What Data Looks Like for This Category
Infrastructure construction documents and materials mainly originate from project construction logs, bidding documents, special construction plans, completion acceptance reports, engineering contracts, and similar sources. Data is updated according to project execution rhythm: on-site records are updated weekly during the construction phase, acceptance materials are synchronized after node acceptance, and full archiving is completed when the project concludes. Most documents have long text structures, containing professional fields such as project number, section, construction date, material usage, technical parameters. Units include cubic meters, tons, square meters, and other engineering units. Some documents include structured tables and drawing summaries.
What Constraints Do These Characteristics Impose on the Vector Model and Index Link
The multi-field long text structure of infrastructure construction documents requires vector models to retain professional terminology and field association information during encoding, to avoid loss of professional semantics. The non-uniform update rhythm based on project nodes requires indexes to support incremental updates, reducing the computing resource consumption of full reconstruction. The large differences in unit and parameter formats across different documents require retaining contextual associations during chunking, to avoid separating units from their corresponding parameters after splitting. The segment length of long texts must adapt to the model’s token limit, while ensuring the integrity of professional paragraphs.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the professional paragraph length of infrastructure construction documents, avoiding damage to the integrity of technical descriptions during splitting |
| Overlap Length | 100–200 characters | Retains contextual associations between adjacent chunks, ensuring cross-chunk coherence of professional terminology |
| Similarity Threshold | 0.72–0.85 | Calibrated through actual testing, adapts to semantic similarity judgment for professional texts, filters low-relevance recall results |
| Single Search Limit | Top 10 results | Balances recall accuracy and retrieval efficiency, adapts to the retrieval needs of professional documents with multi-field associations |
| Maximum Context | 8000 characters | Adapts to the token limit of general-purpose vector models, while covering key information of a single core document |
defaultToken | Calibrated through actual testing | Adapts to the call quota of self-built vector services, ensuring interface call stability |
Three Common Misconfigurations
- Phenomenon: An error stating "This token does not have permission to use the target model" is returned when calling the vector service. Cause: The call key for the vector model is not correctly configured in FastGPT, or the key is not bound to the usage permission of the corresponding embedding service.
- Phenomenon: A prompt indicating no available channels still appears after configuring a third-party vector service channel. Cause: The service address and key of the model are not correctly associated, or the model recognition rules of the channel do not match the built-in logic of FastGPT.
- Phenomenon: Professional terminology and its corresponding unit are split into different paragraphs after chunking. Cause: The chunk length is set too small, or a reasonable overlap length is not configured, leading to truncation of professional expressions.
How to Verify Proper Configuration
- In FastGPT v4.17.0, a typical special construction plan document is uploaded, and the chunked text is checked to confirm it retains complete professional paragraphs and field information.
- A professional query term is entered, the relevance of the recall results is verified against business expectations, and the values of relevant configuration items are adjusted as needed.
- The vector service call logs are reviewed to confirm that the token usage of interface calls does not exceed the configured quota.
- A newly added acceptance report is uploaded, and the index is confirmed to correctly synchronize the new content without triggering full reconstruction.
The values provided here are common starting points and should be evaluated against the samples of the respective users.
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.