mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
mv base cache to schema (#9953)
if you remove all other imports from langchain.init it exposes a circular dep
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@ -4,7 +4,6 @@ from importlib import metadata
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from typing import Optional
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from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
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from langchain.cache import BaseCache
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from langchain.chains import (
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ConversationChain,
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LLMBashChain,
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@ -40,6 +39,7 @@ from langchain.prompts import (
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Prompt,
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PromptTemplate,
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)
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from langchain.schema.cache import BaseCache
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from langchain.schema.prompt_template import BasePromptTemplate
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from langchain.utilities.arxiv import ArxivAPIWrapper
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from langchain.utilities.golden_query import GoldenQueryAPIWrapper
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@ -26,7 +26,6 @@ import inspect
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import json
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import logging
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import warnings
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from abc import ABC, abstractmethod
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from datetime import timedelta
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from typing import (
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TYPE_CHECKING,
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@ -35,7 +34,6 @@ from typing import (
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Dict,
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List,
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Optional,
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Sequence,
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Tuple,
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Type,
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Union,
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@ -46,17 +44,18 @@ from sqlalchemy import Column, Integer, String, create_engine, select
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from sqlalchemy.engine.base import Engine
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from sqlalchemy.orm import Session
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from langchain.utils import get_from_env
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try:
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from sqlalchemy.orm import declarative_base
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except ImportError:
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from sqlalchemy.ext.declarative import declarative_base
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from langchain.embeddings.base import Embeddings
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from langchain.load.dump import dumps
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from langchain.load.load import loads
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from langchain.schema import ChatGeneration, Generation
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from langchain.schema.cache import RETURN_VAL_TYPE, BaseCache
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from langchain.utils import get_from_env
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from langchain.vectorstores.redis import Redis as RedisVectorstore
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logger = logging.getLogger(__file__)
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@ -64,8 +63,6 @@ logger = logging.getLogger(__file__)
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if TYPE_CHECKING:
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import momento
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RETURN_VAL_TYPE = Sequence[Generation]
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def _hash(_input: str) -> str:
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"""Use a deterministic hashing approach."""
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@ -105,22 +102,6 @@ def _load_generations_from_json(generations_json: str) -> RETURN_VAL_TYPE:
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)
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class BaseCache(ABC):
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"""Base interface for cache."""
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@abstractmethod
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def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string."""
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@abstractmethod
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Update cache based on prompt and llm_string."""
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@abstractmethod
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def clear(self, **kwargs: Any) -> None:
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"""Clear cache that can take additional keyword arguments."""
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class InMemoryCache(BaseCache):
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"""Cache that stores things in memory."""
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@ -1,5 +1,6 @@
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"""**Schemas** are the LangChain Base Classes and Interfaces."""
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from langchain.schema.agent import AgentAction, AgentFinish
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from langchain.schema.cache import BaseCache
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from langchain.schema.chat_history import BaseChatMessageHistory
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from langchain.schema.document import BaseDocumentTransformer, Document
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from langchain.schema.exceptions import LangChainException
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@ -39,6 +40,7 @@ RUN_KEY = "__run"
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Memory = BaseMemory
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__all__ = [
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"BaseCache",
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"BaseMemory",
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"BaseStore",
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"AgentFinish",
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24
libs/langchain/langchain/schema/cache.py
Normal file
24
libs/langchain/langchain/schema/cache.py
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@ -0,0 +1,24 @@
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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, Optional, Sequence
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from langchain.schema.output import Generation
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RETURN_VAL_TYPE = Sequence[Generation]
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class BaseCache(ABC):
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"""Base interface for cache."""
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@abstractmethod
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def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string."""
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@abstractmethod
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Update cache based on prompt and llm_string."""
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@abstractmethod
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def clear(self, **kwargs: Any) -> None:
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"""Clear cache that can take additional keyword arguments."""
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