This is an automated email from the ASF dual-hosted git repository. xtsong pushed a commit to branch main in repository https://gitbox.apache.org/repos/asf/flink-agents.git
commit bbf61d010e52e9c21a9b132897832607474c6e47 Author: WenjinXie <[email protected]> AuthorDate: Thu Jan 15 19:44:44 2026 +0800 [doc] Update documents after using constant in resource descriptor. --- docs/content/docs/development/chat_models.md | 32 +++++++-------- docs/content/docs/development/embedding_models.md | 12 +++--- docs/content/docs/development/react_agent.md | 4 +- docs/content/docs/development/vector_stores.md | 32 +++++++-------- docs/content/docs/development/workflow_agent.md | 4 +- .../docs/get-started/quickstart/react_agent.md | 45 +++++++++++----------- .../docs/get-started/quickstart/workflow_agent.md | 6 +-- 7 files changed, 68 insertions(+), 67 deletions(-) diff --git a/docs/content/docs/development/chat_models.md b/docs/content/docs/development/chat_models.md index d9195aa5..d6659425 100644 --- a/docs/content/docs/development/chat_models.md +++ b/docs/content/docs/development/chat_models.md @@ -65,7 +65,7 @@ class MyAgent(Agent): @staticmethod def ollama_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OllamaChatModelConnection, + clazz=Constant.OLLAMA_CHAT_MODEL_CONNECTION, base_url="http://localhost:11434";, request_timeout=30.0 ) @@ -74,7 +74,7 @@ class MyAgent(Agent): @staticmethod def ollama_chat_model() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OllamaChatModelSetup, + clazz=Constant.OLLAMA_CHAT_MODEL_SETUP, connection="ollama_connection", model="qwen3:8b", temperature=0.7 @@ -106,14 +106,14 @@ class MyAgent(Agent): public class MyAgent extends Agent { @ChatModelConnection public static ResourceDescriptor ollamaConnection() { - return ResourceDescriptor.Builder.newBuilder(OllamaChatModelConnection.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_CONNECTION) .addInitialArgument("endpoint", "http://localhost:11434";) .build(); } @ChatModelSetup public static ResourceDescriptor ollamaChatModel() { - return ResourceDescriptor.Builder.newBuilder(OllamaChatModelSetup.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_SETUP) .addInitialArgument("connection", "ollamaConnection") .addInitialArgument("model", "qwen3:8b") .build(); @@ -196,7 +196,7 @@ Azure AI is only supported in Java currently. public class MyAgent extends Agent { @ChatModelConnection public static ResourceDescriptor azureAIConnection() { - return ResourceDescriptor.Builder.newBuilder(AzureAIChatModelConnection.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.AZURE_CHAT_MODEL_CONNECTION) .addInitialArgument("endpoint", "https://your-resource.inference.ai.azure.com";) .addInitialArgument("apiKey", "your-api-key-here") .build(); @@ -204,7 +204,7 @@ public class MyAgent extends Agent { @ChatModelSetup public static ResourceDescriptor azureAIChatModel() { - return ResourceDescriptor.Builder.newBuilder(AzureAIChatModelSetup.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.AZURE_CHAT_MODEL_SETUP) .addInitialArgument("connection", "azureAIConnection") .addInitialArgument("model", "gpt-4o") .build(); @@ -272,7 +272,7 @@ class MyAgent(Agent): @staticmethod def anthropic_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=AnthropicChatModelConnection, + clazz=Constant.ANTHROPIC_CHAT_MODEL_CONNECTION, api_key="your-api-key-here", # Or set ANTHROPIC_API_KEY env var max_retries=3, timeout=60.0 @@ -282,7 +282,7 @@ class MyAgent(Agent): @staticmethod def anthropic_chat_model() -> ResourceDescriptor: return ResourceDescriptor( - clazz=AnthropicChatModelSetup, + clazz=Constant.ANTHROPIC_CHAT_MODEL_SETUP, connection="anthropic_connection", model="claude-sonnet-4-20250514", max_tokens=2048, @@ -383,7 +383,7 @@ class MyAgent(Agent): @staticmethod def ollama_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OllamaChatModelConnection, + clazz=Constant.OLLAMA_CHAT_MODEL_CONNECTION, base_url="http://localhost:11434";, request_timeout=120.0 ) @@ -392,7 +392,7 @@ class MyAgent(Agent): @staticmethod def my_chat_model() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OllamaChatModelSetup, + clazz=Constant.OLLAMA_CHAT_MODEL_CONNECTION, connection="ollama_connection", model="qwen3:8b", temperature=0.7, @@ -410,7 +410,7 @@ class MyAgent(Agent): public class MyAgent extends Agent { @ChatModelConnection public static ResourceDescriptor ollamaConnection() { - return ResourceDescriptor.Builder.newBuilder(OllamaChatModelConnection.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_CONNECTION) .addInitialArgument("endpoint", "http://localhost:11434";) .addInitialArgument("requestTimeout", 120) .build(); @@ -418,7 +418,7 @@ public class MyAgent extends Agent { @ChatModelSetup public static ResourceDescriptor ollamaChatModel() { - return ResourceDescriptor.Builder.newBuilder(OllamaChatModelSetup.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_SETUP) .addInitialArgument("connection", "ollamaConnection") .addInitialArgument("model", "qwen3:8b") .build(); @@ -495,7 +495,7 @@ class MyAgent(Agent): @staticmethod def openai_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIChatModelConnection, + clazz=Constant.OPENAI_CHAT_MODEL_CONNECTION, api_key="your-api-key-here", # Or set OPENAI_API_KEY env var api_base_url="https://api.openai.com/v1";, max_retries=3, @@ -506,7 +506,7 @@ class MyAgent(Agent): @staticmethod def openai_chat_model() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIChatModelSetup, + clazz=Constant.OPENAI_CHAT_MODEL_SETUP, connection="openai_connection", model="gpt-4", temperature=0.7, @@ -569,7 +569,7 @@ class MyAgent(Agent): @staticmethod def tongyi_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=TongyiChatModelConnection, + clazz=Constant.TONGYI_CHAT_MODEL_CONNECTION, api_key="your-api-key-here", # Or set DASHSCOPE_API_KEY env var request_timeout=60.0 ) @@ -578,7 +578,7 @@ class MyAgent(Agent): @staticmethod def tongyi_chat_model() -> ResourceDescriptor: return ResourceDescriptor( - clazz=TongyiChatModelSetup, + clazz=Constant.TONGYI_CHAT_MODEL_SETUP, connection="tongyi_connection", model="qwen-plus", temperature=0.7, diff --git a/docs/content/docs/development/embedding_models.md b/docs/content/docs/development/embedding_models.md index d24912c6..288d300d 100644 --- a/docs/content/docs/development/embedding_models.md +++ b/docs/content/docs/development/embedding_models.md @@ -69,7 +69,7 @@ class MyAgent(Agent): @staticmethod def openai_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelConnection, + clazz=Constant.OPENAI_EMBEDDING_MODEL_CONNECTION, api_key="your-api-key-here", base_url="https://api.openai.com/v1";, request_timeout=30.0 @@ -79,7 +79,7 @@ class MyAgent(Agent): @staticmethod def openai_embedding() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelSetup, + clazz=Constant.OPENAI_EMBEDDING_MODEL_SETUP, connection="openai_connection", model="your-embedding-model-here" ) @@ -136,7 +136,7 @@ class MyAgent(Agent): @staticmethod def ollama_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OllamaEmbeddingModelConnection, + clazz=Constant.OLLAMA_EMBEDDING_MODEL_CONNECTION, base_url="http://localhost:11434";, request_timeout=30.0 ) @@ -145,7 +145,7 @@ class MyAgent(Agent): @staticmethod def ollama_embedding() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OllamaEmbeddingModelSetup, + clazz=Constant.OLLAMA_EMBEDDING_MODEL_SETUP, connection="ollama_connection", model="nomic-embed-text", truncate=True, @@ -185,7 +185,7 @@ class MyAgent(Agent): @staticmethod def openai_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelConnection, + clazz=Constant.OPENAI_EMBEDDING_MODEL_CONNECTION, api_key="your-api-key-here", base_url="https://api.openai.com/v1";, request_timeout=30.0, @@ -196,7 +196,7 @@ class MyAgent(Agent): @staticmethod def openai_embedding() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelSetup, + clazz=Constant.OPENAI_CHAT_MODEL_SETUP, connection="openai_connection", model="your-embedding-model-here", encoding_format="float" diff --git a/docs/content/docs/development/react_agent.md b/docs/content/docs/development/react_agent.md index a8de5072..c1dbfa5b 100644 --- a/docs/content/docs/development/react_agent.md +++ b/docs/content/docs/development/react_agent.md @@ -65,7 +65,7 @@ We use `ResourceDescriptor` to describe the chat model, includes chat model type {{< tab "Python" >}} ```python chat_model_descriptor = ResourceDescriptor( - clazz=OllamaChatModelSetup, + clazz=Constant.OLLAMA_CHAT_MODEL_SETUP, connection="my_ollama_connection", model="qwen3:8b", tools=["my_tool1, my_tool2"], @@ -76,7 +76,7 @@ chat_model_descriptor = ResourceDescriptor( {{< tab "Java" >}} ```java ResourceDescriptor chatModelDescriptor = - ResourceDescriptor.Builder.newBuilder(OllamaChatModelSetup.class.getName()) + ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_SETUP) .addInitialArgument("connection", "myOllamaConnection") .addInitialArgument("model", "qwen3:8b") .addInitialArgument( diff --git a/docs/content/docs/development/vector_stores.md b/docs/content/docs/development/vector_stores.md index a86feeef..e628108d 100644 --- a/docs/content/docs/development/vector_stores.md +++ b/docs/content/docs/development/vector_stores.md @@ -147,7 +147,7 @@ class MyAgent(Agent): @staticmethod def openai_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelConnection, + clazz=Constant.OPENAI_EMBEDDING_MODEL_CONNECTION, api_key="your-api-key-here" ) @@ -155,7 +155,7 @@ class MyAgent(Agent): @staticmethod def openai_embedding() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelSetup, + clazz=Constant.OPENAI_EMBEDDING_MODEL_SETUP, connection="openai_connection", model="your-embedding-model-here" ) @@ -165,7 +165,7 @@ class MyAgent(Agent): @staticmethod def chroma_store() -> ResourceDescriptor: return ResourceDescriptor( - clazz=ChromaVectorStore, + clazz=Constant.CHROMA_VECTOR_STORE, embedding_model="openai_embedding", collection="my_chroma_store" ) @@ -199,22 +199,22 @@ public class MyAgent extends Agent { @EmbeddingModelConnection public static ResourceDescriptor embeddingConnection() { - return ResourceDescriptor.Builder.newBuilder(OpenAIEmbeddingModelConnection.class.getName()) - .addInitialArgument("api_key", "your-api-key-here") + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_EMBEDDING_MODEL_CONNECTION) + .addInitialArgument("host", "http://localhost:11434";) .build(); } @EmbeddingModelSetup public static ResourceDescriptor embeddingModel() { - return ResourceDescriptor.Builder.newBuilder(OpenAIEmbeddingModelSetup.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_EMBEDDING_MODEL_SETUP) .addInitialArgument("connection", "embeddingConnection") - .addInitialArgument("model", "text-embedding-3-small") + .addInitialArgument("model", "nomic-embed-text") .build(); } @VectorStore public static ResourceDescriptor vectorStore() { - return ResourceDescriptor.Builder.newBuilder(ElasticsearchVectorStore.