diff --git a/README.md b/README.md index 1ca4ab1..2fb7f99 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ # ๐ŸŒŸ Awesome LLM Apps -A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more.** This repository features LLM apps that use models from openai logo**OpenAI** , anthropic logo**Anthropic**, google logo**Google**, X logo**xAI** and open-source models like alibaba logo**Qwen** or meta logo**Llama** that you can run locally on your computer. +A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more.** This repository features LLM apps that use models from openai logo**OpenAI** , anthropic logo**Anthropic**, google logo**Google**, X logo**xAI** and open-source models like alibaba logo**Qwen** or meta logo**Llama** that you can run locally on your computer.

@@ -47,6 +47,15 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen + + + - - -
+ + TinyFish + +
+ + TinyFish + + 		Tiger Data @@ -56,6 +65,8 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen Tiger Data MCP
Speechmatics @@ -65,17 +76,6 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen Speechmatics
- - Okara - -
- - Okara AI - - 		Become a Sponsor @@ -138,6 +138,7 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen * [๐Ÿงฒ AI Competitor Intelligence Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_competitor_intelligence_agent_team/) * [๐Ÿ’ฒ AI Finance Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_finance_agent_team/) * [๐ŸŽจ AI Game Design Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_game_design_agent_team/) +* [๐Ÿงญ AG2 Adaptive Research Team](advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/) * [๐Ÿ‘จโ€โš–๏ธ AI Legal Agent Team (Cloud & Local)](advanced_ai_agents/multi_agent_apps/agent_teams/ai_legal_agent_team/) * [๐Ÿ’ผ AI Recruitment Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_recruitment_agent_team/) * [๐Ÿ  AI Real Estate Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_real_estate_agent_team) @@ -181,6 +182,8 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen * [๐Ÿ“  RAG with Database Routing](rag_tutorials/rag_database_routing/) * [๐Ÿ–ผ๏ธ Vision RAG](rag_tutorials/vision_rag/) * [๐Ÿฉบ RAG Failure Diagnostics Clinic](rag_tutorials/rag_failure_diagnostics_clinic/) +* [๐Ÿ•ธ๏ธ Knowledge Graph RAG with Citations](rag_tutorials/knowledge_graph_rag_citations/) + ### ๐Ÿ’พ LLM Apps with Memory Tutorials @@ -221,7 +224,7 @@ A curated collection of **Awesome LLM apps built with RAG, AI Agents, Multi-agen - Memory; callbacks; Plugins - Simple multiโ€‘agent; Multiโ€‘agent patterns -openai logo [OpenAI Agents SDK Crash Course](ai_agent_framework_crash_course/openai_sdk_crash_course/) +openai logo [OpenAI Agents SDK Crash Course](ai_agent_framework_crash_course/openai_sdk_crash_course/) - Starter agent; function calling; structured outputs - Tools: builtโ€‘in, function, thirdโ€‘party integrations - Memory; callbacks; evaluation diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/README.md b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/README.md new file mode 100644 index 0000000..619139d --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/README.md @@ -0,0 +1,52 @@ +# AG2 Adaptive Research Team + +A Streamlit app that blends agent teamwork with agent-enabled routing and fallback, built entirely on AG2. + +## What This Shows + +- **Agent teamwork**: explicit roles and sequential handoffs +- **Agent-enabled routing**: a clear decision step with local-doc vs web fallback +- **AG2-first implementation**: no Microsoft AutoGen dependency; installs via `ag2[openai]` + +## Features + +- Local document upload (PDF, TXT, MD) +- Routing decision based on document coverage +- Optional web fallback via SearxNG +- Verifier step to check evidence sufficiency +- Final synthesis with citations + +## How To Run + +1. Install dependencies: + +```bash +pip install -r requirements.txt +``` + +2. Run the app: + +```bash +streamlit run app.py +``` + +3. Provide your OpenAI API key in the sidebar and ask a question. + +## How It Works + +1. **Triage Agent** decides whether the question should be answered from local docs or the web. +2. **Local/Web Research Agent** collects evidence. +3. **Verifier Agent** checks evidence strength. +4. **Synthesizer Agent** produces the final answer with citations. + +## Optional Add-ons (AG2 0.11) + +- **AG-UI protocol integration** for richer UI rendering +- **OpenTelemetry tracing** for debugging multi-agent workflows + +These are optional and not required to run this example. + +## Notes + +- Default model used is `gpt-5-nano`. You can change it in the sidebar before running a query. +- Web fallback uses the SearxNG public instance at `https://searxng.site/search`. This instance may be rate-limited. diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/agents.py b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/agents.py new file mode 100644 index 0000000..0cecce1 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/agents.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +from typing import Any, Dict, Optional + +from autogen import AssistantAgent + + +DEFAULT_MODEL = "gpt-5-nano" + + +def make_llm_config(api_key: str, model: str = DEFAULT_MODEL, temperature: float = 0.2) -> Dict[str, Any]: + return { + "config_list": [ + { + "api_type": "openai", + "model": model, + "api_key": api_key, + } + ], + "temperature": temperature, + } + + +def build_agents(api_key: str, model: str = DEFAULT_MODEL) -> Dict[str, AssistantAgent]: + llm_config = make_llm_config(api_key=api_key, model=model) + + triage_agent = AssistantAgent( + name="triage_agent", + llm_config=llm_config, + system_message=( + "You are a triage agent for a research team. " + "Classify whether the question can be answered from local documents or needs web research. " + "Respond ONLY with JSON." + ), + ) + + local_research_agent = AssistantAgent( + name="local_research_agent", + llm_config=llm_config, + system_message=( + "You are a local research agent. Use only the provided document excerpts. " + "Return JSON with evidence and a draft answer." + ), + ) + + web_research_agent = AssistantAgent( + name="web_research_agent", + llm_config=llm_config, + system_message=( + "You are a web research agent. Use the provided web search results only. " + "Return JSON with evidence and a draft answer." + ), + ) + + verifier_agent = AssistantAgent( + name="verifier_agent", + llm_config=llm_config, + system_message=( + "You are a verifier. Check evidence sufficiency and identify gaps. " + "Return JSON verdict and gaps." + ), + ) + + synthesizer_agent = AssistantAgent( + name="synthesizer_agent", + llm_config=llm_config, + system_message=( + "You are the final synthesizer. Produce a clear answer with citations to the evidence." + ), + ) + + return { + "triage": triage_agent, + "local": local_research_agent, + "web": web_research_agent, + "verifier": verifier_agent, + "synthesizer": synthesizer_agent, + } + + +def run_agent(agent: AssistantAgent, prompt: str) -> str: + reply = agent.generate_reply(messages=[{"role": "user", "content": prompt}]) + if isinstance(reply, dict): + return reply.get("content", "") or "" + return str(reply) diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/app.py b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/app.py new file mode 100644 index 0000000..e33cfe5 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/app.py @@ -0,0 +1,71 @@ +import os +import streamlit as st + +from agents import DEFAULT_MODEL +from router import run_pipeline +from tools import build_local_index, load_documents + +SEARXNG_BASE_URL = "https://searxng.site/search" + + +st.set_page_config(page_title="AG2 Adaptive Research Team", layout="wide") + +st.title("AG2 Adaptive Research Team") +st.caption("Agent teamwork + agent-enabled routing, built with AG2") + +with st.sidebar: + st.header("API Configuration") + api_key = st.text_input("OpenAI API Key", type="password") + model = st.text_input("Model", value=DEFAULT_MODEL) + web_enabled = st.toggle("Enable Web Fallback", value=True) + st.markdown( + "Web fallback uses a public SearxNG instance, which may be rate-limited." + ) + +st.subheader("1. Upload Local Documents") +files = st.file_uploader( + "Upload PDFs or text files", + type=["pdf", "txt", "md"], + accept_multiple_files=True, +) + +st.subheader("2. Ask a Question") +question = st.text_area("Research question") + +run_clicked = st.button("Run Research") + +if run_clicked: + if not api_key: + st.error("Please provide your OpenAI API key.") + st.stop() + + if not question.strip(): + st.error("Please enter a research question.") + st.stop() + + os.environ["OPENAI_API_KEY"] = api_key + + documents = load_documents(files or []) + local_index = build_local_index(documents) + + with st.spinner("Running the AG2 team..."): + result = run_pipeline( + question=question, + local_chunks=local_index, + api_key=api_key, + model=model, + web_enabled=web_enabled, + searxng_base_url=SEARXNG_BASE_URL, + ) + + st.subheader("Routing Decision") + st.json(result.get("triage", {})) + + st.subheader("Evidence") + st.json(result.get("evidence", [])) + + st.subheader("Verifier") + st.json(result.get("verifier", {})) + + st.subheader("Final Answer") + st.markdown(result.get("final_answer", "")) diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/requirements.txt b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/requirements.txt new file mode 100644 index 0000000..e589c67 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/requirements.txt @@ -0,0 +1,3 @@ +ag2[openai]>=0.11.0 +streamlit>=1.33.0 +pypdf>=4.2.0 diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/router.py b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/router.py new file mode 100644 index 0000000..87f25f8 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/router.py @@ -0,0 +1,141 @@ +from __future__ import annotations + +import json +import re +from typing import Any, Dict, List + +from agents import build_agents, run_agent +from tools import Chunk, run_searxng, search_local + + +def _extract_json(text: str) -> Dict[str, Any]: + match = re.search(r"\{.*\}", text, re.DOTALL) + if not match: + return {} + try: + return json.loads(match.group(0)) + except json.JSONDecodeError: + return {} + + +def _summarize_chunks(chunks: List[Chunk]) -> str: + lines = [] + for chunk in chunks: + snippet = chunk.text[:300].strip() + lines.append(f"- {chunk.doc_name} [chunk {chunk.chunk_id}]: {snippet}") + return "\n".join(lines) + + +def run_pipeline( + question: str, + local_chunks: List[Chunk], + api_key: str, + model: str, + web_enabled: bool, + searxng_base_url: str, +) -> Dict[str, Any]: + agents = build_agents(api_key=api_key, model=model) + + doc_summary = "No local documents provided." + if local_chunks: + doc_names = sorted({chunk.doc_name for chunk in local_chunks}) + doc_summary = f"Local docs: {', '.join(doc_names)} (total chunks: {len(local_chunks)})" + + triage_prompt = f""" +Question: {question} +{doc_summary} + +Decide the best route. Output JSON with keys: +- route: "local" or "web" +- confidence: number 0 to 1 +- rationale: short string +""" + triage_raw = run_agent(agents["triage"], triage_prompt) + triage = _extract_json(triage_raw) + + route = triage.get("route", "local") + if not local_chunks and web_enabled: + route = "web" + + evidence: List[Dict[str, Any]] = [] + draft_answer = "" + + if route == "web" and web_enabled: + search_results = run_searxng(question, base_url=searxng_base_url, max_results=5) + formatted_results = "\n".join( + [ + f"- {item.get('title', 'Untitled')} | {item.get('link', '')} | {item.get('snippet', '')}" + for item in search_results + ] + ) + web_prompt = f""" +Question: {question} + +Web results: +{formatted_results} + +Return JSON with keys: +- evidence: list of {{source, summary}} +- draft_answer: string +""" + web_raw = run_agent(agents["web"], web_prompt) + web_json = _extract_json(web_raw) + evidence = web_json.get("evidence", []) + draft_answer = web_json.get("draft_answer", "") + else: + hits = search_local(question, local_chunks, top_k=5) + formatted_hits = _summarize_chunks(hits) + local_prompt = f""" +Question: {question} + +Document excerpts: +{formatted_hits} + +Return JSON with keys: +- evidence: list of {{source, summary}} +- draft_answer: string +""" + local_raw = run_agent(agents["local"], local_prompt) + local_json = _extract_json(local_raw) + evidence = local_json.get("evidence", []) + draft_answer = local_json.get("draft_answer", "") + + verifier_prompt = f""" +Question: {question} + +Draft answer: +{draft_answer} + +Evidence: +{json.dumps(evidence, indent=2)} + +Return JSON with keys: +- verdict: "sufficient" or "insufficient" +- gaps: list of short strings +""" + verifier_raw = run_agent(agents["verifier"], verifier_prompt) + verifier = _extract_json(verifier_raw) + + synth_prompt = f""" +Question: {question} + +Draft answer: +{draft_answer} + +Evidence: +{json.dumps(evidence, indent=2)} + +Verifier verdict: +{json.dumps(verifier, indent=2)} + +Provide the final answer with clear citations to the evidence sources. +""" + final_answer = run_agent(agents["synthesizer"], synth_prompt) + + return { + "route": route, + "triage": triage, + "evidence": evidence, + "verifier": verifier, + "final_answer": final_answer, + } diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/tools.py b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/tools.py new file mode 100644 index 0000000..03d7ea6 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/tools.py @@ -0,0 +1,105 @@ +from __future__ import annotations + +import re +from dataclasses import dataclass +from typing import Iterable, List + +from pypdf import PdfReader +from autogen.tools.experimental import SearxngSearchTool + + +@dataclass +class Document: + name: str + text: str + + +@dataclass +class Chunk: + doc_name: str + chunk_id: int + text: str + + +def _clean_text(text: str) -> str: + return re.sub(r"\s+", " ", text).strip() + + +def _tokenize(text: str) -> List[str]: + return re.findall(r"[a-zA-Z0-9]+", text.lower()) + + +def load_documents(uploaded_files: Iterable) -> List[Document]: + documents: List[Document] = [] + + for file in uploaded_files: + name = file.name + if name.lower().endswith(".pdf"): + reader = PdfReader(file) + pages_text = [] + for page in reader.pages: + pages_text.append(page.extract_text() or "") + text = _clean_text("\n".join(pages_text)) + else: + raw = file.read() + try: + text = raw.decode("utf-8") + except UnicodeDecodeError: + text = raw.decode("latin-1") + text = _clean_text(text) + + if text: + documents.append(Document(name=name, text=text)) + + return documents + + +def chunk_text(text: str, chunk_size: int = 800, overlap: int = 120) -> List[str]: + words = text.split() + if not words: + return [] + + chunks: List[str] = [] + start = 0 + while start < len(words): + end = min(len(words), start + chunk_size) + chunk = " ".join(words[start:end]) + chunks.append(chunk) + if end == len(words): + break + start = max(0, end - overlap) + + return chunks + + +def build_local_index(documents: List[Document]) -> List[Chunk]: + index: List[Chunk] = [] + for doc in documents: + chunks = chunk_text(doc.text) + for idx, chunk in enumerate(chunks, start=1): + index.append(Chunk(doc_name=doc.name, chunk_id=idx, text=chunk)) + return index + + +def search_local(query: str, index: List[Chunk], top_k: int = 5) -> List[Chunk]: + query_tokens = set(_tokenize(query)) + scored = [] + for chunk in index: + chunk_tokens = set(_tokenize(chunk.text)) + overlap = len(query_tokens & chunk_tokens) + if overlap == 0: + continue + scored.append((overlap, chunk)) + + scored.sort(key=lambda item: item[0], reverse=True) + return [item[1] for item in scored[:top_k]] + + +def run_searxng(query: str, base_url: str, max_results: int = 5) -> List[dict]: + tool = SearxngSearchTool(base_url=base_url) + results = tool(query=query, max_results=max_results) + if isinstance(results, dict): + return results.get("results", []) + if isinstance(results, list): + return results + return [] diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/README.md b/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/README.md index a11adb3..29948c6 100644 --- a/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/README.md +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/README.md @@ -13,12 +13,12 @@ https://github.com/user-attachments/assets/a0befa3a-f4c3-400d-9790-4b9e37254405 - **CEO Agent**: Strategic leader and final decision maker - Analyzes startup ideas using structured evaluation - Makes strategic decisions across product, technical, marketing, and financial domains - - Uses AnalyzeStartupTool