Data Characteristics in This Category
Data for retail chain clinical trial pre-screening primarily comes from customer health records, prescription records, OTC drug purchase history, health questionnaire feedback, and some wearable device data collected at their stores. This data typically exists in structured (e.g., medical record fields in databases, drug codes) and semi-structured (e.g., handwritten notes from doctors or pharmacists, health consultation records) formats. Data updates frequently; customer purchase behavior and health consultations generate new data in real-time or near real-time. Document structures vary, including standardized electronic prescriptions, non-standardized health consultation texts, and questionnaire results entered through store systems. Key fields include patient ID, diagnostic codes (e.g., ICD-10), generic drug names, dosages, purchase dates, and critical physiological indicators (e.g., blood pressure, blood glucose levels). Units follow common medical and pharmaceutical standards.
Constraints from These Characteristics on Citation and Traceability
Retail chain data characteristics impose specific requirements on citation and traceability. High-frequency updates of consumer behavior data necessitate ensuring data timeliness in citations to avoid pre-screening deviations from outdated information. Diverse document structures require FastGPT's knowledge base to effectively process various data source formats and accurately pinpoint original information. For example, the system must identify key symptom descriptions from unstructured health consultation text and use them as citation evidence. The specialized nature of fields and units, such as drug dosage units and physiological indicator ranges, requires the citation system to accurately understand and present them, preventing misjudgments due to unit confusion. Furthermore, citing large amounts of anonymized or de-identified consumer data requires ensuring no personal privacy leakage. The system must perform necessary de-identification when tracing back to original data or only cite de-identified summary information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters (characters) | Balances the average length of health consultation records in retail scenarios with retrieval efficiency, ensuring a single citation segment contains sufficient context. |
Recall count (Recall Count) | 8-12 entries (items) | Considering that consumer data may come from multiple sources and time points, increasing the recall quantity helps cover more comprehensive background information. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | User queries in retail scenarios may contain colloquialisms. A higher threshold helps filter out irrelevant fuzzy matches, improving accuracy. |
Rerank result count (Rerank Return Count) | 4-6 entries (items) | After initially screening a larger number of potentially relevant items, reranking selects the most direct and best-matching few items for final citation. |
maxContext | 3000-4000 characters (characters) | Ensures enough historical conversation context is included when processing multi-turn pre-screening dialogues, improving citation accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles large batches of health questionnaires or historical record files uploaded by stores, ensuring file parsing does not time out. |
Three Common Mistakes
- The response content is empty, but citation sources are provided. This typically occurs when the knowledge base retrieves relevant segments, but their content is too fragmented or lacks sufficient context, preventing the model from generating a meaningful answer while still outputting the original citation based on its mechanism.
- Citation sources cannot be opened or display "cannot be effective." This may stem from issues with password-free sharing link configurations or improper backend permission settings, preventing unauthorized users from accessing original documents.
- The citation mark is not displayed at the end of the answer paragraph. This usually results from a mismatch between the frontend rendering logic and the backend return data format, or in specific versions (e.g., 4.9.7), the display method of citation marks has been adjusted. Frontend component compatibility and configuration require checking.
How to Confirm Proper Configuration
- Perform end-to-end testing: Simulate consumer questions and observe if FastGPT's responses include accurate citation sources. Verify if clicking the source opens the original text and if the content matches the citation.
- Check log output: Review detailed logs for each query in the FastGPT backend. Confirm if configurations like
Recall count(Recall Count) andSimilarity threshold(Similarity Threshold) are effective as expected and record the correct citation document IDs. - Observe after batch data import: Import a batch of representative retail chain data (e.g., prescriptions, questionnaires in different formats). Observe FastGPT's indexing and retrieval performance for this data to validate the effectiveness of
Chunk size(Segment Length) and file parsing parameters. - Compare behavior across versions: After upgrading FastGPT (e.g., from 4.9.6 to 4.9.7), compare the display behavior of citation marks. Ensure new version functionality aligns with expectations and no unexpected regressions occur.
The values provided are common starting points and should be measured against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.