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.ELASTICSEARCH_VECTOR_STORE) .addInitialArgument("embedding_model", "embeddingModel") .addInitialArgument("host", "http://localhost:9200";) .addInitialArgument("index", "my_documents") @@ -293,7 +293,7 @@ class MyAgent(Agent): @staticmethod def openai_connection() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelConnection, + clazz=Constant.OPENAI_EMBEDDING_MODEL_CONNECTION, api_key="your-api-key-here" ) @@ -301,7 +301,7 @@ class MyAgent(Agent): @staticmethod def openai_embedding() -> ResourceDescriptor: return ResourceDescriptor( - clazz=OpenAIEmbeddingModelSetup, + clazz=Constant.OPENAI_EMBEDDING_MODEL_SETUP, connection="openai_connection", model="your-embedding-model-here" ) @@ -311,7 +311,7 @@ class MyAgent(Agent): @staticmethod def chroma_store() -> ResourceDescriptor: return ResourceDescriptor( - clazz=ChromaVectorStore, + clazz=Constant.CHROMA_VECTOR_STORE, embedding_model="openai_embedding", persist_directory="/path/to/chroma/data", # For persistent storage collection="my_documents", @@ -338,7 +338,7 @@ ChromaDB supports multiple deployment modes: @staticmethod def chroma_store() -> ResourceDescriptor: return ResourceDescriptor( - clazz=ChromaVectorStore, + clazz=Constant.CHROMA_VECTOR_STORE, embedding_model="your_embedding_model", collection="my_documents" # No connection configuration needed for in-memory mode @@ -351,7 +351,7 @@ def chroma_store() -> ResourceDescriptor: @staticmethod def chroma_store() -> ResourceDescriptor: return ResourceDescriptor( - clazz=ChromaVectorStore, + clazz=Constant.CHROMA_VECTOR_STORE, embedding_model="your_embedding_model", persist_directory="/path/to/chroma/data", collection="my_documents" @@ -364,7 +364,7 @@ def chroma_store() -> ResourceDescriptor: @staticmethod def chroma_store() -> ResourceDescriptor: return ResourceDescriptor( - clazz=ChromaVectorStore, + clazz=Constant.CHROMA_VECTOR_STORE, embedding_model="your_embedding_model", host="your-chroma-server.com", port=8000, @@ -378,7 +378,7 @@ def chroma_store() -> ResourceDescriptor: @staticmethod def chroma_store() -> ResourceDescriptor: return ResourceDescriptor( - clazz=ChromaVectorStore, + clazz=Constant.CHROMA_VECTOR_STORE, embedding_model="your_embedding_model", api_key="your-chroma-cloud-api-key", collection="my_documents" @@ -428,7 +428,7 @@ Here's how to define an Elasticsearch vector store in your Java agent: ```java @VectorStore public static ResourceDescriptor vectorStore() { - return ResourceDescriptor.Builder.newBuilder(ElasticsearchVectorStore.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.ELASTICSEARCH_VECTOR_STORE) .addInitialArgument("embedding_model", "embeddingModel") .addInitialArgument("host", "http://localhost:9200";) .addInitialArgument("index", "my_documents") diff --git a/docs/content/docs/development/workflow_agent.md b/docs/content/docs/development/workflow_agent.md index 76431d0b..29e1c097 100644 --- a/docs/content/docs/development/workflow_agent.md +++ b/docs/content/docs/development/workflow_agent.md @@ -73,7 +73,7 @@ class ReviewAnalysisAgent(Agent): def review_analysis_model() -> ResourceDescriptor: """ChatModel which focus on review analysis.""" return ResourceDescriptor( - clazz=OllamaChatModelSetup, + clazz=Constant.OLLAMA_CHAT_MODEL_SETUP, connection="ollama_server", model="qwen3:8b", prompt="review_analysis_prompt", @@ -139,7 +139,7 @@ public class ReviewAnalysisAgent extends Agent { @ChatModelSetup public static ResourceDescriptor reviewAnalysisModel() { - return ResourceDescriptor.Builder.newBuilder(OllamaChatModelSetup.