What this category of data looks like
Investment research data sources include internal business ledgers, corporate/retail credit reports, macroeconomic monitoring data, external central bank and banking regulatory documents, industry association research reports, and public market trading data. Update frequencies cover real-time (regulatory policy releases), daily (market trading data), weekly (industry analysis reports), and T+1 (internal business updates). Documents include structured indicator tables, semi-structured PDF research reports, and unstructured announcement texts, with fields such as asset_scale and risk_level, and units of 100 million yuan and BP. The overall data volume is large, with mixed formats.
Constraints imposed by these characteristics on database and operations
Multi-source and multi-format data requires the database to support structured storage, semi-structured parsing, and unstructured indexing, and needs to be compatible with different field types and unit verification. Mixed real-time and T+1 update frequencies require task scheduling configured with a combination of incremental and full synchronization to avoid excessive resource consumption from full synchronization. Large data volume and business peaks require database sharding and connection pools to disperse read and write pressure. Financial data involves sensitive information, so the operations link must meet Class 3 Cybersecurity Protection Level requirements, with data encryption and access audit configurations.
Configuration guidelines
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host1:port1,host2:port2/database?replicaSet=rs0&authSource=admin | Adapts to high-availability replica set deployment requirements for relevant use cases, supports multi-node fault tolerance |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Adapts to the parsing time required for large annual research reports and regulatory compilation documents |
RECALL_TOP_K | Top 20 entries | Covers the recall scope of multi-source investment research data, avoids missing key information |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance unstructured financial texts, improves recall accuracy |
DB_SHARD_COUNT | 4–8 shards based on data volume | Disperses read and write pressure from massive investment research data, adapts to business peak loads |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch upload of large industry research reports and regulatory documents |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common misconfigurations
- Symptom: The MongoDB collection
chat_historyreturns empty data, and conversation records cannot be viewed. Cause: Authentication parameters forMONGODB_URIare not configured, or insufficient connection pool configuration causes session connection interruption. - Symptom: Workflow runs return a
504 Gateway Timeouterror. Cause:PARSE_FILE_TIMEOUT_SECONDSis set too low to complete the parsing process for large investment research documents. - Symptom: Locally deployed MongoDB on Win10 cannot be connected, with the error
Connection refused. Cause: Firewall permissions for the default MongoDB port 27017 are not opened, or the local address inMONGODB_URIis not adapted for public network access.
How to verify successful configuration
- Run the command
mongo --eval "db.getCollection('chat_history').findOne({})"to check if non-empty conversation records are returned, verifying the validity of theMONGODB_URIconfiguration. - Upload a single investment research document larger than 1000 MB, wait for the preset timeout period, then check the parsing status to confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration adapts to the document size. - Initiate multiple concurrent knowledge base query requests, observe the database read and write load, and adjust the values of
DB_SHARD_COUNTandMONGO_CONNECTION_POOL_SIZEto meet business peak requirements. - Input test questions that are covered and not covered by the knowledge base respectively, and check whether tool calls and return results conform to preset rules.
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.