File: models.py

package info (click to toggle)
python-moto 5.1.18-3
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid
  • size: 116,520 kB
  • sloc: python: 636,725; javascript: 181; makefile: 39; sh: 3
file content (314 lines) | stat: -rw-r--r-- 12,328 bytes parent folder | download
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
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
"""AgentsforBedrockBackend class with methods for supported APIs."""

from typing import Any, Optional

from moto.bedrockagent.exceptions import (
    ConflictException,
    ResourceNotFoundException,
    ValidationException,
)
from moto.core.base_backend import BackendDict, BaseBackend
from moto.core.common_models import BaseModel
from moto.core.utils import unix_time
from moto.moto_api._internal import mock_random
from moto.utilities.paginator import paginate
from moto.utilities.tagging_service import TaggingService
from moto.utilities.utils import get_partition


class Agent(BaseModel):
    def __init__(
        self,
        agent_name: str,
        agent_resource_role_arn: str,
        region_name: str,
        account_id: str,
        client_token: Optional[str],
        instruction: Optional[str],
        foundation_model: Optional[str],
        description: Optional[str],
        idle_session_ttl_in_seconds: Optional[int],
        customer_encryption_key_arn: Optional[str],
        prompt_override_configuration: Optional[dict[str, Any]],
    ):
        self.agent_name = agent_name
        self.client_token = client_token
        self.instruction = instruction
        self.foundation_model = foundation_model
        self.description = description
        self.idle_session_ttl_in_seconds = idle_session_ttl_in_seconds
        self.agent_resource_role_arn = agent_resource_role_arn
        self.customer_encryption_key_arn = customer_encryption_key_arn
        self.prompt_override_configuration = prompt_override_configuration
        self.region_name = region_name
        self.account_id = account_id
        self.created_at = unix_time()
        self.updated_at = unix_time()
        self.prepared_at = unix_time()
        self.agent_status = "PREPARED"
        self.agent_id = self.agent_name + str(mock_random.uuid4())[:8]
        self.agent_arn = f"arn:{get_partition(self.region_name)}:bedrock:{self.region_name}:{self.account_id}:agent/{self.agent_id}"
        self.agent_version = "1.0"
        self.failure_reasons: list[str] = []
        self.recommended_actions = ["action"]

    def to_dict(self) -> dict[str, Any]:
        dct = {
            "agentId": self.agent_id,
            "agentName": self.agent_name,
            "agentArn": self.agent_arn,
            "agentVersion": self.agent_version,
            "clientToken": self.client_token,
            "instruction": self.instruction,
            "agentStatus": self.agent_status,
            "foundationModel": self.foundation_model,
            "description": self.description,
            "idleSessionTTLInSeconds": self.idle_session_ttl_in_seconds,
            "agentResourceRoleArn": self.agent_resource_role_arn,
            "customerEncryptionKeyArn": self.customer_encryption_key_arn,
            "createdAt": self.created_at,
            "updatedAt": self.updated_at,
            "preparedAt": self.prepared_at,
            "failureReasons": self.failure_reasons,
            "recommendedActions": self.recommended_actions,
            "promptOverrideConfiguration": self.prompt_override_configuration,
        }
        return {k: v for k, v in dct.items() if v}

    def dict_summary(self) -> dict[str, Any]:
        dct = {
            "agentId": self.agent_id,
            "agentName": self.agent_name,
            "agentStatus": self.agent_status,
            "description": self.description,
            "updatedAt": self.updated_at,
            "latestAgentVersion": self.agent_version,
        }
        return {k: v for k, v in dct.items() if v}


