[Redis]: Vector database added. (#2032)

docs/components/vectordbs/dbs/redis.mdx

@@ -0,0 +1,44 @@
+[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
+
+### Installation
+```bash
+pip install redis redisvl
+```
+
+Redis Stack using Docker:
+```bash
+docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
+```
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "redis",
+        "config": {
+            "collection_name": "mem0",
+            "embedding_model_dims": 1536,
+            "redis_url": "redis://localhost:6379"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Let's see the available parameters for the `redis` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `redis_url` | The URL of the Redis server | `None` |

docs/components/vectordbs/overview.mdx

@@ -14,6 +14,7 @@ See the list of supported vector databases below.
   <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
   <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
   <Card title="Azure AI Search" href="/components/vectordbs/dbs/azure_ai_search"></Card>
+  <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
 </CardGroup>
 
 ## Usage

docs/mint.json

@@ -111,7 +111,8 @@
                 "components/vectordbs/dbs/chroma",
                 "components/vectordbs/dbs/pgvector",
                 "components/vectordbs/dbs/milvus",
-                "components/vectordbs/dbs/azure_ai_search"
+                "components/vectordbs/dbs/azure_ai_search",
+                "components/vectordbs/dbs/redis"
               ]
             }
           ]

mem0/configs/vector_stores/redis.py

@@ -0,0 +1,26 @@
+from typing import Any, Dict
+
+from pydantic import BaseModel, Field, model_validator
+
+
+# TODO: Upgrade to latest pydantic version
+class RedisDBConfig(BaseModel):
+    redis_url: str = Field(..., description="Redis URL")
+    collection_name: str = Field("mem0", description="Collection name")
+    embedding_model_dims: int = Field(1536, description="Embedding model dimensions")
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.model_fields.keys())
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+        if extra_fields:
+            raise ValueError(
+                f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}"
+            )
+        return values
+
+    model_config = {
+        "arbitrary_types_allowed": True,
+    }

mem0/utils/factory.py

@@ -65,6 +65,7 @@ class VectorStoreFactory:
         "pgvector": "mem0.vector_stores.pgvector.PGVector",
         "milvus": "mem0.vector_stores.milvus.MilvusDB",
         "azure_ai_search": "mem0.vector_stores.azure_ai_search.AzureAISearch",
+        "redis": "mem0.vector_stores.redis.RedisDB",
     }
 
     @classmethod

mem0/vector_stores/configs.py

@@ -16,6 +16,7 @@ class VectorStoreConfig(BaseModel):
         "pgvector": "PGVectorConfig",
         "milvus": "MilvusDBConfig",
         "azure_ai_search": "AzureAISearchConfig",
+        "redis": "RedisDBConfig",
     }
 
     @model_validator(mode="after")

