Explore the core features and advantages of the DMflow.chat platform. This article delves into its memory templates, scenario management, graphical flow design, and resource calling mechanisms, assisting developers and enterprises in building intelligent chatbots with precise responses and natural interactions through intuitive operations.
Introduction: A Conversational Design Tool That is Both Intuitive and Powerful
In the process of building chatbots, many people are often deterred by complex code and tedious logic. The emergence of DMflow.chat is precisely to solve this pain point. This is a powerful platform born for creating intelligent conversations. It is not just a tool, but more like a design partner who understands you.
Through an intuitive graphical interface, users can easily create personalized conversation experiences like stacking blocks. Whether it is a simple Q&A or complex logic involving multiple system integrations, DMflow can provide sufficient flexibility and support. Its core advantage lies in lowering the technical threshold while retaining extremely high flexibility, allowing focus to return to “conversation design” itself, rather than being hijacked by technical details.
Memory Templates: Giving the Bot a “Brain”
An excellent chatbot must have memory capabilities. If the bot cannot remember the user’s name, preferences, or what was just said, the conversation will appear stiff and disjointed. DMflow.chat ensures that the bot can call the correct information in different scenarios through a refined “Memory Template” design, which divides data storage into four dimensions.
1. User Parameters
This is equivalent to the bot’s “long-term memory” of this user. You can use it to store the user’s basic data, such as name, membership level, accumulated points, etc.
- Supported Types: Text, Number, Time (seconds).
- Application Scenario: When the user returns next time, the bot still remembers their name or the product category they purchased last time.
2. User Tags
The tag function is a sharp tool for precise marketing. This is like putting a sticky note on the user, marking their interests or behavioral characteristics.
- Main Use: Segmented broadcasting.
- Application Scenario: If a user frequently asks about “beauty products”, the system automatically tags them as a “Beauty Lover”. Subsequent marketing campaigns can send specific offers to this tag group, significantly improving conversion rates.
3. Bot Parameters
This belongs to global settings, similar to Environment Variables in programming. These parameters are the same for all users and do not vary from person to person.
- Main Use: Storing fixed resource keys (API Keys), system setting values, or unified welcome messages.
- Advantage: When you need to change an API key, you only need to modify it here, without adjusting every conversation node one by one.
4. Session Storage
This is the bot’s “short-term memory”, used to handle the currently ongoing conversation context.
- Supported Types: Text, Number, Time (seconds), List.
- Lifecycle Mechanism: This is the most interesting part of this feature. Every time a chat is opened, the system creates a Session that lasts for 30 minutes.
- Auto Extension: As long as the conversation continues, this 30-minute countdown will be reset continuously.
- Auto Destruction: Once the conversation ends and is idle for more than 30 minutes, these temporary data will disappear automatically. This ensures the effective use of system resources and prevents expired conversation logic from interfering with the next interaction.
Scenario Management: Diverse Conversation Stages
Real conversations often happen in different contexts. DMflow.chat supports “Multi-Scenario” design, which means you can design different “rooms” or “modes” for the bot.
This is particularly important in Messenger’s OPEN_THREAD function, or when web-embedded bots need to perform page jumps. Through scenario settings, developers can specify that after a user clicks a specific link, the conversation starts directly from a specific node, rather than starting from scratch every time. This makes the user experience much smoother, as if the bot really knows where the user just came from.
Domain Call: Connecting Different Bot Intelligences
In large projects, we might split functions into multiple different bots (domains), and then have a main bot coordinate them. This is like different departments in a company, where the operator is responsible for transferring calls to professionals.
But when performing a domain call, there is a key rule that must be followed: The called domain must be connected first and published as “Production Version (Prod)”. If you try to call a domain that is still in development or unpublished, the system will report an error.
Common Error Code Analysis
Understanding error codes helps to quickly troubleshoot problems:
- 10001: Domain not found. This is usually because the domain has been deleted, or an incorrect ID was entered in the settings.
- 10002: Domain is closed. Please check the status switch of that domain.
- 11000: Unknown error. This usually involves system-level anomalies; it is recommended to contact platform technical support.
Resource Call: Bridge to the Outside World
If a bot is not connected to the internet, its capabilities are ultimately limited. DMflow.chat allows calling external HTTP requests (APIs) directly through “Resource Call” nodes, allowing the bot to query weather, check order status, or connect to third-party CRM systems.
Extremely Important Limitation: The maximum connection time set by the system is only 10 seconds. This is a hard metric. This means your external API must respond fast enough. If your API involves complex calculations or database queries and does not respond within 10 seconds, the bot will determine that the connection has timed out. Therefore, when designing APIs, be sure to prioritize performance optimization and avoid using them for tasks that require long processing times.
Graphical Flow Design: Five Major Nodes Analysis
The soul of DMflow lies in its visual flow editor. Here we introduce the five key nodes that make up the conversation flow:
- Resource Node: Specifically used to execute the aforementioned HTTP requests to obtain data from outside.
- Reply Node: The most basic node, used to send text, images, or card messages to the user.
- Variable Node: Responsible for writing and processing data. Whether updating user tags or recording the phone number just entered into the Session, it is done through this node.
- Scenario Node: This is used to modify the bot’s status.
- Call Node: Used to trigger other services or domains, achieving cross-bot collaboration.
Version Release and Management
In software development, the separation of “Test” and “Production” environments is crucial. DMflow.chat has a built-in comprehensive version control mechanism.
Each bot has two independent branches: dev (Development) and prod (Production).
- Dev Branch: Your sandbox. Here you can try out new conversation logic and modify nodes to your heart’s content, without worrying about affecting real online users.
- Prod Branch: Stable online environment. After testing in the development version confirms there are no errors, push the version to the production version.
In addition, the platform supports multi-version selection. If a problem is found after a new version goes online, it can be quickly rolled back to the old version, providing extremely high security and fault tolerance for enterprise-level applications.
Frequently Asked Questions (FAQ)
Q1: Why does my “Domain Call” keep showing an error and cannot connect successfully? This is usually because the called domain has not been published. Please ensure that the target bot you want to call has completed the connection in “Domain Settings” and the status of that domain has been published as “Production Version (Prod)”. If it is still in the development version or has been closed, the system will return a 10001 or 10002 error code.
Q2: How long will the data in Session Storage be kept? The default survival time for a Session is 30 minutes. This is a rolling mechanism; as long as the user continues to interact with the bot within 30 minutes, the time will be recalculated. Data will only be cleared when the conversation ends and has been idle for 30 minutes.
Q3: Can I handle large file uploads or complex calculations through Resource Call nodes? Not recommended. The HTTP request for Resource Call has a strict 10-second timeout limit. Any request that does not complete the response within 10 seconds will be forcibly interrupted. It is recommended to use this feature for lightweight data queries or command sending.
Q4: What is the difference between User Tags and User Parameters? The two have different purposes. “User Parameters” are suitable for storing specific data values (e.g., John Doe, phone number, remaining points 500); while “User Tags” are used for classification and grouping (e.g., VIP customer, interested in sports), mainly used for subsequent precise broadcasting and marketing campaigns.
