CSO Policy Deployment and Upgrade

Data Characteristics for CSO Policies

CSO (Chief Scientific Officer) policy documents include internal scientific research management regulations, project approval processes, ethical review standards, intellectual property protection details, and related Standard Operating Procedures (SOPs). Data sources typically come from internal file management systems, enterprise knowledge bases, or formal documents issued by compliance departments. Document update frequency is relatively low, usually revised quarterly or semi-annually after policy and regulatory changes, organizational structure adjustments, or annual reviews. Document structures primarily consist of hierarchical regulations and detailed operational steps, containing numerous technical terms, flowcharts, and approval nodes. Fields and units involve project numbers, approvers, dates, version numbers, section numbers, risk level assessments, and numerical ranges for specific operational parameters (e.g., experimental sample size, reaction time in minutes or hours).

Constraints Imposed by Data Characteristics on Deployment and Upgrade

The low update frequency of CSO policy documents means a large initial data synchronization volume during deployment, but subsequent incremental updates will have less pressure. Therefore, focus on the efficiency of the initial data import. The complex hierarchical structure and specialized terminology in the documents require semantic integrity during text segmentation to avoid splitting critical information. The emphasis on version and section numbers dictates that the knowledge base must effectively identify and link revisions between different versions when processing document updates. Additionally, the need for internal network deployment means network connectivity configuration for the model and knowledge base services is crucial. This is especially true for external large language model API calls, which must be resolved through an internal network proxy or local model deployment. Numerical ranges and units in the documents require precise matching during retrieval to prevent misunderstandings due to inconsistent units.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CSO policy documents may contain many charts and detailed descriptions, leading to large single file sizes.
`Chunk size` (Segment Length)	`800 characters` (characters)	Maintain semantic integrity of policy clauses and SOP steps, preventing truncation of critical information.
`maxContext`	`32000 token`	Policy Q&A often requires a long context to understand complex clauses and cases.
`Similarity threshold` (Similarity Threshold)	`0.78`	Ensure retrieved results are highly relevant to policy text, reducing interference from inaccurate information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large or structurally complex PDF/Word documents can take a long time.
`VECTOR_STORE_CHUNK_BATCH_SIZE`	`1000`	Improve vectorization batch processing efficiency when initially importing a large number of policy documents.

Common Pitfalls

Symptom: After importing documents, some policy clauses yield incomplete or semantically broken Q&A results. Reason: The Chunk size (segment length) is set too small, causing policy clauses to be unreasonably split during vectorization.
Symptom: Unable to connect to external large language model APIs in an intranet environment, or API calls return Connection timed out. Reason: The http_proxy or https_proxy environment variables are not configured correctly, or the proxy server address is wrong, preventing the FastGPT service from accessing the internet or intranet API services via the proxy.
Symptom: Unable to upload more files after the knowledge base file limit is reached, or an upload prompts Knowledge Base limit reached. Reason: The open-source version of FastGPT has a default knowledge base limit of 30. Modify MAX_KNOWLEDGE_BASES_PER_APP and other related environment variables to increase this limit.

How to Verify Correct Configuration

Upload a typical CSO policy PDF file through the FastGPT management interface. Observe if the file parsing and vectorization process completes smoothly without errors.
Use several core policy clauses or SOP steps as questions in the FastGPT test interface. Verify that the retrieved original text snippets are complete and accurate, and that the answers meet expectations.
Check system logs to confirm that interactions with external large language model APIs are normal, with no HTTP 4xx or 5xx error codes, specifically looking for 200 OK responses.
Attempt to upload multiple large policy files until the preset knowledge base limit is reached. Confirm that the system behaves as expected, for example, if it can continue adding knowledge bases up to the preset limit.

Note: 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.