What the data for this category looks like
Data sources for chemical pharmaceutical financial reports mainly include statutory disclosure platforms of domestic and overseas stock exchanges, annual, semi-annual and quarterly reports published by pharmaceutical companies, and public industry R&D pipeline databases. Updates follow statutory disclosure cycles, with reports of different periods released at fixed time points. Document structures usually include sections such as management discussion and analysis, R&D expenditure details, product line revenue breakdown, clinical trial pipeline progress, and core financial indicators. Fields include R&D expenditure amount, revenue amount of each product line, inventory book value, accounts receivable balance, etc. Units are mostly RMB ten thousand or hundred million yuan; some overseas disclosure reports use USD for pricing.
What constraints do these characteristics impose on the model access and configuration link
Chemical pharmaceutical financial reports are generally long, with core data scattered across multiple modules, which imposes higher requirements on the model's context window capacity. Multi-dimensional segmented fields require precise matching, which imposes clear constraints on the knowledge base's recall precision and number of recalled entries. Fixed disclosure cycles allow knowledge base synchronization updates to be planned according to fixed nodes, but adaptation to different pharmaceutical companies' disclosure format differences is needed, requiring support for custom parsing rules. The combination of long text paragraphs and segmented fields requires that document splitting granularity balances context integrity and retrieval precision, avoiding loss of key information or excessive redundant fragments.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Chemical pharmaceutical single financial report documents are long, need to cover core text of multiple modules such as R&D, revenue, pipeline, to avoid context overflow |
chunkSize | 1000–1500 characters | Paragraphs such as R&D pipeline descriptions and revenue details in financial reports are relatively long; this granularity can retain paragraph integrity while facilitating precise retrieval |
overlapRate | 10–20% | Split text fragments need to retain context association, avoiding key information being truncated at fragment boundaries |
recallTopK | Top 8–10 entries | Financial reports have many core fields, need to recall a sufficient number of relevant fragments to support complete analysis |
similarityThreshold | 0.75–0.85 | Need to precisely match segmented fields such as R&D and revenue in financial reports, avoiding irrelevant fragments interfering with analysis results |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single financial report contains a large amount of text and table parsing; this duration covers the parsing time of most compliant financial reports |
The parameter values provided on this page are common recommended starting points for configuration. 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.
Three common mistakes
- Phenomenon: A locally deployed 14B parameter model returns a
500 Internal Server Errorafter enabling the knowledge base, and can interact normally without the knowledge base. Version: 4.8.14. Cause: The total length of text fragments recalled by the knowledge base exceeds the upper limit of the model's configuredmaxContextparameter, causing context overflow. - Phenomenon: After configuring a local TTS model access, the workflow calls the TTS node and returns a
Model connection failederror. Cause: The access port and API path of the local TTS model were not correctly filled into FastGPT's model access configuration items, or the cross-domain access permission of the model was not enabled. - Phenomenon: A limit of 300 words for answers is set in the prompt of the large model node, but the generated content far exceeds this length. Cause: The model's
maxOutputTokensparameter was not configured at the same time, or the word count constraint in the prompt was not correctly parsed by the model, resulting in uncontrolled output length.
How to confirm the configuration is complete
- Upload a single complete chemical pharmaceutical financial report PDF, check whether the parsed text fragments cover core fields such as R&D expenditure, revenue composition, pipeline information, and verify whether the parsing time is within the range set by the
PARSE_FILE_TIMEOUT_SECONDSparameter. - Initiate a query for specific R&D projects or product lines in the financial report, check whether the recalled text fragments contain relevant content, and verify whether the number of recalled entries matches the setting of the
recallTopKparameter. - Test the interaction between the locally deployed large model and the connected knowledge base, check whether it can correctly extract numerical fields from the financial report and generate structured analysis, and verify whether context overflow errors occur.
- After configuring TTS access, trigger the workflow to generate audio, check whether the audio is normally generated and playable, and verify whether the access parameters of the TTS model match the local deployment configuration.
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.