What this type of data looks like
Investment research data for urban commercial banks comes from multiple sources: internal credit approval ledgers, regional macroeconomic monitoring data, public research reports from peer institutions, local regulatory policy documents, and public business information of local physical enterprises. Data update rhythms vary significantly: regulatory policies are updated immediately upon release, regional economic data is updated monthly, peer research reports follow their own publication cycles, and credit ledgers are updated in real time as business is processed.
Document structures include structured tables (such as credit limits, customer classifications), semi-structured policy documents, and unstructured research meeting minutes. Fields include dedicated units and formats: for example, credit limits are measured in ten thousand yuan, and regulatory document numbers follow local standard formats.
Constraints on Model Access and Configuration
Mixed structured and unstructured data sources require model access to support parsing and adaptation for multiple document types, to avoid missing key fields with a single parsing mode. Large differences in data update cycles require configuring incremental synchronization rules for different scenarios, to match the update frequencies of different data sources. Localized industry terminology and dedicated field formats require adding custom term mappings in model configuration, to reduce misunderstandings of local business terms by the model. Some investment research data involves internal compliance requirements, so dedicated data transfer channels must be reserved during model access, to ensure data security.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Use a domestic vector model fine-tuned for the financial domain, such as bge-large-zh-v1.5 | Investment research data for urban commercial banks contains a large number of financial exclusive terms. A vector model adapted for financial fine-tuning can improve field recall accuracy |
llm_api_base | Configure as the dedicated model proxy address for urban commercial banks | Some investment research data involves internal compliance requirements. A dedicated gateway must be used for transfer to avoid data leaks |
top_k | Set to 5-8 | Investment research data for urban commercial banks has high field density. Too many recalled entries will introduce irrelevant information, while too few will lead to insufficient coverage |
similarity_threshold | Set to 0.72-0.78 | Balance the recall accuracy of structured field matching and unstructured text, and adapt to terminology differences in regional economic data |
parse_mode | Enable the dual mode of "structured parsing + general parsing" | Adapt to structured tables in credit ledgers and unstructured text in policy documents, and improve the completeness of document parsing |
max_context | Set to 8000-12000 characters | Investment research data for urban commercial banks includes long-text industry research reports. Sufficient context ensures the model understands complete business logic |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Configuration Mistakes
- Symptom: A
403 Forbiddenerror is returned when calling the vector model, but the connection works normally during curl testing. Cause: The API key and request headers of the vector model are not correctly configured in FastGPT's model configuration, causing the gateway to block the request. - Symptom: Structured field extraction results are empty after uploading a credit ledger. Cause: The structured parsing mode for the corresponding document type is not enabled, so the model cannot recognize table structures and dedicated field formats.
- Symptom: Generated investment research reports do not meet preset field format requirements. Cause: Custom output format verification rules are not configured, and only prompt words are used to guide model generation, which cannot enforce constraints on output structure.
How to Confirm the Configuration Is Complete
- Upload a single credit ledger sample, check whether the extracted fields after parsing match the preset structured mapping rules, to confirm that the parsing mode configuration is effective.
- Initiate a query containing regional economic terminology, verify that the recall results include corresponding data entries, to confirm that the vector model and similarity threshold configuration are adapted to business needs.
- Call the model to generate structured output, check whether the returned content meets preset field requirements, to confirm that the format constraint configuration is correct.
- Simulate internal data transfer, initiate a request through the dedicated gateway, to confirm that the model proxy address configuration can normally connect to the model service.
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.