Harrison/redis cache (#3766)

Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>

docs/ecosystem/redis.md

@@ -0,0 +1,79 @@
+# Redis
+
+This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Redis wrappers.
+
+## Installation and Setup
+- Install the Redis Python SDK with `pip install redis`
+
+## Wrappers
+
+### Cache
+
+The Cache wrapper allows for [Redis](https://redis.io) to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.
+
+#### Standard Cache
+The standard cache is the Redis bread & butter of use case in production for both [open source](https://redis.io) and [enterprise](https://redis.com) users globally.
+
+To import this cache:
+```python
+from langchain.cache import RedisCache
+```
+
+To use this cache with your LLMs:
+```python
+import langchain
+import redis
+
+redis_client = redis.Redis.from_url(...)
+langchain.llm_cache = RedisCache(redis_client)
+```
+
+#### Semantic Cache
+Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.
+
+To import this cache:
+```python
+from langchain.cache import RedisSemanticCache
+```
+
+To use this cache with your LLMs:
+```python
+import langchain
+import redis
+
+# use any embedding provider...
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+redis_url = "redis://localhost:6379"
+
+langchain.llm_cache = RedisSemanticCache(
+    embedding=FakeEmbeddings(),
+    redis_url=redis_url
+)
+```
+
+### VectorStore
+
+The vectorstore wrapper turns Redis into a low-latency [vector database](https://redis.com/solutions/use-cases/vector-database/) for semantic search or LLM content retrieval.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Redis
+```
+
+For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](../modules/indexes/vectorstores/examples/redis.ipynb).
+
+### Retriever
+
+The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call `.as_retriever()` on the base vectorstore class.
+
+### Memory
+Redis can be used to persist LLM conversations.
+
+#### Vector Store Retriever Memory
+
+For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](../modules/memory/types/vectorstore_retriever_memory.ipynb).
+
+#### Chat Message History Memory
+For a detailed example of Redis to cache conversation message history, see [this notebook](../modules/memory/examples/redis_chat_message_history.ipynb).

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

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "f69f6283",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "64005d1f",
    "metadata": {},
    "outputs": [
@@ -60,8 +60,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 14.2 ms, sys: 4.9 ms, total: 19.1 ms\n",
+      "CPU times: user 26.1 ms, sys: 21.5 ms, total: 47.6 ms\n",
-      "Wall time: 1.1 s\n"
+      "Wall time: 1.68 s\n"
      ]
     },
     {
@@ -70,7 +70,7 @@
        "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "c8a1cb2b",
    "metadata": {},
    "outputs": [
@@ -91,8 +91,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 162 µs, sys: 7 µs, total: 169 µs\n",
+      "CPU times: user 238 µs, sys: 143 µs, total: 381 µs\n",
-      "Wall time: 175 µs\n"
+      "Wall time: 1.76 ms\n"
      ]
     },
     {
@@ -101,7 +101,7 @@
        "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,9 +214,18 @@
     "## Redis Cache"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c5c9a4d5",
+   "metadata": {},
+   "source": [
+    "### Standard Cache\n",
+    "Use [Redis](../../../../ecosystem/redis.md) to cache prompts and responses."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "39f6eb0b",
    "metadata": {},
    "outputs": [],
@@ -225,15 +234,35 @@
     "# (make sure your local Redis instance is running first before running this example)\n",
     "from redis import Redis\n",
     "from langchain.cache import RedisCache\n",
+    "\n",
     "langchain.llm_cache = RedisCache(redis_=Redis())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "28920749",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.88 ms, sys: 8.75 ms, total: 15.6 ms\n",
+      "Wall time: 1.04 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%%time\n",
     "# The first time, it is not yet in cache, so it should take longer\n",
@@ -242,16 +271,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "94bf9415",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.59 ms, sys: 610 µs, total: 2.2 ms\n",
+      "Wall time: 5.58 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%%time\n",
     "# The second time it is, so it goes faster\n",
     "llm(\"Tell me a joke\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "82be23f6",
+   "metadata": {},
+   "source": [
+    "### Semantic Cache\n",
+    "Use [Redis](../../../../ecosystem/redis.md) to cache prompts and responses and evaluate hits based on semantic similarity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "64df3099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.cache import RedisSemanticCache\n",
+    "\n",
+    "\n",
+    "langchain.llm_cache = RedisSemanticCache(\n",
+    "    redis_url=\"redis://localhost:6379\",\n",
+    "    embedding=OpenAIEmbeddings()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8e91d3ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 351 ms, sys: 156 ms, total: 507 ms\n",
+      "Wall time: 3.37 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nWhy don't scientists trust atoms?\\nBecause they make up everything.\""
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The first time, it is not yet in cache, so it should take longer\n",
+    "llm(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "df856948",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.25 ms, sys: 2.72 ms, total: 8.97 ms\n",
+      "Wall time: 262 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nWhy don't scientists trust atoms?\\nBecause they make up everything.\""
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The second time, while not a direct hit, the question is semantically similar to the original question,\n",
+    "# so it uses the cached result!\n",
+    "llm(\"Tell me one joke\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "684eab55",

