What the data for this category looks like
The data for this category primarily comes from work order systems, customer interview records, CRM systems, and product roadmaps. Updates occur weekly or when each product version is released. Daily processed documents include demand pool lists, demand assessment forms, customer feedback records, and version planning descriptions. Fields typically include demand number, customer identifier, demand description, priority, assessment status, and scheduling node. No additional custom units are used. Most data is structured text, with some unstructured content from original customer voice transcriptions.
Constraints Imposed on Knowledge Base Retrieval and Recall
Dispersed data sources require cross-system field association during retrieval. Unstable update frequencies may cause the knowledge base index to lag behind business changes. Structured fields such as priority and scheduling node in demand pool lists require precise matching. Ambiguous recall must be avoided for unstructured customer feedback descriptions. A single retrieval must cover multiple document types, so the number of recalled entries must be limited to avoid context overload. Additionally, recall results for demand-related data must strictly match the business scenario, and irrelevant content that affects demand assessment and scheduling decisions must not be introduced.
Configuration Settings
similarity | 0.75–0.85 | Semantic similarity matching customer demand descriptions; filters low-related historical demand records |
|---|---|---|
limit | Top 8 entries | Controls the total token count of recall results, adapting to the average length of demand pool documents |
searchMode | hybrid | Retrieves structured fields and unstructured text simultaneously, covering multiple data types in the demand pool |
usingReRank | Enabled | Optimizes the ranking of recall results, prioritizing high-priority demands |
datasetSearchUsingExtensionQuery | Disabled | Prevents generation of additional queries that cause recall to deviate from original demand content |
collectionIds | Demand pool-specific collection ID | Isolates the demand pool knowledge base from other business data, preventing cross-category recall errors |
Three Common Mistakes
- Retrieval results directly enter the question classification node, creating an infinite loop. The cause is that a branch jump to skip classification is not configured after AI dialogue output, causing the workflow to repeatedly execute classification logic.
- An error occurs when uploading PDF files larger than 2MB. The cause is that chunking parameters are not adjusted to adapt to long text, causing the text understanding model to fail to complete parsing.
- The knowledge base index remains in a running state with no progress. The cause is that the Docker deployment environment has not been allocated sufficient CPU resources, or the external connected model has an unstable network connection, causing the indexing task to block.
How to Verify Successful Configuration
- Upload a test demand assessment form document, retrieve core keywords, and verify that the similarity of recall results falls within the preset range.
- Trigger a workflow test, confirm that the AI dialogue output automatically jumps to subsequent links, and does not repeatedly enter the question classification node.
- View the Docker container resource monitoring panel, confirm that the indexing task does not show persistent abnormal memory or CPU usage.
- Test uploading documents of different sizes in FastGPT v4.17.0, confirm that no errors occur and the indexing progress completes normally.
Note: The configuration values listed above are common starting points. Always validate against your own sample data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.