What the data for this category looks like
Typical supervision data comes from elderly care service information platforms, civil affairs statistics systems, fire inspection records, and institution operation ledgers. Data updates occur monthly. Daily documents include institution filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. Core fields include institution name, filing number, bed count, inspection date, rectification status, and other items. Units use standardized formats such as pieces, person-times, and dates.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
The monthly update cycle of data requires multi-turn dialogue to limit retrieval to the most recent 1-2 cycles, preventing outdated supervision data from being included. Mixed use of multiple document types requires prompt engineering to clearly define retrieval rules for different scenarios, such as filing, subsidies, and inspections. The need for standardized fields and cross-department data collaboration requires binding institution-specific parameters in multi-turn dialogue. This ensures retrieval results accurately match the current supervision target.
Configuration Settings
This document uses FastGPT v4.17.0. Specific parameters are listed below:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Elderly care supervision documents are mostly structured short paragraphs. This length fully covers the core information of a single filing or inspection record. |
| Overlap length | 100–150 characters | Monthly updated documents contain cross-block associated data. This overlap length connects associated fields across adjacent blocks. |
| Similarity threshold | 0.75–0.85 | Supervision data has a high level of standardization. This range filters irrelevant data while retaining valid associated records. |
| Single search limit | Top 3 entries | Supervision scenarios prioritize matching the latest monthly data. Excessive results increase the context load of multi-turn dialogue. |
variables configuration | Bind institution ID, inspection cycle | Multi-turn dialogue must limit data to the current supervision institution to avoid confusion across different institutions. |
| Maximum context window | 6000 tokens | Party building Q&A combines policy documents and institution monthly operation data. This window covers multiple core documents. |
Three Common Configuration Mistakes
- Symptom: Empty results return when calling
messagesduring multi-turn dialogue. Cause: The institution-specificvariablesparameter is not correctly bound. The retrieval scope covers all institution data, so it cannot match the target institution of the current dialogue. - Symptom: Uploaded supervision documents cannot be read in dialogue. Cause: Chunk length is set beyond the system processing limit. This causes document chunking failure and prevents valid vector indexes from being generated.
- Symptom: Vector index construction progress stalls. Cause: No reasonable single search limit is set. This leads to loading excessive redundant data during indexing, blocking the construction process.
How to Verify Successful Configuration
- Enter the filing number of the target supervision institution. Check if the dialogue returns only exclusive data for that institution.
- Upload a monthly service quality inspection form. Launch a multi-turn dialogue to ask about rectification status. Confirm the returned content includes core fields from the corresponding ledger.
- Adjust the similarity threshold. Compare changes in the number of retrieval results to confirm the threshold configuration takes effect.
- View the chat logs for
chatId. Confirm that the context of each interaction includes exclusive parameters for the current institution.
The values provided are common starting points and should be measured against the reader's 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 2026-09-28.