What the Data for This Category Looks Like
Core users in the delivery effect optimization scenario include ad optimizers, growth analysts, and agency operations staff. Daily documents primarily consist of ad daily reports, ad weekly reports, price adjustment records, material test sheets, and optimization review documents. Data sources cover ad platform backends, attribution systems, CRM lead databases, and landing page data. Update frequency is mostly daily or real-time. Document structures mix text paragraphs, structured tables, and list items. Fields mostly involve ad spend, click volume, conversion cost, budget amount, and similar metrics. Units include numerical units such as yuan, times, and pieces. There is no unified fixed long-form narrative structure.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Since core users’ daily documents mix text and structured tables, and data sources are scattered, the parsing process must adapt to mixed-format content. It must avoid breaking the integrity of table data during splitting. The real-time or daily update rhythm requires the parsing process to control time consumption. It must avoid delaying subsequent abnormal indicator positioning and budget allocation work. Documents contain precise numerical fields with units. Chunking must retain the integrity of a single logical unit. Single price adjustment records or single sets of test data must not be split into multiple chunks. At the same time, business logic of optimization documents is highly correlated. Chunking must match conventional units for ad analysis, to facilitate subsequent retrieval and application.
Configuration Settings
Configuration settings are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of single logical blocks in ad-related documents, retains complete units of price adjustment records and material test data |
chunkSplitter | Split by paragraph + table | Adapts to the mixed text and table structure in daily documents, avoids breaking the integrity of table data during splitting |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the batch document processing rhythm of daily updates, avoids interrupting the parsing process due to timeout |
customPdfParse | Enable table recognition mode | Adapts to structured table data in material test sheets and price adjustment records, improves parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the size of daily/weekly report packages uploaded in batches by agencies, avoids being blocked due to oversized files |
Three Common Mistakes
- After uploading price adjustment records or test sheets in Excel format, field misalignment or content loss occurs in the vectorized index. The cause is that the table recognition parsing configuration is not enabled, resulting in structured tables being split into scattered text chunks.
- When uploading multiple daily report documents in batches, a timeout error is triggered for the parsing task. The cause is that the value of
PARSE_FILE_TIMEOUT_SECONDSis too small, which does not match the overall parsing time of batch documents. - Complete budget allocation records cannot be obtained when retrieving chunked content. The cause is that the value of
chunkSizeis too small, splitting a single complete budget adjustment record into two or more chunks.
How to Verify Correct Configuration
- Upload a single material test sheet, check whether the parsed chunked content retains complete table rows and corresponding field information, with no content truncation.
- Check the system parsing logs, confirm that no timeout error is triggered for batch document tasks, matching the set duration of
PARSE_FILE_TIMEOUT_SECONDS. - Test uploading a batch document package within 1000 MB, confirm that the upload and parsing processes can be completed normally, with no file block prompt.
- Retrieve the chunked content, verify that a single price adjustment record or review section is completely retained in a single chunk, with no logical split breakage.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.