What the data for this category looks like
Software development investment research data primarily comes from code repository commit records, merge request documents, internal architecture design drafts, wikis and Issue discussions of open source projects, official documentation of technology stacks, and dependency library change logs.
Data update rhythms cover high-frequency and low-frequency scenarios. Code commit records update daily. Architecture documents and technical whitepapers update per project phase or quarterly.
Document structures include long text, structured fields, and short fragments. Fields include submitter, changed lines of code, associated requirement ID, and more. Units include timestamps, version numbers, lines of code, and more.
What constraints these characteristics impose on the "context and token" workflow
Software development investment research data mixes long text architecture documents, structured fields, and short code fragments. High-frequency updated commit records generate large volumes of incremental data. Context recall must balance information completeness and token usage control.
Structured fields such as dependency library versions, commit hashes require precise matching. This prevents low-relevance content from being included in the context. Long documents consume large amounts of tokens, which may exceed the model's context window limit. Content blocks must be split appropriately.
High-frequency incremental data also requires regular refreshing of the recall pool. This prevents context from lagging behind latest code changes.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Software development investment research data includes long architecture documents and short code fragments. This range balances token consumption and information completeness |
topK | Top 8–12 entries | Investment research data comes from diverse sources. A sufficient number of associated documents must be recalled to cover technical dependencies and code change records |
similarityThreshold | 0.72–0.85 | Precise matching of code changes and dependency library versions is required to avoid low-relevance content being included in the context |
rerankTopN | Top 3–5 entries | Code-related content has strong relevance. Rearranging results reduces context token usage |
AIPROXY_API_ENDPOINT | Proxy address of the deployment node | Used to connect to model APIs, bypass cross-origin and access restrictions |
AIPROXY_API_TOKEN | Secret key bound to the proxy node | Verifies API call permissions and prevents unauthorized access |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Model API calls fail, returning 401 Unauthorized or 403 Forbidden. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, leading to proxy verification failure or inability to access model services. - Phenomenon: E11000 duplicate key error collection: wisegpt is returned during knowledge base classification matching tasks. Cause: A unique identifier field that already exists is used when adding a new classification, leading to database primary key conflict.
- Phenomenon: Single recalled data blocks are too large, causing model processing timeouts or token consumption exceeding limits. Cause: The
chunkSizeparameter is not adjusted, and the default long segmentation setting is used, which does not match the mixed long and short document characteristics of software development investment research data.
How to confirm configuration is complete
- Call the proxy interface to verify the configuration validity of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and obtain a normal model response. - Upload a single typical investment research document, and check that the length of the segmented content blocks matches the preset
chunkSizerange. - Run a recall test to confirm that the number and relevance of recall results meet business requirements.
- Check the context token usage log to confirm that it does not exceed the context window limit of the currently used model.
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.