Vector Models and Indexing for Plastics and Rubber Marketing

What the data for this category looks like

Marketing content data for this category originates from supply chain finance promotion materials from financial institutions serving plastics and rubber enterprises, internal product and technical documents, dealer promotion materials, and application guidelines published by industry associations. The update schedule adjusts with new product launches, marketing campaigns, or industry standard revisions, with no fixed cycle. Most individual documents include product grades, performance parameters such as tensile strength and melt flow index, applicable scenarios, compliance certification information, and supporting financial service scripts. Fields include standardized product codes, parameter values and units, application scenario tags, and some documents include links to high-resolution material assets.

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

The mixed data characteristics of this category create multiple constraints for the vector models and indexing workflow. First, documents contain structured technical parameters, unstructured marketing scripts, and financial service content, requiring vector encoding logic adapted to mixed data types. Second, technical parameters carry specific units such as MPa, g/10min. Failure to standardize these units will interfere with semantic similarity calculations, so unit standardization embedding must be completed during the preprocessing stage. Third, content length varies significantly, with short marketing scripts and long product documents coexisting, so index sharding by content type is required. Fourth, there is no fixed update cycle and batch updates are supported, so the indexing service must support incremental synchronization logic to avoid resource consumption from full reindexing.

How to set configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	This category's documents mix short marketing scripts and long technical parameter documents. 800–1200 characters balances parameter integrity and semantic coherence, avoids splitting parameters into overly short segments, and prevents semantic redundancy from overly long segments.
`chunk_overlap`	`50–100 characters`	Technical parameters have associated logic across segments. Overlapping segments preserve parameter context and improve retrieval accuracy.
`embedding_model`	`bge-large-zh-v1.5 / based on actual testing`	This category includes specialized chemical terminology and marketing scripts. The bge series has strong adaptation for Chinese professional text. Local deployment avoids external interface exceptions and meets data privacy requirements for financial scenarios.
`recall_top_k`	`Top 8–12 results`	Marketing content matching requires a balance between precision and coverage. 8–12 results covers the needs of script and parameter combination requirements across different scenarios.
`embedding_api_timeout`	`30–60 seconds`	Localized model loading and inference take a certain amount of time. The timeout setting must match the model's runtime rhythm to avoid prematurely terminating valid requests.
`index_incremental`	`Enabled`	This category has no fixed update cycle and supports batch updates. Incremental indexing reduces resource consumption from full reindexing and adapts to business update schedules.

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 settings.

Three common mistakes

Phenomenon: The vector model interface returns 503 Service Unavailable, and logs show no available nodes for the text-embedding model under the default group. Cause: No localized vector model is configured. When relying on external interfaces, node overload or interface rate limiting causes service unavailability.
Phenomenon: The knowledge base deployed via Docker continuously displays "Indexing" with no progress updates. Cause: Incremental indexing is not enabled, and the full indexing task is not completed during batch document import, or the chunk_size setting is too small leading to an excessive number of segments, causing indexing task backlog.
Phenomenon: Semantic matching of technical parameters in vector retrieval results is low, and cannot be associated with the corresponding product. Cause: No preprocessing for parameter units is performed, and encoding is done directly on plain text, leading to unit differences such as MPa, g/10min being judged as semantic differences, or the selected vector model is not adapted to specialized chemical text.

How to confirm the configuration is correct

Check the vector model's inference logs to confirm that single encoding time matches the configured embedding_api_timeout threshold, with no timeout errors.
Import a single test document, check the indexing progress page to confirm that the number of segments matches the chunk_size setting, with no abnormal segments.
Initiate a test query, verify that the retrieval results include the corresponding product's technical parameters and marketing scripts, and adjust similarity_threshold to match business matching precision requirements.
Import batch updated documents, check whether the indexing task only processes new content, with no resource consumption prompts for full reindexing.

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.