Python (AI/ML)

Python AI & Machine Learning

Overview

Learn how to build AI-powered Q&A applications using OpenAI and LangChain with Sravz financial data. This section covers large language models (LLMs), RAG (Retrieval-Augmented Generation), and building intelligent financial assistants.

What You’ll Learn

  • OpenAI API: GPT-4 and GPT-3.5 integration
  • LangChain: Framework for LLM application development
  • RAG Architecture: Retrieval-Augmented Generation for accurate answers
  • Vector Databases: Semantic search over financial documents
  • Prompt Engineering: Effective prompts for financial Q&A
  • Context Management: Handling large financial datasets

Key Technologies

  • OpenAI: GPT-4, GPT-3.5-turbo, embeddings
  • LangChain: LLM orchestration framework
  • Vector Stores: Pinecone, Chroma, FAISS for semantic search
  • Embeddings: Text embedding for similarity search
  • Python Libraries: openai, langchain, pandas, numpy

Documentation Index

  1. OpenAI Q&A Application - Build Q&A application on Sravz financial data using OpenAI
  2. LangChain Q&A Application - Build Q&A application using LangChain framework

Use Cases

Financial Q&A

  • Answer questions about stock fundamentals
  • Explain financial metrics and ratios
  • Compare company performance
  • Analyze earnings reports

Document Processing

  • Extract insights from 10-K/10-Q filings
  • Summarize earnings call transcripts
  • Process research reports
  • Analyze news sentiment

Intelligent Assistants

  • Portfolio analysis chatbot
  • Investment research assistant
  • Risk assessment advisor
  • Market insights generator

Architecture Patterns

OpenAI Direct Integration

User Query → OpenAI API → GPT-4 → Response
                ↓
          Context from Sravz DB

LangChain RAG Pipeline

User Query → Embedding → Vector Search → Context Retrieval
                                             ↓
                               LangChain → LLM → Response

Key Concepts

Retrieval-Augmented Generation (RAG)

  1. Embed Documents: Convert financial docs to vectors
  2. Store Vectors: Save in vector database
  3. Query Processing: Embed user question
  4. Semantic Search: Find relevant documents
  5. Context Injection: Add context to prompt
  6. LLM Generation: Generate accurate answer

Prompt Engineering

  • System Prompts: Define AI assistant behavior
  • Few-shot Examples: Provide example Q&A pairs
  • Context Window: Manage token limits (4k, 8k, 32k)
  • Temperature Control: Adjust response creativity

Data Sources

Sravz Financial Data

  • Stock fundamentals (P/E, EPS, revenue, etc.)
  • Historical price data
  • Earnings reports
  • Company descriptions
  • Sector/industry information

Integration Points

  • MongoDB: Document retrieval
  • S3: PDF/document storage
  • DuckDB: Analytical queries
  • Redis: Caching for performance

OpenAI vs. LangChain

OpenAI Direct

Pros:

  • Simple API integration
  • Direct control over prompts
  • Lower latency
  • Easier debugging

Cons:

  • Manual context management
  • Limited tooling
  • More boilerplate code

LangChain Framework

Pros:

  • Modular components
  • RAG pipelines built-in
  • Multiple LLM support
  • Rich ecosystem

Cons:

  • Learning curve
  • Abstraction overhead
  • Dependency complexity

Getting Started

  1. Start Simple: Begin with OpenAI Q&A Application
  2. Scale Up: Advance to LangChain Q&A Application
  3. Experiment: Try different prompts and models
  4. Optimize: Improve context retrieval and caching

Best Practices

Cost Management

  • Use GPT-3.5 for simple queries
  • Cache Responses: Avoid duplicate API calls
  • Limit Context: Only include relevant data
  • Batch Processing: Process multiple queries together

Quality Control

  • Validate Answers: Cross-reference with source data
  • Fact-Checking: Verify financial numbers
  • Confidence Scores: Track answer reliability
  • Fallback Logic: Handle uncertainty gracefully

Performance

  • Vector Database: Fast semantic search
  • Caching: Redis for frequent queries
  • Async Processing: Non-blocking API calls
  • Rate Limiting: Respect API quotas

Advanced Topics

  • Fine-tuning: Custom models for financial domain
  • Function Calling: Enable LLM to query databases
  • Agents: Multi-step reasoning and tool use
  • Memory: Maintain conversation context
  • Evaluation: Measure Q&A accuracy

Source Code: backend-py | Related: IBKR | Analytics

Follow Us: