Mulit-Agentic-Hackthon-Project

Crime Analysis Multi-Agent Research Assistant

This repository implements a multi-agent research assistant integrating:

• RAG Agent: Uses Pinecone (index: crime-reports) with metadata filtering (namespaces like 2023q1, 2024q2, etc.) to retrieve historical crime records
• Web Search Agent: Uses Tavily Search API for real-time web search on crime trends and incidents.
• Snowflake Agent: Queries structured crime statistics stored in Snowflake to generate summary and charts.
• ImageGenerator Agent: Uses image generation capabilities to build “contextual” images.
• Comparision Agent: Uses Snowflake data to compare data amongst different citites to compare the safety in each city against each other.

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📚 Crime Analysis Agentic Architecture Workflow

DEMO: https://northeastern-my.sharepoint.com/:v:/g/personal/mate_r_northeastern_edu/Efem9GpbHaBBpiGy6ZTCAioB6bqzmorEBhrIoxRSSLF_0Q?e=hwlnvd&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D

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Overview

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⚡️ Prerequisites

• Python 3.10+
• Poetry for dependency management
• Docker (optional)
• AWS account for S3 access
• Pinecone account
• Snowflake account
• Tavily Search API key

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🧠 Architecture Components

1. State Management

• Uses CrimeGPTState TypedDict for maintaining pipeline state.
• Tracks inputs, intermediate results, and final outputs.
• Manages conversation history and agent reasoning steps.

2. Node Structure

• start_node: Entry point, processes initial query.
• web_search_node: Real-time web data collection.
• rag_search_node: Historical document retrieval (Pinecone).
• snowflake_node: Crime statistics querying.
• agent_node: LLM-based analysis integration.
• report_generator_node: Final report compilation.

3. Flow Sequence

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🔄 Data Processing

1. Input Processing

• Query analysis and search type determination.
• Period selection for targeted analysis.

2. Parallel Data Collection

• Web scraping for current information.
• RAG-based document retrieval.
• Snowflake crime statistics querying.

3. Analysis Integration

• Claude 3 Haiku model for analysis.
• Tool-augmented reasoning.
• Multi-source data synthesis.

4. Report Generation

• Structured output formatting.
• Key findings compilation.
• Source attribution.

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🚀 Key Features

• Hybrid search capabilities (vector + metadata)
• Error handling and recovery
• State persistence across nodes
• Parallel execution paths
• Modular tool integration
• Visualization support

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🔧 Tools Integration

• Web Search: Tavily Search API integration
• RAG: Pinecone vector store
• Database: Snowflake connection
• LLM: Anthropic Claude 3
• Visualization: Graphviz

Deployed links

• FASTAPI = https://fastapi-crimereport-app-84150543519.us-central1.run.app/docs
• STREAMLIT = https://crime-report-analyzer.streamlit.app/

Deployed links

• FASTAPI = https://fastapi-crimereport-app-84150543519.us-central1.run.app/docs
• STREAMLIT = https://crime-report-analyzer.streamlit.app/

🛠️ Environment Setup

1. Environment Variables

Create a .env file with:

PINECONE_API_KEY=your_key
OPENAI_API_KEY=your_key
SERP_API_KEY=your_key
SNOWFLAKE_USER=your_user
SNOWFLAKE_PASSWORD=your_password
SNOWFLAKE_ACCOUNT=your_account
SNOWFLAKE_WAREHOUSE=your_warehouse
SNOWFLAKE_DATABASE=your_database
SNOWFLAKE_SCHEMA=your_schema
AWS_SERVER_PUBLIC_KEY=your_key
AWS_SERVER_SECRET_KEY=your_key
AWS_REGION=your_region
 

## 
## # Using Make
make backend

# Manual
uvicorn main:app --host 0.0.0.0 --port 8080 --reload


##Start Frontend

# Using Make
make frontend

# Manual
streamlit run streamlit_app.py --server.port 8501

##  Docker Deployment

make fastapibuild
make streamlitbuild


## Run Containers

make fastapirun
make streamlitrun