Published solution: remove compare_agent.py

compare_agent.py

@@ -1,145 +0,0 @@
-"""
-CompareAgent – LangGraph implementation that builds a comparative table for three entities.
-
-Usage:
-    python compare_agent.py "Chroma, FAISS, Qdrant"
-
-The script will:
-1. Ask LLM to generate 3‑5 comparison criteria.
-2. For each entity × criterion pair perform a Tavily search and collect short notes.
-3. Build a markdown table with rows = criteria, columns = entities.
-4. Produce a verdict sentence.
-"""
-
-import os
-import sys
-from typing import TypedDict, List, Dict
-
-from langgraph.graph import StateGraph
-from langchain_openai import ChatOpenAI
-from langchain_tavily import TavilySearch
-
-# ---------- State definition ----------
-class CompareState(TypedDict):
-    entities: List[str]
-    criteria: List[str]
-    findings: Dict[str, List[str]]  # entity -> list of notes per criterion
-    final_table: str | None
-    verdict: str | None
-
-# ---------- LLM and tools ----------
-llm = ChatOpenAI(temperature=0.2)
-search_tool = TavilySearch(api_key=os.getenv("TAVILY_API_KEY"))
-
-# ---------- Node functions ----------
-async def plan_criteria(state: CompareState) -> Dict:
-    """Generate comparison criteria based on entities."""
-    prompt = (
-        f"You are an expert in evaluating technologies.\n"
-        f"Given the following entities: {', '.join(state['entities'])}.\n"
-        f"Provide 3‑5 concise criteria that would be useful for comparing them."
-    )
-    response = await llm.agenerate([prompt])
-    text = response.generations[0][0].text.strip()
-    # split by newlines or commas
-    crits = [c.strip() for c in text.replace('\n', ',').split(',') if c.strip()]
-    return {"criteria": crits}
-
-async def research_entity(state: CompareState) -> Dict:
-    """Perform Tavily search for the next unprocessed entity × criterion pair."""
-    # find first entity with missing notes
-    for ent in state['entities']:
-        notes = state['findings'].get(ent, [])
-        if len(notes) < len(state['criteria']):
-            idx = len(notes)
-            crit = state['criteria'][idx]
-            query = f"{ent} {crit}"
-            result = await search_tool.ainvoke(query=query, max_results=1)
-            snippet = result.get('results', [{}])[0].get('content', 'No info')
-            notes.append(snippet[:200])  # truncate
-            state['findings'][ent] = notes
-            break
-    return {"findings": state['findings']}
-
-async def build_table(state: CompareState) -> Dict:
-    """Create markdown table from findings."""
-    header = "| Criterion | " + " | ".join(state['entities']) + " |\n"
-    separator = "|---|" + "---|" * len(state['entities']) + "\n"
-    rows = []
-    for i, crit in enumerate(state['criteria']):
-        row_cells = [crit]
-        for ent in state['entities']:
-            notes = state['findings'].get(ent, [])
-            note = notes[i] if i < len(notes) else ""
-            row_cells.append(note)
-        rows.append("| " + " | ".join(row_cells) + " |\n")
-    table = header + separator + "".join(rows)
-    return {"final_table": table}
-
-async def verdict(state: CompareState) -> Dict:
-    """Generate a short recommendation based on the table."""
-    prompt = (
-        f"You are an analyst. Based on the following comparison table:\n\n"
-        f"{state['final_table']}\n\n"
-        f"Provide 2‑3 sentences recommending which entity is best for each use case.")
-    response = await llm.agenerate([prompt])
-    text = response.generations[0][0].text.strip()
-    return {"verdict": text}
-
-# ---------- Graph construction ----------
-def create_graph() -> StateGraph:
-    graph = StateGraph(CompareState)
-    graph.add_node("plan_criteria", plan_criteria)
-    graph.add_node("research_entity", research_entity)
-    graph.add_node("build_table", build_table)
-    graph.add_node("verdict", verdict)
-
-    # start -> plan_criteria
-    graph.set_entry_point("plan_criteria")
-
-    # after criteria, loop research until all pairs processed
-    def should_continue(state: CompareState) -> bool:
-        return any(len(notes) < len(state['criteria']) for notes in state.get('findings', {}).values())
-
-    graph.add_conditional_edges(
-        "plan_criteria",
-        lambda s: "research_entity" if should_continue(s) else "build_table",
-        {"research_entity": "research_entity", "build_table": "build_table"},
-    )
-
-    # after research, decide again
-    graph.add_conditional_edges(
-        "research_entity",
-        lambda s: "research_entity" if should_continue(s) else "build_table",
-        {"research_entity": "research_entity", "build_table": "build_table"},
-    )
-
-    # after table, verdict
-    graph.add_edge("build_table", "verdict")
-    graph.set_finish_node("verdict")
-    return graph
-
-# ---------- Main execution ----------
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Usage: python compare_agent.py 'entity1, entity2, entity3'")
-        sys.exit(1)
-    entities = [e.strip() for e in sys.argv[1].split(',')]
-    if len(entities) != 3:
-        print("Please provide exactly three entities separated by commas.")
-        sys.exit(1)
-
-    initial_state: CompareState = {
-        "entities": entities,
-        "criteria": [],
-        "findings": {},
-        "final_table": None,
-        "verdict": None,
-    }
-
-    graph = create_graph()
-    result = graph.invoke(initial_state)
-    print("\n=== Comparison Table ===")
-    print(result["final_table"])
-    print("\n=== Verdict ===")
-    print(result["verdict"])