What This Category of Data Looks Like
Railway and highway investment research data primarily comes from official road network operation systems, industry regulatory reports, maintenance ledgers, bidding announcements, and policy documents. Structured data includes fields such as line number, operating mileage, daily traffic volume, and maintenance cost, with units of kilometers, trips/day, and ten thousand yuan respectively. Unstructured documents include annual operation reports and policy interpretation PDFs. Update frequencies vary significantly: traffic data is updated daily, maintenance ledgers are updated monthly, and policy documents are updated immediately upon release. Individual document lengths range widely, from hundreds-of-word notices to tens of thousands-word annual analysis reports.
Constraints Imposed on the "Context and Token" Workflow
The multi-source, heterogeneous nature of railway and highway investment research data leads to wide variation in single-round retrieved context content and notable fluctuations in token consumption. Differentiated recall trigger logic is required for data with different update frequencies to avoid invalid token usage. Unfiltered multi-field structured data introduces redundant information that occupies valid context token quotas. Direct import of ultra-long documents exceeds the model's context window, causing content truncation or parsing errors, so targeted chunking rules are necessary.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8192–16384 token | Adapts to per-chunk token quotas after splitting long railway and highway documents, avoiding content truncation |
recall_top_k | Top 3–5 results | Reduces token usage from redundant structured data fields while retaining core investment research information |
rerank_top_n | Top 2–3 results | Streamlines token consumption for retrieved results, given the small correlation difference in railway and highway data |
chunk_size | 1024–2048 characters | Controls per-chunk length when splitting ultra-long documents, adapting to model context window limits |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing time for large-scale road network operation reports, avoiding early termination of parsing |
UPLOAD_FILE_MAX_SIZE | 500 MB | Allows full upload of large documents such as railway and highway annual analysis reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and testing on deployment-specific samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Model-generated investment research conclusions deviate significantly from actual road network data, and the interface shows a low context matching score. Cause: No precise recall rules are configured for railway and highway structured fields, leading to retrieval of irrelevant unstructured documents that occupy valid context token quotas.
- Phenomenon: Rerank model calls fail, with
500 Internal Server Errorreturned in container logs. Cause: Environment variable parameters for the rerank model are not correctly configured in docker-compose.yml, causing model dependency packages to fail to load properly. - Phenomenon: Knowledge base credit consumption far exceeds the preset threshold, and background billing logs show abnormally high token usage per conversation. Cause: No reasonable limit is set for
chunk_size, and ultra-long documents are not split, leading to per-round context token usage exceeding the configured range.
How to Verify Proper Configuration
- Upload a single typical railway and highway operation document, check the parsed chunk status, and adjust the
chunk_sizeconfiguration until no abnormal truncation occurs in per-chunk content. - Launch targeted investment research queries, check the retrieved context list, and adjust the
recall_top_kconfiguration until only core relevant data is retained. - Test rerank model calls, check container logs, and adjust relevant environment variables until no loading errors occur.
- Check token consumption records, compare consumption levels before and after adjusting configurations, and confirm that parameter values match the token requirements of business data.
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.