Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
Data for this category comes from national public resource trading platforms, local public resource trading platforms, engineering approval systems, and bidding and tendering supervision systems. The update frequency is daily. Documents processed daily include bidding announcements, pre-qualification documents, bidding documents, tender documents, bid evaluation reports, successful bid candidate announcements, and successful bid result announcements. Core fields of the data structure include project number, section number, qualification requirements, bid quotation, bid evaluation expert records, and successful bid information. Each document is linked to its corresponding project entity, and there is no unified cross-document global primary key.
What constraints these characteristics impose on model access and configuration
Differences in document formats across multiple platforms require model access to support non-standard PDF, Word, and structured table data. Corresponding parsing adaptation rules must be configured. Massive daily updated project data requires that the context window for model calls must support long text processing, to avoid truncation of core section and qualification information. Core fields include professional content such as qualifications, performance, and bills of quantities. This requires the model to accurately match fields and business rules. It also requires limiting the randomness of generated content to avoid deviations from bidding and tendering requirements. In addition, file review requires tracing back to original materials. This requires model calls to retain original text citation links to facilitate subsequent verification and accountability.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple documents spliced for a single project, preventing core section and qualification information from being truncated |
maxTokens | 2000–3000 token | Controls the output length of single-round generated content, adapting to the structured output requirements of file review |
quoteMaxToken | 1500–2000 token | Retains sufficient original text citation fragments to meet the traceability requirements of file review |
temperature | 0.1–0.3 | Reduces the randomness of generated content, ensuring the accuracy of professional content such as qualifications and bills of quantities |
AIPROXY_API_ENDPOINT | Fill in according to the access address provided by the platform | Interface address for connecting third-party models, ensuring normal model call links |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Completes identity verification for model calls, preventing unauthorized access |
Three common errors
- A third-party model passes tests on a transit platform, but throws an error when tested after creating a model in FastGPT. The cause is failure to correctly configure the corresponding relationship between
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, or a mismatch between the interface format of the transit platform and FastGPT's access requirements. - The model used for knowledge base query is inconsistent with the model used when uploading files, leading to abnormal query results. The cause is failure to clearly distinguish model parameters for document embedding and query in the configuration, and failure to bind corresponding models according to business needs.
- The TTS model test prompts
'KokoroModel' object has no attribute 'mode'. The cause is that FastGPT has not configured exclusive call adaptation rules for non-text generation models, and has mistakenly applied the parameter logic of text models to the TTS model.
How to confirm the configuration is correct
- Call the model test interface, input core fragments of a single bidding document, and check whether the returned results include correct section and qualification information, confirming that the context and output length configurations do not trigger content truncation.
- View the model call log, check that the values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the parameters provided by the platform, confirming that identity verification has passed. - Upload a test tender document, trigger the document parsing and embedding process, check whether the embedded text retains the core fields of the original text, confirming that the original text citation configuration meets traceability requirements.
- Adjust the
temperatureparameter and compare the generated results, confirming that the randomness of the generated content meets business requirements, with no obvious professional content deviations.
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.