mirror of
https://github.com/hwchase17/langchain.git
synced 2026-01-30 05:47:54 +00:00
move schema to a directory
This commit is contained in:
Harrison Chase
@@ -1,401 +0,0 @@
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"""Common schema objects."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import (
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Any,
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Dict,
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Generic,
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List,
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NamedTuple,
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Optional,
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Sequence,
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TypeVar,
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Union,
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)
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from pydantic import BaseModel, Extra, Field, root_validator
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def get_buffer_string(
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messages: List[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
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) -> str:
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"""Get buffer string of messages."""
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string_messages = []
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for m in messages:
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if isinstance(m, HumanMessage):
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role = human_prefix
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elif isinstance(m, AIMessage):
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role = ai_prefix
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elif isinstance(m, SystemMessage):
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role = "System"
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elif isinstance(m, ChatMessage):
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role = m.role
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else:
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raise ValueError(f"Got unsupported message type: {m}")
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string_messages.append(f"{role}: {m.content}")
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return "\n".join(string_messages)
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class AgentAction(NamedTuple):
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"""Agent's action to take."""
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tool: str
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tool_input: Union[str, dict]
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log: str
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class AgentFinish(NamedTuple):
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"""Agent's return value."""
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return_values: dict
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log: str
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class Generation(BaseModel):
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"""Output of a single generation."""
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text: str
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"""Generated text output."""
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generation_info: Optional[Dict[str, Any]] = None
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"""Raw generation info response from the provider"""
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"""May include things like reason for finishing (e.g. in OpenAI)"""
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# TODO: add log probs
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class BaseMessage(BaseModel):
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"""Message object."""
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content: str
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additional_kwargs: dict = Field(default_factory=dict)
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@property
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@abstractmethod
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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class HumanMessage(BaseMessage):
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"""Type of message that is spoken by the human."""
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example: bool = False
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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return "human"
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class AIMessage(BaseMessage):
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"""Type of message that is spoken by the AI."""
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example: bool = False
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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return "ai"
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class SystemMessage(BaseMessage):
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"""Type of message that is a system message."""
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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return "system"
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class ChatMessage(BaseMessage):
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"""Type of message with arbitrary speaker."""
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role: str
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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return "chat"
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def _message_to_dict(message: BaseMessage) -> dict:
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return {"type": message.type, "data": message.dict()}
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def messages_to_dict(messages: List[BaseMessage]) -> List[dict]:
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return [_message_to_dict(m) for m in messages]
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def _message_from_dict(message: dict) -> BaseMessage:
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_type = message["type"]
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if _type == "human":
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return HumanMessage(**message["data"])
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elif _type == "ai":
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return AIMessage(**message["data"])
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elif _type == "system":
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return SystemMessage(**message["data"])
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elif _type == "chat":
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return ChatMessage(**message["data"])
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else:
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raise ValueError(f"Got unexpected type: {_type}")
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def messages_from_dict(messages: List[dict]) -> List[BaseMessage]:
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return [_message_from_dict(m) for m in messages]
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class ChatGeneration(Generation):
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"""Output of a single generation."""
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text = ""
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message: BaseMessage
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@root_validator
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def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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values["text"] = values["message"].content
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return values
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class ChatResult(BaseModel):
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"""Class that contains all relevant information for a Chat Result."""
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generations: List[ChatGeneration]
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"""List of the things generated."""
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llm_output: Optional[dict] = None
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"""For arbitrary LLM provider specific output."""
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class LLMResult(BaseModel):
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"""Class that contains all relevant information for an LLM Result."""
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generations: List[List[Generation]]
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"""List of the things generated. This is List[List[]] because
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each input could have multiple generations."""
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llm_output: Optional[dict] = None
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"""For arbitrary LLM provider specific output."""
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class PromptValue(BaseModel, ABC):
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@abstractmethod
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def to_string(self) -> str:
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"""Return prompt as string."""
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@abstractmethod
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def to_messages(self) -> List[BaseMessage]:
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"""Return prompt as messages."""
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class BaseMemory(BaseModel, ABC):
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"""Base interface for memory in chains."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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@abstractmethod
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def memory_variables(self) -> List[str]:
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"""Input keys this memory class will load dynamically."""
