Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Business data for state-owned capital operation companies comes from four main sources: state-owned assets supervision and reporting systems, internal business approval ledgers, property registration archives, and investment and financing project documents. Update frequency varies by module: Property transfer and investment and financing project data updates in real time with each process node. Salary calculation and performance evaluation data updates monthly or quarterly. Each business document is split by approval stage. It includes fields such as business ID, responsible entity, approval time limit, and required material list. All units comply with state-owned assets system standards, including working days, ten thousand yuan, and headcount.
Constraints on multi-turn dialogue and prompt engineering
The split of business data by approval module and standardized field requirements mean prompt engineering for multi-turn dialogue must be configured separately for each business scenario. This avoids cross-module confusion. Real-time updated business data requires dialogue context to sync with the latest process node status. Static cached data cannot be used. When a single document contains multiple fields, prompt engineering must explicitly name the fields to extract. This prevents large language models from missing key information. Business ID is the core identifier. It must be bound to the session identifier via chatId during multi-turn dialogue. The messages field must retain full content of every conversation to maintain context coherence. variables can pass dynamic business parameters such as project ID. This ensures accurate cross-stage process tracing.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Matches the paragraph length of business documents, avoids breaking approval process context logic when splitting |
| Single recall count | Top 3 entries | State-owned capital operation business documents have many fields. Too many recalls exceed context capacity. 3 entries cover core approval node information |
| Context window | 4000 token | Adapts to context length of multi-turn business dialogues, meets cross-stage process tracing needs |
| Streaming output toggle | Enabled | Improves interactive experience for long business process guidance, lets users receive step prompts in real time |
| Context memory duration | 3600 seconds | Matches average time spent on state-owned assets business processing, ensures full process context is retained during dialogue |
| Field filtering configuration | Only retain business ID, approval node, material list | Filters redundant fields, prevents large language models from being disrupted by non-essential information |
Three common mistakes
- Streaming output fails to enable: The dialogue interface shows no streaming typing effect, and content loads all at once. This occurs if the streaming output toggle is not enabled in the dialogue configuration, or if the current large language model interface does not support streaming transmission.
- No matching references after adjusting recall limit: After setting the single recall count to 2000, the large language model output does not reference any recalled content. This happens if business document field identifiers do not match prompt requirements, the large language model cannot identify valid reference fragments, or recalled documents have low relevance to the current dialogue question.
- Knowledge base paragraph IDs appear in results: Retrieval results include paragraph ID fields such as business ID and talent ID. This occurs if no field filtering rules are configured, and the prompt does not explicitly exclude non-business-essential identifier fields.
How to confirm configuration is correct
- Initiate a single-turn business consultation. Check if the dialogue interface shows a streaming typing effect to confirm the streaming output configuration is active.
- Adjust the single recall count to different ranges. Initiate a cross-stage business consultation. Check if the number of referenced recalled document fragments matches the configuration requirements to confirm the reference configuration meets business needs.
- Upload a test document containing business ID and approval node. Initiate relevant consultation. Check if the output excludes non-essential paragraph ID fields to confirm the field filtering configuration is active.
- Initiate two identical process dialogues with a time interval between them. Check if the dialogue context retains previous business information to confirm the context memory duration matches average business processing time.
The values listed here are common starting points, and 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.