What the Data for This Category Looks Like
Engineering consulting research report data comes primarily from project approval documents, cost analysis reports, bidding technical specifications, and industry-specific research reports in the architectural decoration sector. New data is added irregularly with no fixed update cycle, as projects progress or industry policies adjust. Individual document lengths vary widely. Documents include structured fields such as project number, construction location, floor area, total cost, and construction period requirements, alongside large blocks of unstructured text like technical plans and risk assessments. Units follow standard engineering measurement standards including square meters, ten thousand yuan, and cubic meters.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The multi-type data structure of engineering consulting research reports requires indexes to support both text embedding and structured field matching. This avoids numerical query bias caused by relying solely on text vectors. The non-fixed update rhythm requires an incremental indexing mechanism. This reduces resource consumption from full index rebuilding. The wide range of document lengths requires a reasonable chunking strategy. This avoids breaking contextual connections of professional content. The multi-field attribute requirements require vector models to adapt to specialized terminology embedding effects. This improves query accuracy.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Open-source domestic or compliant commercial vector models | Engineering consulting research reports contain a large number of specialized terms related to infrastructure and cost estimation. Domestic models have stronger semantic adaptation to industry terms while meeting data compliance requirements |
chunk_size | 800–1200 characters | Research reports include long technical descriptions and structured tables. Too short chunking will break the contextual association of parameters, while too long chunking will exceed the context window of the vector model |
index_type | Hybrid index (text + structured fields) | Research reports contain both text content and structured data such as cost and area. Using only text vectors cannot accurately meet numerical query needs |
recall_top_k | 10–15 results | Engineering consulting queries mostly target precise technical parameters or solution comparisons. Too many recalled results will increase reranking workload, while too few will fail to cover relevant documents |
similarity_threshold | 0.72–0.85 | Semantic similarity of industry-specific terms is relatively high. A threshold that is too low will introduce irrelevant documents, while a threshold that is too high will miss relevant content |
incremental_index_enable | Enabled | Project document updates follow no fixed schedule. Incremental indexing avoids resource consumption and service interruptions caused by full index rebuilding |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A 403 Forbidden error appears when calling the vector model, but normal embedding results are obtainable via curl testing. Cause: The ollama port has not been configured with an access whitelist, or the FastGPT vector model call key is not bound to the access permission of the corresponding instance.
- Symptom: Duplicate project document entries remain after merging the knowledge base index. Cause: The
document_deduplicationconfiguration item is not enabled, or the deduplication rule does not match the unique identifier field of the research report (such as project number). - Symptom: Non-administrator users cannot perform add or delete operations on the vector database. Cause: Only the root user permission is configured for the vector database, no fine-grained access accounts are created for business roles, and permissions are not synchronized via an external management system.
How to Confirm Correct Configuration
- Upload a single engineering consulting research report, then check the vector embedding task running logs to confirm the called model and configuration item parameters match.
- Launch a query that includes structured parameters to verify the hybrid index can recall matching document entries.
- Add a new research report for an existing project, trigger the index merging operation, then check if automatic deduplication completes.
- Use a non-administrator account to connect to the vector database and perform add or delete operations, then verify the permission configuration takes effect.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.