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@abstractmethod
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def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
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"""Return key-value pairs given the text input to the chain.
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If None, return all memories
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"""
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@abstractmethod
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def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Save the context of this model run to memory."""
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@abstractmethod
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def clear(self) -> None:
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"""Clear memory contents."""
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class BaseChatMessageHistory(ABC):
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"""Base interface for chat message history
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See `ChatMessageHistory` for default implementation.
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"""
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"""
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Example:
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.. code-block:: python
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class FileChatMessageHistory(BaseChatMessageHistory):
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storage_path: str
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session_id: str
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@property
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def messages(self):
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with open(os.path.join(storage_path, session_id), 'r:utf-8') as f:
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messages = json.loads(f.read())
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return messages_from_dict(messages)
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def add_message(self, message: BaseMessage) -> None:
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messages = self.messages.append(_message_to_dict(message))
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with open(os.path.join(storage_path, session_id), 'w') as f:
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json.dump(f, messages)
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def clear(self):
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with open(os.path.join(storage_path, session_id), 'w') as f:
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f.write("[]")
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"""
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messages: List[BaseMessage]
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def add_user_message(self, message: str) -> None:
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"""Add a user message to the store"""
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self.add_message(HumanMessage(content=message))
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def add_ai_message(self, message: str) -> None:
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"""Add an AI message to the store"""
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self.add_message(AIMessage(content=message))
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def add_message(self, message: BaseMessage) -> None:
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"""Add a self-created message to the store"""
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raise NotImplementedError
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@abstractmethod
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def clear(self) -> None:
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"""Remove all messages from the store"""
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class Document(BaseModel):
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"""Interface for interacting with a document."""
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page_content: str
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metadata: dict = Field(default_factory=dict)
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class BaseRetriever(ABC):
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@abstractmethod
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def get_relevant_documents(self, query: str) -> List[Document]:
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"""Get documents relevant for a query.
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Args:
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query: string to find relevant documents for
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Returns:
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List of relevant documents
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"""
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@abstractmethod
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async def aget_relevant_documents(self, query: str) -> List[Document]:
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"""Get documents relevant for a query.
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Args:
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query: string to find relevant documents for
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Returns:
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List of relevant documents
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"""
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# For backwards compatibility
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Memory = BaseMemory
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T = TypeVar("T")
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class BaseOutputParser(BaseModel, ABC, Generic[T]):
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"""Class to parse the output of an LLM call.
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Output parsers help structure language model responses.
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"""
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@abstractmethod
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def parse(self, text: str) -> T:
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"""Parse the output of an LLM call.
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A method which takes in a string (assumed output of a language model )
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and parses it into some structure.
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Args:
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text: output of language model
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Returns:
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structured output
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"""
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def parse_with_prompt(self, completion: str, prompt: PromptValue) -> Any:
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"""Optional method to parse the output of an LLM call with a prompt.
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The prompt is largely provided in the event the OutputParser wants
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to retry or fix the output in some way, and needs information from
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the prompt to do so.
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Args:
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completion: output of language model
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prompt: prompt value
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Returns:
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structured output
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"""
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return self.parse(completion)
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def get_format_instructions(self) -> str:
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"""Instructions on how the LLM output should be formatted."""
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raise NotImplementedError
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@property
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def _type(self) -> str:
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"""Return the type key."""
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raise NotImplementedError(
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f"_type property is not implemented in class {self.__class__.__name__}."
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" This is required for serialization."
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)
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def dict(self, **kwargs: Any) -> Dict:
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"""Return dictionary representation of output parser."""
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output_parser_dict = super().dict()
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output_parser_dict["_type"] = self._type
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return output_parser_dict
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class OutputParserException(ValueError):
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"""Exception that output parsers should raise to signify a parsing error.
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This exists to differentiate parsing errors from other code or execution errors
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that also may arise inside the output parser. OutputParserExceptions will be
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available to catch and handle in ways to fix the parsing error, while other
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errors will be raised.