langchain/cache.py

@@ -1,4 +1,5 @@
 """Beta Feature: base interface for cache."""
+import hashlib
 import json
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type, cast
@@ -12,11 +13,18 @@ try:
 except ImportError:
     from sqlalchemy.ext.declarative import declarative_base
 
+from langchain.embeddings.base import Embeddings
 from langchain.schema import Generation
+from langchain.vectorstores.redis import Redis as RedisVectorstore
 
 RETURN_VAL_TYPE = List[Generation]
 
 
+def _hash(_input: str) -> str:
+    """Use a deterministic hashing approach."""
+    return hashlib.md5(_input.encode()).hexdigest()
+
+
 class BaseCache(ABC):
     """Base interface for cache."""
 
@@ -117,6 +125,8 @@ class SQLiteCache(SQLAlchemyCache):
 class RedisCache(BaseCache):
     """Cache that uses Redis as a backend."""
 
+    # TODO - implement a TTL policy in Redis
+
     def __init__(self, redis_: Any):
         """Initialize by passing in Redis instance."""
         try:
@@ -130,28 +140,30 @@ class RedisCache(BaseCache):
             raise ValueError("Please pass in Redis object.")
         self.redis = redis_
 
-    def _key(self, prompt: str, llm_string: str, idx: int) -> str:
+    def _key(self, prompt: str, llm_string: str) -> str:
-        """Compute key from prompt, llm_string, and idx."""
+        """Compute key from prompt and llm_string"""
-        return str(hash(prompt + llm_string)) + "_" + str(idx)
+        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."""
-        idx = 0
         generations = []
-        while self.redis.get(self._key(prompt, llm_string, idx)):
+        # Read from a Redis HASH
-            result = self.redis.get(self._key(prompt, llm_string, idx))
+        results = self.redis.hgetall(self._key(prompt, llm_string))
-            if not result:
+        if results:
-                break
+            for _, text in results.items():
-            elif isinstance(result, bytes):
+                generations.append(Generation(text=text))
-                result = result.decode()
-            generations.append(Generation(text=result))
-            idx += 1
         return generations if generations else None
 
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
         """Update cache based on prompt and llm_string."""
-        for i, generation in enumerate(return_val):
+        # Write to a Redis HASH
-            self.redis.set(self._key(prompt, llm_string, i), generation.text)
+        key = self._key(prompt, llm_string)
+        self.redis.hset(
+            key,
+            mapping={
+                str(idx): generation.text for idx, generation in enumerate(return_val)
+            },
+        )
 
     def clear(self, **kwargs: Any) -> None:
         """Clear cache. If `asynchronous` is True, flush asynchronously."""
@@ -159,6 +171,106 @@ class RedisCache(BaseCache):
         self.redis.flushdb(asynchronous=asynchronous, **kwargs)
 
