langchain[minor], community[minor], core[minor]: Async Cache support and AsyncRedisCache

libs/community/langchain_community/cache.py

@@ -350,20 +350,24 @@ def clear(self, **kwargs: Any) -> None:
 
 
 class RedisCache(BaseCache):
-    """Cache that uses Redis as a backend."""
+    """
+    Cache that uses Redis as a backend. Allows to use either sync or
+    async Redis client. Depending on the client passed, you are expected
+    to use either sync or async methods.
+    """
 
     def __init__(self, redis_: Any, *, ttl: Optional[int] = None):
         """
         Initialize an instance of RedisCache.
 
         This method initializes an object with Redis caching capabilities.
         It takes a `redis_` parameter, which should be an instance of a Redis
-        client class, allowing the object to interact with a Redis
-        server for caching purposes.
+        client class (`redis.Redis` or `redis.asyncio.Redis`), allowing the object
+        to interact with a Redis server for caching purposes.
 
         Parameters:
             redis_ (Any): An instance of a Redis client class
-                (e.g., redis.Redis) used for caching.
+                (`redis.Redis` or `redis.asyncio.Redis`) to be used for caching.
                 This allows the object to communicate with a
                 Redis server for caching operations.
             ttl (int, optional): Time-to-live (TTL) for cached items in seconds.
@@ -372,26 +376,42 @@ def __init__(self, redis_: Any, *, ttl: Optional[int] = None):
                 have an automatic expiration.
         """
         try:
-            from redis import Redis
+            from redis import Redis as SyncRedis
+            from redis.asyncio import Redis as AsyncRedis
         except ImportError:
             raise ValueError(
                 "Could not import redis python package. "
                 "Please install it with `pip install redis`."
             )
-        if not isinstance(redis_, Redis):
-            raise ValueError("Please pass in Redis object.")
+        if isinstance(redis_, SyncRedis):
+            self._async = False
+        elif isinstance(redis_, AsyncRedis):
+            self._async = True
+        else:
+            raise ValueError("Please pass a valid Redis client.")
         self.redis = redis_
         self.ttl = ttl
 
     def _key(self, prompt: str, llm_string: str) -> str:
         """Compute key from prompt and llm_string"""
         return _hash(prompt + llm_string)
 
-    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
-        """Look up based on prompt and llm_string."""
+    def __ensure_sync(self, function_name: str = "function"):
+        if self._async:
+            raise ValueError(f"Cannot use sync {function_name} with async Redis client")
+
+    @staticmethod
+    def __ensure_generation_type(return_val: RETURN_VAL_TYPE):
+        for gen in return_val:
+            if not isinstance(gen, Generation):
+                raise ValueError(
+                    "RedisCache only supports caching of normal LLM generations, "
+                    f"got {type(gen)}"
+                )
+
+    @staticmethod
+    def __get_generations(results: dict[str | bytes, str | bytes]) -> list[Generation]:
         generations = []
-        # Read from a Redis HASH
-        results = self.redis.hgetall(self._key(prompt, llm_string))
         if results:
             for _, text in results.items():
                 try:
@@ -408,35 +428,69 @@ def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
                     generations.append(Generation(text=text))
         return generations if generations else None
 
+    def __configure_pipeline_for_update(self, key, pipe, return_val):
+        pipe.hset(
+            key,
+            mapping={
+                str(idx): dumps(generation) for idx, generation in enumerate(return_val)
+            },
+        )
+        if self.ttl is not None:
+            pipe.expire(key, self.ttl)
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        self.__ensure_sync("lookup")
+        # Read from a Redis HASH
+        results = self.redis.hgetall(self._key(prompt, llm_string))
+        return self.__get_generations(results)
+
+    async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string. Async version."""
+        if not self._async:
+            return await super().alookup(prompt, llm_string)
+        results = await self.redis.hgetall(self._key(prompt, llm_string))
+        return self.__get_generations(results)
+
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
         """Update cache based on prompt and llm_string."""
-        for gen in return_val:
-            if not isinstance(gen, Generation):
-                raise ValueError(
-                    "RedisCache only supports caching of normal LLM generations, "
-                    f"got {type(gen)}"
-                )
-        # Write to a Redis HASH
+        self.__ensure_sync("update")
+        self.__ensure_generation_type(return_val)
         key = self._key(prompt, llm_string)
 
         with self.redis.pipeline() as pipe:
-            pipe.hset(
-                key,
-                mapping={
-                    str(idx): dumps(generation)
-                    for idx, generation in enumerate(return_val)
-                },
-            )
-            if self.ttl is not None:
-                pipe.expire(key, self.ttl)
-
+            self.__configure_pipeline_for_update(key, pipe, return_val)
             pipe.execute()
 
