
|
||||
assert isinstance(adapter, expected_chat_type)
|
||||
|
||||
|
||||
def test_create_chat_adapter__when_model_not_supported() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
create_chat_adapter("unsupported-model")
|
||||
@ -21,6 +21,8 @@ from ai21.models import (
|
||||
from ai21.models.responses.segmentation_response import Segment
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
J2_CHAT_MODEL_NAME = "j2-ultra"
|
||||
JAMBA_CHAT_MODEL_NAME = "jamba-instruct-preview"
|
||||
DUMMY_API_KEY = "test_api_key"
|
||||
|
||||
BASIC_EXAMPLE_LLM_PARAMETERS = {
|
||||
@ -39,6 +41,23 @@ BASIC_EXAMPLE_LLM_PARAMETERS = {
|
||||
),
|
||||
}
|
||||
|
||||
BASIC_EXAMPLE_CHAT_PARAMETERS = {
|
||||
"num_results": 3,
|
||||
"max_tokens": 20,
|
||||
"min_tokens": 10,
|
||||
"temperature": 0.5,
|
||||
"top_p": 0.5,
|
||||
"top_k_return": 0,
|
||||
"frequency_penalty": Penalty(scale=0.2, apply_to_numbers=True),
|
||||
"presence_penalty": Penalty(scale=0.2, apply_to_stopwords=True),
|
||||
"count_penalty": Penalty(
|
||||
scale=0.2,
|
||||
apply_to_punctuation=True,
|
||||
apply_to_emojis=True,
|
||||
),
|
||||
"n": 3,
|
||||
}
|
||||
|
||||
SEGMENTS = [
|
||||
Segment(
|
||||
segment_type="normal_text",
|
||||
@ -82,6 +101,23 @@ BASIC_EXAMPLE_LLM_PARAMETERS_AS_DICT = {
|
||||
).to_dict(),
|
||||
}
|
||||
|
||||
BASIC_EXAMPLE_CHAT_PARAMETERS_AS_DICT = {
|
||||
"num_results": 3,
|
||||
"max_tokens": 20,
|
||||
"min_tokens": 10,
|
||||
"temperature": 0.5,
|
||||
"top_p": 0.5,
|
||||
"top_k_return": 0,
|
||||
"frequency_penalty": Penalty(scale=0.2, apply_to_numbers=True).to_dict(),
|
||||
"presence_penalty": Penalty(scale=0.2, apply_to_stopwords=True).to_dict(),
|
||||
"count_penalty": Penalty(
|
||||
scale=0.2,
|
||||
apply_to_punctuation=True,
|
||||
apply_to_emojis=True,
|
||||
).to_dict(),
|
||||
"n": 3,
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mocked_completion_response(mocker: MockerFixture) -> Mock:
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""Test chat model integration."""