and MakeStrategicDecision tools + - Uses AnalyzeProjectRequirements tool - **CTO Agent**: Technical architecture and feasibility expert - Evaluates technical requirements and feasibility - Provides architecture decisions - - Uses QueryTechnicalRequirements and EvaluateTechnicalFeasibility tools + - Uses CreateTechnicalSpecification tool - **Product Manager Agent**: Product strategy specialist - Defines product strategy and roadmap @@ -41,18 +41,18 @@ The agency uses specialized tools built with OpenAI Schema for structured analys - **Analysis Tools**: AnalyzeProjectRequirements for market evaluation and analysis of startup idea - **Technical Tools**: CreateTechnicalSpecification for technical assessment -### ๐Ÿ”„ Asynchronous Communication +### ๐Ÿค Multi-Agent Collaboration -The agency operates in async mode, enabling: -- Parallel processing of analyses from different agents -- Efficient multi-agent collaboration -- Real-time communication between agents -- Non-blocking operations for better performance +The agency coordinates five specialists through explicit communication flows: +- CEO drives strategic oversight across the team +- CTO and Developer collaborate on implementation feasibility +- Product and Client Success coordinate roadmap and go-to-market planning +- Each analysis is returned in a dedicated section in the Streamlit UI ### ๐Ÿ”— Agent Communication Flows - CEO โ†”๏ธ All Agents (Strategic Oversight) - CTO โ†”๏ธ Developer (Technical Implementation) -- Product Manager โ†”๏ธ Marketing Manager (Go-to-Market Strategy) +- Product Manager โ†”๏ธ Client Success Manager (Go-to-Market Strategy) - Product Manager โ†”๏ธ Developer (Feature Implementation) - (and more!) diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/agency.py b/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/agency.py index dfcddae..97c6ca6 100644 --- a/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/agency.py +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/agency.py @@ -23,9 +23,6 @@ class AnalyzeProjectRequirements(BaseTool): def run(self) -> str: """Analyzes project and stores results in shared state""" - if self.context is None: - raise RuntimeError("Tool context is not available.") - if self.context.get("project_analysis", None) is not None: raise ValueError("Project analysis already exists. Please proceed with technical specification.") @@ -64,9 +61,6 @@ class CreateTechnicalSpecification(BaseTool): def run(self) -> str: """Creates technical specification based on analysis""" - if self.context is None: - raise RuntimeError("Tool context is not available.") - project_analysis = self.context.get("project_analysis", None) if project_analysis is None: raise ValueError("Please analyze project requirements first using AnalyzeProjectRequirements tool.") @@ -248,7 +242,6 @@ def main() -> None: (product_manager, developer), (product_manager, client_manager), ], - shared_files_folder="shared_files", ) # Prepare project info diff --git a/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/requirements.txt b/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/requirements.txt index 76201a4..f3947eb 100644 --- a/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/requirements.txt +++ b/advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/requirements.txt @@ -1,3 +1,3 @@ -python-dotenv==1.0.1 +python-dotenv==1.1.1 agency-swarm==1.7.0 streamlit diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/.env.example b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/.env.example new file mode 100644 index 0000000..369607b --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/.env.example @@ -0,0 +1,5 @@ +# Backend (Python) +GOOGLE_API_KEY="your_google_api_key_here" + +# Frontend (Next.js) - optional, defaults to localhost:8000 +AGENT_URL=http://localhost:8000/ diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/README.md b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/README.md new file mode 100644 index 0000000..e48b5e4 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/README.md @@ -0,0 +1,216 @@ +# ๐ŸŽฎ AI Negotiation Battle Simulator + +### A Real-Time Agent vs Agent Showdown with AG-UI! + +Watch two AI agents battle it out in an epic used car negotiation! Built with **Google ADK** for the backend agents and **AG-UI + CopilotKit** for a jaw-dropping reactive frontend. + +## โœจ Features + +- **๐Ÿค– Dual AI Agents**: Buyer vs Seller with distinct personalities and negotiation strategies +- **๐Ÿ”„ AG-UI Protocol**: Real-time streaming of agent actions, tool calls, and state changes +- **๐Ÿ’… Jaw-Dropping UI**: Animated battle arena with live negotiation timeline +- **๐ŸŽญ 8 Unique Personalities**: 4 buyers + 4 sellers with different negotiation styles +- **๐Ÿ“Š Generative UI**: Custom React components render tool calls in real-time +- **๐Ÿ”— Shared State**: Agent state syncs bidirectionally with the frontend + +## ๐Ÿ—๏ธ Architecture + +``` +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚ Next.js + CopilotKit Frontend โ”‚ +โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ +โ”‚ โ”‚ Battle Arenaโ”‚ โ”‚ VS Display โ”‚ โ”‚Chat Sidebar โ”‚ โ”‚ +โ”‚ โ”‚ Timeline โ”‚ โ”‚ Buyer/Seller โ”‚ โ”‚ (AG-UI) โ”‚ โ”‚ +โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ AG-UI Events โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ CopilotKit Runtime โ”‚ + โ”‚ (/api/copilotkit) โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ HTTP/SSE + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ FastAPI + AG-UI โ”‚ + โ”‚ ADK Middleware โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ ADK Negotiation Agent โ”‚ + โ”‚ (Battle Master) โ”‚ + โ”‚ โ”‚ + โ”‚ Tools: โ”‚ + โ”‚ โ€ข configure_negotiationโ”‚ + โ”‚ โ€ข start_negotiation โ”‚ + โ”‚ โ€ข buyer_make_offer โ”‚ + โ”‚ โ€ข seller_respond โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +## ๐Ÿš€ Quick Start + +### Prerequisites + +- Python 3.11+ +- Node.js 18+ +- Google AI API Key ([Get one here](https://aistudio.google.com/)) + +### 1. Clone and Navigate + +```bash +git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git +cd advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator +``` + +### 2. Set Up Backend + +```bash +cd backend +pip install -r requirements.txt + +# Create .env file +echo "GOOGLE_API_KEY=your_api_key_here" > .env + +# Start the backend +python agent.py +``` + +The backend will start on `http://localhost:8000` + +### 3. Set Up Frontend + +```bash +cd frontend +npm install + +# Start the frontend +npm run dev +``` + +The frontend will start on `http://localhost:3000` + +### 4. Start Negotiating! ๐ŸŽฎ + +Open `http://localhost:3000` and tell the Battle Master: +- "Start a negotiation for a used car" +- "Show me available scenarios" +- "Use Desperate Dan as buyer and Shark Steve as seller" + +## ๐ŸŽญ Personalities + +### Buyers +| Personality | Emoji | Style | +|-------------|-------|-------| +| Desperate Dan | ๐Ÿ˜ฐ | Needs car TODAY, weak poker face | +| Analytical Alex | ๐Ÿงฎ | Cites every data point, very logical | +| Cool-Hand Casey | ๐Ÿ˜Ž | Master of the walkaway bluff | +| Fair-Deal Fran | ๐Ÿค | Just wants a win-win | + +### Sellers +| Personality | Emoji | Style | +|-------------|-------|-------| +| Shark Steve | ๐Ÿฆˆ | Never drops more than 5% | +| By-The-Book Beth | ๐Ÿ“Š | Goes strictly by KBB | +| Motivated Mike | ๐Ÿ˜… | Really needs to sell | +| Drama Queen Diana | ๐ŸŽญ | Everything is "final offer" | + +## ๐Ÿ“ Project Structure + +``` +ai_negotiation_battle_simulator/ +โ”œโ”€โ”€ README.md +โ”œโ”€โ”€ .env.example +โ”‚ +โ”œโ”€โ”€ backend/ # Python ADK + AG-UI +โ”‚ โ”œโ”€โ”€ agent.py # Main agent with tools +โ”‚ โ”œโ”€โ”€ requirements.txt +โ”‚ โ”œโ”€โ”€ config/ +โ”‚ โ”‚ โ”œโ”€โ”€ personalities.py # 8 unique personalities +โ”‚ โ”‚ โ””โ”€โ”€ scenarios.py # 3 negotiation scenarios +โ”‚ โ””โ”€โ”€ agents/ +โ”‚ โ”œโ”€โ”€ buyer_agent.py +โ”‚ โ”œโ”€โ”€ seller_agent.py +โ”‚ โ””โ”€โ”€ orchestrator.py +โ”‚ +โ””โ”€โ”€ frontend/ # Next.js + CopilotKit + โ”œโ”€โ”€ package.json + โ”œโ”€โ”€ src/ + โ”‚ โ””โ”€โ”€ app/ + โ”‚ โ”œโ”€โ”€ layout.tsx # CopilotKit provider + โ”‚ โ”œโ”€โ”€ page.tsx # Battle Arena UI + โ”‚ โ”œโ”€โ”€ globals.css # Battle animations + โ”‚ โ””โ”€โ”€ api/ + โ”‚ โ””โ”€โ”€ copilotkit/ + โ”‚ โ””โ”€โ”€ route.ts # CopilotKit runtime + โ””โ”€โ”€ tailwind.config.js +``` + +## ๐ŸŽฌ Sample Battle + +``` +๐Ÿ”” NEGOTIATION BEGINS: 2019 Honda Civic EX + +๐Ÿ“‹ ASKING PRICE: $15,500 + +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” + +๐Ÿ˜Ž COOL-HAND CASEY (Round 1): +"I've seen similar Civics go for less. $11,500 seems fair +given the market. Cash in hand today." + +๐Ÿฆˆ SHARK STEVE (Round 1): +"$15,000. This car is pristine. I've got two other +interested buyers coming this weekend." + +๐Ÿ˜Ž COOL-HAND CASEY (Round 2): +"$12,500 is my limit. Take it or I walk." + +๐Ÿฆˆ SHARK STEVE (Round 2): +*considers* "$14,000. Final offer." + +๐Ÿ˜Ž COOL-HAND CASEY (Round 3): +"$13,000. Meet me in the middle." + +๐Ÿฆˆ SHARK STEVE (Round 3): +"...$13,500 and you've got a deal." + +๐Ÿ˜Ž COOL-HAND CASEY (Round 4): +"$13,250. Final answer." + +๐Ÿฆˆ SHARK STEVE (Round 4): +"Deal. ๐Ÿค" + +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” + +โœ… DEAL CLOSED AT $13,250 ๐ŸŽ‰ + Buyer saved: $2,250 (14.5% off asking) +``` + +## ๐Ÿง  How It Works + +1. **User Request**: You tell the Battle Master what kind of negotiation to run +2. **Configuration**: The agent sets up the scenario and personalities +3. **Tool Calls**: The agent alternates between `buyer_make_offer` and `seller_respond` tools +4. **AG-UI Streaming**: Each tool call streams to the frontend via AG-UI protocol +5. **Generative UI**: Custom React components render each offer/response beautifully +6. **Shared State**: The negotiation timeline updates in real-time +7. **Outcome**: Deal or no-deal is celebrated with animations! + +## ๐Ÿ“š Learn More + +- [Google ADK Documentation](https://google.github.io/adk-docs/) +- [AG-UI Protocol Docs](https://docs.ag-ui.com/) +- [CopilotKit Documentation](https://docs.copilotkit.ai/) + +## ๐Ÿค Contributing + +Feel free to add: +- New negotiation scenarios (salary, apartment, contracts) +- Additional personality types +- More dramatic UI effects +- Cross-framework agents (LangChain, CrewAI via A2A) + +--- + +*May the best negotiator win!* ๐Ÿ† diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agent.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agent.py new file mode 100644 index 0000000..a077453 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agent.py @@ -0,0 +1,443 @@ +""" +AI Negotiation Battle Simulator - Backend Agent + +This module creates an ADK agent wrapped with AG-UI middleware for +real-time negotiation between AI buyer and seller agents. +""" + +import os +from typing import Optional +from dotenv import load_dotenv + +from google.adk.agents import LlmAgent +from google.adk.tools import FunctionTool, ToolContext +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from ag_ui_adk import ADKAgent, add_adk_fastapi_endpoint + +from config.scenarios import SCENARIOS, get_scenario +from config.personalities import ( + BUYER_PERSONALITIES, + SELLER_PERSONALITIES, + get_personality_prompt +) + +# Load environment variables +load_dotenv() + + +# ============================================================================ +# NEGOTIATION STATE (shared between tools) +# ============================================================================ + +class NegotiationState: + """Tracks the state of the negotiation.""" + def __init__(self): + self.reset() + + def reset(self): + self.scenario_id = "craigslist_civic" + self.buyer_personality = "cool_hand_casey" + self.seller_personality = "by_the_book_beth" + self.rounds = [] + self.current_round = 0 + self.status = "setup" # setup, negotiating, deal, no_deal + self.final_price = None + self.buyer_budget = 13000 + self.seller_minimum = 12500 + self.asking_price = 15500 + +# Global state +negotiation_state = NegotiationState() + + +# ============================================================================ +# NEGOTIATION TOOLS +# ============================================================================ + +def configure_negotiation( + scenario_id: str = "craigslist_civic", + buyer_personality: str = "cool_hand_casey", + seller_personality: str = "by_the_book_beth", + tool_context: Optional[ToolContext] = None +) -> dict: + """ + Configure the negotiation scenario and personalities. + + Args: + scenario_id: The scenario to use (craigslist_civic, vintage_guitar, apartment_sublet) + buyer_personality: Buyer's personality (desperate_dan, analytical_alex, cool_hand_casey, fair_deal_fran) + seller_personality: Seller's personality (shark_steve, by_the_book_beth, motivated_mike, drama_queen_diana) + + Returns: + Configuration summary + """ + scenario = get_scenario(scenario_id) + + negotiation_state.reset() + negotiation_state.scenario_id = scenario_id + negotiation_state.buyer_personality = buyer_personality + negotiation_state.seller_personality = seller_personality + negotiation_state.asking_price = scenario["asking_price"] + negotiation_state.buyer_budget = scenario["buyer_budget"] + negotiation_state.seller_minimum = scenario["seller_minimum"] + negotiation_state.status = "ready" + + buyer_p = BUYER_PERSONALITIES[buyer_personality] + seller_p = SELLER_PERSONALITIES[seller_personality] + + return { + "status": "configured", + "scenario": { + "title": scenario["title"], + "item": scenario["item"]["name"], + "asking_price": scenario["asking_price"], + "fair_market_value": scenario["fair_market_value"] + }, + "buyer": { + "name": buyer_p["name"], + "emoji": buyer_p["emoji"], + "budget": scenario["buyer_budget"] + }, + "seller": { + "name": seller_p["name"], + "emoji": seller_p["emoji"], + "minimum": scenario["seller_minimum"] + } + } + + +def start_negotiation(tool_context: Optional[ToolContext] = None) -> dict: + """ + Start the negotiation battle! + + Returns: + Initial negotiation state and instructions + """ + if negotiation_state.status != "ready": + return {"error": "Please configure the negotiation first using configure_negotiation"} + + scenario = get_scenario(negotiation_state.scenario_id) + negotiation_state.status = "negotiating" + negotiation_state.current_round = 1 + + return { + "status": "started", + "round": 1, + "scenario": scenario["title"], + "item": scenario["item"]["name"], + "asking_price": scenario["asking_price"], + "message": f"๐Ÿ”” NEGOTIATION BEGINS! The battle for the {scenario['item']['name']} is ON!" + } + + +def buyer_make_offer( + offer_amount: int, + message: str, + reasoning: str = "", + tool_context: Optional[ToolContext] = None +) -> dict: + """ + Buyer makes an offer to the seller. + + Args: + offer_amount: The dollar amount being offered + message: What the buyer says to the seller + reasoning: Internal reasoning for this offer + + Returns: + Offer details and status + """ + if negotiation_state.status != "negotiating": + return {"error": "Negotiation not in progress"} + + scenario = get_scenario(negotiation_state.scenario_id) + buyer_p = BUYER_PERSONALITIES[negotiation_state.buyer_personality] + + round_data = { + "round": negotiation_state.current_round, + "type": "buyer_offer", + "offer_amount": offer_amount, + "message": message, + "reasoning": reasoning, + "buyer_name": buyer_p["name"], + "buyer_emoji": buyer_p["emoji"] + } + + negotiation_state.rounds.append(round_data) + + return { + "status": "offer_made", + "round": negotiation_state.current_round, + "offer": offer_amount, + "message": message, + "buyer": f"{buyer_p['emoji']} {buyer_p['name']}", + "percent_of_asking": round(offer_amount / scenario["asking_price"] * 100, 1) + } + + +def seller_respond( + action: str, + counter_amount: Optional[int] = None, + message: str = "", + reasoning: str = "", + tool_context: Optional[ToolContext] = None +) -> dict: + """ + Seller responds to the buyer's offer. + + Args: + action: One of 'accept', 'counter', 'reject', 'walk' + counter_amount: If countering, the counter offer amount + message: What the seller says to the buyer + reasoning: Internal reasoning for this decision + + Returns: + Response details and updated negotiation status + """ + if negotiation_state.status != "negotiating": + return {"error": "Negotiation not in progress"} + + scenario = get_scenario(negotiation_state.scenario_id) + seller_p = SELLER_PERSONALITIES[negotiation_state.seller_personality] + + round_data = { + "round": negotiation_state.current_round, + "type": "seller_response", + "action": action, + "counter_amount": counter_amount, + "message": message, + "reasoning": reasoning, + "seller_name": seller_p["name"], + "seller_emoji": seller_p["emoji"] + } + + negotiation_state.rounds.append(round_data) + + result = { + "status": "response_given", + "round": negotiation_state.current_round, + "action": action, + "message": message, + "seller": f"{seller_p['emoji']} {seller_p['name']}" + } + + if action == "accept": + # Get the last buyer offer + last_offer = None + for r in reversed(negotiation_state.rounds): + if r["type"] == "buyer_offer": + last_offer = r["offer_amount"] + break + + negotiation_state.status = "deal" + negotiation_state.final_price = last_offer + + result["outcome"] = "DEAL" + result["final_price"] = last_offer + result["savings"] = scenario["asking_price"] - last_offer + result["percent_off"] = round((scenario["asking_price"] - last_offer) / scenario["asking_price"] * 100, 1) + + elif action == "walk": + negotiation_state.status = "no_deal" + result["outcome"] = "SELLER_WALKED" + + elif action == "counter": + result["counter_amount"] = counter_amount + negotiation_state.current_round += 1 + + else: # reject + negotiation_state.current_round += 1 + + return result + + +def get_negotiation_state(tool_context: Optional[ToolContext] = None) -> dict: + """ + Get the current state of the negotiation. + + Returns: + Full negotiation state including history + """ + scenario = get_scenario(negotiation_state.scenario_id) + buyer_p = BUYER_PERSONALITIES[negotiation_state.buyer_personality] + seller_p = SELLER_PERSONALITIES[negotiation_state.seller_personality] + + return { + "status": negotiation_state.status, + "scenario": scenario["title"], + "item": scenario["item"]["name"], + "asking_price": scenario["asking_price"], + "current_round": negotiation_state.current_round, + "buyer": { + "name": buyer_p["name"], + "emoji": buyer_p["emoji"], + "budget": negotiation_state.buyer_budget + }, + "seller": { + "name": seller_p["name"], + "emoji": seller_p["emoji"], + "minimum": negotiation_state.seller_minimum + }, + "rounds": negotiation_state.rounds, + "final_price": negotiation_state.final_price + } + + +def get_available_scenarios(tool_context: Optional[ToolContext] = None) -> dict: + """ + Get all available negotiation scenarios. + + Returns: + List of available scenarios with details + """ + scenarios = [] + for key, s in SCENARIOS.items(): + scenarios.append({ + "id": key, + "title": s["title"], + "emoji": s["emoji"], + "description": s["description"], + "item": s["item"]["name"], + "asking_price": s["asking_price"] + }) + return {"scenarios": scenarios} + + +def get_available_personalities(tool_context: Optional[ToolContext] = None) -> dict: + """ + Get all available buyer and seller personalities. + + Returns: + Available personalities for both buyer and seller + """ + buyers = [] + for key, p in BUYER_PERSONALITIES.items(): + buyers.append({ + "id": key, + "name": p["name"], + "emoji": p["emoji"], + "description": p["description"] + }) + + sellers = [] + for key, p in SELLER_PERSONALITIES.items(): + sellers.append({ + "id": key, + "name": p["name"], + "emoji": p["emoji"], + "description": p["description"] + }) + + return {"buyers": buyers, "sellers": sellers} + + +# ============================================================================ +# ADK AGENT DEFINITION +# ============================================================================ + +negotiation_agent = LlmAgent( + name="NegotiationBattleAgent", + model="gemini-3-flash-preview", + description="AI Negotiation Battle Simulator - orchestrates dramatic negotiations between buyer and seller agents", + instruction=""" +You are the NEGOTIATION BATTLE MASTER! ๐ŸŽฎ You orchestrate epic negotiations between AI buyer and seller agents. + +YOUR ROLE: +You manage a negotiation battle where a Buyer agent and a Seller agent negotiate over an item. +You play BOTH roles, switching between them to create a dramatic back-and-forth negotiation. + +AVAILABLE TOOLS: +- get_available_scenarios: See what scenarios are available +- get_available_personalities: See buyer/seller personality options +- configure_negotiation: Set up the scenario and personalities +- start_negotiation: Begin the battle! +- buyer_make_offer: Make an offer as the buyer +- seller_respond: Respond as the seller (accept/counter/reject/walk) +- get_negotiation_state: Check current negotiation status + +HOW TO RUN A NEGOTIATION: +1. First, use configure_negotiation to set up the scenario and personalities +2. Use start_negotiation to begin +3. Alternate between buyer_make_offer and seller_respond +4. Play each role authentically based on their personality! +5. Continue until a deal is reached or someone walks away + +PERSONALITY GUIDELINES: +- Each personality has distinct traits - embody them fully! +- Buyers have budgets they shouldn't exceed +- Sellers have minimums they won't go below +- Create DRAMA and tension in the negotiation +- Make the dialogue feel real and entertaining + +IMPORTANT: +- When the user asks to start a negotiation, first show them the options +- Let them pick scenario and personalities, or use defaults +- Once configured, run the full negotiation automatically +- Provide colorful commentary between rounds +- Celebrate deals and mourn walkways dramatically! + +Be entertaining, dramatic, and make this feel like a real negotiation showdown! ๐ŸŽญ +""", + tools=[ + FunctionTool(get_available_scenarios), + FunctionTool(get_available_personalities), + FunctionTool(configure_negotiation), + FunctionTool(start_negotiation), + FunctionTool(buyer_make_offer), + FunctionTool(seller_respond), + FunctionTool(get_negotiation_state), + ] +) + + +# ============================================================================ +# AG-UI + FASTAPI SETUP +# ============================================================================ + +# Create ADK middleware agent +adk_negotiation_agent = ADKAgent( + adk_agent=negotiation_agent, + app_name="negotiation_battle", + user_id="battle_user", + session_timeout_seconds=3600, + use_in_memory_services=True +) + +# Create FastAPI app +app = FastAPI( + title="AI Negotiation Battle Simulator", + description="Watch AI agents battle it out in epic negotiations!", + version="1.0.0" +) + +# Add CORS middleware for frontend +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Add AG-UI endpoint +add_adk_fastapi_endpoint(app, adk_negotiation_agent, path="/") + + +# ============================================================================ +# MAIN +# ============================================================================ + +if __name__ == "__main__": + import uvicorn + + if not os.getenv("GOOGLE_API_KEY"): + print("โš ๏ธ Warning: GOOGLE_API_KEY not set!") + print(" Get your key from: https://aistudio.google.com/") + print() + + port = int(os.getenv("PORT", 8000)) + print(f"๐ŸŽฎ AI Negotiation Battle Simulator starting on port {port}") + print(f" AG-UI endpoint: http://localhost:{port}/") + + uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/__init__.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/__init__.py new file mode 100644 index 0000000..3c3b237 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/__init__.py @@ -0,0 +1,11 @@ +"""Negotiation agents module.""" + +from .buyer_agent import create_buyer_agent +from .seller_agent import create_seller_agent +from .orchestrator import NegotiationOrchestrator + +__all__ = [ + "create_buyer_agent", + "create_seller_agent", + "NegotiationOrchestrator", +] diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/buyer_agent.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/buyer_agent.py new file mode 100644 index 0000000..c1db096 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/buyer_agent.py @@ -0,0 +1,79 @@ +"""Buyer agent for the negotiation battle simulator.""" + +from google.adk.agents import LlmAgent + + +def create_buyer_agent( + scenario: dict, + personality_prompt: str = "", + model: str = "gemini-3-flash-preview" +) -> LlmAgent: + """Create a buyer agent configured for a specific negotiation scenario. + + Args: + scenario: The negotiation scenario configuration + personality_prompt: Optional personality traits to inject + model: The LLM model to use + + Returns: + Configured LlmAgent for the buyer role + """ + + item = scenario["item"] + + base_instruction = f"""You are a BUYER in a negotiation for a {item['name']}. + +=== THE SITUATION === +{scenario['buyer_context']} + +=== WHAT YOU'RE BUYING === +Item: {item['name']} +Asking Price: ${scenario['asking_price']:,} +Your Budget: ${scenario['buyer_budget']:,} +Fair Market Value: ~${scenario['fair_market_value']:,} + +Positive aspects: +{chr(10).join(f' + {p}' for p in item.get('positives', []))} + +Issues you can leverage: +{chr(10).join(f' - {i}' for i in item.get('issues', []))} + +=== YOUR STAKES === +{scenario['buyer_stakes']} + +=== YOUR SECRET (influences your behavior, but never state directly) === +{scenario['buyer_secret']} + +=== NEGOTIATION RULES === +1. NEVER exceed your budget of ${scenario['buyer_budget']:,} unless absolutely necessary +2. Start lower than you're willing to pay - leave room to negotiate +3. Use the item's issues to justify lower offers +4. Stay in character throughout +5. React authentically to the seller's counteroffers +6. Know when to walk away (you have other options) + +=== YOUR GOAL === +Get the {item['name']} for the best possible price, ideally under ${scenario['fair_market_value']:,}. +But you really want this item, so find the balance between value and closing the deal. + +{personality_prompt} + +=== RESPONSE FORMAT === +When making an offer, respond with a JSON object like this: +{{ + "offer_amount": 12000, + "message": "What you say to the seller - be in character, conversational!", + "reasoning": "Brief internal reasoning for this offer", + "confidence": 7, + "willing_to_walk": false +}} + +Always respond with valid JSON. Make your message sound natural and in-character! +""" + + return LlmAgent( + name="buyer_agent", + model=model, + description=f"Buyer negotiating for {item['name']}", + instruction=base_instruction, + ) diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/orchestrator.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/orchestrator.py new file mode 100644 index 0000000..c8c3a60 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/orchestrator.py @@ -0,0 +1,425 @@ +"""Orchestrator for managing negotiation flow between buyer and seller agents.""" + +import asyncio +import json +from dataclasses import dataclass, field +from typing import Optional, Literal, Generator +from datetime import datetime + +from google.adk.agents import LlmAgent +from google.adk.runners import InMemoryRunner +from google.genai import types + +from .buyer_agent import create_buyer_agent +from .seller_agent import create_seller_agent + + +# ============================================================================ +# NEGOTIATION STATE +# ============================================================================ + +@dataclass +class NegotiationRound: + """A single round of negotiation.""" + round_number: int + buyer_offer: Optional[int] = None + buyer_message: str = "" + buyer_reasoning: str = "" + seller_action: Optional[str] = None # "counter", "accept", "reject", "walk" + seller_counter: Optional[int] = None + seller_message: str = "" + seller_reasoning: str = "" + timestamp: datetime = field(default_factory=datetime.now) + + +@dataclass +class NegotiationState: + """Full state of the negotiation.""" + scenario_id: str + status: Literal["ongoing", "deal", "buyer_walked", "seller_walked", "no_deal"] = "ongoing" + asking_price: int = 0 + current_offer: int = 0 + rounds: list[NegotiationRound] = field(default_factory=list) + final_price: Optional[int] = None + max_rounds: int = 10 + + @property + def round_count(self) -> int: + return len(self.rounds) + + @property + def is_complete(self) -> bool: + return self.status != "ongoing" + + def get_negotiation_history(self) -> str: + """Format the negotiation history for agent context.""" + if not self.rounds: + return "No offers yet. This is the opening round." + + lines = [] + for r in self.rounds: + lines.append(f"Round {r.round_number}:") + if r.buyer_offer: + lines.append(f" Buyer offered: ${r.buyer_offer:,}") + lines.append(f" Buyer said: \"{r.buyer_message}\"") + if r.seller_action: + if r.seller_action == "accept": + lines.append(f" Seller ACCEPTED!") + elif r.seller_action == "counter" and r.seller_counter: + lines.append(f" Seller countered: ${r.seller_counter:,}") + elif r.seller_action == "reject": + lines.append(f" Seller rejected the offer") + elif r.seller_action == "walk": + lines.append(f" Seller walked away!") + if r.seller_message: + lines.append(f" Seller said: \"{r.seller_message}\"") + lines.append("") + + return "\n".join(lines) + + +# ============================================================================ +# RESPONSE PARSERS +# ============================================================================ + +def parse_buyer_response(response_text: str) -> dict: + """Parse buyer agent response into structured data.""" + # Try to extract JSON from the response + try: + # Look for JSON in the response + if "{" in response_text and "}" in response_text: + start = response_text.find("{") + end = response_text.rfind("}") + 1 + json_str = response_text[start:end] + data = json.loads(json_str) + return { + "offer_amount": data.get("offer_amount", 0), + "message": data.get("message", ""), + "reasoning": data.get("reasoning", ""), + "confidence": data.get("confidence", 5), + "willing_to_walk": data.get("willing_to_walk", False) + } + except (json.JSONDecodeError, ValueError): + pass + + # Fallback: extract offer from text + import re + amount_match = re.search(r'\$?([\d,]+)', response_text) + offer = int(amount_match.group(1).replace(",", "")) if amount_match else 0 + + return { + "offer_amount": offer, + "message": response_text[:500], + "reasoning": "Extracted from response", + "confidence": 5, + "willing_to_walk": False + } + + +def parse_seller_response(response_text: str) -> dict: + """Parse seller agent response into structured data.""" + try: + if "{" in response_text and "}" in response_text: + start = response_text.find("{") + end = response_text.rfind("}") + 1 + json_str = response_text[start:end] + data = json.loads(json_str) + return { + "action": data.get("action", "counter").lower(), + "counter_amount": data.get("counter_amount"), + "message": data.get("message", ""), + "reasoning": data.get("reasoning", ""), + "firmness": data.get("firmness", 5) + } + except (json.JSONDecodeError, ValueError): + pass + + # Fallback parsing + response_lower = response_text.lower() + action = "counter" + if "accept" in response_lower or "deal" in response_lower: + action = "accept" + elif "walk" in response_lower or "goodbye" in response_lower: + action = "walk" + elif "reject" in response_lower: + action = "reject" + + import re + amount_match = re.search(r'\$?