What this category of data looks like
Data sources for rural commercial bank research reports include regional industry analysis produced by internal credit research teams, county-level economic data publicly released by local agricultural and rural affairs departments, and agricultural industry reports from cooperating third-party institutions. There are two update cadences: regular industry reports are updated quarterly, while special research reports for sudden industry changes have no fixed update cycle. Each research report typically includes four fixed modules: research background, regional industry status, credit risk assessment, and business optimization suggestions. Fields include agricultural loan disbursement scale (unit: 100 million yuan), number of farmer credit-covered households (unit: households), and quantitative indicators related to industry revenue. Document length typically hovers around tens of thousands of characters.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
The multi-source data nature of rural commercial bank research reports requires the knowledge base parsing step to support unified processing of different formats. This prevents index gaps caused by differences in data sources. The mixed update cadence of regular and temporary reports requires the recall step to support filtering retrieval results by update time. It also requires configuring incremental updates to balance index overhead and data timeliness. Single documents with long length and fixed structure require precise chunking strategies. This preserves the semantic integrity of core modules such as credit risk assessment and business optimization suggestions, and prevents invalid cross-module recall. Fields with unit-bound quantitative data require retaining the binding between fields and units during parsing. This ensures precise matching during retrieval and prevents semantic errors caused by lost unit information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | The modules in rural commercial bank research reports are relatively long. Chunking at 800–1200 characters preserves contextual connections within modules, and avoids damaging the semantic integrity of core modules such as credit risk assessment and business optimization suggestions |
Recall count | Top 8–10 results | Retrieval needs for rural commercial bank research reports mostly target precise regional industry or credit-related information. 8–10 results cover core module content across different reports, and avoid excessive redundant recall |
Similarity threshold | 0.75–0.85 | Research report content has high professionality. A threshold of 0.75 filters low-relevance generic content, while the 0.85 upper limit retains precise matching results for specific regions |
Incremental Update Trigger Frequency | 2 times per day | Regular reports are updated quarterly, and temporary reports can be triggered manually. 2 daily incremental updates balance index overhead and data timeliness |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single research reports have long length. The parsing process requires sufficient time to complete format conversion and field extraction, and avoid timeout interruptions |
maxContext | 4000–5000 characters | Recalled chunked content needs to combine context to generate responses. A 4000–5000 character context window covers complete research report module information, and ensures response coherence |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume and business rules. Each scenario requires targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Issue: After upgrading to FastGPT 4.9.7, when consecutive questions about research reports are submitted, subsequent replies cannot link to regional industry information from previous conversations, resulting in off-topic responses. Cause: The
maxContextparameter is not configured to enable context association. Each retrieval only parses the current question independently, and does not retain the semantic context of historical conversations. - Issue: Knowledge base recall results only match research report titles or vague industry statements, and cannot accurately hit core module content such as credit risk assessment and regional agricultural policies. Cause: The
chunk retrievalconfiguration is not enabled. Only global keyword matching is performed on the full document, and chunked recall is not performed according to the fixed structure of research reports. - Issue: When running code to obtain user identification in a workflow, a
localStorage is not definederror is returned. Cause: The browser-exclusivelocalStorageobject is called in workflow code executed on the server side. The standardized user identification interface fields provided by the platform are not used.
How to confirm correct configuration
- Upload a single rural commercial bank research report, check the parsed chunk results, confirm the chunk length matches the configured
Chunk sizerange, and each chunk retains complete module semantics. - Submit a retrieval request containing specific regional credit policies, verify that the number of recall results does not exceed the configured
Recall count, and the similarity scores meet the requirements of theSimilarity threshold. - Trigger a manual incremental update, wait for the configured update cycle to end, retrieve the newly uploaded temporary research report content, and confirm normal recall.
- Debug the user identification acquisition logic in the workflow, confirm there are no
localStoragerelated errors, and can obtain user identification fields that meet business requirements.
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.