What this type of data looks like
Vehicle industry investment research data comes from official automaker announcements, Ministry of Industry and Information Technology (MIIT) motor vehicle product announcements, third-party testing institution reports, publicly available industry association statistical documents, and quarterly and annual financial reports of publicly listed automakers. Update frequency: Financial reports are updated quarterly, vehicle model parameters are updated when model facelifts are released, and test reports are published alongside vehicle review cycles. Document structures include structured parameter tables (with fields such as vehicle model number, CLTC range, maximum power, wheelbase, with units of none, kilometers, kilowatts, and millimeters respectively), semi-structured review reports, and unstructured industry research reports.
What constraints do these characteristics impose on the context and token workflow?
Structured parameter tables have a large number of fields, so single documents use more tokens than general consumer goods documents. Semi-structured review reports and long-period financial report fragments contain extensive long paragraphs. Even after splitting individual documents, they may still use a significant number of tokens. Investment research scenarios require comparing multiple cross-period documents. The context for a single round of dialogue must accommodate parameters for at least 3 vehicle models and 1 financial report fragment. An overly small context window will cause core information to be truncated. Token density varies significantly across languages. Chinese text uses more tokens per character than English, so long text inputs are prone to triggering token overflow limits. Regularly updated documents must prioritize retrieving the latest version when recalled. Otherwise, context information will be outdated.
How to set configuration parameters
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Vehicle investment research requires comparing multiple financial reports, model parameters, and review reports. The context for a single round of dialogue must accommodate complete parameters for at least 3 vehicle models and 1 quarterly financial report fragment, which corresponds to a token count of approximately 8000–12000 |
quoteMaxToken | 1500–2000 characters | Single recalled vehicle documents (such as the complete parameter table for one vehicle model) must have their length controlled to avoid excessive token usage, while covering core parameter fields |
RECALL_TOP_N | Top 6–8 results | Investment research scenarios require balancing multi-dimensional information. Too many recalled entries will exceed the context window, while too few will miss key supply chain or competitor data |
SPLIT_CHUNK_SIZE | 1000–1500 characters | Vehicle test reports and research reports have long paragraphs. After splitting, the integrity of parameters and conclusions must be maintained, and field associations must not be broken |
SIMILARITY_THRESHOLD | 0.75–0.85 | Distinguish core parameters from marginal information, avoid recalling irrelevant non-investment research documents, and cover updated content for the same vehicle model across different cycles |
RE_RANK_TOP_N | Top 3–5 results | Rearrange recalled results to retain the most relevant investment research information, reduce context token consumption, and ensure core information is not lost |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
Phenomenon 1
After configuring quoteMaxToken, the returned answer only includes a small number of parameter fragments. Cause: The parameter was mistakenly understood as limiting the length of question input, but it is actually used to limit the token usage of a single recalled document. Setting it too low will truncate core parameter content.
Phenomenon 2
Dialogue works normally when entering English vehicle model codes, but returns a 413 Request Entity Too Large error when entering full Chinese vehicle names or multi-vehicle comparison questions. Cause: Chinese text has higher token density than English, and the total token count of combined multi-vehicle parameter data exceeds the preset context window threshold.
Phenomenon 3
When starting a cross-round investment research dialogue, search results do not link to vehicle parameter requirements from previous conversations, and only return independent document fragments. Cause: Context-aware recall configuration is not enabled, or maxContext is set too small to accommodate historical conversation and current search related information.
How to confirm correct configuration
- Upload a complete vehicle parameter table document, check the parsed chunked content, and confirm that
SPLIT_CHUNK_SIZEdoes not break the association between parameter fields. - Submit an investment research question involving a comparison of 3 vehicle models, check the number of context recall entries returned by the system, and verify that
RECALL_TOP_NandRE_RANK_TOP_Nvalues match requirements. - Input a long paragraph of industry research report fragment, check whether a token overflow error is triggered, and confirm that
maxContextandquoteMaxTokenconfigurations can cover the current input length. - Launch 3 consecutive relevant investment research dialogues, check whether the system retains vehicle parameter information from previous conversations, and verify that the context association function works properly.
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.