Knowledge Base Retrieval and Recall for Process Validation

Data Characteristics

Process validation regulation data primarily originates from internal quality management system documents. These include process validation plans, reports, deviation handling records, and change control documents. These documents are typically in PDF, Word, or scanned image formats, characterized by high degrees of structured and semi-structured content. The update frequency is relatively low, with revisions usually occurring due to regulatory changes, significant process adjustments, or periodic reviews, which can span several months to years. Documents contain extensive specialized terminology, charts, flowcharts, specific batch numbers, equipment IDs, and critical process parameters (e.g., temperature, pressure, time) along with their units of measurement.

Constraints on Knowledge Base Retrieval and Recall

The specialized and structured nature of process validation documents requires precise identification and processing of domain-specific terminology during knowledge base retrieval, avoiding generalized recall. The low update frequency means that after initial knowledge base construction, maintenance costs are relatively manageable. However, incremental updates must ensure consistency and proper linking between new and old document versions. Charts and flowcharts within documents pose a challenge for purely text-based vector retrieval, necessitating consideration of multimodal processing or enhanced text descriptions. The presence of specific fields like batch numbers and equipment IDs requires support for metadata-based filtering or exact matching during retrieval to narrow the recall scope. The existence of units of measurement demands that retrieval results accurately present numerical information, preventing misinterpretations due to unit confusion.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Process validation documents have strong logical paragraph structures. Maintaining a moderate length ensures contextual completeness and prevents truncation of critical information.
`Overlap Length`	`100 characters` (characters)	Ensures necessary semantic continuity between paragraphs, reducing context loss due to chunking, suitable for long process descriptions.
`Recall count` (Recall Count)	`Top 5 entries` (top 5)	Experience shows that recalling the top 5 items usually covers core information. More items increase the processing burden on the subsequent large language model.
`Similarity threshold` (Similarity Threshold)	`0.75`	High repetition of domain-specific terminology allows for a higher threshold to improve precision and filter out irrelevant general descriptions.
`Rerank result count` (Reranked Return Count)	`3 entries` (3 items)	In conjunction with `Recall count`, this re-ranks the initial recall results, selecting the 3 most relevant items for presentation.
`chunk_overlap_ratio`	`0.2`	For specific structured documents, this parameter helps maintain content coherence during chunking, preventing critical information from being split.

Common Pitfalls

Symptom: A user query exactly matches content in a knowledge base chunk, but the chunk is not recalled. Reason: The Similarity threshold (Similarity Threshold) is set too high, causing even perfectly matching text to be filtered out due to minor vector calculation differences.
Symptom: Critical parameter values and units are disconnected or missing in the returned process validation report. Reason: Document parsing did not adequately identify and extract structured data from tables or specific formats, preventing this information from being effectively indexed in the knowledge base.
Symptom: In a self-hosted environment, the custom retriever count cannot be increased beyond 6. Reason: Environment variables or configuration file limits, such as MAX_CUSTOM_RETRIEVER_COUNT, have not been adjusted as needed.

Verification Steps

Conduct a series of test questions containing specialized terminology and specific parameters. Check if the recall results include the expected chunk and verify the completeness and accuracy of the chunk content.
For documents containing non-plain text content like tables and flowcharts, check if the recall results correctly reference or describe this content. Evaluate for any information loss.
Use FastGPT's logs or monitoring interface to observe key metrics such as Recall count (Recall Count) and Similarity Score. Confirm that these align with expected settings and that no abnormal error messages are present.

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.