 
+class RedisSemanticCache(BaseCache):
+    """Cache that uses Redis as a vector-store backend."""
+
+    # TODO - implement a TTL policy in Redis
+
+    def __init__(
+        self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2
+    ):
+        """Initialize by passing in the `init` GPTCache func
+
+        Args:
+            redis_url (str): URL to connect to Redis.
+            embedding (Embedding): Embedding provider for semantic encoding and search.
+            score_threshold (float, 0.2):
+
+        Example:
+        .. code-block:: python
+            import langchain
+
+            from langchain.cache import RedisSemanticCache
+            from langchain.embeddings import OpenAIEmbeddings
+
+            langchain.llm_cache = RedisSemanticCache(
+                redis_url="redis://localhost:6379",
+                embedding=OpenAIEmbeddings()
+            )
+
+        """
+        self._cache_dict: Dict[str, RedisVectorstore] = {}
+        self.redis_url = redis_url
+        self.embedding = embedding
+        self.score_threshold = score_threshold
+
+    def _index_name(self, llm_string: str) -> str:
+        hashed_index = _hash(llm_string)
+        return f"cache:{hashed_index}"
+
+    def _get_llm_cache(self, llm_string: str) -> RedisVectorstore:
+        index_name = self._index_name(llm_string)
+
+        # return vectorstore client for the specific llm string
+        if index_name in self._cache_dict:
+            return self._cache_dict[index_name]
+
+        # create new vectorstore client for the specific llm string
+        try:
+            self._cache_dict[index_name] = RedisVectorstore.from_existing_index(
+                embedding=self.embedding,
+                index_name=index_name,
+                redis_url=self.redis_url,
+            )
+        except ValueError:
+            redis = RedisVectorstore(
+                embedding_function=self.embedding.embed_query,
+                index_name=index_name,
+                redis_url=self.redis_url,
+            )
+            _embedding = self.embedding.embed_query(text="test")
+            redis._create_index(dim=len(_embedding))
+            self._cache_dict[index_name] = redis
+
+        return self._cache_dict[index_name]
+
+    def clear(self, **kwargs: Any) -> None:
+        """Clear semantic cache for a given llm_string."""
+        index_name = self._index_name(kwargs["llm_string"])
+        if index_name in self._cache_dict:
+            self._cache_dict[index_name].drop_index(
+                index_name=index_name, delete_documents=True, redis_url=self.redis_url
+            )
+            del self._cache_dict[index_name]
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        llm_cache = self._get_llm_cache(llm_string)
+        generations = []
+        # Read from a Hash
+        results = llm_cache.similarity_search_limit_score(
+            query=prompt,
+            k=1,
+            score_threshold=self.score_threshold,
+        )
+        if results:
+            for document in results:
+                for text in document.metadata["return_val"]:
+                    generations.append(Generation(text=text))
+        return generations if generations else None
+
+    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
+        """Update cache based on prompt and llm_string."""
+        llm_cache = self._get_llm_cache(llm_string)
+        # Write to vectorstore
+        metadata = {
+            "llm_string": llm_string,
+            "prompt": prompt,
+            "return_val": [generation.text for generation in return_val],
+        }
+        llm_cache.add_texts(texts=[prompt], metadatas=[metadata])
+
+
 class GPTCache(BaseCache):
     """Cache that uses GPTCache as a backend."""
 

langchain/vectorstores/__init__.py

@@ -13,11 +13,13 @@ from langchain.vectorstores.myscale import MyScale, MyScaleSettings
 from langchain.vectorstores.opensearch_vector_search import OpenSearchVectorSearch
 from langchain.vectorstores.pinecone import Pinecone
 from langchain.vectorstores.qdrant import Qdrant
+from langchain.vectorstores.redis import Redis
 from langchain.vectorstores.supabase import SupabaseVectorStore
 from langchain.vectorstores.weaviate import Weaviate
 from langchain.vectorstores.zilliz import Zilliz
 
