What this type of data looks like
Telecommunications service investment research data mainly comes from operator public financial reports, telecommunications industry association standard documents, real-time base station operation logs, spectrum allocation announcements, and 5G/6G technology white papers. Update cycles cover real-time (operation logs), monthly (industry monitoring reports), quarterly (financial reports), and annual (technology white papers). Document structures include structured technical parameter tables, unstructured policy documents, and long-form technical explanations. Fields include professional metrics such as frequency band (unit: MHz), base station density (unit: units/square kilometer), monthly traffic (unit: TB), and others.
Constraints imposed on context and token handling
The multiple update frequencies of telecommunications service data require frequent knowledge base refreshes. Otherwise, old data will occupy context token space. The mixed structure of long-form technical documents and structured parameter tables leads to high per-document token consumption. Improper segmentation can cause context overflow. The presence of specialized terminology and multi-unit fields requires the context to retain precise semantic associations. Otherwise, low-relevance content will interfere with recall results and add unnecessary token redundancy. The high-frequency updates of real-time operation logs also require the context window to support rapid replacement of outdated data, avoiding invalid token buildup.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
maxContextToken | 12288-16384 | Matches the total token limit of most large models, adapts to context recall needs for long documents in the telecommunications industry |
Similarity threshold | 0.5-0.6 | Filters low-relevance general technical content, avoids invalid tokens occupying context space |
Recall count | Top 3-5 | Long telecommunications documents have high per-document length. Excessive recall will exceed the context token limit |
Chunk size | 800-1200 characters | Balances professional content completeness and token usage efficiency, avoids breaking semantic associations of tables and formulas |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing time required for large telecommunications white papers, avoids forced interruption of the parsing process |
maxOutputToken | 12288 | Matches the output token limit of most large models, avoids forced truncation of investment research results |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Knowledge base recall results have low matching degree with target content. Even when the similarity threshold is set to 0.4, required documents cannot be retrieved. Reason: Semantic similarity calculation for specialized telecommunications industry terms is easily disturbed by general words. A 0.4 threshold is too low, leading to a large number of irrelevant documents being included in the context.
- Phenomenon: Large model output is truncated at 12288 tokens. Logs show that the sum of total input tokens and output tokens exceeds the limit. Reason: The sum of
maxContextTokenandmaxOutputTokenis not restricted. Total token consumption exceeds the range supported by the large model, leading to forced truncation of output. - Phenomenon: Timeout errors occur when parsing large telecommunications white papers. Reason:
PARSE_FILE_TIMEOUT_SECONDSis set too short, and does not match the time required for long document parsing.
How to confirm configurations are correctly set
- Upload 1-2 typical telecommunications industry documents, check if parsed segments have no obvious content breaks, and match preset segment length requirements.
- Initiate an investment research query, check if the number of recall results meets business needs, with no excessive redundant content occupying context space.
- Check large model output logs, confirm there are no forced truncation prompts, and total token consumption does not exceed the limit supported by the large model.
- Adjust the similarity threshold, compare relevance changes of recall results, and confirm the threshold setting meets the matching needs of the business scenario.
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.