finished ai-consultant agent

[3rd-Son]

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✅ Type of Change

• ✨ New Project/Feature
• 🐞 Bug Fix
• 📚 Documentation Update
• 🔨 Refactor or Other

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• My README.md follows the official .github/README_TEMPLATE.md.
• I have included clear installation and usage instructions in my README.md.
• I have added a GIF or screenshot to the assets folder and included it in my README.md.

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EntelligenceAI PR Summary

This PR adds a complete AI Consultant Agent application for assessing company AI readiness with memory-powered conversations and web research capabilities.

• Streamlit UI with dual-tab interface for assessments and memory-based chat queries
• Memori v3 integration for automatic conversation capture and persistent SQLite storage
• ExaAI-powered case study research combined with LangChain and GPT-4o-mini reasoning
• Pydantic models for structured company profiles and research snippets
• Complete project setup with pyproject.toml, README documentation, and Streamlit theme configuration
• Python 3.11+ requirement with dependencies for LangChain, OpenAI, ExaAI, SQLAlchemy, and Streamlit

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Walkthrough

This PR introduces a new AI Consultant Agent application that assesses company AI readiness using Streamlit for the UI, Memori v3 for persistent memory, and ExaAI for web research capabilities. The implementation includes a complete project structure with dependency management via pyproject.toml, comprehensive documentation, and a workflow that combines LangChain reasoning with real-time case study research. The application features a dual-tab interface for conducting AI assessments and querying historical data through memory-powered conversations. All components are configured for Python 3.11+ with SQLite-based memory storage and OpenAI GPT-4o-mini integration.

Changes

File(s)	Summary
memory_agents/ai_consultant_agent/app.py	Implemented Streamlit-based AI consultant application with Memori v3 integration, featuring dual-tab interface (AI Assessment and Memory), API key management sidebar, session state handling, and automatic LLM interaction capture.
memory_agents/ai_consultant_agent/workflow.py	Created AI assessment workflow with Pydantic models (CompanyProfile, ResearchSnippet), ExaAI case study search function, and main assessment orchestration using GPT-4o-mini to generate structured markdown consulting reports.
memory_agents/ai_consultant_agent/pyproject.toml	Added project configuration for ai-consultant-agent v0.1.0 with Python 3.11+ requirement and dependencies including LangChain, ExaAI, Memori 3.0.0b3, SQLAlchemy, OpenAI, Streamlit, Pydantic, and python-dotenv.
memory_agents/ai_consultant_agent/README.md	Added comprehensive documentation covering installation with uv package manager, API key configuration (OpenAI and ExaAI), usage instructions, feature descriptions (AI readiness assessment, use-case recommendations, cost analysis, web research, persistent memory), and project structure overview.
memory_agents/ai_consultant_agent/.streamlit/config.toml	Added Streamlit theme configuration file with light mode base theme and commented-out customization options for colors.
memory_agents/job_search_agent/.env.example.local-backup	Added environment configuration template with placeholders for EXA_API_KEY and NEBIUS_API_KEY.

Sequence Diagram

This diagram shows the interactions between components:

sequenceDiagram
    actor User
    participant UI as Streamlit UI
    participant Profile as CompanyProfile
    participant Workflow as workflow.run_ai_assessment
    participant OpenAI as OpenAI Client
    participant Memori as Memori v3
    participant DB as SQLite Database

    Note over UI,DB: Initialization Phase
    UI->>DB: Create engine & session
    UI->>OpenAI: Initialize client with API key
    UI->>Memori: Initialize Memori(conn=SessionLocal)
    Memori->>OpenAI: Register OpenAI client
    Memori-->>UI: Return wrapped client
    UI->>Memori: Set attribution(entity_id, process_id)
    UI->>Memori: config.storage.build()
    Memori->>DB: Create/verify tables

    Note over User,DB: AI Assessment Flow
    User->>UI: Enter company details<br/>(name, industry, size, goals, etc.)
    User->>UI: Click "Run AI Assessment"
    UI->>Profile: Create CompanyProfile(company_name, industry, ...)
    Profile-->>UI: Return profile object
    UI->>Workflow: run_ai_assessment(profile, openai_client)
    Workflow->>OpenAI: chat.completions.create()<br/>(assessment generation)
    Note over OpenAI,Memori: Memori automatically captures<br/>this conversation
    Memori->>DB: Store conversation context
    OpenAI-->>Workflow: Return assessment markdown
    Workflow-->>UI: Return (assessment_markdown, snippets)
    UI->>UI: Store in session_state
    UI->>User: Display assessment results

    Note over User,DB: Memory-Powered Q&A Flow
    User->>UI: Enter question in chat
    UI->>UI: Append to memory_messages
    UI->>UI: Build full_prompt with<br/>latest assessment context
    UI->>OpenAI: chat.completions.create()<br/>(model="gpt-4o-mini")
    Note over OpenAI,Memori: Memori captures Q&A automatically
    Memori->>DB: Store question & response
    OpenAI-->>UI: Return response text
    UI->>UI: Append response to memory_messages
    UI->>User: Display response in chat

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