 __all__ = [
+    "Redis",
     "ElasticVectorSearch",
     "FAISS",
     "VectorStore",

langchain/vectorstores/redis.py

@@ -4,11 +4,21 @@ from __future__ import annotations
 import json
 import logging
 import uuid
-from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Type
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Mapping,
+    Optional,
+    Tuple,
+    Type,
+)
 
 import numpy as np
 from pydantic import BaseModel, root_validator
-from redis.client import Redis as RedisType
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
@@ -18,23 +28,30 @@ from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from redis.client import Redis as RedisType
+    from redis.commands.search.query import Query
+
 
 # required modules
 REDIS_REQUIRED_MODULES = [
     {"name": "search", "ver": 20400},
+    {"name": "searchlight", "ver": 20400},
 ]
 
 
-def _check_redis_module_exist(client: RedisType, modules: List[dict]) -> None:
+def _check_redis_module_exist(client: RedisType, required_modules: List[dict]) -> None:
     """Check if the correct Redis modules are installed."""
     installed_modules = client.module_list()
     installed_modules = {
         module[b"name"].decode("utf-8"): module for module in installed_modules
     }
-    for module in modules:
+    for module in required_modules:
-        if module["name"] not in installed_modules or int(
+        if module["name"] in installed_modules and int(
             installed_modules[module["name"]][b"ver"]
-        ) < int(module["ver"]):
+        ) >= int(module["ver"]):
+            return
+    # otherwise raise error
     error_message = (
         "You must add the RediSearch (>= 2.4) module from Redis Stack. "
         "Please refer to Redis Stack docs: https://redis.io/docs/stack/"
@@ -65,6 +82,24 @@ def _redis_prefix(index_name: str) -> str:
 
 
 class Redis(VectorStore):
+    """Wrapper around Redis vector database.
+
+    To use, you should have the ``redis`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import Redis
+            from langchain.embeddings import OpenAIEmbeddings
+
+            embeddings = OpenAIEmbeddings()
+            vectorstore = Redis(
+                redis_url="redis://username:password@localhost:6379"
+                index_name="my-index",
+                embedding_function=embeddings.embed_query,
+            )
+    """
+
     def __init__(
         self,
         redis_url: str,
@@ -99,33 +134,92 @@ class Redis(VectorStore):
         self.metadata_key = metadata_key
         self.vector_key = vector_key
 
+    def _create_index(self, dim: int = 1536) -> None:
+        try:
+            from redis.commands.search.field import TextField, VectorField
+            from redis.commands.search.indexDefinition import IndexDefinition, IndexType
+        except ImportError:
+            raise ValueError(
+                "Could not import redis python package. "
+                "Please install it with `pip install redis`."
+            )
+
+        # Check if index exists
+        if not _check_index_exists(self.client, self.index_name):
+            # Constants
+            distance_metric = (
+                "COSINE"  # distance metric for the vectors (ex. COSINE, IP, L2)
+            )
+            schema = (
+                TextField(name=self.content_key),
+                TextField(name=self.metadata_key),
+                VectorField(
+                    self.vector_key,
+                    "FLAT",
+                    {
+                        "TYPE": "FLOAT32",
+                        "DIM": dim,
+                        "DISTANCE_METRIC": distance_metric,
+                    },
+                ),
+            )
+            prefix = _redis_prefix(self.index_name)
+
+            # Create Redis Index
+            self.client.ft(self.index_name).create_index(
+                fields=schema,
+                definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
+            )
+
     def add_texts(
         self,
         texts: Iterable[str],
         metadatas: Optional[List[dict]] = None,
+        embeddings: Optional[List[List[float]]] = None,
+        keys: Optional[List[str]] = None,
+        batch_size: int = 1000,
         **kwargs: Any,
     ) -> List[str]:
-        """Add texts data to an existing index."""
+        """Add more texts to the vectorstore.
-        prefix = _redis_prefix(self.index_name)
+
-        keys = kwargs.get("keys")
+        Args:
+            texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
+            metadatas (Optional[List[dict]], optional): Optional list of metadatas.
+                Defaults to None.
+            embeddings (Optional[List[List[float]]], optional): Optional pre-generated
+                embeddings. Defaults to None.
+            keys (Optional[List[str]], optional): Optional key values to use as ids.
+                Defaults to None.
+            batch_size (int, optional): Batch size to use for writes. Defaults to 1000.
+
+        Returns:
+            List[str]: List of ids added to the vectorstore
+        """
         ids = []
+        prefix = _redis_prefix(self.index_name)
+
         # Write data to redis
         pipeline = self.client.pipeline(transaction=False)
         for i, text in enumerate(texts):
-            # Use provided key otherwise use default key
+            # Use provided values by default or fallback
             key = keys[i] if keys else _redis_key(prefix)
             metadata = metadatas[i] if metadatas else {}
+            embedding = embeddings[i] if embeddings else self.embedding_function(text)
             pipeline.hset(
                 key,
                 mapping={
                     self.content_key: text,
-                    self.vector_key: np.array(
+                    self.vector_key: np.array(embedding, dtype=np.float32).tobytes(),
-                        self.embedding_function(text), dtype=np.float32
-                    ).tobytes(),
                     self.metadata_key: json.dumps(metadata),
                 },
             )
             ids.append(key)
+
+            # Write batch
+            if i % batch_size == 0:
+                pipeline.execute()
+
+        # Cleanup final batch
         pipeline.execute()
         return ids
 