([\d,]+)', response_text) + counter = int(amount_match.group(1).replace(",", "")) if amount_match else None + + return { + "action": action, + "counter_amount": counter, + "message": response_text[:500], + "reasoning": "Extracted from response", + "firmness": 5 + } + + +# ============================================================================ +# NEGOTIATION ORCHESTRATOR +# ============================================================================ + +class NegotiationOrchestrator: + """Manages the negotiation between buyer and seller agents.""" + + def __init__( + self, + scenario: dict, + buyer_personality: str = "", + seller_personality: str = "", + max_rounds: int = 10, + model: str = "gemini-3-flash-preview" + ): + self.scenario = scenario + self.model = model + self.max_rounds = max_rounds + + # Create agents + self.buyer_agent = create_buyer_agent(scenario, buyer_personality, model) + self.seller_agent = create_seller_agent(scenario, seller_personality, model) + + # Create runners + self.buyer_runner = InMemoryRunner( + agent=self.buyer_agent, + app_name="negotiation_buyer" + ) + self.seller_runner = InMemoryRunner( + agent=self.seller_agent, + app_name="negotiation_seller" + ) + + # Session IDs + self.buyer_session_id = "buyer_session" + self.seller_session_id = "seller_session" + + # Initialize state + self.state = NegotiationState( + scenario_id=scenario["id"], + asking_price=scenario["asking_price"], + max_rounds=max_rounds + ) + + async def _run_agent(self, runner: InMemoryRunner, session_id: str, prompt: str) -> str: + """Run an agent and return its response.""" + user_content = types.Content( + role='user', + parts=[types.Part(text=prompt)] + ) + + response_text = "" + async for event in runner.run_async( + user_id="negotiation", + session_id=session_id, + new_message=user_content + ): + if event.is_final_response() and event.content: + response_text = event.content.parts[0].text.strip() + + return response_text + + async def _get_buyer_offer(self, context: str) -> dict: + """Get an offer from the buyer agent.""" + prompt = f""" +=== CURRENT NEGOTIATION STATUS === +Asking Price: ${self.state.asking_price:,} +Rounds so far: {self.state.round_count} +Max rounds before walkaway: {self.max_rounds} + +=== HISTORY === +{self.state.get_negotiation_history()} + +=== YOUR TURN === +{context} + +Respond with a JSON object containing: +- "offer_amount": your offer in dollars (integer) +- "message": what you say to the seller (be in character!) +- "reasoning": brief explanation of your strategy +- "confidence": how confident you are 1-10 +- "willing_to_walk": true/false if you'd walk away if rejected +""" + + response = await self._run_agent(self.buyer_runner, self.buyer_session_id, prompt) + return parse_buyer_response(response) + + async def _get_seller_response(self, offer: int, buyer_message: str) -> dict: + """Get a response from the seller agent.""" + prompt = f""" +=== CURRENT NEGOTIATION STATUS === +Your Asking Price: ${self.state.asking_price:,} +Rounds so far: {self.state.round_count} +Max rounds: {self.max_rounds} + +=== HISTORY === +{self.state.get_negotiation_history()} + +=== BUYER'S CURRENT OFFER === +Amount: ${offer:,} +What they said: "{buyer_message}" + +=== YOUR TURN === +Decide how to respond. You can ACCEPT, COUNTER, REJECT, or WALK. + +Respond with a JSON object containing: +- "action": one of "accept", "counter", "reject", "walk" +- "counter_amount": if countering, your counter price (integer) +- "message": what you say to the buyer (be in character!) +- "reasoning": brief explanation of your decision +- "firmness": how firm you are on this position 1-10 +""" + + response = await self._run_agent(self.seller_runner, self.seller_session_id, prompt) + return parse_seller_response(response) + + def run_negotiation_sync(self) -> Generator[dict, None, None]: + """Run the full negotiation synchronously, yielding events. + + Yields dicts with: + - type: "start", "buyer_offer", "seller_response", "deal", "no_deal", "walk" + - data: relevant data for the event + """ + + async def _run(): + events = [] + + events.append({ + "type": "start", + "data": { + "scenario": self.scenario["title"], + "item": self.scenario["item"]["name"], + "asking_price": self.state.asking_price, + "max_rounds": self.max_rounds + } + }) + + # Initial context for buyer + buyer_context = "Make your opening offer for this item. Start strong but leave room to negotiate." + + while not self.state.is_complete and self.state.round_count < self.max_rounds: + round_num = self.state.round_count + 1 + current_round = NegotiationRound(round_number=round_num) + + # Get buyer's offer + try: + buyer_data = await self._get_buyer_offer(buyer_context) + current_round.buyer_offer = buyer_data["offer_amount"] + current_round.buyer_message = buyer_data["message"] + current_round.buyer_reasoning = buyer_data["reasoning"] + self.state.current_offer = buyer_data["offer_amount"] + + events.append({ + "type": "buyer_offer", + "data": { + "round": round_num, + "offer": buyer_data["offer_amount"], + "message": buyer_data["message"], + "reasoning": buyer_data["reasoning"], + "confidence": buyer_data["confidence"], + "willing_to_walk": buyer_data["willing_to_walk"] + } + }) + + except Exception as e: + events.append({"type": "error", "data": {"agent": "buyer", "error": str(e)}}) + break + + # Get seller's response + try: + seller_data = await self._get_seller_response( + buyer_data["offer_amount"], + buyer_data["message"] + ) + + current_round.seller_action = seller_data["action"] + current_round.seller_message = seller_data["message"] + current_round.seller_reasoning = seller_data["reasoning"] + if seller_data["counter_amount"]: + current_round.seller_counter = seller_data["counter_amount"] + + events.append({ + "type": "seller_response", + "data": { + "round": round_num, + "action": seller_data["action"], + "counter": seller_data["counter_amount"], + "message": seller_data["message"], + "reasoning": seller_data["reasoning"], + "firmness": seller_data["firmness"] + } + }) + + # Handle seller's decision + if seller_data["action"] == "accept": + self.state.status = "deal" + self.state.final_price = buyer_data["offer_amount"] + self.state.rounds.append(current_round) + + events.append({ + "type": "deal", + "data": { + "final_price": buyer_data["offer_amount"], + "rounds": round_num, + "savings": self.state.asking_price - buyer_data["offer_amount"], + "percent_off": round((self.state.asking_price - buyer_data["offer_amount"]) / self.state.asking_price * 100, 1) + } + }) + break + + elif seller_data["action"] == "walk": + self.state.status = "seller_walked" + self.state.rounds.append(current_round) + + events.append({ + "type": "walk", + "data": { + "who": "seller", + "round": round_num, + "last_offer": buyer_data["offer_amount"] + } + }) + break + + elif seller_data["action"] == "reject": + buyer_context = f"Your offer of ${buyer_data['offer_amount']:,} was rejected. The seller said: \"{seller_data['message']}\". Make a new offer or walk away." + + else: # counter + buyer_context = f"The seller countered with ${seller_data['counter_amount']:,}. They said: \"{seller_data['message']}\". Make your next move." + + except Exception as e: + events.append({"type": "error", "data": {"agent": "seller", "error": str(e)}}) + break + + self.state.rounds.append(current_round) + + # Max rounds reached + if self.state.status == "ongoing": + self.state.status = "no_deal" + events.append({ + "type": "no_deal", + "data": { + "reason": "max_rounds", + "rounds": self.state.round_count, + "last_offer": self.state.current_offer + } + }) + + return events + + # Run the async function and yield events + events = asyncio.run(_run()) + for event in events: + yield event + + def get_summary(self) -> dict: + """Get a summary of the negotiation.""" + return { + "scenario": self.scenario["title"], + "item": self.scenario["item"]["name"], + "status": self.state.status, + "asking_price": self.state.asking_price, + "final_price": self.state.final_price, + "rounds": self.state.round_count, + "savings": (self.state.asking_price - self.state.final_price) if self.state.final_price else None + } diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/seller_agent.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/seller_agent.py new file mode 100644 index 0000000..3c884bf --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/agents/seller_agent.py @@ -0,0 +1,86 @@ +"""Seller agent for the negotiation battle simulator.""" + +from google.adk.agents import LlmAgent + + +def create_seller_agent( + scenario: dict, + personality_prompt: str = "", + model: str = "gemini-3-flash-preview" +) -> LlmAgent: + """Create a seller agent configured for a specific negotiation scenario. + + Args: + scenario: The negotiation scenario configuration + personality_prompt: Optional personality traits to inject + model: The LLM model to use + + Returns: + Configured LlmAgent for the seller role + """ + + item = scenario["item"] + + base_instruction = f"""You are a SELLER in a negotiation for your {item['name']}. + +=== THE SITUATION === +{scenario['seller_context']} + +=== WHAT YOU'RE SELLING === +Item: {item['name']} +Your Asking Price: ${scenario['asking_price']:,} +Your Minimum (walk away below this): ${scenario['seller_minimum']:,} +Fair Market Value: ~${scenario['fair_market_value']:,} + +Why it's worth the price: +{chr(10).join(f' + {p}' for p in item.get('positives', []))} + +Issues you may need to address: +{chr(10).join(f' - {i}' for i in item.get('issues', []))} + +=== YOUR STAKES === +{scenario['seller_stakes']} + +=== YOUR SECRET (influences your behavior, but never state directly) === +{scenario['seller_secret']} + +=== NEGOTIATION RULES === +1. NEVER go below your minimum of ${scenario['seller_minimum']:,} +2. Start firm - you've priced it fairly +3. Counter lowball offers with smaller concessions +4. Highlight the positives to justify your price +5. Stay in character throughout +6. Create urgency when appropriate ("I have other interested buyers") +7. Know when to stand firm vs. when to close the deal + +=== YOUR GOAL === +Sell the {item['name']} for the best possible price, ideally at or above ${scenario['fair_market_value']:,}. +But you do need to sell, so find the balance between maximizing value and closing the deal. + +{personality_prompt} + +=== RESPONSE FORMAT === +When responding to an offer, respond with a JSON object like this: +{{ + "action": "counter", + "counter_amount": 14500, + "message": "What you say to the buyer - be in character, conversational!", + "reasoning": "Brief internal reasoning for this decision", + "firmness": 7 +}} + +For "action", use one of: +- "accept" - You accept the offer (no counter_amount needed) +- "counter" - You make a counteroffer (include counter_amount) +- "reject" - You reject outright but don't walk away +- "walk" - You're done negotiating + +Always respond with valid JSON. Make your message sound natural and in-character! +""" + + return LlmAgent( + name="seller_agent", + model=model, + description=f"Seller of {item['name']}", + instruction=base_instruction, + ) diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/__init__.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/__init__.py new file mode 100644 index 0000000..957cb48 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/__init__.py @@ -0,0 +1,9 @@ +from .personalities import BUYER_PERSONALITIES, SELLER_PERSONALITIES +from .scenarios import SCENARIOS, get_scenario + +__all__ = [ + "BUYER_PERSONALITIES", + "SELLER_PERSONALITIES", + "SCENARIOS", + "get_scenario", +] diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/personalities.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/personalities.py new file mode 100644 index 0000000..9a802db --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/personalities.py @@ -0,0 +1,193 @@ +"""Agent personality configurations for the negotiation simulator.""" + +from typing import TypedDict + + +class PersonalityConfig(TypedDict): + """Configuration for an agent personality.""" + name: str + emoji: str + description: str + traits: list[str] + opening_style: str + concession_rate: str # How quickly they give ground + walkaway_threshold: str # When they'll walk away + secret_motivation: str + + +# ============================================================================ +# BUYER PERSONALITIES +# ============================================================================ + +BUYER_PERSONALITIES: dict[str, PersonalityConfig] = { + "desperate_dan": { + "name": "Desperate Dan", + "emoji": "๐Ÿ˜ฐ", + "description": "Needs the car TODAY. Terrible poker face.", + "traits": [ + "Reveals too much about urgency", + "Makes emotional appeals", + "Caves quickly under pressure", + "Genuinely nice but easily manipulated" + ], + "opening_style": "Start at 75% of asking, mention time pressure early", + "concession_rate": "Fast - will increase offer by 5-8% each round", + "walkaway_threshold": "Very high - will go up to 95% of budget before walking", + "secret_motivation": "New job starts Monday, public transit is 2 hours each way" + }, + + "analytical_alex": { + "name": "Analytical Alex", + "emoji": "๐Ÿงฎ", + "description": "Cites every data point. Very logical, somewhat robotic.", + "traits": [ + "Quotes KBB, Edmunds, and market data constantly", + "Breaks down value into itemized components", + "Unemotional, focused on fair market value", + "Respects logic, immune to emotional manipulation" + ], + "opening_style": "Start at exactly market value minus depreciation factors", + "concession_rate": "Slow and calculated - only moves when given new data", + "walkaway_threshold": "Firm - walks if price exceeds data-backed value by 10%", + "secret_motivation": "Has analyzed 47 similar listings, knows exact fair price" + }, + + "cool_hand_casey": { + "name": "Cool-Hand Casey", + "emoji": "๐Ÿ˜Ž", + "description": "Master of the walkaway bluff. Ice in their veins.", + "traits": [ + "Never shows eagerness, always seems ready to leave", + "Uses strategic silence", + "Mentions other options constantly", + "Extremely patient, will wait out the seller" + ], + "opening_style": "Lowball at 65% of asking, seem indifferent", + "concession_rate": "Glacial - small moves only after long pauses", + "walkaway_threshold": "Will actually walk at fair value, not bluffing", + "secret_motivation": "Has two backup cars lined up, genuinely doesn't care" + }, + + "fair_deal_fran": { + "name": "Fair-Deal Fran", + "emoji": "๐Ÿค", + "description": "Just wants everyone to win. Seeks middle ground.", + "traits": [ + "Proposes split-the-difference solutions", + "Acknowledges seller's perspective", + "Builds rapport before negotiating", + "Values relationship over small dollar amounts" + ], + "opening_style": "Start at 85% of asking, explain reasoning kindly", + "concession_rate": "Moderate - moves to meet in the middle", + "walkaway_threshold": "Medium - walks if seller is unreasonable, not just expensive", + "secret_motivation": "Believes in karma, wants seller to feel good about deal" + } +} + + +# ============================================================================ +# SELLER PERSONALITIES +# ============================================================================ + +SELLER_PERSONALITIES: dict[str, PersonalityConfig] = { + "shark_steve": { + "name": "Shark Steve", + "emoji": "๐Ÿฆˆ", + "description": "Never drops more than 5%. Take it or leave it attitude.", + "traits": [ + "Creates artificial scarcity", + "Never makes the first concession", + "Uses high-pressure tactics", + "Dismissive of lowball offers" + ], + "opening_style": "Price is firm, mentions multiple interested buyers", + "concession_rate": "Minimal - 1-2% per round maximum", + "walkaway_threshold": "Will pretend to walk to create urgency", + "secret_motivation": "Actually has car payment due and needs to sell" + }, + + "by_the_book_beth": { + "name": "By-The-Book Beth", + "emoji": "๐Ÿ“Š", + "description": "Goes strictly by KBB. Reasonable but firm.", + "traits": [ + "References official valuations", + "Provides documentation for pricing", + "Fair but won't go below market value", + "Responds well to logical arguments" + ], + "opening_style": "Asks KBB private party value, shows service records", + "concession_rate": "Steady - will adjust based on condition factors", + "walkaway_threshold": "Won't go below KBB fair condition price", + "secret_motivation": "Has no rush, will wait for right buyer" + }, + + "motivated_mike": { + "name": "Motivated Mike", + "emoji": "๐Ÿ˜…", + "description": "Really needs to sell. More flexible than he wants to be.", + "traits": [ + "Mentions reasons for selling", + "Open to creative deals", + "Shows nervousness about timeline", + "Accepts reasonable offers quickly" + ], + "opening_style": "Prices competitively, emphasizes quick sale", + "concession_rate": "Fast - will drop 3-5% per round", + "walkaway_threshold": "Low - very reluctant to lose a serious buyer", + "secret_motivation": "Already bought the new car, paying two car payments" + }, + + "drama_queen_diana": { + "name": "Drama Queen Diana", + "emoji": "๐ŸŽญ", + "description": "Everything is 'my final offer' (it's never final).", + "traits": [ + "Theatrical reactions to offers", + "Claims emotional attachment to car", + "Uses guilt and stories", + "Actually negotiable despite protests" + ], + "opening_style": "Tells the car's 'story', prices emotionally", + "concession_rate": "Appears slow but actually moderate after drama", + "walkaway_threshold": "Threatens to walk constantly but never does", + "secret_motivation": "Car holds memories of ex, secretly wants it gone" + } +} + + +def get_personality_prompt(role: str, personality_key: str) -> str: + """Generate a personality-specific prompt addition for an agent. + + Args: + role: Either 'buyer' or 'seller' + personality_key: Key from the personality dictionary + + Returns: + A string to append to the agent's base instructions + """ + personalities = BUYER_PERSONALITIES if role == "buyer" else SELLER_PERSONALITIES + p = personalities.get(personality_key) + + if not p: + return "" + + return f""" +YOUR PERSONALITY: {p['emoji']} {p['name']} +{p['description']} + +YOUR TRAITS: +{chr(10).join(f'- {trait}' for trait in p['traits'])} + +NEGOTIATION STYLE: +- Opening Approach: {p['opening_style']} +- How You Concede: {p['concession_rate']} +- When You Walk Away: {p['walkaway_threshold']} + +SECRET (never reveal directly, but it influences your behavior): +{p['secret_motivation']} + +Stay in character! Your personality should come through in how you phrase offers, +react to counteroffers, and handle pressure. +""" diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/scenarios.py b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/scenarios.py new file mode 100644 index 0000000..f65c977 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/config/scenarios.py @@ -0,0 +1,227 @@ +"""Negotiation scenario configurations.""" + +from typing import TypedDict + + +class ScenarioConfig(TypedDict): + """Configuration for a negotiation scenario.""" + id: str + title: str + emoji: str + description: str + + # The item being negotiated + item: dict # name, details, condition, etc. + + # Pricing + asking_price: int + fair_market_value: int + buyer_budget: int + seller_minimum: int + + # Context + buyer_context: str + seller_context: str + + # Stakes + buyer_stakes: str + seller_stakes: str + + # Drama elements + buyer_secret: str + seller_secret: str + twist: str # Something that could be revealed mid-negotiation + + +# ============================================================================ +# SCENARIOS +# ============================================================================ + +SCENARIOS: dict[str, ScenarioConfig] = { + "craigslist_civic": { + "id": "craigslist_civic", + "title": "The Craigslist Showdown", + "emoji": "๐Ÿš—", + "description": "A classic used car negotiation with secrets on both sides.", + + "item": { + "name": "2019 Honda Civic EX", + "year": 2019, + "make": "Honda", + "model": "Civic EX", + "mileage": 45000, + "color": "Lunar Silver Metallic", + "condition": "Excellent", + "issues": ["Minor scratch on rear bumper", "Small chip in windshield"], + "positives": [ + "Single owner", + "Full service records", + "New tires (6 months ago)", + "No accidents", + "Garage kept" + ] + }, + + "asking_price": 15500, + "fair_market_value": 14000, + "buyer_budget": 13000, + "seller_minimum": 12500, + + "buyer_context": """ +You're a recent college graduate who just landed your first real job. +The commute is 25 miles each way, and public transit would take 2 hours. +You've saved up $13,000 over the past year, with a secret $500 emergency buffer. +You've been looking for 3 weeks and this is the best car you've seen. +""", + + "seller_context": """ +You bought this Civic new for $24,000 and it's been your daily driver. +You're upgrading to an SUV because your family is growing. +KBB says private party value is $14,000-$15,000 for excellent condition. +You've already put a deposit on the new car and want to close this sale. +""", + + "buyer_stakes": "Job starts Monday. No car means either decline the job or brutal commute.", + "seller_stakes": "New SUV deposit is non-refundable. Need this money for the down payment.", + + "buyer_secret": "You could technically go up to $13,500 using your emergency fund, but you really don't want to.", + "seller_secret": "The 'other interested buyer' you might mention? They're very flaky and probably won't show.", + + "twist": "The seller's spouse mentioned at the test drive that they 'need this gone before the baby comes in 2 weeks.'" + }, + + "vintage_guitar": { + "id": "vintage_guitar", + "title": "The Vintage Axe", + "emoji": "๐ŸŽธ", + "description": "A musician hunts for their dream guitar at a local shop.", + + "item": { + "name": "1978 Fender Stratocaster", + "year": 1978, + "make": "Fender", + "model": "Stratocaster", + "condition": "Very Good", + "issues": ["Some fret wear", "Non-original tuning pegs", "Case shows age"], + "positives": [ + "All original electronics", + "Original pickups (legendary CBS-era)", + "Great neck feel", + "Authentic relic'd finish", + "Plays beautifully" + ] + }, + + "asking_price": 8500, + "fair_market_value": 7500, + "buyer_budget": 7000, + "seller_minimum": 6500, + + "buyer_context": """ +You're a professional session musician who's been searching for a late-70s Strat +with the right feel. You've played through dozens and this one just speaks to you. +Your budget is $7,000 but this is The One. +""", + + "seller_context": """ +You run a vintage guitar shop. This Strat came from an estate sale where you +paid $4,500. It's been in the shop for 3 months and floor space is money. +You need at least $6,500 to keep margins healthy. +""", + + "buyer_stakes": "You have a big studio session next week. The right guitar could define your sound.", + "seller_stakes": "Rent is due and you've got two more estate buys coming in that need funding.", + + "buyer_secret": "You could stretch to $7,500 by selling your backup amp, but you'd really rather not.", + "seller_secret": "You've had this for 90 days and need to move inventory. Might take $6,200.", + + "twist": "The buyer mentions they're recording with a famous producer who loves vintage gear." + }, + + "apartment_sublet": { + "id": "apartment_sublet", + "title": "The Sublet Standoff", + "emoji": "๐Ÿ ", + "description": "Negotiating rent for a 3-month summer sublet in a hot market.", + + "item": { + "name": "Studio Apartment Sublet", + "type": "Studio apartment", + "location": "Downtown, 5 min walk to transit", + "duration": "3 months (June-August)", + "condition": "Recently renovated", + "issues": ["Street noise", "No dishwasher", "Coin laundry"], + "positives": [ + "Great location", + "In-unit washer/dryer", + "Rooftop access", + "Utilities included", + "Furnished" + ] + }, + + "asking_price": 2200, # per month + "fair_market_value": 2000, + "buyer_budget": 1800, + "seller_minimum": 1700, + + "buyer_context": """ +You're a summer intern at a tech company. They're paying $5,000/month stipend. +Housing eats into that significantly. You need something walkable to the office. +""", + + "seller_context": """ +You're leaving for a 3-month work trip and need to cover rent while gone. +Your rent is $1,600/month. You're hoping to make a small profit or at least break even. +""", + + "buyer_stakes": "Your internship starts in 2 weeks. You need housing locked down.", + "seller_stakes": "You leave in 10 days. Empty apartment means paying double rent.", + + "buyer_secret": "Company will reimburse up to $2,000/month but you'd love to pocket the difference.", + "seller_secret": "You've had two other inquiries but they fell through. Getting nervous.", + + "twist": "The sublet includes your neighbor's cat-sitting duties (easy, cat is chill)." + } +} + + +def get_scenario(scenario_id: str) -> ScenarioConfig: + """Get a scenario by ID. + + Args: + scenario_id: The scenario identifier + + Returns: + The scenario configuration + + Raises: + KeyError: If scenario not found + """ + if scenario_id not in SCENARIOS: + raise KeyError(f"Unknown scenario: {scenario_id}. Available: {list(SCENARIOS.keys())}") + return SCENARIOS[scenario_id] + + +def format_item_description(scenario: ScenarioConfig) -> str: + """Format the item being negotiated into a readable description.""" + item = scenario["item"] + + lines = [ + f"**{item['name']}**", + "", + "Condition: " + item.get("condition", "Good"), + "", + "Positives:", + ] + + for positive in item.get("positives", []): + lines.append(f" โœ“ {positive}") + + lines.append("") + lines.append("Issues to Note:") + + for issue in item.get("issues", []): + lines.append(f" โ€ข {issue}") + + return "\n".join(lines) diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/requirements.txt b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/requirements.txt new file mode 100644 index 0000000..db557b2 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/backend/requirements.txt @@ -0,0 +1,7 @@ +google-adk>=1.5.0 +google-genai>=1.0.0 +ag-ui-adk>=0.1.0 +fastapi>=0.115.0 +uvicorn>=0.34.0 +python-dotenv>=1.0.0 +pydantic>=2.0.0 diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/.gitignore b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/.gitignore new file mode 100644 index 0000000..7e7ddd4 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/.gitignore @@ -0,0 +1,4 @@ +node_modules/ +.next/ +.env +.env.local diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/next-env.d.ts b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/next-env.d.ts new file mode 100644 index 0000000..830fb59 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/next-env.d.ts @@ -0,0 +1,6 @@ +/// +/// +/// + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/next.config.js b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/next.config.js new file mode 100644 index 0000000..a843cbe --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/next.config.js @@ -0,0 +1,6 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, +} + +module.exports = nextConfig diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/package-lock.json b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/package-lock.json new file mode 100644 index 0000000..b55c8a4 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/package-lock.json @@ -0,0 +1,12887 @@ +{ + "name": 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"negotiation-battle-frontend", + "version": "1.0.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "next lint" + }, + "dependencies": { + "@ag-ui/client": "^0.0.43", + "@copilotkit/react-core": "^1.8.0", + "@copilotkit/react-ui": "^1.8.0", + "@copilotkit/runtime": "^1.8.0", + "next": "^15.0.0", + "react": "^19.0.0", + "react-dom": "^19.0.0", + "framer-motion": "^11.0.0", + "lucide-react": "^0.460.0" + }, + "devDependencies": { + "@types/node": "^22.0.0", + "@types/react": "^19.0.0", + "@types/react-dom": "^19.0.0", + "autoprefixer": "^10.4.0", + "postcss": "^8.4.0", + "tailwindcss": "^3.4.0", + "typescript": "^5.0.0" + } +} diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/postcss.config.js b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/postcss.config.js new file mode 100644 index 0000000..33ad091 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/api/copilotkit/route.ts b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/api/copilotkit/route.ts new file mode 100644 index 0000000..042183a --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/api/copilotkit/route.ts @@ -0,0 +1,30 @@ +import { + CopilotRuntime, + ExperimentalEmptyAdapter, + copilotRuntimeNextJSAppRouterEndpoint, +} from "@copilotkit/runtime"; +import { HttpAgent } from "@ag-ui/client"; +import { NextRequest } from "next/server"; + +// Service adapter for CopilotKit runtime +const serviceAdapter = new ExperimentalEmptyAdapter(); + +// Create the CopilotRuntime with our ADK agent +const runtime = new CopilotRuntime({ + agents: { + negotiation_agent: new HttpAgent({ + url: process.env.AGENT_URL || "http://localhost:8000/", + }), + }, +}); + +// Export POST handler for the API route +export const POST = async (req: NextRequest) => { + const { handleRequest } = copilotRuntimeNextJSAppRouterEndpoint({ + runtime, + serviceAdapter, + endpoint: "/api/copilotkit", + }); + + return handleRequest(req); +}; diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/globals.css b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/globals.css new file mode 100644 index 0000000..a7abc16 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/globals.css @@ -0,0 +1,102 @@ +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Playfair+Display:ital,wght@0,400;0,600;0,700;1,400&display=swap'); + +@tailwind base; +@tailwind components; +@tailwind utilities; + +/* Warm Editorial Palette - Stone & Paper */ +:root { + /* Core Colors */ + --bg-app: #FAF8F5; + /* Warm cream paper background */ + --bg-card: #FFFFFF; + /* Clean white card surface */ + --bg-subtle: #F3F0EB; + /* Subtle separation background */ + + --text-primary: #1C1917; + /* Warm black (Stone 900) */ + --text-secondary: #44403C; + /* Dark stone (Stone 700) */ + --text-tertiary: #78716C; + /* Medium stone (Stone 500) */ + + --border-light: #E7E5E4; + /* Light stone border */ + --border-medium: #D6D3D1; + /* Medium stone border */ + + /* Accents - Muted & Sophisticated */ + --accent-gold: #D97706; + /* Editorial Gold */ + --accent-emerald: #059669; + /* Editorial Green */ + --accent-rose: #E11D48; + /* Editorial Rose */ +} + +/* Base Styles */ +body { + background-color: var(--bg-app); + color: var(--text-primary); + font-family: 'Inter', sans-serif; + -webkit-font-smoothing: antialiased; + min-height: 100vh; +} + +/* Typography Utilities */ +.font-serif { + font-family: 'Playfair Display', serif; +} + +.font-sans { + font-family: 'Inter', sans-serif; +} + +/* Component Utilities */ +@layer components { + + /* Minimal Editorial Card */ + .editorial-card { + background-color: var(--bg-card); + border: 1px solid var(--border-light); + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05); + @apply rounded-xl transition-all duration-300; + } + + .editorial-card:hover { + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08); + transform: translateY(-2px); + } + + /* Chat Bubble Base */ + .chat-bubble { + @apply flex items-start gap-4 max-w-2xl p-4 rounded-2xl relative mb-2; + } + + .chat-bubble.buyer { + @apply ml-auto bg-white border border-stone-200 text-stone-900 rounded-tr-sm; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.04); + } + + .chat-bubble.seller { + @apply mr-auto bg-stone-50 border border-stone-200 text-stone-900 rounded-tl-sm; + } +} + +/* Animations */ +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(10px); + } + + to { + opacity: 1; + transform: translateY(0); + } +} + +.animate-fade-in { + animation: fadeIn 0.5s ease-out forwards; +} \ No newline at end of file diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/layout.tsx b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/layout.tsx new file mode 100644 index 0000000..6fc8111 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/layout.tsx @@ -0,0 +1,28 @@ +import type { Metadata } from "next"; +import { CopilotKit } from "@copilotkit/react-core"; +import "@copilotkit/react-ui/styles.css"; +import "./globals.css"; + +export const metadata: Metadata = { + title: "๐ŸŽฎ AI Negotiation Battle Simulator", + description: "Watch AI agents battle it out in epic negotiations!", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + + + {children} + + + + ); +} diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/page.tsx b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/page.tsx new file mode 100644 index 0000000..b8a5b65 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/src/app/page.tsx @@ -0,0 +1,719 @@ +"use client"; + +import { useState, useEffect } from "react"; +import { useCoAgent, useCopilotAction } from "@copilotkit/react-core"; +import { motion, AnimatePresence } from "framer-motion"; +import { + Swords, + DollarSign, + TrendingDown, + TrendingUp, + Handshake, + XCircle, + Trophy, + Play, + RotateCcw, + Sparkles, + Zap +} from "lucide-react"; + +// Types for agent state +type NegotiationRound = { + round: number; + type: "buyer_offer" | "seller_response"; + offer_amount?: number; + action?: string; + counter_amount?: number; + message: string; + buyer_name?: string; + buyer_emoji?: string; + seller_name?: string; + seller_emoji?: string; +}; + +type Scenario = { + id: string; + title: string; + emoji: string; + item: string; + asking_price: number; + description: string; +}; + +type Personality = { + id: string; + name: string; + emoji: string; + description: string; +}; + +type AgentState = { + status: "setup" | "ready" | "negotiating" | "deal" | "no_deal"; + scenario?: { + title: string; + item: string; + asking_price: number; + }; + buyer?: { + name: string; + emoji: string; + budget: number; + }; + seller?: { + name: string; + emoji: string; + minimum: number; + }; + rounds: NegotiationRound[]; + current_round: number; + final_price?: number; +}; + +// Offer Card Component - Editorial Style +function OfferCard({ round, isLatest }: { round: NegotiationRound; isLatest: boolean }) { + const isBuyer = round.type === "buyer_offer"; + + return ( + + {/* Round Indicator */} +
+ Round {round.round} +
+ + {/* Header */} +
+ + {isBuyer ? round.buyer_emoji : round.seller_emoji} + + + {isBuyer ? round.buyer_name : round.seller_name} + +
+ + {/* Offer Content */} +
+ {isBuyer && round.offer_amount && ( +
+ Offer + + ${round.offer_amount.toLocaleString()} + +
+ )} + + {!isBuyer && round.action && ( +
+ Response + {round.action === "accept" && ( + + Deal Accepted + + )} + {round.action === "counter" && ( + + ${round.counter_amount?.toLocaleString()} + + )} + {round.action === "reject" && ( + + Offer Rejected + + )} + {round.action === "walk" && ( + + Ended Negotiation + + )} +
+ )} + + {/* Message Body */} +

