What the data for this category looks like
In agrochemical marketing and customer acquisition scenarios, data is primarily sourced from internal enterprise product filing documents, agricultural technology promotion manuals, field trial records, and compliance promotional materials. Data updates are triggered by new product launches, adjustments to pesticide registration policies, and quarterly agricultural technology guidance updates, with no fixed real-time update rhythm. Document structures include structured parameters and long-text descriptions, with some documents including dosage comparison tables. Fields cover active ingredient content, registered crops, application dosage, product name, and registration certificate number. Active ingredient content uses g/L and % as units, while application dosage uses ml/mu and kg/ha as units.
What constraints these characteristics impose on vector models and indexing
Structured parameters in agrochemical documents are tightly bound to long text. When chunking vectors, the association between parameters and context must be retained to avoid losing key semantics after splitting. Data is primarily updated in batches with non-real-time scheduling, so indexing must adapt to scheduled refresh rhythms instead of real-time synchronization, balancing indexing performance and data timeliness. Some documents include tabular dosage comparison data. Vector chunking must retain the row and column structure association of tables to avoid semantic breakage after disassembly. The field contains a high proportion of professional chemical and agricultural technical terms. Vector models must have domain semantic understanding capabilities, otherwise parameter matching deviations will occur.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Agrochemical marketing documents contain long-text agricultural guidance and parameterized product descriptions. This chunk length retains the binding relationship between active ingredients, application dosages and context, avoiding semantic chunking breakage |
chunk_overlap | 80–120 characters | Parameters in agrochemical documents are tightly bound to context. The overlap length ensures semantic coherence across chunks, preventing loss of key information |
embedding_model | Determined through actual testing | The agrochemical field includes professional chemical terms and agricultural scenarios. A vector model that supports domain semantic understanding must be matched |
index_refresh_strategy | Scheduled full refresh | Agrochemical data updates are triggered by new product launches and policy adjustments, with non-real-time batch updates. Scheduled refresh balances indexing performance and data timeliness |
recall_top_k | 6–8 results | Agrochemical marketing content needs to accurately match users' agricultural consulting or product selection needs. Too many recall results will interfere with decision-making, while too few will fail to cover similar scenarios |
similarity_threshold | 0.75–0.85 | Parameter matching for agrochemical products requires high precision. A threshold that is too low will introduce irrelevant product information, while a threshold that is too high will fail to cover similar agricultural application scenarios |
The parameter values provided on this page are all common starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three common mistakes
- After importing a self-hosted Qdrant vector database, recall results are empty. The vector database connection address and authentication key are not correctly configured in FastGPT, causing the index to fail to read external data normally.
- After enabling the Tencent Hunyuan embedding model, the task reports an error "model channel not configured". The API key and interface address of the corresponding model are not bound on the model supplier management page, causing index call failure.
- The recalled product parameters after chunking do not match the original text. The chunk length is set too small, which cuts the binding relationship between active ingredients, application dosages and corresponding usage scenarios during splitting, resulting in vector semantic deviation.
How to confirm the configuration is correct
- Upload a single agrochemical product specification, check the vector chunking results, confirm that key parameters and context are not split and broken.
- Initiate a test query, verify the similarity score of the recall results matches the configured threshold, confirm that the threshold setting meets business requirements.
- Trigger an index refresh task, check the index update log, confirm that the external vector database or model channel connection is normal.
- Import a batch of agrochemical documents, check the index construction progress and time consumption, confirm that the configured refresh strategy matches the data update rhythm.
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.