What data for this category looks like
Party building learning data for the port and shipping industry mainly comes from official documents released by the group’s party affairs department, study records from subordinate branches, party building requirement documents from superior transportation systems, internal training materials and meeting minutes. Data is updated in monthly centralized batches, with supplementary content added for new policies on an ongoing basis. Most documents use structured formats, including titles, body paragraphs, and attachment links. Fields cover publishing department, publish date, topic category, and applicable positions. Document sizes typically range from hundreds of KB to several MB.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The position classification field in party building data requires multi-turn dialogue to adjust answer professionalism based on user role. Avoid overly general statements for frontline crew and shore-based management positions. Attachment links and long text content in documents require multi-turn dialogue to retain contextual association, and avoid losing key policy paragraphs. The monthly update rhythm requires configuring a regular synchronization mechanism to ensure Q&A content always matches the latest official statements. Embedded video transcription content requires adapting chunk length for long text processing, to avoid exceeding model context limits.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph structure of port party building documents, avoid single chunk content exceeding model capacity limits |
| Chunk Overlap Length | 150–200 characters | Retain cross-chunk contextual association, ensure coherent interpretation of party building policies |
variables configuration | Preset position tags by user role | Adapt to the position classification field in documents, accurately match corresponding learning materials |
chatId session management | Bind internal port employee IDs | Retain historical context of multi-turn dialogue, track user study progress and question trajectory |
| Trigger synchronization frequency | 1 time per month | Match the monthly update rhythm of party building materials, ensure data timeliness |
| Maximum context length | 4000–6000 tokens | Adapt to historical questions and answers in multi-turn dialogue, avoid truncation of key content |
Three common configuration mistakes
- Phenomenon: Chinese and English opening lines cannot function simultaneously in multi-turn dialogue, only a single language version is displayed. Cause: No language switching trigger rule configured in
variables, no binding of user language usage scenarios. - Phenomenon: Embedded images fail to load normally in imported Markdown format party building documents. Cause: No image domain name auto-completion rule configured in the document synchronization process, domain name replacement only triggered during Word import.
- Phenomenon: Context is truncated after importing long documents, making it impossible to fully answer associated party building policy questions. Cause: No reasonable chunk length and overlap length configured, leading to key policy paragraphs being split and lost.
How to verify correct configuration
- Use the FastGPT v4.17.0 platform to initiate test sessions, select different position roles to ask questions, and verify if answer content matches corresponding categorized party building materials.
- Upload Markdown format party building documents with embedded images, and check if images load normally in the conversation.
- Trigger the tool call process, confirm the final answer only contains content intended for party members, with no intermediate logs displayed.
- Import long documents exceeding the model context length, initiate associated questions, verify if the answer covers complete policy content, and check if the
messageshistorical context is correctly retained.
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.