xintongsong commented on code in PR #439:
URL: https://github.com/apache/flink-agents/pull/439#discussion_r2693385896
##########
.github/workflows/ci.yml:
##########
@@ -173,6 +188,15 @@ jobs:
java-version: [ '17' ]
steps:
- uses: actions/checkout@v4
+ - name: Free disk space
+ uses:
jlumbroso/free-disk-space@54081f138730dfa15788a46383842cd2f914a1be
+ with:
+ tool-cache: true
+ android: true
+ dotnet: true
+ haskell: true
+ large-packages: true
+ docker-images: true
Review Comment:
Why do we need this step?
##########
python/flink_agents/e2e_tests/e2e_tests_resource_cross_language/vector_store_cross_language_agent.py:
##########
@@ -0,0 +1,207 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#################################################################################
+import os
+import time
+
+import pytest
+
+from flink_agents.api.agents.agent import Agent
+from flink_agents.api.decorators import (
+ action,
+ embedding_model_connection,
+ embedding_model_setup,
+ vector_store,
+)
+from flink_agents.api.embedding_models.java_embedding_model import (
+ JavaEmbeddingModelConnection,
+ JavaEmbeddingModelSetup,
+)
+from flink_agents.api.events.context_retrieval_event import (
+ ContextRetrievalRequestEvent,
+ ContextRetrievalResponseEvent,
+)
+from flink_agents.api.events.event import InputEvent, OutputEvent
+from flink_agents.api.resource import ResourceDescriptor, ResourceType
+from flink_agents.api.runner_context import RunnerContext
+from flink_agents.api.vector_stores.java_vector_store import (
+ JavaCollectionManageableVectorStore,
+)
+from flink_agents.api.vector_stores.vector_store import (
+ CollectionManageableVectorStore,
+ Document,
+)
+from flink_agents.integrations.embedding_models.local.ollama_embedding_model
import (
+ OllamaEmbeddingModelConnection,
+ OllamaEmbeddingModelSetup,
+)
+
+TEST_COLLECTION = "test_collection"
+MAX_RETRIES_TIMES = 10
+
+class VectorStoreCrossLanguageAgent(Agent):
+ """Example agent demonstrating cross-language embedding model testing."""
+
+ @embedding_model_connection
+ @staticmethod
+ def embedding_model_connection() -> ResourceDescriptor:
+ """EmbeddingModelConnection responsible for ollama model service
connection."""
+ if os.environ.get("EMBEDDING_TYPE") == "JAVA":
+ return ResourceDescriptor(
+ clazz=JavaEmbeddingModelConnection,
+
java_clazz="org.apache.flink.agents.integrations.embeddingmodels.ollama.OllamaEmbeddingModelConnection",
+ host="http://localhost:11434";,
+ )
+ return ResourceDescriptor(
+ clazz=OllamaEmbeddingModelConnection,
+ host="http://localhost:11434";,
+ )
+
+ @embedding_model_setup
+ @staticmethod
+ def embedding_model() -> ResourceDescriptor:
+ """EmbeddingModel which focus on math, and reuse
ChatModelConnection."""
+ if os.environ.get("EMBEDDING_TYPE") == "JAVA":
+ return ResourceDescriptor(
+ clazz=JavaEmbeddingModelSetup,
+
java_clazz="org.apache.flink.agents.integrations.embeddingmodels.ollama.OllamaEmbeddingModelSetup",
+ connection="embedding_model_connection",
+ model=os.environ.get(
+ "OLLAMA_EMBEDDING_MODEL", "nomic-embed-text:latest"
+ ),
+ )
+ return ResourceDescriptor(
+ clazz=OllamaEmbeddingModelSetup,
+ connection="embedding_model_connection",
+ model=os.environ.get("OLLAMA_EMBEDDING_MODEL",
"nomic-embed-text:latest"),
+ )
+
+ @vector_store
+ @staticmethod
+ def vector_store() -> ResourceDescriptor:
+ """Vector store setup for knowledge base."""
+ return ResourceDescriptor(
+ clazz=JavaCollectionManageableVectorStore,
+
java_clazz="org.apache.flink.agents.integrations.vectorstores.elasticsearch.ElasticsearchVectorStore",
+ embedding_model="embedding_model",
+ host=os.environ.get("ES_HOST", "http://localhost:9200";),
Review Comment:
The test case should not assume that it's always executed in the GHA
pipeline and the ES service always exist. We can set the ES_HOST env in the ci
pipeline. And if the env is not set, the test should be skipped.
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