What Data for This Category Looks Like
Party building learning data in the iron and steel smelting field mainly comes from official documents issued by higher-level party committees, records of three regular party branch meetings and one monthly party member learning session, party building activity archives, and specialized learning materials combined with production positions. Data update rhythms fall into two categories: fixed cycles and immediate triggers. Theoretical learning documents released by higher-level party committees are updated monthly, in-plant party building activity updates are synchronized every two weeks, and synchronization is completed within 24 hours after major policy releases. The core fields of document structure include title, body, issuing unit, and release date. Some materials contain embedded images or OCR text from scanned documents. The length of individual documents varies widely, with standardized identifiers including document ID, affiliated party branch, content category, and other similar fields.
Constraints Imposed on Vector Models and Indexes
The multi-dimensional characteristics of iron and steel smelting party building data impose clear constraints on vector model and index configuration. First, document length varies widely and includes mixed text and image materials, requiring chunking strategies adapted to different text lengths and text-image associated content to avoid breaking contextual connections. Second, the data includes standardized metadata fields, requiring support for filtered retrieval based on metadata such as affiliated party branch and content category to improve retrieval accuracy. Third, real-time updated data sources require indexes to support incremental synchronization to avoid resource waste caused by full index reconstruction. Finally, some materials combine specific production position requirements, requiring that vector retrieval results match the position scenario to avoid retrieving irrelevant content.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the average length of iron and steel smelting party building documents, avoid breaking the contextual connection between body text and production-related content |
| Overlap Length | 100–150 characters | Retain contextual connections between chunks, prevent disconnection between cross-chunk party building policies and production requirements |
| Similarity Threshold | 0.72–0.85 | Filter low-relevance retrieval results, meet the strictness requirements of party building content |
| Single Search Limit | Top 10 results | Control the total length of returned results to avoid exceeding the model context window limit |
maxToken | 4096 token | Adapt to the input length limit of mainstream embedding models, compatible with vectorization of mixed text-image documents |
| Reranking | Enabled, use top 3 results | Improve the ranking priority of highly relevant results, meet the accuracy requirements of party building Q&A |
| API Request Token | Fill in the defaultToken of the corresponding model | Complete identity verification for model calls, resolve the issue of unavailable model selection |
Three Common Misconfigurations
- Phenomenon: Retrieval results do not include party building promotional materials with images, only returning plain text content. Cause: Joint text-image vectorization configuration is not enabled, so only vectors for the plain text part of the document are generated, without associating OCR text features from images.
- Phenomenon: The latest embedding models such as Zhipu Embedding-3 cannot be selected in the interface. Cause: The API address and key of the corresponding model are not filled in the
defaultTokenconfiguration item, or FastGPT v4.17.0 or a later version is not in use. - Phenomenon: Retrieved content after importing the knowledge base includes non-party building production documents. Cause: Index filtering rules are not configured based on the document content category field, resulting in failure to filter out only party building-related data sources.
How to Confirm Proper Configuration
- Upload a party building document with images, perform a retrieval, and check whether the results include the image-related content of the document.
- Enter the model configuration page, confirm that the target embedding model appears in the optional list.
- Upload documents of different categories, add metadata filtering conditions during retrieval, and confirm that only documents matching the specified category are retrieved.
- Perform a complete incremental index synchronization, check whether the index progress is completed normally with no error logs.
The values provided 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.