class KnowledgeBase(BaseModel):
    def __init__(
        self,
        name: str,
        role_arn: str,
        region_name: str,
        account_id: str,
        knowledge_base_configuration: dict[str, Any],
        storage_configuration: dict[str, Any],
        client_token: Optional[str],
        description: Optional[str],
    ):
        self.client_token = client_token
        self.name = name
        self.description = description
        self.role_arn = role_arn
        if knowledge_base_configuration["type"] != "VECTOR":
            raise ValidationException(
                "Validation error detected: "
                f"Value '{knowledge_base_configuration['type']}' at 'knowledgeBaseConfiguration' failed to satisfy constraint: "
                "Member must contain 'type' as 'VECTOR'"
            )
        self.knowledge_base_configuration = knowledge_base_configuration
        if storage_configuration["type"] not in [
            "OPENSEARCH_SERVERLESS",
            "PINECONE",
            "REDIS_ENTERPRISE_CLOUD",
            "RDS",
        ]:
            raise ValidationException(
                "Validation error detected: "
                f"Value '{storage_configuration['type']}' at 'storageConfiguration' failed to satisfy constraint: "
                "Member 'type' must be one of: OPENSEARCH_SERVERLESS | PINECONE | REDIS_ENTERPRISE_CLOUD | RDS"
            )
        self.storage_configuration = storage_configuration
        self.region_name = region_name
        self.account_id = account_id
        self.knowledge_base_id = self.name + str(mock_random.uuid4())[:8]
        self.knowledge_base_arn = f"arn:{get_partition(self.region_name)}:bedrock:{self.region_name}:{self.account_id}:knowledge-base/{self.knowledge_base_id}"
        self.created_at = unix_time()
        self.updated_at = unix_time()
        self.status = "Active"
        self.failure_reasons: list[str] = []

    def to_dict(self) -> dict[str, Any]:
        dct = {
            "knowledgeBaseId": self.knowledge_base_id,
            "name": self.name,
            "knowledgeBaseArn": self.knowledge_base_arn,
            "description": self.description,
            "roleArn": self.role_arn,
            "knowledgeBaseConfiguration": self.knowledge_base_configuration,
            "storageConfiguration": self.storage_configuration,
            "status": self.status,
            "createdAt": self.created_at,
            "updatedAt": self.updated_at,
            "failureReasons": self.failure_reasons,
        }
        return {k: v for k, v in dct.items() if v}

    def dict_summary(self) -> dict[str, Any]:
        dct = {
            "knowledgeBaseId": self.knowledge_base_id,
            "name": self.name,
            "description": self.description,
            "status": self.status,
            "updatedAt": self.updated_at,
        }
        return {k: v for k, v in dct.items() if v}


class AgentsforBedrockBackend(BaseBackend):
    """Implementation of AgentsforBedrock APIs."""

    PAGINATION_MODEL = {
        "list_agents": {
            "input_token": "next_token",
            "limit_key": "max_results",
            "limit_default": 100,
            "unique_attribute": "agent_id",
        },
        "list_knowledge_bases": {
            "input_token": "next_token",
            "limit_key": "max_results",
            "limit_default": 100,
            "unique_attribute": "knowledge_base_id",
        },
    }

    def __init__(self, region_name: str, account_id: str):
        super().__init__(region_name, account_id)
        self.agents: dict[str, Agent] = {}
        self.knowledge_bases: dict[str, KnowledgeBase] = {}
        self.tagger = TaggingService()

    def _list_arns(self) -> list[str]:
        return [agent.agent_arn for agent in self.agents.values()] + [
            knowledge_base.knowledge_base_arn
            for knowledge_base in self.knowledge_bases.values()
        ]

    def create_agent(
        self,
        agent_name: str,
        agent_resource_role_arn: str,
        client_token: Optional[str],
        instruction: Optional[str],
        foundation_model: Optional[str],
        description: Optional[str],
        idle_session_ttl_in_seconds: Optional[int],
        customer_encryption_key_arn: Optional[str],
        tags: Optional[dict[str, str]],
        prompt_override_configuration: Optional[dict[str, Any]],
    ) -> Agent:
        agent = Agent(
            agent_name,
            agent_resource_role_arn,
            self.region_name,
            self.account_id,
            client_token,
            instruction,
            foundation_model,
            description,
            idle_session_ttl_in_seconds,
            customer_encryption_key_arn,
            prompt_override_configuration,
        )
        self.agents[agent.agent_id] = agent
        if tags:
            self.tag_resource(agent.agent_arn, tags)
        return agent

    def get_agent(self, agent_id: str) -> Agent:
        if agent_id not in self.agents:
            raise ResourceNotFoundException(f"Agent {agent_id} not found")
        return self.agents[agent_id]

