What this category of data looks like
The data is mainly sourced from the unit’s party building work archives, party building learning records from frontline teams, and party building guidance documents for the oil and gas industry issued by superior authorities. Update rhythms are adjusted per superior deployments and internal learning plans, with no fixed cycle but stable monthly cumulative update volume. Most documents use structured formats, containing fields such as title, main body, release date, affiliated party branch, and associated exploration and development project number. Most text uses standard Chinese characters, some associated fields use project number formats, and overall document length ranges from hundreds to thousands of words.
Constraints imposed on vector models and indexing by these characteristics
The structured field associated with exploration and development project numbers requires retaining this field during indexing to enable targeted retrieval of party building content for specific projects, preventing irrelevant cross-project content from being returned. The lack of a fixed update cycle requires configuring incremental indexing logic that only processes newly added or updated documents, reducing redundant computation overhead. Documents have a hierarchical structure including titles and main bodies; during chunking, titles must be concatenated as preceding context to improve the topic relevance of vector retrieval. Some documents use short-form encoding for the project number field, so field concatenation must be completed before vector generation to ensure complete semantic association.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | The main body of party building documents for oil and gas exploration and development is mostly of medium length. This range ensures complete semantics within each chunk and avoids semantic breaks between chunks. |
| Overlap Length | 100–150 characters | Partial context retention after chunking ensures coherent semantics across chunks, adapting to the paragraph structure of party building documents. |
maxToken | 4000 token | Adapted to the vector model input limit of FastGPT v4.17.0, this setting ensures complete semantic coverage for a single chunk of documents while avoiding exceeding model processing limits. |
| Similarity Threshold | 0.72–0.78 | Party building documents have strong topic relevance. This range filters low-relevance content while retaining valid retrieval results. |
| Single Search Limit | Top 8 results | The relevance of oil and gas exploration and development party building content is concentrated. Too many retrieved results increase context redundancy, while too few may miss relevant content. |
defaultToken | Calibrated through actual testing | Resource limits vary across deployment environments, and default token allocation must be adjusted based on actual call volume to ensure retrieval stability. |
Three common configuration errors
- The symptom: Matching documents are retrieved, but the large language model returns no relevant content. The cause: Failure to correctly concatenate retrieved document content into the context window, or the context window exceeding the
maxTokenlimit, resulting in content truncation. - The symptom: Knowledge base query responses time out, and logs show prolonged retrieval phase duration. The cause: Failure to configure incremental indexing, with full indexing traversing all historical documents during each retrieval, leading to excessive computation overhead.
- The symptom: Vector retrieval results do not match the actual party building topic, and irrelevant exploration and development technical documents are retrieved. The cause: Failure to concatenate the party building document’s topic field and main body when generating vectors, resulting in missing semantic association.
How to verify correct configuration
- Review indexing logs to confirm only newly added or updated documents are processed, verifying that the incremental indexing configuration is active.
- Manually input party building-related query terms, and check that the number of retrieved documents matches the single search limit configuration.
- Inspect vector generation logs to confirm that titles and structured fields have been concatenated into the main body content, verifying that the semantic concatenation configuration is correct.
- Adjust query terms and observe similarity results, confirming that the relevance of retrieved results aligns with the preset threshold judgment logic.
The values provided 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.