|
||||
from typing import List, Optional, cast
|
||||
from typing import cast
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
import pytest
|
||||
@ -7,24 +7,18 @@ from ai21 import MissingApiKeyError
|
||||
from ai21.models import ChatMessage, Penalty, RoleType
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
BaseMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.messages import (
|
||||
ChatMessage as LangChainChatMessage,
|
||||
)
|
||||
from langchain_core.pydantic_v1 import SecretStr
|
||||
from pytest import CaptureFixture, MonkeyPatch
|
||||
|
||||
from langchain_ai21.chat_models import (
|
||||
ChatAI21,
|
||||
_convert_message_to_ai21_message,
|
||||
_convert_messages_to_ai21_messages,
|
||||
)
|
||||
from tests.unit_tests.conftest import (
|
||||
BASIC_EXAMPLE_LLM_PARAMETERS,
|
||||
BASIC_EXAMPLE_LLM_PARAMETERS_AS_DICT,
|
||||
BASIC_EXAMPLE_CHAT_PARAMETERS,
|
||||
BASIC_EXAMPLE_CHAT_PARAMETERS_AS_DICT,
|
||||
DUMMY_API_KEY,
|
||||
temporarily_unset_api_key,
|
||||
)
|
||||
@ -43,7 +37,7 @@ def test_initialization__when_default_parameters_in_init() -> None:
|
||||
|
||||
|
||||
def test_initialization__when_custom_parameters_in_init() -> None:
|
||||
model = "j2-mid"
|
||||
model = "j2-ultra"
|
||||
num_results = 1
|
||||
max_tokens = 10
|
||||
min_tokens = 20
|
||||
@ -79,101 +73,6 @@ def test_initialization__when_custom_parameters_in_init() -> None:
|
||||
assert count_penalty == count_penalty
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_human_message",
|
||||
"when_ai_message",
|
||||
],
|
||||
argnames=["message", "expected_ai21_message"],
|
||||
argvalues=[
|
||||
(
|
||||
HumanMessage(content="Human Message Content"),
|
||||
ChatMessage(role=RoleType.USER, text="Human Message Content"),
|
||||
),
|
||||
(
|
||||
AIMessage(content="AI Message Content"),
|
||||
ChatMessage(role=RoleType.ASSISTANT, text="AI Message Content"),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_convert_message_to_ai21_message(
|
||||
message: BaseMessage, expected_ai21_message: ChatMessage
|
||||
) -> None:
|
||||
ai21_message = _convert_message_to_ai21_message(message)
|
||||
assert ai21_message == expected_ai21_message
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_system_message",
|
||||
"when_langchain_chat_message",
|
||||
],
|
||||
argnames=["message"],
|
||||
argvalues=[
|
||||
(SystemMessage(content="System Message Content"),),
|
||||
(LangChainChatMessage(content="Chat Message Content", role="human"),),
|
||||
],
|
||||
)
|
||||
def test_convert_message_to_ai21_message__when_invalid_role__should_raise_exception(
|
||||
message: BaseMessage,
|
||||
) -> None:
|
||||
with pytest.raises(ValueError) as e:
|
||||
_convert_message_to_ai21_message(message)
|
||||
assert e.value.args[0] == (
|
||||
f"Could not resolve role type from message {message}. "
|
||||
f"Only support {HumanMessage.__name__} and {AIMessage.__name__}."
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
ids=[
|
||||
"when_all_messages_are_human_messages__should_return_system_none",
|
||||
"when_first_message_is_system__should_return_system",
|
||||
],
|
||||
argnames=["messages", "expected_system", "expected_messages"],
|
||||
argvalues=[
|
||||
(
|
||||
[
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
HumanMessage(content="Human Message Content 2"),
|
||||
],
|
||||
None,
|
||||
[
|
||||
ChatMessage(role=RoleType.USER, text="Human Message Content 1"),
|
||||
ChatMessage(role=RoleType.USER, text="Human Message Content 2"),
|
||||
],
|
||||
),
|
||||
(
|
||||
[
|
||||