@@ -170,9 +264,30 @@ class Redis(VectorStore):
 
         """
         docs_and_scores = self.similarity_search_with_score(query, k=k)
-
         return [doc for doc, score in docs_and_scores if score < score_threshold]
 
+    def _prepare_query(self, k: int) -> Query:
+        try:
+            from redis.commands.search.query import Query
+        except ImportError:
+            raise ValueError(
+                "Could not import redis python package. "
+                "Please install it with `pip install redis`."
+            )
+        # Prepare the Query
+        hybrid_fields = "*"
+        base_query = (
+            f"{hybrid_fields}=>[KNN {k} @{self.vector_key} $vector AS vector_score]"
+        )
+        return_fields = [self.metadata_key, self.content_key, "vector_score"]
+        return (
+            Query(base_query)
+            .return_fields(*return_fields)
+            .sort_by("vector_score")
+            .paging(0, k)
+            .dialect(2)
+        )
+
     def similarity_search_with_score(
         self, query: str, k: int = 4
     ) -> List[Tuple[Document, float]]:
@@ -185,40 +300,22 @@ class Redis(VectorStore):
         Returns:
             List of Documents most similar to the query and score for each
         """
-        try:
-            from redis.commands.search.query import Query
-        except ImportError:
-            raise ValueError(
-                "Could not import redis python package. "
-                "Please install it with `pip install redis`."
-            )
-
         # Creates embedding vector from user query
         embedding = self.embedding_function(query)
 
-        # Prepare the Query
+        # Creates Redis query
-        return_fields = [self.metadata_key, self.content_key, "vector_score"]
+        redis_query = self._prepare_query(k)
-        vector_field = self.vector_key
+
-        hybrid_fields = "*"
-        base_query = (
-            f"{hybrid_fields}=>[KNN {k} @{vector_field} $vector AS vector_score]"
-        )
-        redis_query = (
-            Query(base_query)
-            .return_fields(*return_fields)
-            .sort_by("vector_score")
-            .paging(0, k)
-            .dialect(2)
-        )
         params_dict: Mapping[str, str] = {
             "vector": np.array(embedding)  # type: ignore
             .astype(dtype=np.float32)
             .tobytes()
         }
 
-        # perform vector search
+        # Perform vector search
         results = self.client.ft(self.index_name).search(redis_query, params_dict)
 
