diff --git a/packages/components/nodes/agentflow/ConditionAgent/ConditionAgent.ts b/packages/components/nodes/agentflow/ConditionAgent/ConditionAgent.ts index 6ec809f96..b23dd198f 100644 --- a/packages/components/nodes/agentflow/ConditionAgent/ConditionAgent.ts +++ b/packages/components/nodes/agentflow/ConditionAgent/ConditionAgent.ts @@ -27,7 +27,7 @@ class ConditionAgent_Agentflow implements INode { constructor() { this.label = 'Condition Agent' this.name = 'conditionAgentAgentflow' - this.version = 1.0 + this.version = 1.1 this.type = 'ConditionAgent' this.category = 'Agent Flows' this.description = `Utilize an agent to split flows based on dynamic conditions` @@ -80,6 +80,26 @@ class ConditionAgent_Agentflow implements INode { scenario: '' } ] + }, + { + label: 'Override System Prompt', + name: 'conditionAgentOverrideSystemPrompt', + type: 'boolean', + description: 'Override initial system prompt for Condition Agent', + optional: true + }, + { + label: 'Node System Prompt', + name: 'conditionAgentSystemPrompt', + type: 'string', + rows: 4, + optional: true, + acceptVariable: true, + default: CONDITION_AGENT_SYSTEM_PROMPT, + description: 'Expert use only. Modifying this can significantly alter agent behavior. Leave default if unsure', + show: { + conditionAgentOverrideSystemPrompt: true + } } /*{ label: 'Enable Memory', @@ -242,6 +262,12 @@ class ConditionAgent_Agentflow implements INode { const conditionAgentInput = nodeData.inputs?.conditionAgentInput as string let input = conditionAgentInput || question const conditionAgentInstructions = nodeData.inputs?.conditionAgentInstructions as string + const conditionAgentSystemPrompt = nodeData.inputs?.conditionAgentSystemPrompt as string + const conditionAgentOverrideSystemPrompt = nodeData.inputs?.conditionAgentOverrideSystemPrompt as boolean + let systemPrompt = CONDITION_AGENT_SYSTEM_PROMPT + if (conditionAgentSystemPrompt && conditionAgentOverrideSystemPrompt) { + systemPrompt = conditionAgentSystemPrompt + } // Extract memory and configuration options const enableMemory = nodeData.inputs?.conditionAgentEnableMemory as boolean @@ -277,31 +303,15 @@ class ConditionAgent_Agentflow implements INode { const messages: BaseMessageLike[] = [ { role: 'system', - content: CONDITION_AGENT_SYSTEM_PROMPT + content: systemPrompt }, { role: 'user', - content: `{"input": "Hello", "scenarios": ["user is asking about AI", "default"], "instruction": "Your task is to check and see if user is asking topic about AI"}` + content: `{"input": "Hello", "scenarios": ["user is asking about AI", "user is not asking about AI"], "instruction": "Your task is to check if the user is asking about AI."}` }, { role: 'assistant', - content: `\`\`\`json\n{"output": "default"}\n\`\`\`` - }, - { - role: 'user', - content: `{"input": "What is AIGC?", "scenarios": ["user is asking about AI", "default"], "instruction": "Your task is to check and see if user is asking topic about AI"}` - }, - { - role: 'assistant', - content: `\`\`\`json\n{"output": "user is asking about AI"}\n\`\`\`` - }, - { - role: 'user', - content: `{"input": "Can you explain deep learning?", "scenarios": ["user is interested in AI topics", "default"], "instruction": "Determine if the user is interested in learning about AI"}` - }, - { - role: 'assistant', - content: `\`\`\`json\n{"output": "user is interested in AI topics"}\n\`\`\`` + content: `\`\`\`json\n{"output": "user is not asking about AI"}\n\`\`\`` } ] // Use to store messages with image file references as we do not want to store the base64 data into database @@ -374,15 +384,19 @@ class ConditionAgent_Agentflow implements INode { ) } - let calledOutputName = 'default' + let calledOutputName: string try { const parsedResponse = this.parseJsonMarkdown(response.content as string) - if (!parsedResponse.output) { - throw new Error('Missing "output" key in response') + if (!parsedResponse.output || typeof parsedResponse.output !== 'string') { + throw new Error('LLM response is missing the "output" key or it is not a string.') } calledOutputName = parsedResponse.output } catch (error) { - console.warn(`Failed to parse LLM response: ${error}. Using default output.`) + throw new Error( + `Failed to parse a valid scenario from the LLM's response. Please check if the model is capable of following JSON output instructions. Raw LLM Response: "${ + response.content as string + }"` + ) } // Clean up empty inputs diff --git a/packages/components/nodes/agentflow/prompt.ts b/packages/components/nodes/agentflow/prompt.ts index a5d9cd893..bb68b79c2 100644 --- a/packages/components/nodes/agentflow/prompt.ts +++ b/packages/components/nodes/agentflow/prompt.ts @@ -39,37 +39,45 @@ export const DEFAULT_HUMAN_INPUT_DESCRIPTION_HTML = `