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"""
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def __init__(
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self,
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error: Any,
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observation: str | None = None,
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llm_output: str | None = None,
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send_to_llm: bool = False,
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):
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super(OutputParserException, self).__init__(error)
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if send_to_llm:
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if observation is None or llm_output is None:
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raise ValueError(
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"Arguments 'observation' & 'llm_output'"
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" are required if 'send_to_llm' is True"
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)
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self.observation = observation
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self.llm_output = llm_output
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self.send_to_llm = send_to_llm
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class BaseDocumentTransformer(ABC):
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"""Base interface for transforming documents."""
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@abstractmethod
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def transform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Transform a list of documents."""
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@abstractmethod
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async def atransform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Asynchronously transform a list of documents."""
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48
langchain/schema/__init__.py
Normal file
48
langchain/schema/__init__.py
Normal file
@@ -0,0 +1,48 @@
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from langchain.schema.agents import AgentAction, AgentFinish
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from langchain.schema.chat_message_history import BaseChatMessageHistory
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from langchain.schema.documents import BaseDocumentTransformer, Document
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from langchain.schema.memory import BaseMemory, Memory
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from langchain.schema.messages import (
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AIMessage,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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_message_from_dict,
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_message_to_dict,
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get_buffer_string,
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messages_from_dict,
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messages_to_dict,
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)
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from langchain.schema.output_parser import BaseOutputParser, OutputParserException
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from langchain.schema.outputs import ChatGeneration, ChatResult, Generation, LLMResult
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from langchain.schema.prompts import PromptValue
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from langchain.schema.retriever import BaseRetriever
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__all__ = [
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||||
"AgentAction",
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||||
"AgentFinish",
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"BaseMessage",
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"BaseChatMessageHistory",
|
||||
"Document",
|
||||
"BaseDocumentTransformer",
|
||||
"Memory",
|
||||
"BaseMemory",
|
||||
"ChatResult",
|
||||
"ChatGeneration",
|
||||
"ChatMessage",
|
||||
"HumanMessage",
|
||||
"AIMessage",
|
||||
"SystemMessage",
|
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"get_buffer_string",
|
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"messages_to_dict",
|
||||
"messages_from_dict",
|
||||
"BaseOutputParser",
|
||||
"OutputParserException",
|
||||
"PromptValue",
|
||||
"Generation",
|
||||
"LLMResult",
|
||||
"BaseRetriever",
|
||||
"_message_to_dict",
|
||||
"_message_from_dict",
|
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]
|
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18
langchain/schema/agents.py
Normal file
18
langchain/schema/agents.py
Normal file
@@ -0,0 +1,18 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import NamedTuple, Union
|
||||
|
||||
|
||||
class AgentAction(NamedTuple):
|
||||
"""Agent's action to take."""
|
||||
|
||||
tool: str
|
||||
tool_input: Union[str, dict]
|
||||
log: str
|
||||
|
||||
|
||||
class AgentFinish(NamedTuple):
|
||||
"""Agent's return value."""
|
||||
|
||||
return_values: dict
|
||||
log: str
|
||||
54
langchain/schema/chat_message_history.py
Normal file
54
langchain/schema/chat_message_history.py
Normal file
@@ -0,0 +1,54 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List
|
||||
|
||||
from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
|
||||
|
||||
|
||||
class BaseChatMessageHistory(ABC):
|
||||
"""Base interface for chat message history
|
||||
See `ChatMessageHistory` for default implementation.
