mirror of
https://github.com/hwchase17/langchain
synced 2024-11-16 06:13:16 +00:00
263 lines
9.1 KiB
Python
263 lines
9.1 KiB
Python
"""Beta Feature: base interface for cache."""
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import json
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, List, Optional, Tuple
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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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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.schema import Generation
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RETURN_VAL_TYPE = List[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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class InMemoryCache(BaseCache):
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"""Cache that stores things in memory."""
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def __init__(self) -> None:
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"""Initialize with empty cache."""
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self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {}
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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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return self._cache.get((prompt, llm_string), None)
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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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self._cache[(prompt, llm_string)] = return_val
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Base = declarative_base()
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class FullLLMCache(Base): # type: ignore
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"""SQLite table for full LLM Cache (all generations)."""
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__tablename__ = "full_llm_cache"
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prompt = Column(String, primary_key=True)
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llm = Column(String, primary_key=True)
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idx = Column(Integer, primary_key=True)
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response = Column(String)
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class SQLAlchemyCache(BaseCache):
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"""Cache that uses SQAlchemy as a backend."""
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def __init__(self, engine: Engine, cache_schema: Any = FullLLMCache):
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"""Initialize by creating all tables."""
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self.engine = engine
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self.cache_schema = cache_schema
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self.cache_schema.metadata.create_all(self.engine)
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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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stmt = (
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select(self.cache_schema.response)
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.where(self.cache_schema.prompt == prompt)
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.where(self.cache_schema.llm == llm_string)
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.order_by(self.cache_schema.idx)
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)
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with Session(self.engine) as session:
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generations = [Generation(text=row[0]) for row in session.execute(stmt)]
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if len(generations) > 0:
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return generations
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return None
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Look up based on prompt and llm_string."""
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for i, generation in enumerate(return_val):
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item = self.cache_schema(
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prompt=prompt, llm=llm_string, response=generation.text, idx=i
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)
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with Session(self.engine) as session, session.begin():
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session.merge(item)
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class SQLiteCache(SQLAlchemyCache):
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"""Cache that uses SQLite as a backend."""
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def __init__(self, database_path: str = ".langchain.db"):
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"""Initialize by creating the engine and all tables."""
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engine = create_engine(f"sqlite:///{database_path}")
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super().__init__(engine)
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class RedisCache(BaseCache):
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"""Cache that uses Redis as a backend."""
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def __init__(self, redis_: Any):
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"""Initialize by passing in Redis instance."""
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try:
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from redis import Redis
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except ImportError:
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raise ValueError(
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"Could not import redis python package. "
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"Please install it with `pip install redis`."
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)
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if not isinstance(redis_, Redis):
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raise ValueError("Please pass in Redis object.")
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self.redis = redis_
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def _key(self, prompt: str, llm_string: str, idx: int) -> str:
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"""Compute key from prompt, llm_string, and idx."""
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return str(hash(prompt + llm_string)) + "_" + str(idx)
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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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idx = 0
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generations = []
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while self.redis.get(self._key(prompt, llm_string, idx)):
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result = self.redis.get(self._key(prompt, llm_string, idx))
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if not result:
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break
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elif isinstance(result, bytes):
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result = result.decode()
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generations.append(Generation(text=result))
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idx += 1
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return generations if generations else None
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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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for i, generation in enumerate(return_val):
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self.redis.set(self._key(prompt, llm_string, i), generation.text)
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class GPTCache(BaseCache):
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"""Cache that uses GPTCache as a backend."""
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def __init__(self, init_func: Callable[[Any], None]):
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"""Initialize by passing in the `init` GPTCache func
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Args:
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init_func (Callable[[Any], None]): init `GPTCache` function
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Example:
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.. code-block:: python
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import gptcache
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from gptcache.processor.pre import get_prompt
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from gptcache.manager.factory import get_data_manager
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# Avoid multiple caches using the same file,
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causing different llm model caches to affect each other
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i = 0
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file_prefix = "data_map"
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def init_gptcache_map(cache_obj: gptcache.Cache):
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nonlocal i
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cache_path = f'{file_prefix}_{i}.txt'
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cache_obj.init(
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pre_embedding_func=get_prompt,
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data_manager=get_data_manager(data_path=cache_path),
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)
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i += 1
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langchain.llm_cache = GPTCache(init_gptcache_map)
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"""
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try:
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import gptcache # noqa: F401
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except ImportError:
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raise ValueError(
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"Could not import gptcache python package. "
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"Please install it with `pip install gptcache`."
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)
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self.init_gptcache_func: Callable[[Any], None] = init_func
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self.gptcache_dict: Dict[str, Any] = {}
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@staticmethod
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def _update_cache_callback_none(*_: Any, **__: Any) -> None:
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"""When updating cached data, do nothing.
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Because currently only cached queries are processed."""
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return None
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@staticmethod
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def _llm_handle_none(*_: Any, **__: Any) -> None:
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"""Do nothing on a cache miss"""
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return None
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@staticmethod
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def _cache_data_converter(data: str) -> RETURN_VAL_TYPE:
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"""Convert the `data` in the cache to the `RETURN_VAL_TYPE` data format."""
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return [Generation(**generation_dict) for generation_dict in json.loads(data)]
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def _get_gptcache(self, llm_string: str) -> Any:
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"""Get a cache object.
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When the corresponding llm model cache does not exist, it will be created."""
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from gptcache import Cache
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_gptcache = self.gptcache_dict.get(llm_string, None)
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if _gptcache is None:
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_gptcache = Cache()
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self.init_gptcache_func(_gptcache)
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self.gptcache_dict[llm_string] = _gptcache
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return _gptcache
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def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up the cache data.
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First, retrieve the corresponding cache object using the `llm_string` parameter,
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and then retrieve the data from the cache based on the `prompt`.
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"""
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from gptcache.adapter.adapter import adapt
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_gptcache = self.gptcache_dict.get(llm_string)
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if _gptcache is None:
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return None
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res = adapt(
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GPTCache._llm_handle_none,
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GPTCache._cache_data_converter,
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GPTCache._update_cache_callback_none,
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cache_obj=_gptcache,
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prompt=prompt,
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)
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return res
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@staticmethod
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def _update_cache_callback(
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llm_data: RETURN_VAL_TYPE, update_cache_func: Callable[[Any], None]
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) -> None:
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"""Save the `llm_data` to cache storage"""
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handled_data = json.dumps([generation.dict() for generation in llm_data])
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update_cache_func(handled_data)
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Update cache.
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First, retrieve the corresponding cache object using the `llm_string` parameter,
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and then store the `prompt` and `return_val` in the cache object.
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"""
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from gptcache.adapter.adapter import adapt
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_gptcache = self._get_gptcache(llm_string)
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def llm_handle(*_: Any, **__: Any) -> RETURN_VAL_TYPE:
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return return_val
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return adapt(
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llm_handle,
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GPTCache._cache_data_converter,
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GPTCache._update_cache_callback,
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cache_obj=_gptcache,
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cache_skip=True,
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prompt=prompt,
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)
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