What this category of data looks like
Data sources for consumer building materials investment research include manufacturer official product manuals, industry association supply and demand monitoring reports, bid announcements on public resource trading platforms, and terminal retail price monitoring data. Update frequencies vary across sources: product guide prices update monthly, bid announcements are released in real time, and annual industry reports update quarterly. Document structures contain standardized fields such as product parameters (model, thickness, compressive strength), delivery lead time, regional agency policies, compliance certification numbers, and more. Units used follow professional measurement standards including millimeters, megapascals, days, and individual units.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require support for parsing multi-format documents and unifying fields. Custom mapping rules must be configured to adapt to non-standard field names from different sources. Data with different update frequencies need distinct incremental and full synchronization trigger logic to avoid duplicate imports or missed real-time data. Document lengths vary widely, from short product parameter entries to dozens of pages of industry reports. Flexible segmentation strategies must be supported. The presence of professional terminology and non-standard units requires embedding models and entity recognition rules to adapt to building industry-specific vocabulary, to prevent semantic recognition errors.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the document structure of consumer building materials, which includes both short parameter entries and long industry reports. Prevents overly fragmented splits that lose context, or overly long segments that reduce embedding accuracy |
embedding_model | Locally deployed bge-large-zh-v1.5 | Consumer building materials contain a large number of professional terms and non-standard units. This model has good semantic recognition adaptability for Chinese professional texts |
similarity_top_k | Top 6–8 results | Investment research scenarios require associating multi-dimensional bid, quotation, and manufacturer data. Balances recall breadth and result conciseness |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large bidding PDF files and annual industry reports take longer to parse. Prevents document import failures due to timeout |
embedding_batch_size | 32 entries | Balances video memory usage for local deployments and the execution efficiency of embedding tasks |
max_context | 12000 characters | Meets the need to associate multiple historical documents and current questions in multi-round investment research conversations |
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 conduct tests on local samples before finalizing settings.
Three common mistakes
- Symptom: The embedding interface call returns a 500 status code, and the backend log shows "connection refused". Cause: Local embedding model port forwarding rules are not configured, or the correct local service address is not filled in the FastGPT embedding model configuration.
- Symptom: In multi-round investment research conversations, subsequent replies do not associate previously mentioned building material models or regional policies. Cause: Conversation context retention configuration is not enabled, or the
max_contextvalue is less than the total character count of historical conversations and current questions. - Symptom: The "delivery lead time" field from imported manufacturer product manuals is not correctly extracted and associated with the knowledge base. Cause: Custom parsing rules are not configured for the non-standard document structure of consumer building materials, causing the general parser to fail to recognize non-standard field names.
How to confirm the configuration is complete
- Run a connectivity test for the local embedding model, verify that FastGPT can access the embedding service through the configured address and port, and confirm the connection is normal.
- Import a typical consumer building materials industry report and product parameter document, check that the segmented text block lengths fall within the expected configuration range, with no excessive splitting or overly long segments.
- Launch a simulated investment research conversation: first ask a basic question, then ask a follow-up associated question, check that replies associate keywords and document content from historical conversations.
- View the knowledge base field mapping logs, confirm that professional fields from imported documents (such as compressive strength, delivery lead time) are correctly identified and mapped to standard knowledge base fields.
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.