SystemMessage(content="System Message Content 1"),
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
],
|
||||
"System Message Content 1",
|
||||
[
|
||||
ChatMessage(role=RoleType.USER, text="Human Message Content 1"),
|
||||
],
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_convert_messages(
|
||||
messages: List[BaseMessage],
|
||||
expected_system: Optional[str],
|
||||
expected_messages: List[ChatMessage],
|
||||
) -> None:
|
||||
system, ai21_messages = _convert_messages_to_ai21_messages(messages)
|
||||
assert ai21_messages == expected_messages
|
||||
assert system == expected_system
|
||||
|
||||
|
||||
def test_convert_messages_when_system_is_not_first__should_raise_value_error() -> None:
|
||||
messages = [
|
||||
HumanMessage(content="Human Message Content 1"),
|
||||
SystemMessage(content="System Message Content 1"),
|
||||
]
|
||||
with pytest.raises(ValueError):
|
||||
_convert_messages_to_ai21_messages(messages)
|
||||
|
||||
|
||||
def test_invoke(mock_client_with_chat: Mock) -> None:
|
||||
chat_input = "I'm Pickle Rick"
|
||||
|
||||
@ -181,7 +80,7 @@ def test_invoke(mock_client_with_chat: Mock) -> None:
|
||||
model="j2-ultra",
|
||||
api_key=DUMMY_API_KEY,
|
||||
client=mock_client_with_chat,
|
||||
**BASIC_EXAMPLE_LLM_PARAMETERS,
|
||||
**BASIC_EXAMPLE_CHAT_PARAMETERS,
|
||||
)
|
||||
llm.invoke(input=chat_input, config=dict(tags=["foo"]), stop=["\n"])
|
||||
|
||||
@ -190,7 +89,7 @@ def test_invoke(mock_client_with_chat: Mock) -> None:
|
||||
messages=[ChatMessage(role=RoleType.USER, text=chat_input)],
|
||||
system="",
|
||||
stop_sequences=["\n"],
|
||||
**BASIC_EXAMPLE_LLM_PARAMETERS_AS_DICT,
|
||||
**BASIC_EXAMPLE_CHAT_PARAMETERS_AS_DICT,
|
||||
)
|
||||
|
||||
|
||||
@ -207,7 +106,7 @@ def test_generate(mock_client_with_chat: Mock) -> None:
|
||||
llm = ChatAI21(
|
||||
model="j2-ultra",
|
||||
client=mock_client_with_chat,
|
||||
**BASIC_EXAMPLE_LLM_PARAMETERS,
|
||||
**BASIC_EXAMPLE_CHAT_PARAMETERS,
|
||||
)
|
||||
|
||||
llm.generate(messages=[messages0, messages1])
|
||||
@ -226,7 +125,7 @@ def test_generate(mock_client_with_chat: Mock) -> None:
|
||||
ChatMessage(role=RoleType.USER, text=str(messages0[2].content)),
|
||||
],
|
||||
system="",
|
||||
**BASIC_EXAMPLE_LLM_PARAMETERS_AS_DICT,
|
||||
**BASIC_EXAMPLE_CHAT_PARAMETERS_AS_DICT,
|
||||
),
|
||||
call(
|
||||
model="j2-ultra",
|
||||
@ -234,7 +133,7 @@ def test_generate(mock_client_with_chat: Mock) -> None:
|
||||
ChatMessage(role=RoleType.USER, text=str(messages1[1].content)),
|
||||
],
|
||||
system="system message",
|
||||
**BASIC_EXAMPLE_LLM_PARAMETERS_AS_DICT,
|
||||
**BASIC_EXAMPLE_CHAT_PARAMETERS_AS_DICT,
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
@ -9,7 +9,7 @@ from langchain_standard_tests.unit_tests import ChatModelUnitTests
|
||||
from langchain_ai21 import ChatAI21
|
||||
|
||||
|
||||
class TestAI21Standard(ChatModelUnitTests):
|
||||
class TestAI21J2(ChatModelUnitTests):
|
||||
@pytest.fixture
|
||||
def chat_model_class(self) -> Type[BaseChatModel]:
|
||||
return ChatAI21
|
||||
@ -20,3 +20,16 @@ class TestAI21Standard(ChatModelUnitTests):
|
||||
"model": "j2-ultra",
|
||||
"api_key": "test_api_key",
|
||||
}
|
||||
|
||||
|
||||
class TestAI21Jamba(ChatModelUnitTests):
|
||||
@pytest.fixture
|
||||
def chat_model_class(self) -> Type[BaseChatModel]:
|
||||
return ChatAI21
|
||||
|
||||
@pytest.fixture
|
||||
def chat_model_params(self) -> dict:
|
||||
return {
|
||||
"model": "jamba-instruct",
|
||||
"api_key": "test_api_key",
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user