Vector Models and Indexing for Structured Analysis of

Data Characteristics

Psychiatric R&D data primarily originates from Clinical Study Reports (CSRs), medical literature, patent documents, and internal research notes. Document update frequency is relatively low, typically coinciding with clinical trial phases or major research advancements. Document structures are complex, containing extensive unstructured text such as patient visit records, diagnostic criteria, and scale score interpretations. Key fields include disease diagnostic codes (e.g., ICD-10 F00-F99), drug mechanisms of action, target information, clinical symptom descriptions, adverse event reports, and dosage units (e.g., mg, µg). Data often includes psychometric scale results, presented as numerical values combined with textual descriptions.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The complex structure and specialized terminology of psychiatric R&D documents demand high semantic understanding from vector models. Extensive unstructured clinical descriptions and scale interpretations require models to capture subtle semantic differences and contextual relationships. Due to infrequent data updates, real-time requirements for incremental indexing are relaxed. However, consistency and traceability of historical data are more critical. Precise information like disease diagnostic codes and drug targets requires vector indexes to balance exact matching with semantic similarity during recall. Additionally, different scales or diagnostic criteria may have subtle differences. The vectorization process needs to distinguish these differences to avoid incorrect recall or omissions.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–700 characters`	Paragraphs in psychiatric R&D documents are often long, containing multiple medical concepts. This length helps maintain contextual integrity.
`Overlap Length`	`100–150 characters`	Ensures semantic continuity between adjacent paragraphs, preventing critical information from being split.
`Recall Count`	`8–12 items`	Considering the complexity and information density of R&D documents, increasing the recall count improves relevance coverage.
`Similarity Threshold`	`0.75–0.85`	Balances recall and precision, avoids interference from irrelevant information, and ensures accurate matching of medical concepts.
`Rerank Return Count`	`3–5 items`	Reranking models further filter results, providing the most relevant core information and reducing downstream processing load.
`Embedding Model`	Qwen3-Embedding-8B or Baichuan2-Large-Embedding	Models optimized for Chinese medical domains, with strong capabilities in understanding specialized terminology and long texts.

Common Pitfalls

Connection refused or Timeout errors when connecting to a VLLM-deployed Qwen3-Embedding-8B model. This typically indicates the VLLM service is not running correctly, the IP address and port configured in FastGPT do not match, or firewall rules are blocking the connection.
Retrieval results contain numerous irrelevant or duplicate document snippets after knowledge base chunking. This occurs when Chunk Length is set too large or too small, failing to effectively capture semantic units within the document and leading to degraded vectorization quality.
Milvus deployment failure, with logs showing database "milvus" does not exist or pg_hba.conf related errors. This usually means the Docker Compose file includes PostgreSQL-related configurations, but PostgreSQL is not integrated or correctly configured in the actual Milvus deployment.

How to Verify Configuration

Upload a typical clinical trial report containing psychiatric diagnostic criteria and treatment plans. Review the knowledge base chunk preview to confirm that key medical concepts and specialized terminology are not improperly split.
Use a query containing specific disease targets (e.g., Alzheimer's disease) or drug mechanisms of action. Utilize FastGPT's knowledge base retrieval function to observe the relevance of recall results. Adjust the Similarity Threshold until recall results meet expectations.
After configuring an external vector model connection in FastGPT, perform a model connection test. Confirm a 200 OK status code is returned, and check logs for the absence of 500 or 4xx error codes.

The values provided are common starting points and should be measured 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.