+        # Prepare document results
         docs = [
             (
                 Document(
@@ -243,15 +340,15 @@ class Redis(VectorStore):
         vector_key: str = "content_vector",
         **kwargs: Any,
     ) -> Redis:
-        """Construct RediSearch wrapper from raw documents.
+        """Create a Redis vectorstore from raw documents.
         This is a user-friendly interface that:
             1. Embeds documents.
-            2. Creates a new index for the embeddings in the RediSearch instance.
+            2. Creates a new index for the embeddings in Redis.
-            3. Adds the documents to the newly created RediSearch index.
+            3. Adds the documents to the newly created Redis index.
         This is intended to be a quick way to get started.
         Example:
             .. code-block:: python
-                from langchain import RediSearch
+                from langchain.vectorstores import Redis
                 from langchain.embeddings import OpenAIEmbeddings
                 embeddings = OpenAIEmbeddings()
                 redisearch = RediSearch.from_texts(
@@ -261,84 +358,35 @@ class Redis(VectorStore):
                 )
         """
         redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
-        try:
+
-            import redis
-            from redis.commands.search.field import TextField, VectorField
-            from redis.commands.search.indexDefinition import IndexDefinition, IndexType
-        except ImportError:
-            raise ValueError(
-                "Could not import redis python package. "
-                "Please install it with `pip install redis`."
-            )
-        try:
-            # We need to first remove redis_url from kwargs,
-            # otherwise passing it to Redis will result in an error.
         if "redis_url" in kwargs:
             kwargs.pop("redis_url")
-            client = redis.from_url(url=redis_url, **kwargs)
-            # check if redis has redisearch module installed
-            _check_redis_module_exist(client, REDIS_REQUIRED_MODULES)
-        except ValueError as e:
-            raise ValueError(f"Redis failed to connect: {e}")
-
-        # Create embeddings over documents
-        embeddings = embedding.embed_documents(texts)
 
         # Name of the search index if not given
         if not index_name:
             index_name = uuid.uuid4().hex
-        prefix = _redis_prefix(index_name)  # prefix for the document keys
 
-        # Check if index exists
+        # Create instance
-        if not _check_index_exists(client, index_name):
+        instance = cls(
-            # Constants
+            redis_url=redis_url,
-            dim = len(embeddings[0])
+            index_name=index_name,
-            distance_metric = (
+            embedding_function=embedding.embed_query,
-                "COSINE"  # distance metric for the vectors (ex. COSINE, IP, L2)
-            )
-            schema = (
-                TextField(name=content_key),
-                TextField(name=metadata_key),
-                VectorField(
-                    vector_key,
-                    "FLAT",
-                    {
-                        "TYPE": "FLOAT32",
-                        "DIM": dim,
-                        "DISTANCE_METRIC": distance_metric,
-                    },
-                ),
-            )
-            # Create Redis Index
-            client.ft(index_name).create_index(
-                fields=schema,
-                definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
-            )
-
-        # Write data to Redis
-        pipeline = client.pipeline(transaction=False)
-        for i, text in enumerate(texts):
-            key = _redis_key(prefix)
-            metadata = metadatas[i] if metadatas else {}
-            pipeline.hset(
-                key,
-                mapping={
-                    content_key: text,
-                    vector_key: np.array(embeddings[i], dtype=np.float32).tobytes(),
-                    metadata_key: json.dumps(metadata),
-                },
-            )
-        pipeline.execute()
-        return cls(
-            redis_url,
-            index_name,
-            embedding.embed_query,
             content_key=content_key,
             metadata_key=metadata_key,
             vector_key=vector_key,
             **kwargs,
         )
 
