Add: Redis VectorDB support

src/beyondllm/vectordb/__init__.py

@@ -1,3 +1,4 @@
 from .chroma import ChromaVectorDb
 from .pinecone import PineconeVectorDb
-from .weaviate import WeaviateVectorDb
+from .weaviate import WeaviateVectorDb
+from .redis import RedisVectorDb

src/beyondllm/vectordb/redis.py

@@ -0,0 +1,123 @@
+from beyondllm.vectordb.base import VectorDb, VectorDbConfig
+from typing import Optional, Dict
+from dataclasses import dataclass
+import warnings
+import subprocess
+import sys
+
+warnings.filterwarnings("ignore")
+
+@dataclass
+class RedisVectorDb:
+    """
+    Example usage:
+    from beyondllm.vectordb import RedisVectorDb
+    vectordb = RedisVectorDb(
+        host="localhost",
+        port=6379,
+        index_name="MyIndex",
+        password=None,
+        schema=None,
+        additional_headers=None
+    )
+    """
+    host: str
+    port: int
+    index_name: str
+    password: Optional[str] = None  # Optional, for authenticated access
+    schema: Optional[Dict] = None  # Optional, for custom schema
+    additional_headers: Optional[Dict] = None  # Optional, for custom headers
+
+    def __post_init__(self):
+        try:
+            from llama_index.vector_stores.redis import RedisVectorStore
+            from redisvl.schema import IndexSchema
+            import redis
+        except ImportError:
+            user_agree = input("The feature you're trying to use requires additional libraries: llama_index.vector_stores.redis and redis. Would you like to install them now? [y/N]: ")
+            if user_agree.lower() == 'y':
+                subprocess.check_call([sys.executable, "-m", "pip", "install", "llama_index.vector_stores.redis", "redis"])
+                from llama_index.vector_stores.redis import RedisVectorStore
+                from redisvl.schema import IndexSchema
+                import redis
+            else:
+                raise ImportError("The required 'llama_index.vector_stores.redis' and 'redis' libraries are not installed.")
+
+        self.client = redis.Redis(
+            host=self.host,
+            port=self.port,
+            password=self.password,
+            decode_responses=True
+        )
+        self.load()
+
+    def load(self):
+        try:
+            from llama_index.vector_stores.redis import RedisVectorStore
+            from redisvl.schema import IndexSchema
+        except ImportError:
+            raise ImportError("RedisVectorStore library is not installed. Please install it with `pip install llama_index.vector_stores.redis`.")
+
+        try:
+            if self.schema is None:
+                self.schema = {
+                    "index": {
+                        "name": self.index_name,
+                        "prefix": "essay",
+                        "key_separator": ":",
+                    },
+                    "fields": [
+                        {"type": "tag", "name": "id"},
+                        {"type": "tag", "name": "doc_id"},
+                        {"type": "text", "name": "text"},
+                        {"type": "numeric", "name": "updated_at"},
+                        {"type": "tag", "name": "file_name"},
+                        {
+                            "type": "vector",
+                            "name": "vector",
+                            "attrs": {
+                                "dims": 768,
+                                "algorithm": "hnsw",
+                                "distance_metric": "cosine",
+                            },
+                        },
+                    ],
+                }
+
+            index_schema = IndexSchema.from_dict(self.schema)
+            vector_store = RedisVectorStore(
+                redis_client=self.client,
+                schema=index_schema,
+                overwrite=True
+            )
+            self.client = vector_store
+        except Exception as e:
+            raise Exception(f"Failed to load the Redis Vectorstore: {e}")
+
+        return self.client
+
+    def add(self, *args, **kwargs):
+        return self.client.add(*args, **kwargs)
+
+    def stores_text(self, *args, **kwargs):
+        return self.client.stores_text(*args, **kwargs)
+
+    def is_embedding_query(self, *args, **kwargs):
+        return self.client.is_embedding_query(*args, **kwargs)
+
+    def query(self, *args, **kwargs):
+        return self.client.query(*args, **kwargs)
+
+    def insert_document(self, document):
+        self.client.insert(document)
+
+    def delete_index(self):
+        self.client.delete_index()
+
+    def close_connection(self):
+        self.client.close()
+
+    def load_from_kwargs(self, kwargs):
+        embed_config = VectorDbConfig(**kwargs)
+        self.config = embed_config
+        self.load()