Data Characteristics for This Category
Biopharmaceutical CDMO (Contract Development and Manufacturing Organization) product data typically originates from project contracts, process development reports, batch production records, quality control reports, and stability study data. These documents are primarily in PDF, Word, and Excel formats, containing both structured and unstructured information. Data update frequency is closely tied to project progress; new experimental data, analysis results, or production batch records may be generated weekly or even daily. Document structures are complex, often mixing nested tables, charts, chemical structures, reaction flow diagrams, and specialized terminology, abbreviations, and units (e.g., mg/mL, nM, ℃, pH values). Excel table data is frequently used to record experimental parameters, reaction conditions, and analytical results. Table headers can have multiple levels, and data cells often contain both text descriptions and numerical values.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The highly specialized and diverse nature of CDMO data challenges knowledge base text segmentation strategies and entity recognition capabilities. Documents with numerous tables and charts require intelligent parsers to accurately extract information, preventing loss of critical data or context fragmentation. For example, a numerical value in a process parameter table must be understood in conjunction with its corresponding experimental conditions and units. Frequent data updates demand efficient incremental indexing from the knowledge base to ensure retrieval results are current. Furthermore, the widespread use of specialized terminology and abbreviations requires RAG models to handle synonyms and contextual understanding, avoiding recall failures due to vocabulary mismatches. For cross-document linked information, such as all production records and quality inspection reports corresponding to a specific batch number, the knowledge base needs to establish effective linking relationships.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances contextual completeness with information density per segment, suitable for the long descriptive texts found in CDMO reports. |
Chunk Overlap Length (Overlap Length) | 100–150 characters | Ensures critical information across segments is not truncated, maintaining contextual coherence, especially important for documents containing process descriptions. |
Similarity threshold (Similarity Threshold) | Calibrated by measurement, typically 0.75–0.85 | Balances recall rate and accuracy, avoiding retrieval of irrelevant content. Adjustment is required based on actual data and query corpus. |
Recall count (Number of Retrieved Items) | 10–15 items | Ensures enough potentially relevant document snippets are initially retrieved to handle complex queries and cross-referencing needs for multifaceted information. |
Rerank result count (Number of Reranked Items) | 3–5 items | After optimization by a Reranker model, selects the most relevant snippets to present to the user, improving the quality and conciseness of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Addresses situations where parsing large PDFs or complex Excel files may take a long time, preventing files from failing to index due to timeouts. |
Three Common Mistakes
- The knowledge base fails to correctly parse multi-level headers in Excel tables, leading to data-label misalignment and inability to retrieve relevant numerical values when querying specific parameters.
- After uploading documents, critical experimental flow diagrams or chemical structures within some images are not recognized by OCR or linked to text content, resulting in empty results when users query image information.
- Knowledge base index updates are not timely, preventing newly uploaded batch production records from being immediately retrieved, and returning outdated information when querying for the latest progress.
How to Confirm Proper Configuration
- Upload a batch of CDMO reports containing complex tables and charts. Check the
parse_statusin the system logs to confirm it isSUCCESS. Verify that the parsed content includes key numerical values from tables and text within images. - Test with questions containing specialized terminology and abbreviations. Check if the retrieval results cover relevant documents. Verify the performance of the
Similarity threshold(Similarity Threshold) for different query types, ensuring highly relevant documents are prioritized. - Simulate recent project progress by uploading new experimental data documents. Immediately perform a query to confirm if the
Index Update Frequencymeets business timeliness requirements, and check if the latest data can be correctly retrieved.
The values provided are common starting points and should be measured against specific 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.