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fix(backend): Append prompt into the conversations output & Remove unused output pin on SmartDecisionBlock (#9550)
### Changes 🏗️ Append prompt into the conversations output & Remove unused output pin on SmartDecisionBlock. ### Checklist 📋 #### For code changes: - [ ] I have clearly listed my changes in the PR description - [ ] I have made a test plan - [ ] I have tested my changes according to the test plan: <!-- Put your test plan here: --> - [ ] ... <details> <summary>Example test plan</summary> - [ ] Create from scratch and execute an agent with at least 3 blocks - [ ] Import an agent from file upload, and confirm it executes correctly - [ ] Upload agent to marketplace - [ ] Import an agent from marketplace and confirm it executes correctly - [ ] Edit an agent from monitor, and confirm it executes correctly </details> #### For configuration changes: - [ ] `.env.example` is updated or already compatible with my changes - [ ] `docker-compose.yml` is updated or already compatible with my changes - [ ] I have included a list of my configuration changes in the PR description (under **Changes**) <details> <summary>Examples of configuration changes</summary> - Changing ports - Adding new services that need to communicate with each other - Secrets or environment variable changes - New or infrastructure changes such as databases </details>
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@@ -79,12 +79,7 @@ class SmartDecisionMakerBlock(Block):
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)
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class Output(BlockSchema):
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prompt: str = SchemaField(description="The prompt sent to the language model.")
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error: str = SchemaField(description="Error message if the API call failed.")
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function_signatures: list[dict[str, Any]] = SchemaField(
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description="The function signatures that are sent to the language model."
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)
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tools: Any = SchemaField(description="The tools that are available to use.")
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finished: str = SchemaField(
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description="The finished message to display to the user."
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@@ -331,6 +326,9 @@ class SmartDecisionMakerBlock(Block):
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for c in response.tool_calls
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)
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input_data.conversation_history.append(
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llm.Message(role=llm.MessageRole.USER, content=response.prompt)
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)
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input_data.conversation_history.append(
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llm.Message(role=llm.MessageRole.ASSISTANT, content=assistant_response)
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)
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