|
||||
"""
|
||||
|
||||
"""
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
class FileChatMessageHistory(BaseChatMessageHistory):
|
||||
storage_path: str
|
||||
session_id: str
|
||||
|
||||
@property
|
||||
def messages(self):
|
||||
with open(os.path.join(storage_path, session_id), 'r:utf-8') as f:
|
||||
messages = json.loads(f.read())
|
||||
return messages_from_dict(messages)
|
||||
|
||||
def add_message(self, message: BaseMessage) -> None:
|
||||
messages = self.messages.append(_message_to_dict(message))
|
||||
with open(os.path.join(storage_path, session_id), 'w') as f:
|
||||
json.dump(f, messages)
|
||||
|
||||
def clear(self):
|
||||
with open(os.path.join(storage_path, session_id), 'w') as f:
|
||||
f.write("[]")
|
||||
"""
|
||||
|
||||
messages: List[BaseMessage]
|
||||
|
||||
def add_user_message(self, message: str) -> None:
|
||||
"""Add a user message to the store"""
|
||||
self.add_message(HumanMessage(content=message))
|
||||
|
||||
def add_ai_message(self, message: str) -> None:
|
||||
"""Add an AI message to the store"""
|
||||
self.add_message(AIMessage(content=message))
|
||||
|
||||
def add_message(self, message: BaseMessage) -> None:
|
||||
"""Add a self-created message to the store"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def clear(self) -> None:
|
||||
"""Remove all messages from the store"""
|
||||
29
langchain/schema/documents.py
Normal file
29
langchain/schema/documents.py
Normal file
@@ -0,0 +1,29 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Sequence
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class BaseDocumentTransformer(ABC):
|
||||
"""Base interface for transforming documents."""
|
||||
|
||||
@abstractmethod
|
||||
def transform_documents(
|
||||
self, documents: Sequence[Document], **kwargs: Any
|
||||
) -> Sequence[Document]:
|
||||
"""Transform a list of documents."""
|
||||
|
||||
@abstractmethod
|
||||
async def atransform_documents(
|
||||
self, documents: Sequence[Document], **kwargs: Any
|
||||
) -> Sequence[Document]:
|
||||
"""Asynchronously transform a list of documents."""
|
||||
|
||||
|
||||
class Document(BaseModel):
|
||||
"""Interface for interacting with a document."""
|
||||
|
||||
page_content: str
|
||||
metadata: dict = Field(default_factory=dict)
|
||||
39
langchain/schema/memory.py
Normal file
39
langchain/schema/memory.py
Normal file
@@ -0,0 +1,39 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from pydantic import BaseModel, Extra
|
||||
|
||||
|
||||
class BaseMemory(BaseModel, ABC):
|
||||
"""Base interface for memory in chains."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def memory_variables(self) -> List[str]:
|
||||
"""Input keys this memory class will load dynamically."""
|
||||
|
||||
@abstractmethod
|
||||
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Return key-value pairs given the text input to the chain.
|
||||
|
||||
If None, return all memories
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
|
||||
"""Save the context of this model run to memory."""
|
||||
|
||||
@abstractmethod
|
||||
def clear(self) -> None:
|
||||
"""Clear memory contents."""
|
||||
|
||||
|
||||
Memory = BaseMemory
|
||||
106
langchain/schema/messages.py
Normal file
106
langchain/schema/messages.py
Normal file
@@ -0,0 +1,106 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import abstractmethod
|
||||
from typing import List
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class BaseMessage(BaseModel):
|
||||
"""Message object."""
|
||||
|
||||
content: str
|
||||
additional_kwargs: dict = Field(default_factory=dict)
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def type(self) -> str:
|
||||
"""Type of the message, used for serialization."""
|
||||
|
||||
|
||||
class HumanMessage(BaseMessage):
|
||||
"""Type of message that is spoken by the human."""
|
||||
|
||||
example: bool = False
|
||||
|
||||
@property
|
||||
def type(self) -> str:
|
||||
"""Type of the message, used for serialization."""
|
||||
return "human"
|
||||
|
||||
|
||||
class AIMessage(BaseMessage):
|
||||
"""Type of message that is spoken by the AI."""
|
||||
|
||||
example: bool = False
|
||||
|
||||
@property
|
||||
def type(self) -> str:
|
||||
"""Type of the message, used for serialization."""
|
||||
return "ai"
|
||||
|
||||
|
||||
class SystemMessage(BaseMessage):
|
||||
"""Type of message that is a system message."""
|
||||
|
||||
@property
|
||||
def type(self) -> str:
|
||||
"""Type of the message, used for serialization."""
|
||||
return "system"
|
||||
|
||||
|
||||
class ChatMessage(BaseMessage):
|
||||
"""Type of message with arbitrary speaker."""
|
||||
|
||||
role: str
|
||||
|
||||
@property
|
||||
def type(self) -> str:
|
||||
"""Type of the message, used for serialization."""
