Knowledge Base Retrieval and Recall for Batch Record Review

Data Characteristics

Batch record review data primarily originates from pharmaceutical manufacturing batch production records, batch inspection records, deviation investigation reports, and change control records. These documents are typically stored as PDFs, Word files, or scanned images. Some systems export data as structured XML or CSV files. Data updates frequently, with new data generated after each production batch or quality event. Document structures are complex, containing numerous tables, charts, handwritten annotations, and specific terminology. Fields include production date, batch number, material batch number, operator, equipment number, critical process parameters (e.g., temperature, pressure, time), deviation description, investigation conclusion, and Corrective and Preventive Actions (CAPA). Units involve temperature (℃), pressure (MPa), time (min/h), volume (L/mL), and mass (kg/g). The same field may have multiple expressions across different records.

Constraints on Knowledge Base Retrieval and Recall

The coexistence of highly structured and semi-structured batch record data requires robust document parsing capabilities from the knowledge base. Accurate identification of numerous tables and charts is essential to extract key parameters. Frequent data updates mean the knowledge base needs to support efficient incremental indexing and version management to ensure timely retrieval results. Specialized biopharmaceutical terminology and abbreviations in documents require the vector model to have strong domain understanding, preventing recall deficiencies due to vocabulary differences. Diverse fields and units, along with their varied expressions across documents, necessitate considering both exact and fuzzy matching strategies. For example, querying "cooling time for batch number XX" should accurately extract and compare the cooling time field value from different batch record formats and understand the conversion between min and hours.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Key information in batch records often appears in longer paragraphs or table rows. Overly short segments may cut off context, affecting semantic completeness.
`Chunk overlap` (Segment Overlap)	`100 characters`	Ensures information at segment boundaries is not lost, especially in scenarios involving cross-segment related process parameters and deviation descriptions.
`Recall count` (Recall Count)	`Top 8–12 items`	Batch record review requires comprehensive consideration of multiple related documents or document snippets. Appropriately increasing the recall count improves coverage.
`Similarity threshold` (Similarity Threshold)	`0.75–0.82`	Batch record review demands high accuracy. A threshold that is too low introduces excessive noise, while one that is too high may miss relevant information with slightly lower similarity.
`Rerank result count` (Rerank Return Count)	`Top 5 items`	Reranks the initial recall results to prioritize the most relevant key information, reducing the burden on engineers for filtering.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Considering that batch record files are typically large, containing complex tables and multiple pages, a longer parsing time is needed to avoid timeout errors.

Three Common Mistakes

Knowledge base search results are empty or return irrelevant content. This may be due to incorrect identification of table structures during document parsing, leading to unindexed key data.
API call results are inconsistent with online conversations, especially when stream is set to false. This could be because the detail parameter was not passed correctly in the API call or due to behavioral differences between versions.
Chinese content appears garbled after importing a CSV file. This is typically a file encoding issue. Even if encoding is set, if the original file's encoding does not match, it will still display abnormally.

How to Verify Configuration

Select typical batch production records, inspection reports, and other documents. Upload them to the knowledge base and check if the parsed content is complete, especially if table data and key parameters are correctly extracted.
Perform searches for batch numbers and critical process parameters (e.g., "reaction temperature 60℃", "batch deviation number D2023001"). Verify that the recall results include the expected document snippets and evaluate their relevance.
Simulate actual review scenarios by entering complex queries, such as "Find pressure difference anomaly records for batch number P20230101 in the mixing process." Check if the system accurately recalls relevant deviation reports and batch production records, and verify if the returned Recall count (Recall Count) and Rerank result count (Rerank Return Count) match the configured expectations.

Note: The values provided 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 2026-09-21.