Building a context-aware chatbot requires the following key steps:
- Infrastructure setup: Create a new Flow in the Studio workspace, dragging in the input node (receiving user messages), LLM node (e.g., GPT-4), and output node in that order. Establish a basic dialog flow by connecting the lines.
- contextual integration::
- Add Weaviate vector database node, upload product manuals and other knowledge documents to form long-term memory
- Configuring the Conversation History Node to Save Short-Term Session Contexts
- Connect to CRM and other business systems for real-time data via Integration nodes.
- parameter tuningThe LLM node sets the temperature value (0.3-0.7 is the recommended range), turns on the "Context Window" option to control the length of the history message retention, and adjusts the prompt template to direct the model to the key contexts.
- test and verify: Use test cases that include references and follow-up questions (e.g., "What color does the iPhone 14 come in?"). → "Which is the best seller?"). , checking whether the system can maintain dialog coherence.
Advanced tips include setting up conversation status tracking nodes, configuring multi-round fill-in-the-blank templates, etc., all of which are exemplified in the platform's pre-built customer service bot templates.
This answer comes from the articleLamatic.ai: a hosted platform for rapidly building and deploying AI intelligencesThe
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