What the Data for This Category Looks Like
Data for advertising and marketing financing daily reports comes from publicly disclosed financing announcements of advertising and marketing enterprises, aggregated financing updates from industry media, and real-time updates from third-party financing data platforms. Updates are released daily. Each daily report contains multiple financing entries from the same day. Each entry has structured fields and unstructured text. Structured fields include financing entity name, financing round, disclosure date, and advertising verticals. Unstructured text includes financing descriptions, investor background, and other content. Field units follow publicly disclosed pricing standards. Document lengths vary widely: short entries are usually hundreds of characters, while long entries can reach several thousand characters.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Link
The daily incremental update requirement means vector indexes must support high-frequency, small-batch writes, to avoid performance loss from full reindexing. The mixed structured and unstructured document structure requires indexes to support both metadata filtering and semantic vector recall, to narrow retrieval scope and improve efficiency. The wide range of document lengths requires vector models and chunking strategies to adapt to variable-length text inputs, avoiding critical information loss from long text truncation or semantic fragmentation from over-splitting short texts. The timeliness requirement of financing daily reports means retrieval must quickly filter by disclosure date, only recalling entries from the current day or a specified time range to reduce irrelevant data interference.
How to Configure Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | text-embedding-3-small or bge-large-zh-v1.5 | Advertising and marketing financing daily reports contain specialized vertical terminology and structured descriptive text. These models perform well for industry semantic encoding and support variable-length input |
VECTOR_INDEX_TYPE | hnsw (pgvector scenario) | Meets the high-frequency write requirements of daily incremental updates. The hnsw index has better query and write performance than other index types |
| Chunk Length | 800-1200 characters | Balances semantic integrity and vector recall accuracy for individual financing daily reports, avoiding long text truncation or over-splitting of short texts |
| Recall Count | Top 10-20 results | Valid information in financing daily reports is usually concentrated in newly disclosed entries. This range balances recall coverage and context redundancy |
METADATA_FILTER_ENABLE | Enabled | Retrieval for advertising and marketing financing daily reports typically includes metadata conditions such as verticals and rounds. Enabling this can significantly narrow the scope of vector recall |
EMBEDDING_BATCH_SIZE | 32-64 | Adapts to the batch write requirements of daily incremental updates, avoiding interface timeouts from overly large single batches |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, 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: A 500 Internal Server Error is returned when referencing the knowledge base, and logs show a vector model call failure. Cause: The
EMBEDDING_MODELparameter is not configured correctly, or vector embeddings for existing documents are not regenerated after switching models. - Symptom: After uploading financing daily report files, the call status query interface returns success, but corresponding entries cannot be found during retrieval. Cause: The states of file parsing completion and vector index completion are confused, and the update of the vector index stage status flag is not waited for.
- Symptom: pgvector database query latency is too high, and lock waits occur during incremental updates. Cause: The
ivfflatindex is selected for pgvector but thelistsparameter is not adjusted to a reasonable range, or secondary indexes are not created for core metadata fields, leading to low metadata filtering efficiency.
How to Confirm Proper Configuration
- Upload a single test financing daily report document, check the model name shown in the embedding log to confirm it matches the configured
EMBEDDING_MODEL. Retrieve core keywords of the document to verify that semantic matching results meet expectations. - Call the file upload and status query interface, sequentially check the return values of the
parse_statusandvector_index_statusfields, and confirm both are in the completed state before initiating formal retrieval. - Construct a retrieval request with metadata filtering conditions, verify that the filtering logic takes effect, and that retrieval results only include entries that meet the conditions.
- Adjust the
EMBEDDING_BATCH_SIZEparameter, run a single-batch embedding task, and observe the interface return status to ensure the task can complete normally without timeouts.
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.