+ {round.message} +

+
+ + ); +} + +// Deal Banner Component - Editorial Style +function DealBanner({ finalPrice, askingPrice }: { finalPrice: number; askingPrice: number }) { + const savings = askingPrice - finalPrice; + const percentOff = ((savings / askingPrice) * 100).toFixed(1); + + return ( + +
+ +
+

Agreement Reached

+

+ ${finalPrice.toLocaleString()} +

+
+
+

Savings

+

${savings.toLocaleString()}

+
+
+

Discount

+

{percentOff}%

+
+
+ + ); +} + +// ==================== CHAT UI COMPONENTS ==================== + +// Chat Bubble Component +const ChatBubble = ({ round, type }: { round: NegotiationRound; type: "buyer" | "seller" }) => { + const name = type === "buyer" ? round.buyer_name : round.seller_name; + const emoji = type === "buyer" ? round.buyer_emoji : round.seller_emoji; + + return ( + +
{emoji}
+
+
{name}
+
{round.message}
+ {round.offer_amount && ( +
${round.offer_amount.toLocaleString()}
+ )} +
+ + ); +}; + +// Typing Indicator Component +const TypingIndicator = ({ emoji, name }: { emoji: string; name: string }) => ( + + {emoji} +
+ + + +
+ {name} is thinking... + +); + +// Price Tracker Component +const PriceTracker = ({ + currentOffer, + askingPrice, + minimum, + budget +}: { + currentOffer: number; + askingPrice: number; + minimum: number; + budget: number; +}) => { + const range = budget - minimum; + const position = ((currentOffer - minimum) / range) * 100; + + return ( +
+

+ ๐Ÿ’ฐ Price Convergence +

+
+
+
+
+
Min: ${minimum.toLocaleString()}
+
Max: ${budget.toLocaleString()}
+
+
+ ); +}; + +// Main Page Component +export default function NegotiationBattle() { + const [selectedScenario, setSelectedScenario] = useState(null); + const [selectedBuyer, setSelectedBuyer] = useState(null); + const [selectedSeller, setSelectedSeller] = useState(null); + const [scenarios, setScenarios] = useState([]); + const [buyers, setBuyers] = useState([]); + const [sellers, setSellers] = useState([]); + + // Connect to agent state + const { state, setState } = useCoAgent({ + name: "negotiation_agent", + initialState: { + status: "setup", + rounds: [], + current_round: 0, + }, + }); + + // Fetch available scenarios and personalities on mount + useEffect(() => { + const fetchData = async () => { + try { + console.log('[DEBUG] Fetching scenarios and personalities...'); + // Fetch scenarios + const scenariosRes = await fetch('http://localhost:8000/get_available_scenarios'); + const scenariosData = await scenariosRes.json(); + console.log('[DEBUG] Scenarios fetched:', scenariosData); + if (scenariosData.scenarios) { + setScenarios(scenariosData.scenarios); + console.log('[DEBUG] Scenarios state updated:', scenariosData.scenarios.length, 'scenarios'); + } + + // Fetch personalities + const personalitiesRes = await fetch('http://localhost:8000/get_available_personalities'); + const personalitiesData = await personalitiesRes.json(); + console.log('[DEBUG] Personalities fetched:', personalitiesData); + if (personalitiesData.buyers && personalitiesData.sellers) { + setBuyers(personalitiesData.buyers); + setSellers(personalitiesData.sellers); + console.log('[DEBUG] Personalities state updated:', personalitiesData.buyers.length, 'buyers,', personalitiesData.sellers.length, 'sellers'); + } + } catch (error) { + console.error('[DEBUG] Error fetching data:', error); + } + }; + fetchData(); + }, []); + + // Debug logging for state changes + useEffect(() => { + console.log('[DEBUG] Current agent state:', state); + console.log('[DEBUG] Scenarios count:', scenarios.length); + console.log('[DEBUG] Buyers count:', buyers.length); + console.log('[DEBUG] Sellers count:', sellers.length); + }, [state, scenarios, buyers, sellers]); + + const handleStartBattle = async () => { + if (!selectedScenario || !selectedBuyer || !selectedSeller) return; + + try { + // Configure the negotiation + const configRes = await fetch('http://localhost:8000/configure_negotiation', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + scenario_id: selectedScenario, + buyer_personality: selectedBuyer, + seller_personality: selectedSeller, + }), + }); + const configData = await configRes.json(); + + // Update state with configuration + setState({ + ...state, + status: "ready", + scenario: configData.scenario, + buyer: configData.buyer, + seller: configData.seller, + }); + + // Start the negotiation + const startRes = await fetch('http://localhost:8000/start_negotiation', { + method: 'POST', + }); + const startData = await startRes.json(); + + if (startData.status === "started") { + setState({ + ...state, + status: "negotiating", + scenario: configData.scenario, + buyer: configData.buyer, + seller: configData.seller, + }); + + // Start the automated negotiation + runNegotiation(); + } + } catch (error) { + console.error('Error starting battle:', error); + } + }; + + const runNegotiation = async () => { + // This will be handled by the agent automatically + // Just need to poll for state updates + const pollInterval = setInterval(async () => { + try { + const stateRes = await fetch('http://localhost:8000/get_negotiation_state'); + const stateData = await stateRes.json(); + + setState({ + status: stateData.status, + scenario: { + title: stateData.scenario, + item: stateData.item, + asking_price: stateData.asking_price, + }, + buyer: stateData.buyer, + seller: stateData.seller, + rounds: stateData.rounds, + current_round: stateData.current_round, + final_price: stateData.final_price, + }); + + if (stateData.status === "deal" || stateData.status === "no_deal") { + clearInterval(pollInterval); + } + } catch (error) { + console.error('Error polling state:', error); + } + }, 1000); + }; + + const handleReset = () => { + setSelectedScenario(null); + setSelectedBuyer(null); + setSelectedSeller(null); + setState({ + status: "setup", + rounds: [], + current_round: 0, + }); + }; + + return ( +
+ {/* Header - Editorial Style */} +
+
+ +

+ The Negotiation +

+

+ Autonomous Agent Simulation +

+ +
+
+ +
+ {/* Setup Phase - Editorial Style */} + {(state.status === "setup" || (!state.status && scenarios.length > 0)) && ( + + {/* Scenario Selection */} +
+
+ Step 1 +

+ Select Context +

+
+
+ {scenarios.map((scenario) => ( + setSelectedScenario(scenario.id)} + className={` + cursor-pointer p-8 rounded-xl border transition-all duration-200 + ${selectedScenario === scenario.id + ? 'bg-bg-card border-text-primary shadow-md ring-1 ring-text-primary' + : 'bg-bg-subtle border-transparent hover:border-border-medium hover:bg-bg-card' + } + `} + > +
{scenario.emoji}
+

{scenario.title}

+

{scenario.item}

+

+ ${scenario.asking_price.toLocaleString()} +

+ + ))} +
+
+ + {/* Character Selection */} + {selectedScenario && ( + +
+ {/* Buyer Selection */} +
+
+ Step 2 +

+ Choose Buyer +

+
+
+ {buyers.map((buyer) => ( + setSelectedBuyer(buyer.id)} + className={` + cursor-pointer p-5 rounded-lg border transition-all + ${selectedBuyer === buyer.id + ? 'bg-bg-card border-text-primary shadow-sm' + : 'bg-bg-subtle border-transparent hover:border-border-medium' + } + `} + > +
{buyer.emoji}
+

