documentation cleanup

Author: tylerhutcherson

redisvl/extensions/llmcache/schema.py

@@ -8,14 +8,24 @@
 
 
 class CacheEntry(BaseModel):
+    """A single cache entry in Redis"""
+
     entry_id: Optional[str] = Field(default=None)
+    """Cache entry identifier"""
     prompt: str
+    """Input prompt or question cached in Redis"""
     response: str
+    """Response or answer to the question, cached in Redis"""
     prompt_vector: List[float]
+    """Text embedding representation of the prompt"""
     inserted_at: float = Field(default_factory=current_timestamp)
+    """Timestamp of when the entry was added to the cache"""
     updated_at: float = Field(default_factory=current_timestamp)
+    """Timestamp of when the entry was updated in the cache"""
     metadata: Optional[Dict[str, Any]] = Field(default=None)
+    """Optional metadata stored on the cache entry"""
     filters: Optional[Dict[str, Any]] = Field(default=None)
+    """Optional filter data stored on the cache entry for customizing retrieval"""
 
     @root_validator(pre=True)
     @classmethod
@@ -43,14 +53,24 @@ def to_dict(self) -> Dict:
 
 
 class CacheHit(BaseModel):
+    """A cache hit based on some input query"""
+
     entry_id: str
+    """Cache entry identifier"""
     prompt: str
+    """Input prompt or question cached in Redis"""
     response: str
+    """Response or answer to the question, cached in Redis"""
     vector_distance: float
+    """The semantic distance between the query vector and the stored prompt vector"""
     inserted_at: float
+    """Timestamp of when the entry was added to the cache"""
     updated_at: float
+    """Timestamp of when the entry was updated in the cache"""
     metadata: Optional[Dict[str, Any]] = Field(default=None)
+    """Optional metadata stored on the cache entry"""
     filters: Optional[Dict[str, Any]] = Field(default=None)
+    """Optional filter data stored on the cache entry for customizing retrieval"""
 
     @root_validator(pre=True)
     @classmethod

redisvl/extensions/llmcache/semantic.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional
 
 from redis import Redis
 
@@ -10,20 +10,15 @@
 )
 from redisvl.index import SearchIndex
 from redisvl.query import RangeQuery
-from redisvl.query.filter import FilterExpression, Tag
-from redisvl.redis.utils import array_to_buffer
-from redisvl.utils.utils import (
-    current_timestamp,
-    deserialize,
-    serialize,
-    validate_vector_dims,
-)
+from redisvl.query.filter import FilterExpression
+from redisvl.utils.utils import current_timestamp, serialize, validate_vector_dims
 from redisvl.utils.vectorize import BaseVectorizer, HFTextVectorizer
 
 
 class SemanticCache(BaseLLMCache):
     """Semantic Cache for Large Language Models."""
 
+    redis_key_field_name: str = "key"
     entry_id_field_name: str = "entry_id"
     prompt_field_name: str = "prompt"
     response_field_name: str = "response"
@@ -55,6 +50,8 @@ def __init__(
                 in Redis. Defaults to None.
             vectorizer (Optional[BaseVectorizer], optional): The vectorizer for the cache.
                 Defaults to HFTextVectorizer.
+            filterable_fields (Optional[List[Dict[str, Any]]]): An optional list of RedisVL fields
+                that can be used to customize cache retrieval with filters.
             redis_client(Optional[Redis], optional): A redis client connection instance.
                 Defaults to None.
             redis_url (str, optional): The redis url. Defaults to redis://localhost:6379.
@@ -81,9 +78,6 @@ def __init__(
                 model="sentence-transformers/all-mpnet-base-v2"
             )
 
-        # Create semantic cache schema
-        schema = SemanticCacheIndexSchema.from_params(name, prefix, vectorizer.dims)
-
         # Process fields
         self.return_fields = [
             self.entry_id_field_name,
@@ -94,18 +88,9 @@ def __init__(
             self.metadata_field_name,
         ]
 
-        if filterable_fields is not None:
-            for filter_field in filterable_fields:
-                if (
-                    filter_field["name"] in self.return_fields
-                    or filter_field["name"] == "key"
-                ):
-                    raise ValueError(
-                        f'{filter_field["name"]} is a reserved field name for the semantic cache schema'
-                    )
-                schema.add_field(filter_field)
-                # Add to return fields too
-                self.return_fields.append(filter_field["name"])
+        # Create semantic cache schema and index
+        schema = SemanticCacheIndexSchema.from_params(name, prefix, vectorizer.dims)
+        schema = self._modify_schema(schema, filterable_fields)
 
         self._index = SearchIndex(schema=schema)
 
@@ -120,6 +105,30 @@ def __init__(
         self.set_threshold(distance_threshold)
         self._index.create(overwrite=False)
 
+    def _modify_schema(
+        self,
+        schema: SemanticCacheIndexSchema,
+        filterable_fields: Optional[List[Dict[str, Any]]] = None,
+    ) -> SemanticCacheIndexSchema:
+        """Modify the base cache schema using the provided filterable fields"""
+
+        if filterable_fields is not None:
+            protected_field_names = set(
+                self.return_fields + [self.redis_key_field_name]
+            )
+            for filter_field in filterable_fields:
+                field_name = filter_field["name"]
+                if field_name in protected_field_names:
+                    raise ValueError(
+                        f"{field_name} is a reserved field name for the semantic cache schema"
+                    )
+                # Add to schema
+                schema.add_field(filter_field)
+                # Add to return fields too
+                self.return_fields.append(field_name)
+
+        return schema
+
     @property
     def index(self) -> SearchIndex:
         """The underlying SearchIndex for the cache.