    @paginate(pagination_model=PAGINATION_MODEL)
    def list_agents(self) -> list[Agent]:
        return list(self.agents.values())

    def delete_agent(
        self, agent_id: str, skip_resource_in_use_check: Optional[bool]
    ) -> tuple[str, str]:
        if agent_id in self.agents:
            if (
                skip_resource_in_use_check
                or self.agents[agent_id].agent_status == "PREPARED"
            ):
                self.agents[agent_id].agent_status = "DELETING"
                agent_status = self.agents[agent_id].agent_status
                del self.agents[agent_id]
            else:
                raise ConflictException(f"Agent {agent_id} is in use")
        else:
            raise ResourceNotFoundException(f"Agent {agent_id} not found")
        return agent_id, agent_status

    def create_knowledge_base(
        self,
        name: str,
        role_arn: str,
        knowledge_base_configuration: dict[str, Any],
        storage_configuration: dict[str, Any],
        client_token: Optional[str],
        description: Optional[str],
        tags: Optional[dict[str, str]],
    ) -> KnowledgeBase:
        knowledge_base = KnowledgeBase(
            name,
            role_arn,
            self.region_name,
            self.account_id,
            knowledge_base_configuration,
            storage_configuration,
            client_token,
            description,
        )
        self.knowledge_bases[knowledge_base.knowledge_base_id] = knowledge_base
        if tags:
            self.tag_resource(knowledge_base.knowledge_base_arn, tags)
        return knowledge_base

    @paginate(pagination_model=PAGINATION_MODEL)
    def list_knowledge_bases(self) -> list[KnowledgeBase]:
        return list(self.knowledge_bases.values())

    def delete_knowledge_base(self, knowledge_base_id: str) -> tuple[str, str]:
        if knowledge_base_id in self.knowledge_bases:
            self.knowledge_bases[knowledge_base_id].status = "DELETING"
            knowledge_base_status = self.knowledge_bases[knowledge_base_id].status
            del self.knowledge_bases[knowledge_base_id]
        else:
            raise ResourceNotFoundException(
                f"Knowledge base {knowledge_base_id} not found"
            )
        return knowledge_base_id, knowledge_base_status

    def get_knowledge_base(self, knowledge_base_id: str) -> KnowledgeBase:
        if knowledge_base_id not in self.knowledge_bases:
            raise ResourceNotFoundException(
                f"Knowledge base {knowledge_base_id} not found"
            )
        return self.knowledge_bases[knowledge_base_id]

    def tag_resource(self, resource_arn: str, tags: dict[str, str]) -> None:
        if resource_arn not in self._list_arns():
            raise ResourceNotFoundException(f"Resource {resource_arn} not found")
        tags_input = TaggingService.convert_dict_to_tags_input(tags or {})
        self.tagger.tag_resource(resource_arn, tags_input)
        return

    def untag_resource(self, resource_arn: str, tag_keys: list[str]) -> None:
        if resource_arn not in self._list_arns():
            raise ResourceNotFoundException(f"Resource {resource_arn} not found")
        self.tagger.untag_resource_using_names(resource_arn, tag_keys)
        return

    def list_tags_for_resource(self, resource_arn: str) -> dict[str, str]:
        if resource_arn not in self._list_arns():
            raise ResourceNotFoundException(f"Resource {resource_arn} not found")
        return self.tagger.get_tag_dict_for_resource(resource_arn)


bedrockagent_backends = BackendDict(AgentsforBedrockBackend, "bedrock")