Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Documents and materials for state-owned industrial funds mainly come from fund establishment plans, capital contribution agreements, filing registration documents, and annual operation reports in internal project libraries. Updates are triggered by project approval or clause revisions, with no fixed cycle. Most documents have fixed headers and structured fields. These fields include total subscribed fund scale (unit: 100 million yuan), term (unit: year), investor qualification requirements, return investment ratio, and more. The length of a single document typically ranges from 10 to 50 pages.
What constraints these characteristics impose on model integration and configuration
Structured fields and fixed document structures require model outputs to strictly match preset field formats, to avoid unit errors or missing fields. Long documents and multi-page content require the context window to accommodate full content, to prevent truncation of critical information. Frequently updated internal documents require knowledge base synchronization frequency to align with project progress, to ensure the timeliness of data used for model calls. Unique professional terms require the model to be bound to industry-specific prompts, to avoid confusion caused by general terminology.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000–15000 token | Documents for state-owned industrial funds include multi-page agreements and due diligence content, requiring full context to avoid truncation of critical clauses |
maxTokens | 3000–4000 token | A single drafted document must cover full clause content, controlling output length to comply with standard fund document formats |
quoteMaxToken | 2000–2500 token | Internal filing documents and regulatory requirement excerpts must be referenced, limiting reference content length to keep outputs focused |
toolChoice | auto | Automatically select whether to call knowledge base retrieval for internal compliance documents based on drafting needs |
temperature | 0.3–0.5 | Fund documents require strict standardization, reducing output randomness to ensure accurate clause wording |
AIPROXY_API_ENDPOINT | Fill in the large model access address configured for local deployment | Adapt to the privately deployed large model link, to comply with data localization requirements |
Three common mistakes
- Phenomenon: The model test pops up the prompt "This is an image model, why doesn't the image option work?" Cause: The
visionparameter was mistakenly set totrue, or a general large model without visual capabilities was integrated, causing irrelevant image options to appear in the interface. - Phenomenon: After restarting the Docker container and large model service, the model test prompts a connection timeout. Cause: The large model service was not started before the FastGPT container according to the startup sequence, resulting in a failed service link establishment.
- Phenomenon: Expired fund filing clauses appear in generated documents. Cause: The
quoteMaxTokenlimit for reference range was not configured, causing the model to call outdated knowledge base content.
How to confirm the configuration is complete
- Run the model test function in FastGPT, input the standard prompt "Draft a preliminary draft of an industrial fund establishment plan", and check whether the output includes preset structured fields.
- View the FastGPT model call logs, confirm that the values of
maxContextandmaxTokensin the request parameters match the configured values. - Restart the Docker container and large model service, then run the test again, verify that the connection status shows normal with no errors.
- Import a fragment of an internal fund filing document as test material, confirm that the model can correctly reference the clause content within it.
The values given 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.