Vector Models and Indexing for Power Grid Equipment

What the data for this category looks like

Financial report data for power grid equipment companies comes from public periodic reports and temporary announcements released by the Shanghai Stock Exchange and Shenzhen Stock Exchange, as well as official annual operational briefings published by companies. Updates primarily follow annual and quarterly periodic reports. Major orders, capacity changes and other matters are updated in real time via temporary announcements. Most documents are in PDF format, and include consolidated balance sheets, income statements, cash flow statements, notes on operating data, segmented business revenue breakdowns, equipment capacity and order data, and more. Fields cover revenue, net profit, R&D expenses, contract liabilities, installed capacity, equipment capacity and other metrics. Units are mostly RMB 10,000, 10,000 kW, and sets/units.

Constraints for vector models and indexing

Power grid equipment company financial reports mostly use long-text structures. A single annual financial report can contain tens of thousands of characters, and includes many nested tables and structured note data. This requires vector models to support long-text segmentation and structured vectorization of table content. Financial reports are updated frequently on a quarterly and annual basis, and disclosure formats vary across companies. This requires support for incremental indexing and unified preprocessing of multi-format documents. Financial reports include industry-specific fields for power grid equipment, such as segmented revenue and installed capacity. This requires vector models to accurately capture the semantics of professional terms in finance and power equipment industries, avoiding semantic drift. Some temporary announcements are short but time-sensitive. This requires adaptation for fast indexing and retrieval of short texts.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`EMBEDDING_MODEL`	`bge-large-zh-v1.5` or `text-embedding-3-large`	Supports context windows larger than 1024, can cover long text segments of power grid equipment financial reports, and performs well in understanding professional term semantics for finance and power equipment
`CHUNK_SIZE`	`800–1200 characters`	Notes in power grid equipment financial reports are mostly hundreds to thousands of characters long. This range balances context completeness and retrieval accuracy, avoiding semantic dilution caused by overly long single segments
`CHUNK_OVERLAP`	`100–150 characters`	Prevents key financial data and business descriptions from being split apart after long text segmentation, ensuring continuous information is fully covered
`RECALL_TOP_K`	`Top 8–12 results`	Core data in power grid equipment financial reports is relatively concentrated. An appropriate number of retrieval results can cover key information such as segmented business revenue and capacity, while avoiding excessive retrieval that introduces noise
`PARSE_TABLE_MODE`	`Structured extraction`	Power grid equipment financial reports include many financial and business capacity tables. Structured extraction preserves the correspondence between table fields and values, improving vectorization accuracy
`INDEX_STORAGE_TYPE`	`MongoDB vector index`	Natively supported by FastGPT, can adapt to frequently updated financial report data, and supports incremental indexing and multi-dimensional queries

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

Three Common Mistakes

Phenomenon: A 400 Bad Request error is returned when calling the vector model, with a prompt that the context length exceeds the limit. Cause: The CHUNK_SIZE parameter is not adjusted according to the long-text characteristics of power grid equipment financial reports, and a single segment of text exceeds the maximum context window of the selected embedding model.
Phenomenon: The exported dataset.csv file only contains the index field and no content field. Cause: The text extraction switch after document parsing is not enabled, or the content field of MongoDB is not correctly associated when configuring INDEX_STORAGE_TYPE, resulting in the original text not being stored.
Phenomenon: Segmented business revenue data is not included in the index retrieval results, and retrieval accuracy is low. Cause: The table structured parsing mode is not enabled, and the business tables in the financial reports are converted into unordered text, making it impossible for the vector model to capture the semantic association of segmented revenue.

How to Verify Proper Configuration

Upload a single annual financial report PDF of a power grid equipment company, and review the parsed text segmentation results. Confirm that the segmentation length matches the preset CHUNK_SIZE range, and the overlapping section retains key financial data.
Navigate to the vector model management page of FastGPT, and verify that the selected embedding model is correctly configured, and its supported context window is larger than the preset CHUNK_SIZE.
Run a knowledge base retrieval test, enter a query statement containing power grid equipment business keywords. Check whether the retrieval results include the financial data of the corresponding business segment, and confirm that the number of retrieved results matches the preset RECALL_TOP_K range.
Export the dataset.csv file of the knowledge base, confirm that the file contains both the index and content fields, and that the content field includes the complete text content of the financial report.

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.