When this decision has to be made
You must finalize your indexing strategy when your knowledge base reaches a certain file scale, when retrieval response delays exceed acceptable thresholds, or when you see irrelevant search results or failed indexing tasks. If your knowledge base only holds a small number of small unstructured files, adjusting your indexing strategy early will add unnecessary upfront configuration, testing costs, and operational resource usage. Once your knowledge base scales to tens of thousands of files, or you encounter issues like multi-knowledge-base load crashes, vector database restarts from indexing tasks, or abnormally low semantic retrieval similarity, delaying adjustments will hurt user search experience, block business workflows, and risk core data failing to be retrieved. You also need to clarify adaptation conditions for each indexing strategy in advance if you plan to add custom indexing, multi-path recall combinations, or reranking models to improve retrieval accuracy. Poor configuration here can trigger new indexing errors. Evaluate your current indexing strategy based on your knowledge base’s file types, retrieval frequency, and business accuracy requirements. For example, if automatically generated indexes for uploaded Excel files cannot be found accurately and require manual custom indexes to work, or if you hit 413 status code embedding errors during bulk indexing tasks, you need to assess whether to switch or adjust your strategy mix.
Criteria matrix
| Candidate | Supported File Types | Index Build Speed | Retrieval Recall Accuracy | System Resource Usage | Error Trigger Probability | Configuration Complexity | Supported Retrieval Scenarios |
|---|---|---|---|---|---|---|---|
| Basic Chunked Indexing (Direct Chunking) | General text files (PDF, MD, etc.); not stated in the documentation; verify in your environment | Not stated in the documentation; verify in your environment | Retains full details, performance drops when too many chunks are created | Not stated in the documentation; verify in your environment | Not stated in the documentation; verify in your environment | Low | Semantic retrieval, full-text retrieval |
| QA Split Indexing | General text files, ideal for documents that can generate clear question-answer pairs | Not stated in the documentation; verify in your environment | Matched content has short length, similarity is easy to distinguish, but details are easily lost | Not stated in the documentation; verify in your environment | Not stated in the documentation; verify in your environment | Medium | Semantic retrieval, question-answer matching |
| Custom Enhanced Indexing | Structured files (Excel, CSV, etc.), supports custom field indexing | Not stated in the documentation; verify in your environment | High accuracy when manually configured, auto-generated indexes require validation | Not stated in the documentation; verify in your environment | Not stated in the documentation; verify in your environment | High manual configuration, medium auto-configuration | Specified field retrieval, semantic retrieval |
| Question-Answer Pair Extraction Mode Indexing | Documents with clear question-answer structure | Not stated in the documentation; verify in your environment | Not stated in the documentation; verify in your environment | Not stated in the documentation; verify in your environment | Higher, 413 status code errors may occur in some scenarios | Medium | Question-answer matching, semantic retrieval |
| Multi-path Recall Combined Strategy | All types of knowledge base files | Not stated in the documentation; verify in your environment | Can improve overall recall effect, requires combination with reranking models for optimization | Higher, needs to run multiple types of retrieval services simultaneously | Not stated in the documentation; verify in your environment | High | Hybrid retrieval, semantic retrieval, full-text retrieval, reranking sorting |
Why each criterion matters
Each criterion directly impacts the success of your indexing strategy deployment. Supported File Types are the foundational prerequisite for indexing strategy effectiveness. Different file types deliver different indexing performance. For example, structured Excel and CSV files may fail to match user search keywords accurately with basic chunked indexing, so you need custom indexing to enable accurate retrieval. Choosing an indexing strategy that does not fit your file types will lead to irrelevant search results, or even failure to retrieve valid data, disrupting normal business workflows. Community-reported issues show that automatically generated indexes for uploaded Excel files cannot be found accurately, requiring manual custom indexes to work. This confirms that matching file types to indexing strategies directly affects retrieval accuracy and usability. Index build speed determines knowledge base launch timelines and bulk import efficiency. When you need to import large numbers of files in bulk, slow index build speeds will extend deployment cycles and increase operational wait times. Slow indexing can also occupy system resources for long periods, disrupting other business operations. Community reports note that vector databases may restart when importing three or more 5,000-word Word documents. This shows you need to prioritize index build speed and resource consumption based on your business needs when selecting an indexing strategy. Retrieval recall accuracy is the