+        # Create embeddings over documents
+        embeddings = embedding.embed_documents(texts)
+
+        # Create the search index
+        instance._create_index(dim=len(embeddings[0]))
+
+        # Add data to Redis
+        instance.add_texts(texts, metadatas, embeddings)
+        return instance
+
     @staticmethod
     def drop_index(
         index_name: str,

tests/integration_tests/cache/test_redis_cache.py

@@ -0,0 +1,55 @@
+"""Test Redis cache functionality."""
+import redis
+
+import langchain
+from langchain.cache import RedisCache, RedisSemanticCache
+from langchain.schema import Generation, LLMResult
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+REDIS_TEST_URL = "redis://localhost:6379"
+
+
+def test_redis_cache() -> None:
+    langchain.llm_cache = RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL))
+    llm = FakeLLM()
+    params = llm.dict()
+    params["stop"] = None
+    llm_string = str(sorted([(k, v) for k, v in params.items()]))
+    langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")])
+    output = llm.generate(["foo"])
+    print(output)
+    expected_output = LLMResult(
+        generations=[[Generation(text="fizz")]],
+        llm_output={},
+    )
+    print(expected_output)
+    assert output == expected_output
+    langchain.llm_cache.redis.flushall()
+
+
+def test_redis_semantic_cache() -> None:
+    langchain.llm_cache = RedisSemanticCache(
+        embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1
+    )
+    llm = FakeLLM()
+    params = llm.dict()
+    params["stop"] = None
+    llm_string = str(sorted([(k, v) for k, v in params.items()]))
+    langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")])
+    output = llm.generate(
+        ["bar"]
+    )  # foo and bar will have the same embedding produced by FakeEmbeddings
+    expected_output = LLMResult(
+        generations=[[Generation(text="fizz")]],
+        llm_output={},
+    )
+    assert output == expected_output
+    # clear the cache
+    langchain.llm_cache.clear(llm_string=llm_string)
+    output = llm.generate(
+        ["bar"]
+    )  # foo and bar will have the same embedding produced by FakeEmbeddings
+    # expect different output now without cached result
+    assert output != expected_output
+    langchain.llm_cache.clear(llm_string=llm_string)

tests/integration_tests/vectorstores/test_redis.py

@@ -1,26 +1,60 @@
 """Test Redis functionality."""
-
 from langchain.docstore.document import Document
 from langchain.vectorstores.redis import Redis
 from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
 
+TEST_INDEX_NAME = "test"
+TEST_REDIS_URL = "redis://localhost:6379"
+TEST_SINGLE_RESULT = [Document(page_content="foo")]
+TEST_RESULT = [Document(page_content="foo"), Document(page_content="foo")]
+
+
+def drop(index_name: str) -> bool:
+    return Redis.drop_index(
+        index_name=index_name, delete_documents=True, redis_url=TEST_REDIS_URL
+    )
+
 
 def test_redis() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
-    docsearch = Redis.from_texts(
+    docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
-        texts, FakeEmbeddings(), redis_url="redis://localhost:6379"
-    )
     output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo")]
+    assert output == TEST_SINGLE_RESULT
+    assert drop(docsearch.index_name)
 
 
 def test_redis_new_vector() -> None:
     """Test adding a new document"""
     texts = ["foo", "bar", "baz"]
-    docsearch = Redis.from_texts(
+    docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
-        texts, FakeEmbeddings(), redis_url="redis://localhost:6379"
+    docsearch.add_texts(["foo"])
+    output = docsearch.similarity_search("foo", k=2)
+    assert output == TEST_RESULT
+    assert drop(docsearch.index_name)
+
+
+def test_redis_from_existing() -> None:
+    """Test adding a new document"""
+    texts = ["foo", "bar", "baz"]
+    Redis.from_texts(
+        texts, FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
+    )
+    # Test creating from an existing
+    docsearch2 = Redis.from_existing_index(
+        FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
+    )
+    output = docsearch2.similarity_search("foo", k=1)
+    assert output == TEST_SINGLE_RESULT
+
+
+def test_redis_add_texts_to_existing() -> None:
+    """Test adding a new document"""
+    # Test creating from an existing
+    docsearch = Redis.from_existing_index(
+        FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
     )
     docsearch.add_texts(["foo"])
     output = docsearch.similarity_search("foo", k=2)
-    assert output == [Document(page_content="foo"), Document(page_content="foo")]
+    assert output == TEST_RESULT
+    assert drop(TEST_INDEX_NAME)