What is financial RAG?什么是金融RAG
Financial RAG is retrieval-augmented generation designed for financial questions and evidence. It retrieves relevant filing text, tables, footnotes, transcripts or structured API records, supplies that evidence to a language model, and attaches traceable citations to the answer. The objective is not merely semantic similarity: it is to retrieve the minimally sufficient, period-correct evidence needed to support each claim.
金融RAG是面向金融问题与证据设计的检索增强生成系统。它从监管文件文本、表格、脚注、电话会逐字稿或结构化API记录中检索相关证据，提供给语言模型，并为答案附加可追溯引用。目标并非仅找到语义相似文本，而是检索足以支持每项主张且期间正确的最小证据集合。
Financial RAG architecture: from filing to answer金融RAG架构：从监管文件到可验证答案
Route questions before retrieval检索前先识别问题类型
A narrative-risk question, a single-table lookup and a multi-period ratio should not use the same path. Classify entity, form, period, task type and required evidence. Exact facts may route to a structured financial-data API; accounting explanations may need filing text; a variance question may need both.
叙述性风险问题、单表查值和多期间比率不应使用同一路径。系统应识别实体、表单、期间、任务类型和所需证据。精确数值可路由到结构化金融数据API，会计解释可能需要监管文件文本，差异分析则可能同时需要两者。
Table-aware ingestion for financial documents面向金融文档的表格感知摄取
Fixed token chunks can sever a number from its row label, column period, unit or footnote. Preserve the document hierarchy—form, section, subsection, page, table and note—and create serialized table facts that travel with enough context to remain interpretable.
固定长度分块可能把数字与行名、列期间、单位或脚注切断。应保留“表单→章节→子章节→页面→表格→附注”的文档层级，并创建携带充分上下文的序列化表格事实，使数字始终可解释。
Narrative chunk叙述文本块
Table fact表格事实
Metadata is part of the evidence元数据本身也是证据
Attach company identifier, form type, filing date, period end, fiscal year, currency, scale, consolidation scope, amendment status and access-control labels. Apply filters before similarity search so an excellent semantic match from the wrong company or period cannot outrank the requested evidence.
每个文本块应附带公司标识、表单类型、提交日期、期末日、财年、货币、数量级、合并范围、修订状态和访问控制标签。先应用过滤，再做相似度搜索，避免错误公司或期间的高语义匹配结果压过目标证据。
Hybrid retrieval for SEC filings and financial reports面向监管文件与财务报告的混合检索
Keyword retrieval关键词检索
Strong for exact accounting terms, line items, form sections, defined metrics and names.适合精确会计术语、报表项目、表单章节、已定义指标与名称。
Semantic retrieval语义检索
Strong for paraphrases, narrative themes, similar risks and natural-language questions.适合同义表达、叙述主题、相似风险与自然语言问题。
Structured retrieval结构化检索
Strong for exact values, time series, ratios and entity-period filtering through APIs.适合通过API获取精确数值、时间序列、比率和实体期间过滤。
Merge and deduplicate candidate evidence, then rerank against the question and required sub-questions. Multi-hop queries should produce an evidence plan: for example, retrieve two comparable revenue values, confirm units and periods, then send them to a calculation tool. Top-k alone does not guarantee coverage.
合并并去重候选证据后，应依据问题及其子问题进行重排。多跳问题需要先生成证据计划，例如检索两个可比收入值，确认单位与期间，再送入计算工具。仅依赖Top-k并不能保证证据覆盖完整。
Financial RAG citations must support the claim金融RAG引用必须真正支持主张
Supported citation有效引用
The cited table cell or passage contains the claimed fact, correct entity, period and unit; derived values link to every input and formula.引用的表格单元格或段落包含所主张事实，并且实体、期间与单位正确；派生值链接到所有输入与公式。
Related but unsupported相关但不支持
The source mentions the topic but not the claimed number, direction, cause or certainty. Similarity is not entailment.来源提到相关主题，却没有支持所声称的数字、方向、原因或确定程度。相似并不等于支持。
Validate numeric tokens as first-class objects把数字作为一等验证对象
Extract every amount, percentage, date and period from the draft. Confirm it appears in cited evidence or comes from an approved calculation with traceable inputs. Check unit, sign, scale, currency and arithmetic. If evidence conflicts, present the conflict or abstain rather than silently selecting one value.
从草稿中提取每个金额、百分比、日期和期间，确认其存在于所引证据中，或来自输入可追溯的获批计算。检查单位、符号、数量级、货币和算术；若证据冲突，应展示冲突或拒答，而不是静默选择某个数值。
How to evaluate a financial RAG system如何评估金融RAG系统
Build a human-verified set from real user tasks, not only synthetic questions. Include simple lookups, table questions, cross-period calculations, multi-document evidence, ambiguous requests, missing answers and adversarial instructions. Score retrieval and generation separately so a fluent answer cannot hide missing evidence.