|
||||
return "chat"
|
||||
|
||||
|
||||
def get_buffer_string(
|
||||
messages: List[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
|
||||
) -> str:
|
||||
"""Get buffer string of messages."""
|
||||
string_messages = []
|
||||
for m in messages:
|
||||
if isinstance(m, HumanMessage):
|
||||
role = human_prefix
|
||||
elif isinstance(m, AIMessage):
|
||||
role = ai_prefix
|
||||
elif isinstance(m, SystemMessage):
|
||||
role = "System"
|
||||
elif isinstance(m, ChatMessage):
|
||||
role = m.role
|
||||
else:
|
||||
raise ValueError(f"Got unsupported message type: {m}")
|
||||
string_messages.append(f"{role}: {m.content}")
|
||||
return "\n".join(string_messages)
|
||||
|
||||
|
||||
def _message_to_dict(message: BaseMessage) -> dict:
|
||||
return {"type": message.type, "data": message.dict()}
|
||||
|
||||
|
||||
def messages_to_dict(messages: List[BaseMessage]) -> List[dict]:
|
||||
return [_message_to_dict(m) for m in messages]
|
||||
|
||||
|
||||
def _message_from_dict(message: dict) -> BaseMessage:
|
||||
_type = message["type"]
|
||||
if _type == "human":
|
||||
return HumanMessage(**message["data"])
|
||||
elif _type == "ai":
|
||||
return AIMessage(**message["data"])
|
||||
elif _type == "system":
|
||||
return SystemMessage(**message["data"])
|
||||
elif _type == "chat":
|
||||
return ChatMessage(**message["data"])
|
||||
else:
|
||||
raise ValueError(f"Got unexpected type: {_type}")
|
||||
|
||||
|
||||
def messages_from_dict(messages: List[dict]) -> List[BaseMessage]:
|
||||
return [_message_from_dict(m) for m in messages]
|
||||
93
langchain/schema/output_parser.py
Normal file
93
langchain/schema/output_parser.py
Normal file
@@ -0,0 +1,93 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, Generic, TypeVar
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.schema.prompts import PromptValue
|
||||
|
||||
T = TypeVar("T")
|
||||
|
||||
|
||||
class BaseOutputParser(BaseModel, ABC, Generic[T]):
|
||||
"""Class to parse the output of an LLM call.
|
||||
|
||||
Output parsers help structure language model responses.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def parse(self, text: str) -> T:
|
||||
"""Parse the output of an LLM call.
|
||||
|
||||
A method which takes in a string (assumed output of a language model )
|
||||
and parses it into some structure.
|
||||
|
||||
Args:
|
||||
text: output of language model
|
||||
|
||||
Returns:
|
||||
structured output
|
||||
"""
|
||||
|
||||
def parse_with_prompt(self, completion: str, prompt: PromptValue) -> Any:
|
||||
"""Optional method to parse the output of an LLM call with a prompt.
|
||||
|
||||
The prompt is largely provided in the event the OutputParser wants
|
||||
to retry or fix the output in some way, and needs information from
|
||||
the prompt to do so.
|
||||
|
||||
Args:
|
||||
completion: output of language model
|
||||
prompt: prompt value
|
||||
|
||||
Returns:
|
||||
structured output
|
||||
"""
|
||||
return self.parse(completion)
|
||||
|
||||
def get_format_instructions(self) -> str:
|
||||
"""Instructions on how the LLM output should be formatted."""
|
||||
raise NotImplementedError
|
||||
|
||||
@property
|
||||
def _type(self) -> str:
|
||||
"""Return the type key."""
|
||||
raise NotImplementedError(
|
||||
f"_type property is not implemented in class {self.__class__.__name__}."
|
||||
" This is required for serialization."
|
||||
)
|
||||
|
||||
def dict(self, **kwargs: Any) -> Dict:
|
||||
"""Return dictionary representation of output parser."""
|
||||
output_parser_dict = super().dict()
|
||||
output_parser_dict["_type"] = self._type
|
||||
return output_parser_dict
|
||||
|
||||
|
||||
class OutputParserException(ValueError):
|
||||
"""Exception that output parsers should raise to signify a parsing error.
