mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-22 09:01:09 +03:00
Henry Heng
7924fbce0d
* agent flow v2 * chat message background * conditon agent flow * add sticky note * update human input dynamic prompt * add HTTP node * add default tool icon * fix export duplicate agentflow v2 * add agentflow v2 marketplaces * refractor memoization, add iteration nodes * add agentflow v2 templates * add agentflow generator * add migration scripts for mysql, mariadb, posrgres and fix date filters for executions * update agentflow chat history config * fix get all flows error after deletion and rename * add previous nodes from parent node * update generator prompt * update run time state when using iteration nodes * prevent looping connection, prevent duplication of start node, add executeflow node, add nodes agentflow, chat history variable * update embed * convert form input to string * bump openai version * add react rewards * add prompt generator to prediction queue * add array schema to overrideconfig * UI touchup * update embedded chat version * fix node info dialog * update start node and loop default iteration * update UI fixes for agentflow v2 * fix async drop down * add export import to agentflowsv2, executions, fix UI bugs * add default empty object to flowlisttable * add ability to share trace link publicly, allow MCP tool use for Agent and Assistant * add runtime message length to variable, display conditions on UI * fix array validation * add ability to add knowledge from vector store and embeddings for agent * add agent tool require human input * add ephemeral memory to start node * update agent flow node to show vs and embeddings icons * feat: add import chat data functionality for AgentFlowV2 * feat: set chatMessage.executionId to null if not found in import JSON file or database * fix: MariaDB execution migration script to utf8mb4_unicode_520_ci --------- Co-authored-by: Ong Chung Yau <33013947+chungyau97@users.noreply.github.com> Co-authored-by: chungyau97 <chungyau97@gmail.com>
76 lines
4.6 KiB
TypeScript
76 lines
4.6 KiB
TypeScript
export const DEFAULT_SUMMARIZER_TEMPLATE = `Progressively summarize the conversation provided and return a new summary.
|
|
|
|
EXAMPLE:
|
|
Human: Why do you think artificial intelligence is a force for good?
|
|
AI: Because artificial intelligence will help humans reach their full potential.
|
|
|
|
New summary:
|
|
The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.
|
|
END OF EXAMPLE
|
|
|
|
Conversation:
|
|
{conversation}
|
|
|
|
New summary:`
|
|
|
|
export const DEFAULT_HUMAN_INPUT_DESCRIPTION = `Summarize the conversation between the user and the assistant, reiterate the last message from the assistant, and ask if user would like to proceed or if they have any feedback.
|
|
- Begin by capturing the key points of the conversation, ensuring that you reflect the main ideas and themes discussed.
|
|
- Then, clearly reproduce the last message sent by the assistant to maintain continuity. Make sure the whole message is reproduced.
|
|
- Finally, ask the user if they would like to proceed, or provide any feedback on the last assistant message
|
|
|
|
## Output Format The output should be structured in three parts in text:
|
|
|
|
- A summary of the conversation (1-3 sentences).
|
|
- The last assistant message (exactly as it appeared).
|
|
- Ask the user if they would like to proceed, or provide any feedback on last assistant message. No other explanation and elaboration is needed.
|
|
`
|
|
|
|
export const DEFAULT_HUMAN_INPUT_DESCRIPTION_HTML = `<p>Summarize the conversation between the user and the assistant, reiterate the last message from the assistant, and ask if user would like to proceed or if they have any feedback. </p>
|
|
<ul>
|
|
<li>Begin by capturing the key points of the conversation, ensuring that you reflect the main ideas and themes discussed.</li>
|
|
<li>Then, clearly reproduce the last message sent by the assistant to maintain continuity. Make sure the whole message is reproduced.</li>
|
|
<li>Finally, ask the user if they would like to proceed, or provide any feedback on the last assistant message</li>
|
|
</ul>
|
|
<h2 id="output-format-the-output-should-be-structured-in-three-parts-">Output Format The output should be structured in three parts in text:</h2>
|
|
<ul>
|
|
<li>A summary of the conversation (1-3 sentences).</li>
|
|
<li>The last assistant message (exactly as it appeared).</li>
|
|
<li>Ask the user if they would like to proceed, or provide any feedback on last assistant message. No other explanation and elaboration is needed.</li>
|
|
</ul>
|
|
`
|
|
|
|
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."
|
|
|
|
- **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
|
|
|
|
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 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.
|
|
`
|