Summarize the conversati ` -export const CONDITION_AGENT_SYSTEM_PROMPT = `You are part of a multi-agent system designed to make agent coordination and execution easy. Your task is to analyze the given input and select one matching scenario from a provided set of scenarios. If none of the scenarios match the input, you should return "default." +export const CONDITION_AGENT_SYSTEM_PROMPT = ` +

You are part of a multi-agent system designed to make agent coordination and execution easy. Your task is to analyze the given input and select one matching scenario from a provided set of scenarios.

-- **Input**: A string representing the user's query or message. -- **Scenarios**: A list of predefined scenarios that relate to the input. -- **Instruction**: Determine if the input fits any of the scenarios. + -## Steps +

Steps

+
    +
  1. Read the input string and the list of scenarios.
    2. +
  2. Analyze the content of the input to identify its main topic or intention.
    3. +
  3. Compare the input with each scenario: Evaluate how well the input's topic or intention aligns with each of the provided scenarios and select the one that is the best fit.
    4. +
  4. Output the result: Return the selected scenario in the specified JSON format.
    5. +
-1. **Read the input string** and the list of scenarios. -2. **Analyze the content of the input** to identify its main topic or intention. -3. **Compare the input with each scenario**: - - If a scenario matches the main topic of the input, select that scenario. - - If no scenarios match, prepare to output "\`\`\`json\n{"output": "default"}\`\`\`" -4. **Output the result**: If a match is found, return the corresponding scenario in JSON; otherwise, return "\`\`\`json\n{"output": "default"}\`\`\`" +

Output Format

+

Output should be a JSON object that names the selected scenario, like this: {"output": ""}. No explanation is needed.

-## Output Format +

Examples

+
    +
  1. +

    Input: {"input": "Hello", "scenarios": ["user is asking about AI", "user is not asking about AI"], "instruction": "Your task is to check if the user is asking about AI."}

    +

    Output: {"output": "user is not asking about AI"}

    +
    2. +
  2. +

    Input: {"input": "What is AIGC?", "scenarios": ["user is asking about AI", "user is asking about the weather"], "instruction": "Your task is to check and see if the user is asking a topic about AI."}

    +

    Output: {"output": "user is asking about AI"}

    +
    3. +
  3. +

    Input: {"input": "Can you explain deep learning?", "scenarios": ["user is interested in AI topics", "user wants to order food"], "instruction": "Determine if the user is interested in learning about AI."}

    +

    Output: {"output": "user is interested in AI topics"}

    +
    4. +
-Output should be a JSON object that either names the matching scenario or returns "\`\`\`json\n{"output": "default"}\`\`\`" if no scenarios match. No explanation is needed. - -## Examples - -1. **Input**: {"input": "Hello", "scenarios": ["user is asking about AI", "default"], "instruction": "Your task is to check and see if user is asking topic about AI"} - **Output**: "\`\`\`json\n{"output": "default"}\`\`\`" - -2. **Input**: {"input": "What is AIGC?", "scenarios": ["user is asking about AI", "default"], "instruction": "Your task is to check and see if user is asking topic about AI"} - **Output**: "\`\`\`json\n{"output": "user is asking about AI"}\`\`\`" - -3. **Input**: {"input": "Can you explain deep learning?", "scenarios": ["user is interested in AI topics", "default"], "instruction": "Determine if the user is interested in learning about AI"} - **Output**: "\`\`\`json\n{"output": "user is interested in AI topics"}\`\`\`" - -## Note -- Ensure that the input scenarios align well with potential user queries for accurate matching -- DO NOT include anything other than the JSON in your response. +

Note

+ `