What This Category of Data Looks Like
Glass industry research report data sources include public statistics from the China Building Glass and Industrial Glass Association, annual and semi-annual reports of listed building materials companies, and public research documents from third-party building materials industry consulting institutions. Update rhythms fall into three categories: quarterly full industry deep reports, monthly updates of segmented category price and supply-demand dynamics, and policy-related reports updated immediately upon release.
Document structures typically include industry macro environment, production capacity and shipment data for segmented glass categories, downstream application proportions, price trends, policy impact analysis, and future trend forecasts. Fields include product specification (e.g. 5mm float glass), unit price (yuan/weight box), monthly shipment volume (10,000 weight boxes), unit energy consumption, publishing institution and publishing date.
Constraints Imposed by These Characteristics on Model Access and Configuration
The multi-source heterogeneous data characteristics of glass research reports require configuring multi-format parsing adaptation parameters to avoid loss of structured tables and professional terminology. Data with distinct update frequencies needs incremental index trigger rules to distinguish quarterly full-index and monthly incremental index tasks, avoiding repeated indexing of old data. Clear unit fields and value types require configuring field extraction rules to retain the association between units and values during embedding, avoiding semantic confusion. Content dense with professional terminology needs custom vocabulary configuration to improve the semantic recognition accuracy of embedding models for building industry professional expressions. Documents vary widely in length, from short dynamic reports of a few pages to long industry whitepapers of dozens of pages, requiring segment length adaptation parameters to avoid long documents being truncated and losing cross-paragraph key logic.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Glass research reports contain a large amount of structured table data on production capacity and prices. Enabling this option retains complete table information |
UPLOAD_FILE_MAX_SIZE | 200 MB | Single large industry whitepapers typically exceed 100MB in size. Reserving sufficient space prevents large documents from being truncated |
maxContext | 8000–12000 characters | Adapts to the long-paragraph logic of glass research reports, retaining sufficient contextual association to accurately convey semantics |
embedding_model | bge-large-zh-v1.5 | This model delivers strong embedding performance for Chinese professional terminology, adapting to the professional expression habits of the building materials industry |
recall_top_k | Top 8 entries | Relevant analysis paragraphs of glass research reports are concentrated in the supply-demand and price modules of segmented categories. 8 entries cover core information while avoiding redundancy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large industry research reports take longer to parse, preventing parsing tasks from being interrupted due to timeout |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: After adding an Ollama model in FastGPT v4.8.21-fix, test calls return the
model not founderror. Cause: The model name entered in FastGPT does not exactly match the name of the model loaded locally by Ollama, or the API request source whitelist for the Ollama server has not been configured. - Issue: When deploying an integrated index model via Docker, the vector model fails to load normally. Cause: The vector model was not mounted to the specified directory in the Docker container, or the local path of the model was not correctly specified in the environment variables.
- Issue: When importing glass research reports as plain text, segment parameters are not adjusted, resulting in long paragraphs being truncated and losing associated information on prices and production capacity. Cause: The default segment length does not adapt to the long table paragraphs of glass research reports, and segment parameters were not adjusted based on document structure.
How to Confirm Successful Configuration
- Upload a single glass industry research report, check if the parsed text retains structured tables and professional terminology to confirm that the document parsing configuration is effective.
- Enter a query containing glass categories and supply-demand data, initiate a model test call, check if the returned results match the query intent to confirm that the embedding and recall configuration adapts to industry content.
- View deployment environment logs to confirm that both the large language model and vector model are loaded normally, with no port conflict or path error prompts.
- Upload a new industry dynamic report, check if the index task only processes the new document to confirm that the incremental update configuration trigger rules meet expectations.
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.