Knowledge Base Retrieval and Recall for CDMO R&D Document

Data Characteristics

CDMO (Contract Development and Manufacturing Organization) R&D document data originates from project reports, experiment records, batch production records, quality standards, analytical method validation reports, and regulatory compliance documents. This data updates frequently, especially during early-stage R&D and production process optimization, with new experimental data and analysis results appearing weekly or even daily. Document structures typically include strict section numbering, figures, tables, appendices, and references. Documents extensively use specialized terminology, abbreviations, and specific table formats. Field content covers compound structures, reaction conditions, yields, purity, stability data, instrument parameters, and units (e.g., nm, ppm, mg/mL, ℃). Data sources are diverse, including internal laboratory systems, LIMS (Laboratory Information Management System) export files, and electronic documents from partners.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The structured nature of CDMO R&D documents presents multiple challenges for knowledge base retrieval and recall. High update frequency requires the knowledge base to have efficient incremental update and indexing mechanisms to avoid data lag. The extensive specialized terminology and abbreviations in documents, if not handled effectively, lead to inaccurate word segmentation and impact retrieval precision. Strict document structures and tabular data require parsers to identify and preserve contextual relationships; otherwise, simple text segmentation loses critical information. For example, compound structures are usually closely linked to their corresponding experimental data. Cross-references exist between different document types, requiring recall results to effectively link information from various sources. Accurate identification of units and numerical values is crucial for quantitative data retrieval; incorrect unit parsing can lead to misjudgments in recall results.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500-800 characters`	Balances contextual completeness and retrieval efficiency. Avoids individual segments being too long, diluting key information, or too short, losing context.
`Recall count` (Number of Retrieved Items)	`10-15 items`	Ensures coverage of sufficient potentially relevant document snippets. Avoids recalling too much irrelevant information, which would increase reranking burden.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Requires testing with actual business data to balance recall rate and accuracy. An initial setting of `0.75-0.85` is suggested.
`Rerank result count` (Number of Reranked Items)	`3-5 items`	Returns a small number of highly relevant results after optimization by the reranking model, improving user experience.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Considers that large batch production records or analysis reports may contain extensive data, leading to longer parsing times.
`maxContext`	`4000-8000 Token`	Provides the longest possible context to the reranking and generation models within LLM context window limitations.

Common Mistakes

Symptom: A user queries for the purity of a specific compound, but recall results contain many irrelevant experimental procedure descriptions, and purity data is missing. Reason: The segmentation strategy did not adequately consider the structural characteristics of tabular data in CDMO documents. This resulted in critical numerical values being incorrectly separated from their associated descriptions, or table row/column header information not being preserved during indexing.
Symptom: After a knowledge base update, specialized terms (e.g., API, CRO) in newly uploaded batch production records are not correctly matched during retrieval. Reason: The tokenizer was not optimized for specialized vocabulary and abbreviations in the biomedical field. This led to new terms being incorrectly segmented or identified as general vocabulary, affecting recall.
Symptom: When querying via the api/v1/chat/completions API, specifying a knowledge base tag yields results inconsistent with expectations. Reason: During data upload using the knowledge base creation interface api/core/datas, documents were not correctly or completely tagged with fine-grained labels. This prevented tag filtering from being precisely effective.

How to Verify Configuration

Select a batch of representative CDMO R&D documents, including different types (experiment records, batch production records, analysis reports). Import them into the knowledge base and check if Chunk size (segment length) and number of segments meet expectations.
Conduct retrieval tests for queries of varying complexity, including specialized terminology, numerical ranges, and cross-document association queries. Examine the similarity score distribution of recall results and manually evaluate the relevance of the top Recall count (number of retrieved items).
Simulate actual user questioning scenarios. Use the api/v1/chat/completions interface for dialogue testing. Observe whether the knowledge points cited in the model's responses are accurate and complete, and evaluate the quality of Rerank result count (number of reranked items).
Regularly track the effectiveness of incremental knowledge base updates. Verify the indexing speed and retrieval availability of newly uploaded documents to ensure that frequently updated documents can be retrieved promptly.

Note: The values provided are common starting points. Measure them against your 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.