应基于真实用户任务建立人工验证测试集，而不是只使用合成问题。测试集要覆盖简单查值、表格问题、跨期计算、多文档证据、模糊请求、答案缺失和对抗性指令；并分别评估检索与生成，避免流畅回答掩盖证据缺失。
| Metric指标 | What it asks衡量问题 | Failure example失败示例 |
|---|---|---|
| Context recall上下文召回 | Was all required evidence retrieved?是否检索到全部必要证据？ | One period missing缺少一个期间 |
| Context precision上下文精度 | How much retrieved content was relevant?检索内容中有多少真正相关？ | Wrong segment crowds prompt错误分部挤占上下文 |
| Citation support引用支持度 | Does evidence entail each claim?证据是否支持每项主张？ | Topic match only仅主题相关 |
| Numeric accuracy数字准确性 | Are values, units and calculations correct?数值、单位和计算是否正确？ | Millions treated as dollars把百万当作元 |
| Completeness完整性 | Were all requested facets answered?是否回答所有所需方面？ | Cause omitted遗漏原因 |
| Abstention quality拒答质量 | Does the system stop when evidence is insufficient?证据不足时是否停止？ | Invented estimate编造估算值 |
- Freeze test documents and expected evidence.冻结测试文档与预期证据。
- Version parser, index, embeddings, reranker, prompt and model.对解析器、索引、嵌入、重排器、提示词与模型进行版本管理。
- Run regression tests after every component change.每次组件变更后运行回归测试。
- Manually audit high-impact and failed cases.人工审计高影响与失败案例。
Financial RAG with QVeris and FMP使用QVeris与FMP构建金融RAG
QVeris can help an agent discover and call authorized FMP APIs when a question is better answered by structured financial data than document similarity. The retrieval router can combine those API records with filing passages and transcript evidence, while preserving source identifiers and tool responses.
当问题更适合通过结构化金融数据而非文档相似度回答时，QVeris可以帮助智能体发现并调用已授权的FMP API。检索路由器可以把API记录与监管文件段落、电话会证据组合，同时保留来源标识和工具响应。
Endpoint availability, fields, history, entitlements and rate limits depend on current FMP documentation and subscription. QVeris does not guarantee source completeness, retrieval accuracy or investment outcomes; validation and accountable review remain required.
端点可用性、字段、历史范围、权限和速率限制取决于当前FMP文档与订阅。QVeris不保证来源完整性、检索准确性或投资结果；验证与明确责任的人工复核仍然必要。
LLM for financial analysis大模型金融分析 · What is a 13F filing?什么是13F文件？ · QVeris documentationQVeris文档
Financial RAG FAQ金融RAG常见问题
What is financial RAG?什么是金融RAG？
It is retrieval-augmented generation adapted to financial evidence, combining document and structured-data retrieval with citations and numerical validation.
它是面向金融证据优化的检索增强生成，结合文档与结构化数据检索、引用和数字验证。
How should financial tables be chunked?金融表格应该怎样分块？
Keep row label, value, unit, period, table title, scope and relevant footnote together, with page coordinates and document metadata.
应把行名、数值、单位、期间、表名、范围和相关脚注放在一起，并保留页码坐标和文档元数据。
Is vector search enough for financial RAG?向量搜索足以支撑金融RAG吗？
Usually not. Exact terminology, structured values and entity-period constraints benefit from keyword search, metadata filters and API retrieval.
通常不足。精确术语、结构化数值与实体期间约束需要关键词检索、元数据过滤和API检索补充。
How do you validate financial RAG citations?如何验证金融RAG引用？
Check whether each citation logically supports the exact claim, including entity, period, unit, direction and certainty.
检查每个引用是否在逻辑上支持准确主张，包括实体、期间、单位、方向和确定程度。
How do you evaluate financial RAG?如何评估金融RAG？
Measure context recall and precision, citation support, numeric accuracy, answer completeness, reproducibility and abstention quality.
衡量上下文召回与精度、引用支持度、数字准确性、回答完整性、可复现性和拒答质量。
Does financial RAG eliminate hallucinations?金融RAG能消除幻觉吗？
No. Parsing, retrieval, citation and generation can each fail. Validation, monitoring and human review are still necessary.
不能。解析、检索、引用和生成都可能失败，仍需验证、监控和人工复核。
References and methodology sources参考资料与方法来源
- ACL Anthology — FinMRAGBench
- T²-RAGBench — financial text and table QA
- FMP — Financial data API documentation
Educational content only; not investment advice. Validate retrieved evidence and calculations before use.本文仅供教育用途，不构成投资建议。使用前必须验证所检索证据与计算。