Optimize the initialization method of GPTCache (#4522)

Optimize the initialization method of GPTCache, so that users can use GPTCache more quickly.

docs/modules/models/llms/examples/llm_caching.ipynb

@@ -408,25 +408,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import gptcache\n",
+    "from gptcache import Cache\n",
+    "from gptcache.manager.factory import manager_factory\n",
     "from gptcache.processor.pre import get_prompt\n",
-    "from gptcache.manager.factory import get_data_manager\n",
     "from langchain.cache import GPTCache\n",
     "\n",
     "# Avoid multiple caches using the same file, causing different llm model caches to affect each other\n",
-    "i = 0\n",
-    "file_prefix = \"data_map\"\n",
     "\n",
-    "def init_gptcache_map(cache_obj: gptcache.Cache):\n",
-    "    global i\n",
-    "    cache_path = f'{file_prefix}_{i}.txt'\n",
+    "def init_gptcache(cache_obj: Cache, llm str):\n",
     "    cache_obj.init(\n",
     "        pre_embedding_func=get_prompt,\n",
-    "        data_manager=get_data_manager(data_path=cache_path),\n",
+    "        data_manager=manager_factory(manager=\"map\", data_dir=f\"map_cache_{llm}\"),\n",
     "    )\n",
-    "    i += 1\n",
     "\n",
-    "langchain.llm_cache = GPTCache(init_gptcache_map)"
+    "langchain.llm_cache = GPTCache(init_gptcache)"
    ]
   },
   {
@@ -506,37 +501,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import gptcache\n",
-    "from gptcache.processor.pre import get_prompt\n",
-    "from gptcache.manager.factory import get_data_manager\n",
-    "from langchain.cache import GPTCache\n",
-    "from gptcache.manager import get_data_manager, CacheBase, VectorBase\n",
     "from gptcache import Cache\n",
-    "from gptcache.embedding import Onnx\n",
-    "from gptcache.similarity_evaluation.distance import SearchDistanceEvaluation\n",
+    "from gptcache.adapter.api import init_similar_cache\n",
+    "from langchain.cache import GPTCache\n",
     "\n",
     "# Avoid multiple caches using the same file, causing different llm model caches to affect each other\n",
-    "i = 0\n",
-    "file_prefix = \"data_map\"\n",
-    "llm_cache = Cache()\n",
     "\n",
+    "def init_gptcache(cache_obj: Cache, llm str):\n",
+    "    init_similar_cache(cache_obj=cache_obj, data_dir=f\"similar_cache_{llm}\")\n",
     "\n",
-    "def init_gptcache_map(cache_obj: gptcache.Cache):\n",
-    "    global i\n",
-    "    cache_path = f'{file_prefix}_{i}.txt'\n",
-    "    onnx = Onnx()\n",
-    "    cache_base = CacheBase('sqlite')\n",
-    "    vector_base = VectorBase('faiss', dimension=onnx.dimension)\n",
-    "    data_manager = get_data_manager(cache_base, vector_base, max_size=10, clean_size=2)\n",
-    "    cache_obj.init(\n",
-    "        pre_embedding_func=get_prompt,\n",
-    "        embedding_func=onnx.to_embeddings,\n",
-    "        data_manager=data_manager,\n",
-    "        similarity_evaluation=SearchDistanceEvaluation(),\n",
-    "    )\n",
-    "    i += 1\n",
-    "\n",
-    "langchain.llm_cache = GPTCache(init_gptcache_map)"
+    "langchain.llm_cache = GPTCache(init_gptcache)"
    ]
   },
   {
@@ -929,7 +903,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -943,7 +917,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,

langchain/cache.py

@@ -1,8 +1,9 @@
 """Beta Feature: base interface for cache."""
 import hashlib
+import inspect
 import json
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type, cast
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast
 
 from sqlalchemy import Column, Integer, String, create_engine, select
 from sqlalchemy.engine.base import Engine
@@ -274,7 +275,12 @@ class RedisSemanticCache(BaseCache):
 class GPTCache(BaseCache):
     """Cache that uses GPTCache as a backend."""
 
-    def __init__(self, init_func: Optional[Callable[[Any], None]] = None):
+    def __init__(
+        self,
+        init_func: Union[
+            Callable[[Any, str], None], Callable[[Any], None], None
+        ] = None,
+    ):
         """Initialize by passing in init function (default: `None`).
 
