Vector Models and Indexing for Optical Module Financial

What data looks like for this category

Data sources for optical module financial reports include public periodic reports disclosed by listed companies, special reports released by optical module industry research institutions, and operational briefings voluntarily disclosed by manufacturers. Listed companies follow fixed quarterly and annual update schedules. Industry reports and manufacturer briefings are released as needed. Single documents typically include report period explanations, core operational data, business segment breakdowns, downstream application classifications, R&D investment details, and other content. Fields and units are as follows: report period uses the YYYY-MM format, shipment volume unit is ten thousand units, revenue unit is RMB yuan, unit price unit is yuan per unit, and R&D investment unit is RMB yuan.

What constraints do these characteristics impose on the vector models and indexing link

The characteristics of optical module financial reports — fixed low-frequency updates, numerous structured fields, and diverse sources — impose multiple constraints on the vector models and indexing link. Fixed low-frequency updates require indexes to support incremental updates without full reindexing, reducing operational costs. The high number of structured fields requires vector models to adapt to structured metadata encoding, and indexes to support multi-field combined recall. Multi-dimensional labeling requirements from downstream application classifications require indexes to support label filtering, avoiding recall of irrelevant data outside the optical module category. The long average document length requires adjusting chunking strategies to preserve semantic associations between fields.

Configuration settings

Configuration Item	Recommended Value	Rationale
`EMBEDDING_MODEL_PATH`	`http://localhost:8000/v1` (local VLLM deployment address)	Structured fields in optical module financial reports require adaptation to the long-text encoding capabilities of general-purpose embedding models. VLLM enables efficient deployment of the Qwen3-Embedding-8B model
`CHUNK_SIZE`	`1000–1500 characters`	Optical module financial reports have dense fields. Excessively long chunks will lose field associations, while excessively short chunks will damage semantic integrity
`RECALL_TOP_K`	`Top 8–12 results`	Financial report analysis needs to cover multi-dimensional operational data. Too many recalled results will introduce irrelevant information, while too few will miss key fields
`VECTOR_STORE_TYPE`	`Milvus`	Optical module financial reports have multi-field combined query requirements. Milvus supports hybrid queries for structured filtering and vector recall
`PARSE_STRUCTURED_DATA`	`Enabled`	Optical module financial reports include standardized operational fields. Enabling this option extracts field-level metadata for vector index optimization
`MAX_DOCUMENT_LENGTH`	`50000 characters`	Listed company annual reports typically contain tens of thousands of characters of operational data. Adapting to this length enables complete processing of single financial report documents

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

A dial tcp 127.0.0.1:5432: connect: connection refused error occurs when starting the Milvus container, and deployment cannot be completed. The cause is failure to modify the default PostgreSQL dependency configuration in the compose file. Structured queries for optical module financial reports do not require additional PostgreSQL storage, and redundant configuration causes startup failure.
A 400 Bad Request error occurs when configuring the Qwen3-Embedding-8B model deployed via VLLM in FastGPT. The cause is failure to correctly align the port and model path of EMBEDDING_MODEL_PATH. Embedding encoding for optical module financial reports needs to match the model's input format, and misaligned configuration causes request failure.
Financial report documents outside the optical module category appear in knowledge base retrieval results, and recall results do not match expectations. The cause is failure to enable the PARSE_STRUCTURED_DATA switch, and failure to extract industry and report period fields from financial reports as filtering conditions. This causes vector recall to not associate with optical module-specific data.

How to confirm configuration is complete

Enter the FastGPT vector database management page, check the status of the connected vector database, confirm that VECTOR_STORE_TYPE matches the configured value, and there are no connection error prompts.
Upload a standard optical module financial report test document, check the number of split document chunks, confirm that it matches the CHUNK_SIZE setting, and there is no over-splitting or incomplete processing.
Initiate a targeted financial report retrieval request, check whether the recall results include optical module operational data for the corresponding report period, and confirm that structured filtering rules are effective.
Check the vector database disk usage statistics, confirm that stored content only includes original documents, split document chunks and embedding vectors, with no redundant data.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.