|
||||
|
||||
This exists to differentiate parsing errors from other code or execution errors
|
||||
that also may arise inside the output parser. OutputParserExceptions will be
|
||||
available to catch and handle in ways to fix the parsing error, while other
|
||||
errors will be raised.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
error: Any,
|
||||
observation: str | None = None,
|
||||
llm_output: str | None = None,
|
||||
send_to_llm: bool = False,
|
||||
):
|
||||
super(OutputParserException, self).__init__(error)
|
||||
if send_to_llm:
|
||||
if observation is None or llm_output is None:
|
||||
raise ValueError(
|
||||
"Arguments 'observation' & 'llm_output'"
|
||||
" are required if 'send_to_llm' is True"
|
||||
)
|
||||
self.observation = observation
|
||||
self.llm_output = llm_output
|
||||
self.send_to_llm = send_to_llm
|
||||
50
langchain/schema/outputs.py
Normal file
50
langchain/schema/outputs.py
Normal file
@@ -0,0 +1,50 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, root_validator
|
||||
|
||||
from langchain.schema.messages import BaseMessage
|
||||
|
||||
|
||||
class Generation(BaseModel):
|
||||
"""Output of a single generation."""
|
||||
|
||||
text: str
|
||||
"""Generated text output."""
|
||||
|
||||
generation_info: Optional[Dict[str, Any]] = None
|
||||
"""Raw generation info response from the provider"""
|
||||
"""May include things like reason for finishing (e.g. in OpenAI)"""
|
||||
# TODO: add log probs
|
||||
|
||||
|
||||
class ChatGeneration(Generation):
|
||||
"""Output of a single generation."""
|
||||
|
||||
text = ""
|
||||
message: BaseMessage
|
||||
|
||||
@root_validator
|
||||
def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
values["text"] = values["message"].content
|
||||
return values
|
||||
|
||||
|
||||
class ChatResult(BaseModel):
|
||||
"""Class that contains all relevant information for a Chat Result."""
|
||||
|
||||
generations: List[ChatGeneration]
|
||||
"""List of the things generated."""
|
||||
llm_output: Optional[dict] = None
|
||||
"""For arbitrary LLM provider specific output."""
|
||||
|
||||
|
||||
class LLMResult(BaseModel):
|
||||
"""Class that contains all relevant information for an LLM Result."""
|
||||
|
||||
generations: List[List[Generation]]
|
||||
"""List of the things generated. This is List[List[]] because
|
||||
each input could have multiple generations."""
|
||||
llm_output: Optional[dict] = None
|
||||
"""For arbitrary LLM provider specific output."""
|
||||
18
langchain/schema/prompts.py
Normal file
18
langchain/schema/prompts.py
Normal file
@@ -0,0 +1,18 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.schema.messages import BaseMessage
|
||||
|
||||
|
||||
class PromptValue(BaseModel, ABC):
|
||||
@abstractmethod
|
||||
def to_string(self) -> str:
|
||||
"""Return prompt as string."""
|
||||
|
||||
@abstractmethod
|
||||
def to_messages(self) -> List[BaseMessage]:
|
||||
"""Return prompt as messages."""
|
||||
30
langchain/schema/retriever.py
Normal file
30
langchain/schema/retriever.py
Normal file
@@ -0,0 +1,30 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List
|
||||
|
||||
from langchain.schema.documents import Document
|
||||
|
||||
|
||||
class BaseRetriever(ABC):
|
||||
@abstractmethod
|
||||
def get_relevant_documents(self, query: str) -> List[Document]:
|
||||
"""Get documents relevant for a query.
|
||||
|
||||
Args:
|
||||
query: string to find relevant documents for
|
||||
|
||||
Returns:
|
||||
List of relevant documents
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
async def aget_relevant_documents(self, query: str) -> List[Document]:
|
||||
"""Get documents relevant for a query.
|
||||
|
||||
Args:
|
||||
query: string to find relevant documents for
|
||||
|
||||
Returns:
|
||||
List of relevant documents
|
||||
"""
|
||||
Reference in New Issue
Block a user