         Args:
@@ -291,19 +297,17 @@ class GPTCache(BaseCache):
 
             # Avoid multiple caches using the same file,
             causing different llm model caches to affect each other
-            i = 0
-            file_prefix = "data_map"
 
-            def init_gptcache_map(cache_obj: gptcache.Cache):
-                nonlocal i
-                cache_path = f'{file_prefix}_{i}.txt'
+            def init_gptcache(cache_obj: gptcache.Cache, llm str):
                 cache_obj.init(
                     pre_embedding_func=get_prompt,
-                    data_manager=get_data_manager(data_path=cache_path),
+                    data_manager=manager_factory(
+                        manager="map",
+                        data_dir=f"map_cache_{llm}"
+                    ),
                 )
-                i += 1
 
-            langchain.llm_cache = GPTCache(init_gptcache_map)
+            langchain.llm_cache = GPTCache(init_gptcache)
 
         """
         try:
@@ -314,30 +318,38 @@ class GPTCache(BaseCache):
                 "Please install it with `pip install gptcache`."
             )
 
-        self.init_gptcache_func: Optional[Callable[[Any], None]] = init_func
+        self.init_gptcache_func: Union[
+            Callable[[Any, str], None], Callable[[Any], None], None
+        ] = init_func
         self.gptcache_dict: Dict[str, Any] = {}
 
-    def _get_gptcache(self, llm_string: str) -> Any:
-        """Get a cache object.
-
-        When the corresponding llm model cache does not exist, it will be created."""
+    def _new_gptcache(self, llm_string: str) -> Any:
+        """New gptcache object"""
         from gptcache import Cache
         from gptcache.manager.factory import get_data_manager
         from gptcache.processor.pre import get_prompt
 
-        _gptcache = self.gptcache_dict.get(llm_string, None)
-        if _gptcache is None:
-            _gptcache = Cache()
-            if self.init_gptcache_func is not None:
-                self.init_gptcache_func(_gptcache)
+        _gptcache = Cache()
+        if self.init_gptcache_func is not None:
+            sig = inspect.signature(self.init_gptcache_func)
+            if len(sig.parameters) == 2:
+                self.init_gptcache_func(_gptcache, llm_string)  # type: ignore[call-arg]
             else:
-                _gptcache.init(
-                    pre_embedding_func=get_prompt,
-                    data_manager=get_data_manager(data_path=llm_string),
-                )
-            self.gptcache_dict[llm_string] = _gptcache
+                self.init_gptcache_func(_gptcache)  # type: ignore[call-arg]
+        else:
+            _gptcache.init(
+                pre_embedding_func=get_prompt,
+                data_manager=get_data_manager(data_path=llm_string),
+            )
         return _gptcache
 
+    def _get_gptcache(self, llm_string: str) -> Any:
+        """Get a cache object.
+
+        When the corresponding llm model cache does not exist, it will be created."""
+
+        return self.gptcache_dict.get(llm_string, self._new_gptcache(llm_string))
+
     def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
         """Look up the cache data.
         First, retrieve the corresponding cache object using the `llm_string` parameter,

tests/integration_tests/cache/test_gptcache.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Union
 
 import pytest
 
@@ -30,9 +30,23 @@ def init_gptcache_map(cache_obj: Cache) -> None:
     init_gptcache_map._i = i + 1  # type: ignore
 
 
+def init_gptcache_map_with_llm(cache_obj: Cache, llm: str) -> None:
+    cache_path = f"data_map_{llm}.txt"
+    if os.path.isfile(cache_path):
+        os.remove(cache_path)
+    cache_obj.init(
+        pre_embedding_func=get_prompt,
+        data_manager=get_data_manager(data_path=cache_path),
+    )
+
+
 @pytest.mark.skipif(not gptcache_installed, reason="gptcache not installed")
-@pytest.mark.parametrize("init_func", [None, init_gptcache_map])
-def test_gptcache_caching(init_func: Optional[Callable[[Any], None]]) -> None:
+@pytest.mark.parametrize(
+    "init_func", [None, init_gptcache_map, init_gptcache_map_with_llm]
+)
+def test_gptcache_caching(
+    init_func: Union[Callable[[Any, str], None], Callable[[Any], None], None]
+) -> None:
     """Test gptcache default caching behavior."""
     langchain.llm_cache = GPTCache(init_func)
     llm = FakeLLM()