Knowledge Base Retrieval and Recall for Semiconductor

What the data for this category looks like

Data sources include vertical semiconductor industry news platforms, daily tracking reports from securities research institutions, and financing disclosure announcements publicly available on domestic and overseas stock exchanges. Updates run daily after market close, syncing newly added semiconductor enterprise financing events from the same day. Document structure primarily uses structured tables. Each daily report contains dozens of financing records. Each record includes full enterprise name, financing round, financing amount (units are mostly ten thousand yuan or hundred million yuan RMB), investor list, disclosure date, affiliated semiconductor sub-sectors (such as wafer manufacturing, EDA tools, design services), and original announcement link. Each individual financing record is 300 to 800 characters long. A complete daily report is usually several thousand to tens of thousands of characters total.

What constraints do these characteristics impose on the knowledge base retrieval and recall link

Many structured fields and unit differences require precise matching and normalization of financing amounts and track tags during retrieval. This prevents recalling records that cross units or are unrelated to the target track. The daily incremental update rhythm requires configuring incremental indexing. This avoids full index rebuilding and reduces computing resource usage. Single-document length varies widely. Short entries risk being overlooked, while long entries need proper chunking to avoid context overflow. Financing events have strong timeliness. Retrieval must prioritize content updated in the last 7 days to ensure results align with the latest daily developments.

How to set configurations

Configuration Item	Recommended Value	Rationale
`embedding_model`	`text-embedding-3-small`	Optimized for structured short text in semiconductor financing daily reports. Compared to `text-embedding-ada-002`, it delivers higher semantic matching accuracy for track tags and amount fields.
`chunk_size`	`800-1200 characters`	Matches the average length of individual financing events. This avoids splitting a single financing record while ensuring complete contextual semantics.
`recall_top_k`	`Top 10 entries`	Semiconductor financing daily reports have a high density of valid events. Recalling 10 entries covers the day's key financing updates, avoiding redundancy or missed content.
`similarity_threshold`	`0.75-0.85`	Filters low-match cross-track records and retains financing events highly relevant to the query semantics.
`parse_incremental`	`Enabled`	Aligns with the daily incremental update rhythm. Only processes newly added daily report files, reducing index rebuilding time.
`max_context_length`	`4000 characters`	Matches the length of post-retrieval context concatenation, avoiding exceeding the large model's context window limits.

The parameter values provided on this page are general recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

Symptom: Calling the knowledge base file upload interface returns a 413 Request Entity Too Large error. Cause: The UPLOAD_FILE_MAX_SIZE parameter was not adjusted. Semiconductor financing daily reports have large individual document sizes, and the default parameter cannot accommodate them.
Symptom: Retrieval result relevance declines after multiple rounds of dialogue, and returns to normal after opening a new conversation. Cause: Automatic cleanup of session context was not configured. Redundant context from multiple interactions occupies valid space for retrieval and recall, leading to reduced matching accuracy.
Symptom: Retrieval results for existing knowledge bases deviate after replacing the embedding_model. Cause: Segment index rebuilding was not performed. Existing embedding vectors were generated using the old model and do not match the vector space of the new model.

How to confirm the configuration is correct

Upload a test semiconductor financing daily report document, check the number of parsed chunks to confirm the chunk_size configuration is active.
Initiate a query containing semiconductor track keywords, verify that the number of returned recall entries matches the recall_top_k configuration.
After replacing the embedding_model, import a historical document and initiate a query. Compare matching results before and after the change to confirm the vector space update is active.
Initiate consecutive multi-round queries, check whether retrieval results show abnormal decay as the number of dialogue rounds increases, to confirm the session context configuration is reasonable.

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.