+    async def aupdate(
+        self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
+    ) -> None:
+        """Update cache based on prompt and llm_string. Async version."""
+        if not self._async:
+            return await super().aupdate(prompt, llm_string, return_val)
+        self.__ensure_generation_type(return_val)
+        key = self._key(prompt, llm_string)
+
+        async with self.redis.pipeline() as pipe:
+            self.__configure_pipeline_for_update(key, pipe, return_val)
+            await pipe.execute()
+
     def clear(self, **kwargs: Any) -> None:
         """Clear cache. If `asynchronous` is True, flush asynchronously."""
+        self.__ensure_sync("clear")
         asynchronous = kwargs.get("asynchronous", False)
         self.redis.flushdb(asynchronous=asynchronous, **kwargs)
 
+    async def aclear(self, **kwargs: Any) -> None:
+        """
+        Clear cache. If `asynchronous` is True, flush asynchronously.
+        Async version.
+        """
+        if not self._async:
+            return await super().aclear(**kwargs)
+        asynchronous = kwargs.get("asynchronous", False)
+        await self.redis.flushdb(asynchronous=asynchronous, **kwargs)
+
 
 class RedisSemanticCache(BaseCache):
     """Cache that uses Redis as a vector-store backend."""

libs/core/langchain_core/caches.py

@@ -4,6 +4,7 @@
 from typing import Any, Optional, Sequence
 
 from langchain_core.outputs import Generation
+from langchain_core.runnables import run_in_executor
 
 RETURN_VAL_TYPE = Sequence[Generation]
 
@@ -22,3 +23,17 @@ def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> N
     @abstractmethod
     def clear(self, **kwargs: Any) -> None:
         """Clear cache that can take additional keyword arguments."""
+
+    async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        return await run_in_executor(None, self.lookup, prompt, llm_string)
+
+    async def aupdate(
+        self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
+    ) -> None:
+        """Update cache based on prompt and llm_string."""
+        return await run_in_executor(None, self.update, prompt, llm_string, return_val)
+
+    async def aclear(self, **kwargs: Any) -> None:
+        """Clear cache that can take additional keyword arguments."""
+        return await run_in_executor(None, self.clear, **kwargs)

libs/core/langchain_core/language_models/chat_models.py

@@ -621,7 +621,7 @@ async def _agenerate_with_cache(
         else:
             llm_string = self._get_llm_string(stop=stop, **kwargs)
             prompt = dumps(messages)
-            cache_val = llm_cache.lookup(prompt, llm_string)
+            cache_val = await llm_cache.alookup(prompt, llm_string)
             if isinstance(cache_val, list):
                 return ChatResult(generations=cache_val)
             else:
@@ -631,7 +631,7 @@ async def _agenerate_with_cache(
                     )
                 else:
                     result = await self._agenerate(messages, stop=stop, **kwargs)
-                llm_cache.update(prompt, llm_string, result.generations)
+                await llm_cache.aupdate(prompt, llm_string, result.generations)
                 return result
 
     @abstractmethod

libs/core/langchain_core/language_models/llms.py

@@ -134,6 +134,26 @@ def get_prompts(
     return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
 
 
+async def aget_prompts(
+    params: Dict[str, Any], prompts: List[str]
+) -> Tuple[Dict[int, List], str, List[int], List[str]]:
+    """Get prompts that are already cached. Async version."""
+    llm_string = str(sorted([(k, v) for k, v in params.items()]))
+    missing_prompts = []
+    missing_prompt_idxs = []
+    existing_prompts = {}
+    llm_cache = get_llm_cache()
+    for i, prompt in enumerate(prompts):
+        if llm_cache:
+            cache_val = await llm_cache.alookup(prompt, llm_string)
+            if isinstance(cache_val, list):
+                existing_prompts[i] = cache_val
+            else:
+                missing_prompts.append(prompt)
+                missing_prompt_idxs.append(i)
+    return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
+
+
 def update_cache(
     existing_prompts: Dict[int, List],
     llm_string: str,
@@ -152,6 +172,24 @@ def update_cache(
     return llm_output
 
 
+async def aupdate_cache(
+    existing_prompts: Dict[int, List],
+    llm_string: str,
+    missing_prompt_idxs: List[int],
+    new_results: LLMResult,
+    prompts: List[str],
+) -> Optional[dict]:
+    """Update the cache and get the LLM output. Async version"""
+    llm_cache = get_llm_cache()
+    for i, result in enumerate(new_results.generations):
+        existing_prompts[missing_prompt_idxs[i]] = result
+        prompt = prompts[missing_prompt_idxs[i]]
+        if llm_cache:
+            await llm_cache.aupdate(prompt, llm_string, result)
+    llm_output = new_results.llm_output
+    return llm_output
+
+
 class BaseLLM(BaseLanguageModel[str], ABC):
     """Base LLM abstract interface.
 
@@ -864,7 +902,7 @@ async def agenerate(
             llm_string,
             missing_prompt_idxs,
             missing_prompts,
-        ) = get_prompts(params, prompts)
+        ) = await aget_prompts(params, prompts)
         disregard_cache = self.cache is not None and not self.cache
         new_arg_supported = inspect.signature(self._agenerate).parameters.get(
             "run_manager"
@@ -912,7 +950,7 @@ async def agenerate(
             new_results = await self._agenerate_helper(
                 missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs
             )
-            llm_output = update_cache(
+            llm_output = await aupdate_cache(
                 existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
             )
             run_info = (