What the data for this category looks like
Survey and design ledger data primarily comes from project survey reports, construction drawing design documents, on-site construction logs, phase acceptance forms, and similar materials. Data update frequency adjusts dynamically with project phases, with concentrated updates at milestones including preliminary design, construction drawing review, and completion acceptance.
The document structure includes two parts: structured fields and unstructured text. Structured fields include project ID, survey point coordinates, material specification and usage, project cost, and similar items, with professional units such as meters, square meters, cubic meters, and kilograms attached. Unstructured content includes survey comments, design change explanations, on-site problem records, and similar content.
What constraints these characteristics impose on model access and configuration
The mixed structure and professional attributes of survey and design ledgers require model access configuration to adapt to multi-type data processing needs.
Structured fields use professional units and exclusive terminology. Models must have accurate format parsing capabilities. Context length configuration must reserve sufficient space to hold complete ledger data.
Dynamic update cycles mean the model call context refresh mechanism must support non-fixed data loading. This prevents analysis deviations caused by data truncation.
Additionally, multi-source scattered document sources require API access configuration to support different data source routing paths. This ensures requests are correctly delivered to the target model.
How to Configure Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | A single-project survey and design ledger typically contains dozens of structured fields and several pages of unstructured records. This range reserves enough space for complete historical data and current query context |
maxTokens | 2000–3000 token | Ledger summary and report generation output requires complete field statistics and analysis conclusions. This range reserves sufficient generation space |
temperature | 0.2–0.4 | This range ensures consistency of professional terminology and data formats, and avoids generating incorrect units or field values |
AIPROXY_API_ENDPOINT | Fill in the address of the deployed model service provider | Adapts access paths for different third-party LLMs, and ensures requests are correctly routed to the target model |
AIPROXY_API_TOKEN | Fill in the valid key provided by the service provider | Completes identity verification for model access, and prevents requests from being intercepted or rejected |
toolChoice | auto | Allows the model to automatically select whether to call tools to process structured ledger data based on query requirements |
Three Common Configuration Mistakes
- After configuring an LLM model mounted via Ollama, returned results are unrelated to the survey and design ledger knowledge base. The cause is incorrect configuration of the
maxContextparameter. This prevents the model from loading complete ledger data, so it only generates content based on general knowledge. - Attempting to access an Ollama-deployed model via the OneAPI channel fails. The cause is incorrect filling of
AIPROXY_API_ENDPOINTwith the Ollama native local address instead of the OneAPI transit address. - Unable to find the corresponding configuration entry when adding rerank functionality. The cause is failing to look for the option in the advanced settings area of the model configuration page, or not confirming that the current FastGPT version supports this functionality.
How to Verify Correct Configuration
- Initiate a query targeting a single survey and design ledger. Verify that the returned results include correct field names and units.
- Test the connected model. Check that the returned response status code is 200, and that there are no identity verification related errors.
- Adjust the
temperatureparameter value. Observe whether the professional terminology consistency of the generated content meets expectations. - Submit a ledger summary request containing multiple fields. Verify that the generated report covers all specified statistical dimensions.
The values listed above are common starting points. They should be evaluated 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.