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_SETUP) .addInitialArgument("connection", "ollamaChatModelConnection") .addInitialArgument("model", "qwen3:8b") .addInitialArgument("prompt", "reviewAnalysisPrompt") diff --git a/docs/content/docs/get-started/quickstart/react_agent.md b/docs/content/docs/get-started/quickstart/react_agent.md index b58a5860..e3fa22e1 100644 --- a/docs/content/docs/get-started/quickstart/react_agent.md +++ b/docs/content/docs/get-started/quickstart/react_agent.md @@ -47,10 +47,11 @@ agents_env = AgentsExecutionEnvironment.get_execution_environment(env) # Add Ollama chat model connection and notify shipping manager tool to be used # by the Agent. agents_env.add_resource( - "ollama_server", - ResourceDescriptor(clazz=OllamaChatModelConnection, request_timeout=120), + "ollama_server", + ResourceType.CHAT_MODEL_CONNECTION, + ResourceDescriptor(clazz=Constant.OLLAMA_CHAT_MODEL_CONNECTION, request_timeout=120), ).add_resource( - "notify_shipping_manager", Tool.from_callable(notify_shipping_manager) + "notify_shipping_manager", ResourceType.TOOL, Tool.from_callable(notify_shipping_manager) ) ``` {{< /tab >}} @@ -88,14 +89,14 @@ Create the ReAct Agent instance, configure the chat model, prompt and the output {{< tab "Python" >}} ```python review_analysis_react_agent = ReActAgent( - chat_model=ResourceDescriptor( - clazz=OllamaChatModelSetup, - connection="ollama_server", - model="qwen3:8b", - tools=["notify_shipping_manager"], - ), - prompt=review_analysis_react_prompt, - output_schema=ProductReviewAnalysisRes, + chat_model=ResourceDescriptor( + clazz=Constant.OLLAMA_CHAT_MODEL_SETUP, + connection="ollama_server", + model="qwen3:8b", + tools=["notify_shipping_manager"], + ), + prompt=review_analysis_react_prompt, + output_schema=ProductReviewAnalysisRes, ) ``` {{< /tab >}} @@ -105,17 +106,17 @@ review_analysis_react_agent = ReActAgent( // Create ReAct agent. ReActAgent reviewAnalysisReactAgent = getReActAgent(); -private static ReActAgent getReActAgent() { - return new ReActAgent( - ResourceDescriptor.Builder.newBuilder(OllamaChatModelSetup.class.getName()) - .addInitialArgument("connection", "ollamaChatModelConnection") - .addInitialArgument("model", "qwen3:8b") - .addInitialArgument( - "tools", Collections.singletonList("notifyShippingManager")) - .build(), - reviewAnalysisReactPrompt(), - CustomTypesAndResources.ProductReviewAnalysisRes.class); -} + private static ReActAgent getReActAgent() { + return new ReActAgent( + ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_SETUP) + .addInitialArgument("connection", "ollamaChatModelConnection") + .addInitialArgument("model", "qwen3:8b") + .addInitialArgument( + "tools", Collections.singletonList("notifyShippingManager")) + .build(), + reviewAnalysisReactPrompt(), + CustomTypesAndResources.ProductReviewAnalysisRes.class); + } ``` {{< /tab >}} diff --git a/docs/content/docs/get-started/quickstart/workflow_agent.md b/docs/content/docs/get-started/quickstart/workflow_agent.md index f7c50005..43c3e3fc 100644 --- a/docs/content/docs/get-started/quickstart/workflow_agent.md +++ b/docs/content/docs/get-started/quickstart/workflow_agent.md @@ -119,7 +119,7 @@ class ReviewAnalysisAgent(Agent): def review_analysis_model() -> ResourceDescriptor: """ChatModel which focus on review analysis.""" return ResourceDescriptor( - clazz=OllamaChatModelSetup, + clazz=Constant.OLLAMA_CHAT_MODEL_SETUP, connection="ollama_server", model="qwen3:8b", prompt="review_analysis_prompt", @@ -185,12 +185,12 @@ public class ReviewAnalysisAgent extends Agent { @ChatModelSetup public static ResourceDescriptor reviewAnalysisModel() { - return ResourceDescriptor.Builder.newBuilder(OllamaChatModelSetup.class.getName()) + return ResourceDescriptor.Builder.newBuilder(Constant.OLLAMA_CHAT_MODEL_SETUP) .addInitialArgument("connection", "ollamaChatModelConnection") .addInitialArgument("model", "qwen3:8b") .addInitialArgument("prompt", "reviewAnalysisPrompt") .addInitialArgument("tools", Collections.singletonList("notifyShippingManager")) - .addInitialArgument("extract_reasoning", "true") + .addInitialArgument("extract_reasoning", true) .build(); }
