Spaces:
Sleeping
Sleeping
File size: 8,371 Bytes
1b7e88c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 |
import base64
import pickle
from typing import Any, Iterable, List, Optional, Tuple
from omagent_core.memories.ltms.ltm_base import LTMBase
from omagent_core.services.connectors.milvus import MilvusConnector
from omagent_core.utils.registry import registry
from pydantic import Field
from pymilvus import (Collection, CollectionSchema, DataType, FieldSchema,
utility)
@registry.register_component()
class VideoMilvusLTM(LTMBase):
milvus_ltm_client: MilvusConnector
storage_name: str = Field(default="default")
dim: int = Field(default=128)
def model_post_init(self, __context: Any) -> None:
pass
def _create_collection(self) -> None:
# Check if collection exists
if not self.milvus_ltm_client._client.has_collection(self.storage_name):
index_params = self.milvus_ltm_client._client.prepare_index_params()
# Define field schemas
key_field = FieldSchema(
name="key", dtype=DataType.VARCHAR, is_primary=True, max_length=256
)
value_field = FieldSchema(
name="value", dtype=DataType.JSON, description="Json value"
)
embedding_field = FieldSchema(
name="embedding",
dtype=DataType.FLOAT_VECTOR,
description="Embedding vector",
dim=self.dim,
)
index_params = self.milvus_ltm_client._client.prepare_index_params()
# Create collection schema
schema = CollectionSchema(
fields=[key_field, value_field, embedding_field],
description="Key-Value storage with embeddings",
)
for field in schema.fields:
if (
field.dtype == DataType.FLOAT_VECTOR
or field.dtype == DataType.BINARY_VECTOR
):
index_params.add_index(
field_name=field.name,
index_name=field.name,
index_type="FLAT",
metric_type="COSINE",
params={"nlist": 128},
)
self.milvus_ltm_client._client.create_collection(
self.storage_name, schema=schema, index_params=index_params
)
# Create index separately after collection creation
print(f"Created storage {self.storage_name} successfully")
def __getitem__(self, key: Any) -> Any:
key_str = str(key)
expr = f'key == "{key_str}"'
res = self.milvus_ltm_client._client.query(
self.storage_name, expr, output_fields=["value"]
)
if res:
value = res[0]["value"]
# value_bytes = base64.b64decode(value_base64)
# value = pickle.loads(value_bytes)
return value
else:
raise KeyError(f"Key {key} not found")
def __setitem__(self, key: Any, value: Any) -> None:
self._create_collection()
key_str = str(key)
# Check if value is a dictionary containing 'value' and 'embedding'
if isinstance(value, dict) and "value" in value and "embedding" in value:
actual_value = value["value"]
embedding = value["embedding"]
else:
raise ValueError(
"When setting an item, value must be a dictionary containing 'value' and 'embedding' keys."
)
# Serialize the actual value and encode it to base64
# value_bytes = pickle.dumps(actual_value)
# value_base64 = base64.b64encode(value_bytes).decode('utf-8')
# Ensure the embedding is provided
if embedding is None:
raise ValueError("An embedding vector must be provided.")
# Check if the key exists and delete it if it does
if key_str in self:
self.__delitem__(key_str)
# Prepare data for insertion (as a list of dictionaries)
data = [
{
"key": key_str,
"value": actual_value,
"embedding": embedding,
}
]
# Insert the new record
self.milvus_ltm_client._client.insert(
collection_name=self.storage_name, data=data
)
def __delitem__(self, key: Any) -> None:
key_str = str(key)
if key_str in self:
expr = f'key == "{key_str}"'
self.milvus_ltm_client._client.delete(self.storage_name, expr)
else:
raise KeyError(f"Key {key} not found")
def __contains__(self, key: Any) -> bool:
key_str = str(key)
expr = f'key == "{key_str}"'
# Adjust the query call to match the expected signature
res = self.milvus_ltm_client._client.query(
self.storage_name, # Pass the collection name as the first argument
filter=expr,
output_fields=["key"],
)
return len(res) > 0
"""
def __len__(self) -> int:
milvus_ltm.collection.flush()
return self.collection.num_entities
"""
def __len__(self) -> int:
expr = 'key != ""' # Expression to match all entities
# self.milvus_ltm_client._client.load(refresh=True)
results = self.milvus_ltm_client._client.query(
self.storage_name, expr, output_fields=["key"], consistency_level="Strong"
)
return len(results)
def keys(self, limit=10) -> Iterable[Any]:
expr = ""
res = self.milvus_ltm_client._client.query(
self.storage_name, expr, output_fields=["key"], limit=limit
)
return (item["key"] for item in res)
def values(self) -> Iterable[Any]:
expr = 'key != ""' # Expression to match all active entities
self.milvus_ltm_client._client.load(refresh=True)
res = self.milvus_ltm_client._client.query(
self.storage_name, expr, output_fields=["value"], consistency_level="Strong"
)
for item in res:
value_base64 = item["value"]
value_bytes = base64.b64decode(value_base64)
value = pickle.loads(value_bytes)
yield value
def items(self) -> Iterable[Tuple[Any, Any]]:
expr = 'key != ""'
res = self.milvus_ltm_client._client.query(
self.storage_name, expr, output_fields=["key", "value"]
)
for item in res:
key = item["key"]
value = item["value"]
# value_bytes = base64.b64decode(value_base64)
# value = pickle.loads(value_bytes)
yield (key, value)
def get(self, key: Any, default: Any = None) -> Any:
try:
return self[key]
except KeyError:
return default
def clear(self) -> None:
expr = (
'key != ""' # This expression matches all records where 'key' is not empty
)
self.milvus_ltm_client._client.delete(self.storage_name, filter=expr)
def pop(self, key: Any, default: Any = None) -> Any:
try:
value = self[key]
self.__delitem__(key)
return value
except KeyError:
if default is not None:
return default
else:
raise
def update(self, other: Iterable[Tuple[Any, Any]]) -> None:
for key, value in other:
self[key] = value
def get_by_vector(
self,
embedding: List[float],
top_k: int = 10,
threshold: float = 0.0,
filter: str = "",
) -> List[Tuple[Any, Any, float]]:
search_params = {
"metric_type": "COSINE",
"params": {"nprobe": 10, "range_filter": 1, "radius": threshold},
}
results = self.milvus_ltm_client._client.search(
self.storage_name,
data=[embedding],
anns_field="embedding",
search_params=search_params,
limit=top_k,
output_fields=["key", "value"],
consistency_level="Strong",
filter=filter,
)
items = []
for match in results[0]:
key = match.get("entity").get("key")
value = match.get("entity").get("value")
items.append((key, value))
return items
|