mem0/vector_stores/redis.py

@@ -0,0 +1,236 @@
+import json
+import logging
+from datetime import datetime
+from functools import reduce
+
+import numpy as np
+import pytz
+import redis
+from redis.commands.search.query import Query
+from redisvl.index import SearchIndex
+from redisvl.query import VectorQuery
+from redisvl.query.filter import Tag
+
+from mem0.vector_stores.base import VectorStoreBase
+
+logger = logging.getLogger(__name__)
+
+# TODO: Improve as these are not the best fields for the Redis's perspective. Might do away with them.
+DEFAULT_FIELDS = [
+    {"name": "memory_id", "type": "tag"},
+    {"name": "hash", "type": "tag"},
+    {"name": "agent_id", "type": "tag"},
+    {"name": "run_id", "type": "tag"},
+    {"name": "user_id", "type": "tag"},
+    {"name": "memory", "type": "text"},
+    {"name": "metadata", "type": "text"},
+    # TODO: Although it is numeric but also accepts string
+    {"name": "created_at", "type": "numeric"},
+    {"name": "updated_at", "type": "numeric"},
+    {
+        "name": "embedding",
+        "type": "vector",
+        "attrs": {"distance_metric": "cosine", "algorithm": "flat", "datatype": "float32"},
+    },
+]
+
+excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at"}
+
+
+class MemoryResult:
+    def __init__(self, id: str, payload: dict, score: float = None):
+        self.id = id
+        self.payload = payload
+        self.score = score
+
+
+class RedisDB(VectorStoreBase):
+    def __init__(
+        self,
+        redis_url: str,
+        collection_name: str,
+        embedding_model_dims: int,
+    ):
+        """
+        Initialize the Redis vector store.
+
+        Args:
+            redis_url (str): Redis URL.
+            collection_name (str): Collection name.
+            embedding_model_dims (int): Embedding model dimensions.
+        """
+        index_schema = {
+            "name": collection_name,
+            "prefix": f"mem0:{collection_name}",
+        }
+
+        fields = DEFAULT_FIELDS.copy()
+        fields[-1]["attrs"]["dims"] = embedding_model_dims
+
+        self.schema = {"index": index_schema, "fields": fields}
+
+        self.client = redis.Redis.from_url(redis_url)
+        self.index = SearchIndex.from_dict(self.schema)
+        self.index.set_client(self.client)
+        self.index.create(overwrite=True)
+
+    # TODO: Implement multiindex support.
+    def create_col(self, name, vector_size, distance):
+        raise NotImplementedError("Collection/Index creation not supported yet.")
+
+    def insert(self, vectors: list, payloads: list = None, ids: list = None):
+        data = []
+        for vector, payload, id in zip(vectors, payloads, ids):
+            # Start with required fields
+            entry = {
+                "memory_id": id,
+                "hash": payload["hash"],
+                "memory": payload["data"],
+                "created_at": int(datetime.fromisoformat(payload["created_at"]).timestamp()),
+                "embedding": np.array(vector, dtype=np.float32).tobytes(),
+            }
+
+            # Conditionally add optional fields
+            for field in ["agent_id", "run_id", "user_id"]:
+                if field in payload:
+                    entry[field] = payload[field]
+
+            # Add metadata excluding specific keys
+            entry["metadata"] = json.dumps({k: v for k, v in payload.items() if k not in excluded_keys})
+
+            data.append(entry)
+        self.index.load(data, id_field="memory_id")
+
+    def search(self, query: list, limit: int = 5, filters: dict = None):
+        conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
+        filter = reduce(lambda x, y: x & y, conditions)
+
+        v = VectorQuery(
+            vector=np.array(query, dtype=np.float32).tobytes(),
+            vector_field_name="embedding",
+            return_fields=["memory_id", "hash", "agent_id", "run_id", "user_id", "memory", "metadata", "created_at"],
+            filter_expression=filter,
+            num_results=limit,
+        )
+
+        results = self.index.query(v)
+
+        return [
+            MemoryResult(
+                id=result["memory_id"],
+                score=result["vector_distance"],
+                payload={
+                    "hash": result["hash"],
+                    "data": result["memory"],
+                    "created_at": datetime.fromtimestamp(
+                        int(result["created_at"]), tz=pytz.timezone("US/Pacific")
+                    ).isoformat(timespec="microseconds"),
+                    **(
+                        {
+                            "updated_at": datetime.fromtimestamp(
+                                int(result["updated_at"]), tz=pytz.timezone("US/Pacific")
+                            ).isoformat(timespec="microseconds")
+                        }
+                        if "updated_at" in result
+                        else {}
+                    ),
+                    **{field: result[field] for field in ["agent_id", "run_id", "user_id"] if field in result},
+                    **{k: v for k, v in json.loads(result["metadata"]).items()},
+                },
+            )
+            for result in results
+        ]
+
+    def delete(self, vector_id):
+        self.index.drop_keys(f"{self.schema['index']['prefix']}:{vector_id}")
+
+    def update(self, vector_id=None, vector=None, payload=None):
+        data = {
+            "memory_id": vector_id,
+            "hash": payload["hash"],
+            "memory": payload["data"],
+            "created_at": int(datetime.fromisoformat(payload["created_at"]).timestamp()),
+            "updated_at": int(datetime.fromisoformat(payload["updated_at"]).timestamp()),
+            "embedding": np.array(vector, dtype=np.float32).tobytes(),
+        }
+
+        for field in ["agent_id", "run_id", "user_id"]:
+            if field in payload:
+                data[field] = payload[field]
+
+        data["metadata"] = json.dumps({k: v for k, v in payload.items() if k not in excluded_keys})
+        self.index.load(data=[data], keys=[f"{self.schema['index']['prefix']}:{vector_id}"], id_field="memory_id")
+
+    def get(self, vector_id):
+        result = self.index.fetch(vector_id)
+        payload = {
+            "hash": result["hash"],
+            "data": result["memory"],
+            "created_at": datetime.fromtimestamp(int(result["created_at"]), tz=pytz.timezone("US/Pacific")).isoformat(
+                timespec="microseconds"
+            ),
+            **(
+                {
+                    "updated_at": datetime.fromtimestamp(
+                        int(result["updated_at"]), tz=pytz.timezone("US/Pacific")
+                    ).isoformat(timespec="microseconds")
+                }
+                if "updated_at" in result
+                else {}
+            ),
+            **{field: result[field] for field in ["agent_id", "run_id", "user_id"] if field in result},
+            **{k: v for k, v in json.loads(result["metadata"]).items()},
+        }
+
+        return MemoryResult(id=result["memory_id"], payload=payload)
+
+    def list_cols(self):
+        return self.index.listall()
+
+    def delete_col(self):
+        self.index.delete()
+
+    def col_info(self, name):
+        return self.index.info()
+
+    def list(self, filters: dict = None, limit: int = None) -> list:
+        """
+        List all recent created memories from the vector store.
+        """
+        conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
+        filter = reduce(lambda x, y: x & y, conditions)
+        query = Query(str(filter)).sort_by("created_at", asc=False)
+        if limit is not None:
+            query = Query(str(filter)).sort_by("created_at", asc=False).paging(0, limit)
+
+        results = self.index.search(query)
+        return [
+            [
+                MemoryResult(
+                    id=result["memory_id"],
+                    payload={
+                        "hash": result["hash"],
+                        "data": result["memory"],
+                        "created_at": datetime.fromtimestamp(
+                            int(result["created_at"]), tz=pytz.timezone("US/Pacific")
+                        ).isoformat(timespec="microseconds"),
+                        **(
+                            {
+                                "updated_at": datetime.fromtimestamp(
+                                    int(result["updated_at"]), tz=pytz.timezone("US/Pacific")
+                                ).isoformat(timespec="microseconds")
+                            }
+                            if result.__dict__.get("updated_at")
+                            else {}
+                        ),
+                        **{
+                            field: result[field]
+                            for field in ["agent_id", "run_id", "user_id"]
+                            if field in result.__dict__
+                        },
+                        **{k: v for k, v in json.loads(result["metadata"]).items()},
+                    },
+                )
+                for result in results.docs
+            ]
+        ]