Financial RAG Synthesizer: LangGraph + Llama 3

The Financial RAG Synthesizer is an agentic AI system built on LangGraph that uses Llama 3.1 to perform multi-step financial reasoning and document synthesis. Unlike basic retrieval-augmented generation (RAG) which simply retrieves text and summarizes it, this synthesizer uses a stateful graph architecture to decide between multiple tools: searching SEC EDGAR filings for historical data, pulling real-time market stats via yfinance, or performing web searches for news sentiment. The system includes an autonomous grader node that evaluates retrieved context for relevance and financial accuracy before passing it to the final reasoning node. This ensures that complex queries—such as comparing three-year R&D spending trends against revenue growth—are handled with the same rigor as a manual analyst review.

BUSINESS PROBLEM

Financial analysts and portfolio managers spend up to 70-80 percent of their time on manual document review and data collection, according to 2024 industry benchmarks from Deloitte and McKinsey. A single deep-dive analysis of a company's financial health requires cross-referencing thousands of pages of SEC filings, quarterly earnings transcripts, and real-time market data. This manual process is not only slow but prone to oversight; missing a single footnote in a 10-K filing can lead to flawed investment theses. Firms operating with lean teams cannot scale their coverage without a system that can synthesize these disparate data sources into a cohesive, verified analysis in seconds rather than hours.

WHO BENEFITS

Hedge funds and asset management firms needing to scale their equity research coverage without increasing headcount. Corporate finance departments at mid-market companies performing competitive intelligence and market trend analysis. Financial advisors who need to generate personalized, data-backed investment reports for high-net-worth clients based on current market shifts.

HOW IT WORKS

1. Query Decomposition: The user submits a complex financial question. Llama 3.1 (70B) breaks the query into logical sub-tasks, such as fetching historical filings, current stock price, and recent news.

2. Stateful Routing: The LangGraph router node analyzes the sub-tasks and directs the flow to specialized nodes. Tasks requiring historical data go to the SEC EDGAR retrieval node, while price queries go to the yfinance node.

3. Parallel Tool Execution: The system executes tool calls in parallel. One node retrieves SEC 10-K filings from a Pinecone vector store, another pulls current market metrics, and a third uses Tavily for real-time sentiment analysis.

4. Relevance Grading: A smaller Llama 3.1 (8B) model acts as a grader, checking each retrieved snippet for financial relevance and factual alignment with the original query. Irrelevant or contradictory data is pruned from the state.

5. Synthesis and Reasoning: The final reasoning node (Llama 3.1 70B or 405B) takes the verified, multi-source context and performs the requested analysis, such as calculating ratios or identifying spending trends.

6. Self-Correction Loop: The synthesizer checks its own output against the retrieved financial figures. If a discrepancy is found, it reruns the reasoning step to ensure mathematical integrity.

7. Output Generation: The system produces a structured report with inline citations, including direct links to the source SEC filings and yfinance data points.

TOOL INTEGRATION

LangGraph: Install via pip install langgraph. Use StateGraph to define the financial reasoning flow. The state should track the question, retrieved context, grader scores, and final synthesis.

Llama 3.1: For production speed, use Groq (Llama-3.1-70b-versatile) or vLLM on a private cloud. The 128k context window is essential for ingestion of full financial reports. Use the 8B model for fast, low-cost grading tasks.

SEC EDGAR API: Use the sec-api library or Edgar-Tools to pull raw filings. These must be pre-chunked and embedded using an embedding model like text-embedding-3-small before storage in Pinecone.

yfinance: Integrate using the yfinance Python library to fetch ticker-specific data. This tool should be defined as a structured tool within the LangGraph graph to allow the agent to pass parameters like ticker symbols and date ranges.

Pinecone: Create a Serverless index with 1536 dimensions. Use namespaces to separate data by ticker or fiscal year to improve retrieval precision and reduce noise.

Groq: Obtain an API key from console.groq.com. Groq is recommended for this workflow due to its high-speed inference, which is critical when the agent needs to perform multiple reasoning loops and grading steps in real-time.

ROI METRICS

Productivity Gain: Firms report a 33 percent median increase in analyst productivity within the first quarter of deployment. Document Review Time: Automated synthesis reduces manual document review time by 70-80 percent (Source: McKinsey Global Institute, 2024). Investment Return: The average ROI for financial AI automation is $3.70 for every $1 invested, with top performers seeing over $10.00 (Source: BCG AI at Scale Report, 2024). Time Saved: Analysts save an average of 15-20 hours per week on data entry and initial document synthesis tasks.

CAVEATS

Financial data accuracy is paramount; while the grader node catches most errors, the system should not be used for high-stakes trading decisions without human oversight. API costs for high-volume SEC filing retrieval can be significant if not optimized with efficient chunking and caching. Llama 3.1 can still hallucinate complex mathematical derivations; all calculated ratios must be verified against the raw numbers provided in the report citations. Real-time market data via yfinance can have 15-minute delays on some exchanges, which must be factored into time-sensitive analyses.