core metric for measuring indexing strategy effectiveness, directly impacting user information access efficiency and accuracy. Different indexing strategies have varying impacts on recall accuracy. For example, basic chunked indexing retains more details but performs poorly when too many chunks are created. QA split indexing has short matched content, making similarity easy to distinguish but prone to losing details. Insufficient recall accuracy will leave users unable to access required information, or even lead to wrong decision-making. Community-reported issues include abnormally low or completely irrelevant semantic retrieval similarity, all linked to poor indexing strategy choices. System resource usage is a critical factor when deploying indexing strategies. Different indexing strategies consume different system resources. For example, multi-path recall combined strategies need to run semantic retrieval, full-text retrieval, and reranking services simultaneously, consuming significant CPU, memory, and network resources. Insufficient system resources will cause indexing task failures, slow retrieval response times, or even system crashes. Community-reported issues include browser crashes from multiple knowledge bases and vector database restarts from large file imports, both linked to excessive resource usage. You must select an indexing strategy based on your deployment environment’s resource limits to avoid failures from exhausted resources. Error trigger probability and configuration complexity directly affect operational difficulty and system stability. High error trigger probability increases operational workload and disrupts normal knowledge base operations. Excessively high configuration complexity increases deployment difficulty and time costs, and can even trigger failures from misconfiguration. Community-reported issues include 413 status code errors from question-answer pair extraction mode, and configuration errors during reranking model deployment. This shows you need to evaluate error trigger probability and configuration complexity in advance, and select more stable, easier-to-maintain indexing strategies.
The cost of switching later
Switching your existing indexing strategy carries multi-dimensional implementation costs. First, you will face data reconfiguration costs: you need to re-parse, chunk, and generate indexes for all uploaded files in your knowledge base. If you use custom indexing or QA split modes, you will also need to adjust corresponding data structures and index configurations, consuming extra storage and computing resources. Second, you will incur business interruption costs: rebuilding indexes will occupy core system resources, which may cause delays or temporary unavailability of existing retrieval services. You need to plan a reasonable maintenance window to minimize impact on business workflows. You will also need to invest in validation work: run full tests for the switched strategy, including retrieval accuracy, response speed, and error trigger probability, to ensure system stability. This requires sufficient manpower and time. Additionally, if you need to change your vector database or retrieval services, you will need to perform data migration and compatibility validation, which carries potential risks of data loss or retrieval failure. For example, switching from basic chunked indexing to multi-path recall combined strategies requires regenerating all vector data, configuring reranking models and full-text retrieval services, and verifying retrieval effects for all file types one by one. This will significantly increase overall implementation time and costs.
When this decision can wait
You can delay finalizing your indexing strategy in several scenarios. First, if your knowledge base only holds a small number of small unstructured files, and your business has low requirements for retrieval accuracy and response speed, you can put this decision on hold. Basic chunked indexing will meet basic retrieval needs, and you do not need to invest extra resources in complex indexing strategy adjustments. Second, if your business is in a rapid iteration phase, and you have not yet clarified the final knowledge base scale or retrieval scenarios, you can maintain your existing indexing strategy temporarily. Wait until your business needs are clear before selecting and adjusting your strategy. Third, if your deployment environment has limited resources and cannot support complex indexing strategies, you can delay this decision to prioritize core business operations. For example, if your knowledge base only contains dozens of small PDF files, and user retrieval needs are simple, you do not need to switch to complex indexing strategies immediately. You can evaluate and adjust your strategy once your knowledge base scales up or retrieval accuracy issues arise.
Keep reading
- Cloud, Community Self-Hosting or Commercial Private Deployment: Six Criteria
- Choosing a Document Parser: Built-in, Enhanced and External Services
References
Next steps
The criteria above can be checked against public documentation and a test deployment. To decide against a specific workload, data boundary and operations setup, contact sales for an assessment; the cloud service can be used first to validate feasibility before choosing a deployment form.
- Contact sales: assess the choice against your conditions
- Get started: validate feasibility on the cloud service
- Pricing: compare what each form covers