{buyer.name}

+ + ))} +
+
+ + {/* Seller Selection */} +
+
+ Step 3 +

+ Choose Seller +

+
+
+ {sellers.map((seller) => ( + setSelectedSeller(seller.id)} + className={` + cursor-pointer p-5 rounded-lg border transition-all + ${selectedSeller === seller.id + ? 'bg-bg-card border-text-primary shadow-sm' + : 'bg-bg-subtle border-transparent hover:border-border-medium' + } + `} + > +
{seller.emoji}
+

{seller.name}

+ + ))} +
+
+
+ + {/* Start Button */} + {selectedBuyer && selectedSeller && ( + + + + Begin Negotiation + + + )} + + )} + + )} + + {/* Battle Phase */} + {(state.status === "ready" || state.status === "negotiating" || state.status === "deal" || state.status === "no_deal") && ( +
+ {/* Scenario Header - Editorial Style */} + {state.scenario && ( + +
+ Negotiation Item +
+

+ {state.scenario.title} +

+

{state.scenario.item}

+
+ Asking Price + + ${state.scenario.asking_price?.toLocaleString()} + +
+ + )} + + {/* Comparison Display - Editorial Two-Column */} + {state.buyer && state.seller && ( +
+ + {/* Buyer Column */} + + Buyer +
{state.buyer.emoji}
+

{state.buyer.name}

+

Budget: ${state.buyer.budget?.toLocaleString()}

+ + + {/* Seller Column */} + + Seller +
{state.seller.emoji}
+

{state.seller.name}

+

Minimum: ${state.seller.minimum?.toLocaleString()}

+ + +
+ )} + + {/* Negotiation Chat */} + {state.rounds && state.rounds.length > 0 && ( +
+
+ + Live Transcript + +
+ + {/* Price Tracker */} + {state.buyer && state.seller && state.rounds.length > 0 && state.rounds[state.rounds.length - 1].offer_amount && ( +
+ +
+ )} + + {/* Chat Bubbles */} +
+ + {state.rounds.map((round, index) => { + const isBuyer = round.type === "buyer_offer" || round.buyer_name; + return ( + + ); + })} + + + {/* Typing Indicator */} + {(state.status === "negotiating" || state.status === "ready") && ( +
+ +
+ )} +
+
+ )} + + {/* Deal Banner */} + {state.status === "deal" && state.final_price && state.scenario && ( +
+ +
+ )} + + {/* No Deal Banner */} + {state.status === "no_deal" && ( + +
+
+ +
+
+

Negotiation Ended

+

No agreement could be reached between the parties.

