HTTP Interface and External Systems for Batch Record Review

Data Characteristics

Batch record review data primarily originates from Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and Laboratory Information Management Systems (LIMS). This data typically exists as structured or semi-structured documents, such as PDFs, Word documents, or XML files. Batch record documents update frequently, closely tied to production batches; one or more new batch records generate upon completion of each production batch. Document structures are complex, encompassing multiple modules like production process parameters, material batches, equipment operation logs, operator signatures, and inspection reports. Fields contain numerous biopharmaceutical-specific terms, such as batch number, product code, production date, expiration date, critical process parameters (e.g., fermenter temperature, pH value, culture time), and testing indicators (e.g., purity, content, microbial limits) along with their units (e.g., ℃, pH, hours, %, cfu/g).

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The diversity of batch record data sources requires HTTP interfaces to have good compatibility, processing data formats from different systems, such as XML and JSON. The update frequency, synchronized with production batches, dictates that external systems must support high-concurrency data fetching or real-time push mechanisms to ensure timely information. Complex and varied document structures mean that when transmitting via HTTP interfaces, clear data models and field mapping rules need definition to avoid information loss or parsing errors. Specifically, the biopharmaceutical-specific fields and units within documents demand higher accuracy in data parsing. For example, the fermenter temperature field might appear with different key names in various systems, and its unit could be Celsius or Fahrenheit; this requires unified conversion and standardization at the interface level.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`600 seconds`	Batch record files are often large and involve aggregation from multiple systems, requiring a longer timeout to prevent interruptions.
`MAX_FILE_SIZE_MB`	`100 MB`	A single batch record document can contain images, charts, and extensive text, potentially exceeding the size of typical text files.
`CHUNK_SIZE_CHARACTERS`	`800–1200 characters`	Ensures each text chunk contains sufficient context while preventing individual chunks from becoming too large and reducing processing efficiency.
`EMBEDDING_BATCH_SIZE`	`32`	Balances memory consumption and processing speed, suitable for handling medium-sized text chunks.
`RECALL_TOP_K`	`8`	Batch record review demands high information completeness; increasing recall count improves relevance coverage.
`THRESHOLD_SIMILARITY`	`0.75`	Ensures recalled document snippets are highly relevant to the query, reducing noise.

Three Common Mistakes

HTTP interface returns a 504 Gateway Timeout error: This typically occurs when external systems experience a long delay in fetching batch record data. The cause is often large batch record file sizes or complex queries taking too long for the backend system to process, combined with HTTP_REQUEST_TIMEOUT_SECONDS being set too short.
Key process parameter fields (e.g., PH_VALUE) are empty or incorrect in the parsed document: This manifests as specific fields in the data model not being populated or populated with incorrect values. The reason is inaccurate data model mapping between the external system and FastGPT, or the format of the field in the source system does not meet expectations, leading to incorrect extraction by the parser.
The model frequently omits critical information when answering batch record-related questions: This appears as the model being unable to provide complete or accurate batch record details. Possible causes include CHUNK_SIZE_CHARACTERS being set too small, leading to excessive fragmentation of context within batch records and scattering critical information across different text chunks, or insufficient RECALL_TOP_K.

How to Verify Configuration

Invoke the external system interface to check if the returned HTTP status code is 200 and verify if the returned data structure matches expectations, especially for biopharmaceutical-specific fields like batch number and expiration date.
Upload a typical batch record document and check the parsed document preview in FastGPT to ensure the document content is complete and accurate, and that key fields (e.g., fermenter temperature, PH value) and their units are correctly identified and extracted.
Use test questions containing batch record-specific terminology to query the model. Verify if the model accurately recalls relevant document snippets and provides answers containing critical batch record information. Determine the appropriate threshold by comparing recall results under different THRESHOLD_SIMILARITY settings.

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.