+ + )} + + {/* Reset Button */} + {(state.status === "deal" || state.status === "no_deal") && ( + + + + Start New Negotiation + + + )} +
+ )} +
+
+ ); +} diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/tailwind.config.js b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/tailwind.config.js new file mode 100644 index 0000000..7d39050 --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/tailwind.config.js @@ -0,0 +1,22 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: [ + './src/pages/**/*.{js,ts,jsx,tsx,mdx}', + './src/components/**/*.{js,ts,jsx,tsx,mdx}', + './src/app/**/*.{js,ts,jsx,tsx,mdx}', + ], + theme: { + extend: { + colors: { + 'battle-blue': '#1e3a5f', + 'battle-red': '#5a1e1e', + 'battle-gold': '#ffd700', + }, + animation: { + 'pulse-slow': 'pulse 3s cubic-bezier(0.4, 0, 0.6, 1) infinite', + 'bounce-slow': 'bounce 2s infinite', + }, + }, + }, + plugins: [], +} diff --git a/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/tsconfig.json b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/tsconfig.json new file mode 100644 index 0000000..f48e7ee --- /dev/null +++ b/advanced_ai_agents/multi_agent_apps/ai_negotiation_battle_simulator/frontend/tsconfig.json @@ -0,0 +1,40 @@ +{ + "compilerOptions": { + "lib": [ + "dom", + "dom.iterable", + "esnext" + ], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": [ + "./src/*" + ] + }, + "target": "ES2017" + }, + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/types/**/*.ts" + ], + "exclude": [ + "node_modules" + ] +} diff --git a/docs/banner/sponsors/tinyfish.png b/docs/banner/sponsors/tinyfish.png new file mode 100644 index 0000000..7936aa9 Binary files /dev/null and b/docs/banner/sponsors/tinyfish.png differ diff --git a/rag_tutorials/knowledge_graph_rag_citations/Dockerfile b/rag_tutorials/knowledge_graph_rag_citations/Dockerfile new file mode 100644 index 0000000..db3c120 --- /dev/null +++ b/rag_tutorials/knowledge_graph_rag_citations/Dockerfile @@ -0,0 +1,12 @@ +FROM python:3.11-slim + +WORKDIR /app + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY knowledge_graph_rag.py . + +EXPOSE 8501 + +CMD ["streamlit", "run", "knowledge_graph_rag.py", "--server.address", "0.0.0.0"] diff --git a/rag_tutorials/knowledge_graph_rag_citations/README.md b/rag_tutorials/knowledge_graph_rag_citations/README.md new file mode 100644 index 0000000..191e204 --- /dev/null +++ b/rag_tutorials/knowledge_graph_rag_citations/README.md @@ -0,0 +1,171 @@ +# ๐Ÿ” Knowledge Graph RAG with Verifiable Citations + +A Streamlit application demonstrating how **Knowledge Graph-based Retrieval-Augmented Generation (RAG)** provides multi-hop reasoning with fully verifiable source attribution. + +## ๐ŸŽฏ What Makes This Different? + +Traditional vector-based RAG finds similar text chunks, but struggles with: +- Questions requiring information from multiple documents +- Complex reasoning chains +- Providing verifiable sources for each claim + +**Knowledge Graph RAG** solves these by: +1. **Building a structured graph** of entities and relationships from documents +2. **Traversing connections** to find related information (multi-hop reasoning) +3. **Tracking provenance** so every claim links back to its source + +## โœจ Features + +| Feature | Description | +|---------|-------------| +| ๐Ÿ”— **Multi-hop Reasoning** | Traverse entity relationships to answer complex questions | +| ๐Ÿ“š **Verifiable Citations** | Every claim includes source document and text | +| ๐Ÿง  **Reasoning Trace** | See exactly how the answer was derived | +| ๐Ÿ  **Fully Local** | Uses Ollama for LLM, Neo4j for graph storage | + +## ๐Ÿš€ Quick Start + +### Prerequisites + +1. **Ollama** - Local LLM inference + ```bash + # Install from https://ollama.ai + ollama pull llama3.2 + ``` + +2. **Neo4j** - Knowledge graph database + ```bash + # Using Docker + docker run -d \ + --name neo4j \ + -p 7474:7474 -p 7687:7687 \ + -e NEO4J_AUTH=neo4j/password \ + neo4j:latest + ``` + +### Installation + +```bash +# Clone and navigate +cd knowledge_graph_rag_citations + +# Install dependencies +pip install -r requirements.txt + +# Run the app +streamlit run knowledge_graph_rag.py +``` + +## ๐Ÿ“– How It Works + +### Step 1: Document โ†’ Knowledge Graph + +``` +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚ Document โ”‚ โ”€โ”€โ–บ โ”‚ LLM Extraction โ”‚ โ”€โ”€โ–บ โ”‚ Knowledge Graph โ”‚ +โ”‚ (Text/PDF) โ”‚ โ”‚ (Entities+Rels) โ”‚ โ”‚ (Neo4j) โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +The LLM extracts: +- **Entities**: People, organizations, concepts, technologies +- **Relationships**: How entities connect (e.g., "works_for", "created", "uses") +- **Provenance**: Source document and chunk for each extraction + +### Step 2: Query โ†’ Multi-hop Traversal + +``` +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚ Query โ”‚ โ”€โ”€โ–บ โ”‚ Find Start โ”‚ โ”€โ”€โ–บ โ”‚ Traverse โ”‚ โ”€โ”€โ–บ โ”‚ Context โ”‚ +โ”‚ โ”‚ โ”‚ Entities โ”‚ โ”‚ Relations โ”‚ โ”‚ + Sourcesโ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +### Step 3: Answer โ†’ Verified Citations + +``` +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚ Context โ”‚ โ”€โ”€โ–บ โ”‚ Generate โ”‚ โ”€โ”€โ–บ โ”‚ Answer with โ”‚ +โ”‚ + Sources โ”‚ โ”‚ Answer โ”‚ โ”‚ [1][2] Citationsโ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ–ผ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ Citation Details โ”‚ + โ”‚ โ€ข Source Doc โ”‚ + โ”‚ โ€ข Source Text โ”‚ + โ”‚ โ€ข Reasoning Path โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +## ๐Ÿ–ฅ๏ธ Usage Example + +### 1. Add a Document + +Paste or select a sample document. The system extracts entities and relationships: + +``` +Document: "GraphRAG was developed by Microsoft Research. + Darren Edge led the project..." + +Extracted: + โ”œโ”€โ”€ Entity: GraphRAG (TECHNOLOGY) + โ”œโ”€โ”€ Entity: Microsoft Research (ORGANIZATION) + โ”œโ”€โ”€ Entity: Darren Edge (PERSON) + โ””โ”€โ”€ Relationship: Darren Edge --[WORKS_FOR]--> Microsoft Research +``` + +### 2. Ask a Question + +``` +Question: "Who developed GraphRAG and what organization are they from?" +``` + +### 3. Get Verified Answer + +``` +Answer: GraphRAG was developed by researchers at Microsoft Research [1], + with Darren Edge leading the project [2]. + +Citations: + [1] Source: AI Research Paper + Text: "GraphRAG is a technique developed by Microsoft Research..." + + [2] Source: AI Research Paper + Text: "...introduced by researchers including Darren Edge..." +``` + +## ๐Ÿ”ง Configuration + +| Setting | Default | Description | +|---------|---------|-------------| +| Neo4j URI | `bolt://localhost:7687` | Neo4j connection string | +| Neo4j User | `neo4j` | Database username | +| Neo4j Password | - | Database password | +| LLM Model | `llama3.2` | Ollama model for extraction/generation | + +## ๐Ÿ—๏ธ Architecture + +``` +knowledge_graph_rag_citations/ +โ”œโ”€โ”€ knowledge_graph_rag.py # Main Streamlit application +โ”œโ”€โ”€ requirements.txt # Python dependencies +โ””โ”€โ”€ README.md # This file +``` + +### Key Components + +- **`KnowledgeGraphManager`**: Neo4j interface for graph operations +- **`extract_entities_with_llm()`**: LLM-based entity/relationship extraction +- **`generate_answer_with_citations()`**: Multi-hop RAG with provenance tracking + +## ๐ŸŽ“ Learn More + +This example is inspired by [VeritasGraph](https://github.com/bibinprathap/VeritasGraph), an enterprise-grade framework for: +- On-premise knowledge graph RAG +- Visual reasoning traces (Veritas-Scope) +- LoRA-tuned LLM integration + +## ๐Ÿ“ License + +MIT License diff --git a/rag_tutorials/knowledge_graph_rag_citations/docker-compose.yml b/rag_tutorials/knowledge_graph_rag_citations/docker-compose.yml new file mode 100644 index 0000000..36da736 --- /dev/null +++ b/rag_tutorials/knowledge_graph_rag_citations/docker-compose.yml @@ -0,0 +1,58 @@ +version: '3.8' + +services: + neo4j: + image: neo4j:latest + container_name: kg-rag-neo4j + ports: + - "7474:7474" # Browser + - "7687:7687" # Bolt + environment: + - NEO4J_AUTH=neo4j/password + - NEO4J_PLUGINS='["apoc"]' + volumes: + - neo4j_data:/data + healthcheck: + test: ["CMD-SHELL", "wget --no-verbose --tries=1 --spider http://localhost:7474 || exit 1"] + interval: 10s + timeout: 5s + retries: 5 + + # Ollama container (optional - can use local installation instead) + ollama: + image: ollama/ollama:latest + container_name: kg-rag-ollama + ports: + - "11434:11434" + volumes: + - ollama_data:/root/.ollama + # Pull the model on startup + entrypoint: ["/bin/sh", "-c"] + command: + - | + /bin/ollama serve & + sleep 5 + /bin/ollama pull llama3.2 + wait + + streamlit: + build: + context: . + dockerfile: Dockerfile + container_name: kg-rag-app + ports: + - "8501:8501" + environment: + - NEO4J_URI=bolt://neo4j:7687 + - NEO4J_USER=neo4j + - NEO4J_PASSWORD=password + - OLLAMA_HOST=http://ollama:11434 + depends_on: + neo4j: + condition: service_healthy + ollama: + condition: service_started + +volumes: + neo4j_data: + ollama_data: diff --git a/rag_tutorials/knowledge_graph_rag_citations/knowledge_graph_rag.py b/rag_tutorials/knowledge_graph_rag_citations/knowledge_graph_rag.py new file mode 100644 index 0000000..64d3e54 --- /dev/null +++ b/rag_tutorials/knowledge_graph_rag_citations/knowledge_graph_rag.py @@ -0,0 +1,521 @@ +""" +Knowledge Graph RAG with Verifiable Citations + +A Streamlit app demonstrating how Knowledge Graph-based RAG provides: +1. Multi-hop reasoning across documents +2. Verifiable source attribution for every claim +3. Transparent reasoning traces + +This example uses Ollama for local LLM inference and Neo4j for the knowledge graph. +""" + +import streamlit as st +import ollama +from ollama import Client as OllamaClient +from neo4j import GraphDatabase +from typing import List, Dict, Tuple +import re +import os +from dataclasses import dataclass +import json +import hashlib + +# Configure Ollama host from environment (for Docker) +OLLAMA_HOST = os.environ.get('OLLAMA_HOST', 'http://localhost:11434') +ollama_client = OllamaClient(host=OLLAMA_HOST) + + +# ============================================================================ +# Data Models +# ============================================================================ + +@dataclass +class Entity: + """Represents an entity extracted from documents.""" + id: str + name: str + entity_type: str + description: str + source_doc: str + source_chunk: str + + +@dataclass +class Relationship: + """Represents a relationship between entities.""" + source: str + target: str + relation_type: str + description: str + source_doc: str + + +@dataclass +class Citation: + """Represents a verifiable citation for a claim.""" + claim: str + source_document: str + source_text: str + confidence: float + reasoning_path: List[str] + + +@dataclass +class AnswerWithCitations: + """Final answer with full attribution.""" + answer: str + citations: List[Citation] + reasoning_trace: List[str] + + +# ============================================================================ +# Knowledge Graph Manager +# ============================================================================ + +class KnowledgeGraphManager: + """Manages the Neo4j knowledge graph for RAG.""" + + def __init__(self, uri: str, user: str, password: str): + self.driver = GraphDatabase.driver(uri, auth=(user, password)) + + def close(self): + self.driver.close() + + def clear_graph(self): + """Clear all nodes and relationships.""" + with self.driver.session() as session: + session.run("MATCH (n) DETACH DELETE n") + + def add_entity(self, entity: Entity): + """Add an entity to the knowledge graph.""" + with self.driver.session() as session: + session.run( + """ + MERGE (e:Entity {id: $id}) + SET e.name = $name, + e.type = $entity_type, + e.description = $description, + e.source_doc = $source_doc, + e.source_chunk = $source_chunk + """, + id=entity.id, + name=entity.name, + entity_type=entity.entity_type, + description=entity.description, + source_doc=entity.source_doc, + source_chunk=entity.source_chunk + ) + + def add_relationship(self, rel: Relationship): + """Add a relationship between entities.""" + with self.driver.session() as session: + session.run( + """ + MATCH (a:Entity {name: $source}) + MATCH (b:Entity {name: $target}) + MERGE (a)-[r:RELATES_TO {type: $rel_type}]->(b) + SET r.description = $description, + r.source_doc = $source_doc + """, + source=rel.source, + target=rel.target, + rel_type=rel.relation_type, + description=rel.description, + source_doc=rel.source_doc + ) + + def find_related_entities(self, entity_name: str, hops: int = 2) -> List[Dict]: + """Find entities related within N hops, with full provenance.""" + with self.driver.session() as session: + result = session.run( + f""" + MATCH path = (start:Entity)-[*1..{hops}]-(related:Entity) + WHERE toLower(start.name) CONTAINS toLower($name) OR toLower(start.description) CONTAINS toLower($name) + RETURN related.name as name, + related.description as description, + related.source_doc as source, + related.source_chunk as chunk, + [r in relationships(path) | r.description] as path_descriptions + LIMIT 20 + """, + name=entity_name, hops=hops + ) + return [dict(record) for record in result] + + def semantic_search(self, query: str) -> List[Dict]: + """Search for relevant entities based on query.""" + with self.driver.session() as session: + # Simple text matching (in production, use vector embeddings) + result = session.run( + """ + MATCH (e:Entity) + WHERE e.name CONTAINS $query + OR e.description CONTAINS $query + RETURN e.name as name, + e.description as description, + e.source_doc as source, + e.source_chunk as chunk, + e.type as type + LIMIT 10 + """, + query=query + ) + return [dict(record) for record in result] + + +# ============================================================================ +# LLM-based Entity Extraction +# ============================================================================ + +def extract_entities_with_llm(text: str, source_doc: str, model: str = "llama3.2") -> Tuple[List[Entity], List[Relationship]]: + """Use LLM to extract entities and relationships from text.""" + + extraction_prompt = f"""Analyze the following text and extract: +1. KEY ENTITIES (people, organizations, concepts, technologies, events) +2. RELATIONSHIPS between these entities + +For each entity, provide: +- name: The entity name +- type: Category (PERSON, ORGANIZATION, CONCEPT, TECHNOLOGY, EVENT, LOCATION) +- description: Brief description based on the text + +For each relationship, provide: +- source: Source entity name +- target: Target entity name +- type: Relationship type (e.g., WORKS_FOR, CREATED, USES, LOCATED_IN) +- description: Description of how they relate + +TEXT: +{text} + +Respond in JSON format: +{{ + "entities": [ + {{"name": "...", "type": "...", "description": "..."}} + ], + "relationships": [ + {{"source": "...", "target": "...", "type": "...", "description": "..."}} + ] +}} +""" + + try: + response = ollama_client.chat( + model=model, + messages=[{"role": "user", "content": extraction_prompt}], + format="json" + ) + + data = json.loads(response['message']['content']) + + entities = [] + for e in data.get('entities', []): + entity_id = hashlib.md5(f"{e['name']}_{source_doc}".encode()).hexdigest()[:12] + entities.append(Entity( + id=entity_id, + name=e['name'], + entity_type=e['type'], + description=e['description'], + source_doc=source_doc, + source_chunk=text[:200] + "..." + )) + + relationships = [] + for r in data.get('relationships', []): + relationships.append(Relationship( + source=r['source'], + target=r['target'], + relation_type=r['type'], + description=r['description'], + source_doc=source_doc + )) + + return entities, relationships + + except Exception as e: + st.warning(f"Entity extraction error: {e}") + return [], [] + + +# ============================================================================ +# Multi-hop RAG with Citations +# ============================================================================ + +def generate_answer_with_citations( + query: str, + graph: KnowledgeGraphManager, + model: str = "llama3.2" +) -> AnswerWithCitations: + """ + Generate an answer using multi-hop graph traversal with full citations. + + This is the core differentiator: every claim is traced back to source documents. + """ + + reasoning_trace = [] + citations = [] + + # Step 1: Initial semantic search + reasoning_trace.append(f"๐Ÿ” Searching knowledge graph for: '{query}'") + initial_results = graph.semantic_search(query) + + if not initial_results: + return AnswerWithCitations( + answer="I couldn't find relevant information in the knowledge graph.", + citations=[], + reasoning_trace=reasoning_trace + ) + + reasoning_trace.append(f"๐Ÿ“Š Found {len(initial_results)} initial entities") + + # Step 2: Multi-hop expansion + all_context = [] + for entity in initial_results[:3]: + reasoning_trace.append(f"๐Ÿ”— Expanding from entity: {entity['name']}") + related = graph.find_related_entities(entity['name'], hops=2) + + for rel in related: + all_context.append({ + "entity": rel['name'], + "description": rel['description'], + "source": rel['source'], + "chunk": rel['chunk'], + "path": rel.get('path_descriptions', []) + }) + reasoning_trace.append(f" โ†’ Found related: {rel['name']}") + + # Step 3: Build context with source tracking + context_parts = [] + source_map = {} + + for i, ctx in enumerate(all_context): + source_key = f"[{i+1}]" + context_parts.append(f"{source_key} {ctx['entity']}: {ctx['description']}") + source_map[source_key] = { + "document": ctx['source'], + "text": ctx['chunk'], + "entity": ctx['entity'] + } + + context_text = "\n".join(context_parts) + reasoning_trace.append(f"๐Ÿ“ Built context from {len(context_parts)} sources") + + # Step 4: Generate answer with citation requirements + answer_prompt = f"""Based on the following knowledge graph context, answer the question. +IMPORTANT: For each claim you make, cite the source using [N] notation. + +CONTEXT: +{context_text} + +QUESTION: {query} + +Provide a comprehensive answer with inline citations [1], [2], etc. for each claim. +""" + + try: + response = ollama_client.chat( + model=model, + messages=[{"role": "user", "content": answer_prompt}] + ) + answer = response['message']['content'] + reasoning_trace.append("โœ… Generated answer with citations") + + # Step 5: Extract and verify citations + citation_refs = re.findall(r'\[(\d+)\]', answer) + + for ref in set(citation_refs): + key = f"[{ref}]" + if key in source_map: + src = source_map[key] + citations.append(Citation( + claim=f"Reference {key}", + source_document=src['document'], + source_text=src['text'], + confidence=0.85, + reasoning_path=[f"Entity: {src['entity']}"] + )) + + reasoning_trace.append(f"๐Ÿ”’ Verified {len(citations)} citations") + + return AnswerWithCitations( + answer=answer, + citations=citations, + reasoning_trace=reasoning_trace + ) + + except Exception as e: + return AnswerWithCitations( + answer=f"Error generating answer: {e}", + citations=[], + reasoning_trace=reasoning_trace + ) + + +# ============================================================================ +# Streamlit UI +# ============================================================================ + +def main(): + st.set_page_config( + page_title="Knowledge Graph RAG with Citations", + page_icon="๐Ÿ”", + layout="wide" + ) + + st.title("๐Ÿ” Knowledge Graph RAG with Verifiable Citations") + st.markdown(""" + This demo shows how **Knowledge Graph-based RAG** provides: + - **Multi-hop reasoning** across connected information + - **Verifiable source attribution** for every claim + - **Transparent reasoning traces** you can audit + + Unlike traditional vector RAG, every answer is traceable to its source documents. + """) + + # Sidebar configuration + st.sidebar.header("โš™๏ธ Configuration") + + neo4j_uri = st.sidebar.text_input("Neo4j URI", "bolt://localhost:7687") + neo4j_user = st.sidebar.text_input("Neo4j User", "neo4j") + neo4j_password = st.sidebar.text_input("Neo4j Password", type="password", value="password") + llm_model = st.sidebar.selectbox("LLM Model", ["llama3.2", "mistral", "phi3"]) + + # Initialize session state + if 'graph_initialized' not in st.session_state: + st.session_state.graph_initialized = False + st.session_state.documents = [] + + # Main content + tab1, tab2, tab3 = st.tabs(["๐Ÿ“„ Add Documents", "โ“ Ask Questions", "๐Ÿ”ฌ View Graph"]) + + with tab1: + st.header("Step 1: Build Knowledge Graph from Documents") + + sample_docs = { + "AI Research Paper": """ + GraphRAG is a technique developed by Microsoft Research that combines knowledge graphs + with retrieval-augmented generation. Unlike traditional RAG which uses vector similarity, + GraphRAG builds a structured knowledge graph from documents, enabling multi-hop reasoning. + The technique was introduced by researchers including Darren Edge and Ha Trinh. + GraphRAG excels at answering complex questions that require connecting information + from multiple sources, such as "What are the relationships between different research projects?" + """, + "Company Report": """ + Acme Corp was founded in 2020 by Jane Smith and John Doe in San Francisco. + The company develops AI-powered analytics tools for enterprise customers. + Their flagship product, DataSense, uses machine learning to analyze business data. + Jane Smith previously worked at Google as a senior engineer on the TensorFlow team. + John Doe was a co-founder of StartupX, which was acquired by Microsoft in 2019. + Acme Corp raised $50 million in Series B funding led by Sequoia Capital. + """ + } + + doc_choice = st.selectbox("Choose sample document:", list(sample_docs.keys())) + doc_text = st.text_area("Or paste your own document:", sample_docs[doc_choice], height=200) + doc_name = st.text_input("Document name:", doc_choice) + + if st.button("๐Ÿ”จ Extract & Add to Knowledge Graph"): + with st.spinner("Extracting entities and relationships..."): + try: + graph = KnowledgeGraphManager(neo4j_uri, neo4j_user, neo4j_password) + entities, relationships = extract_entities_with_llm(doc_text, doc_name, llm_model) + + for entity in entities: + graph.add_entity(entity) + + for rel in relationships: + graph.add_relationship(rel) + + graph.close() + + st.success(f"โœ… Extracted {len(entities)} entities and {len(relationships)} relationships") + + with st.expander("View Extracted Entities"): + for e in entities: + st.write(f"**{e.name}** ({e.entity_type}): {e.description}") + + with st.expander("View Extracted Relationships"): + for r in relationships: + st.write(f"{r.source} --[{r.relation_type}]--> {r.target}: {r.description}") + + st.session_state.graph_initialized = True + st.session_state.documents.append(doc_name) + + except Exception as e: + st.error(f"Error: {e}") + st.info("Make sure Neo4j is running and Ollama has the model pulled.") + + with tab2: + st.header("Step 2: Ask Questions with Verifiable Answers") + + if not st.session_state.graph_initialized: + st.warning("โš ๏ธ Please add documents to the knowledge graph first.") + else: + st.info(f"๐Ÿ“š Knowledge graph contains documents: {', '.join(st.session_state.documents)}") + + query = st.text_input("Enter your question:", "What are the key concepts in GraphRAG and who developed it?") + + if st.button("๐Ÿ” Ask with Citations"): + with st.spinner("Traversing knowledge graph and generating answer..."): + try: + graph = KnowledgeGraphManager(neo4j_uri, neo4j_user, neo4j_password) + result = generate_answer_with_citations(query, graph, llm_model) + graph.close() + + # Display reasoning trace + st.subheader("๐Ÿง  Reasoning Trace") + for step in result.reasoning_trace: + st.write(step) + + # Display answer + st.subheader("๐Ÿ’ฌ Answer") + st.markdown(result.answer) + + # Display citations + st.subheader("๐Ÿ“š Source Citations") + if result.citations: + for i, citation in enumerate(result.citations): + with st.expander(f"Citation {i+1}: {citation.source_document}"): + st.write(f"**Source Document:** {citation.source_document}") + st.write(f"**Source Text:** {citation.source_text}") + st.write(f"**Confidence:** {citation.confidence:.0%}") + st.write(f"**Reasoning Path:** {' โ†’ '.join(citation.reasoning_path)}") + else: + st.info("No specific citations extracted for this answer.") + + except Exception as e: + st.error(f"Error: {e}") + + with tab3: + st.header("๐Ÿ”ฌ Knowledge Graph Visualization") + st.info("This tab shows the structure of your knowledge graph.") + + if st.button("๐Ÿ“Š Show Graph Statistics"): + try: + graph = KnowledgeGraphManager(neo4j_uri, neo4j_user, neo4j_password) + with graph.driver.session() as session: + node_count = session.run("MATCH (n) RETURN count(n) as count").single()['count'] + rel_count = session.run("MATCH ()-[r]->() RETURN count(r) as count").single()['count'] + + col1, col2 = st.columns(2) + col1.metric("Total Entities", node_count) + col2.metric("Total Relationships", rel_count) + + graph.close() + except Exception as e: + st.error(f"Error connecting to Neo4j: {e}") + + if st.button("๐Ÿ—‘๏ธ Clear Graph"): + try: + graph = KnowledgeGraphManager(neo4j_uri, neo4j_user, neo4j_password) + graph.clear_graph() + graph.close() + st.session_state.graph_initialized = False + st.session_state.documents = [] + st.success("Graph cleared!") + except Exception as e: + st.error(f"Error: {e}") + + +if __name__ == "__main__": + main() diff --git a/rag_tutorials/knowledge_graph_rag_citations/requirements.txt b/rag_tutorials/knowledge_graph_rag_citations/requirements.txt new file mode 100644 index 0000000..4dc44b9 --- /dev/null +++ b/rag_tutorials/knowledge_graph_rag_citations/requirements.txt @@ -0,0 +1,3 @@ +streamlit>=1.28.0 +ollama>=0.1.0 +neo4j>=5.0.0