from typing import List, Dict
from pydantic import BaseModel
from agent.adapters import LLMAdapter, EmbeddingsAdapter
from agent.prompts import SYSTEM_ANSWER, BRIEF_PROMPT
from vector.store import FaissStore

class Chunk(BaseModel):
    text: str
    source: str
    title: str

def chunk_text(text: str, source: str, title: str, size=1200, overlap=200) -> List[Chunk]:
    out, i = [], 0
    while i < len(text):
        out.append(Chunk(text=text[i:i+size], source=source, title=title))
        i += size - overlap
    return out

class AgentGraph:
    def __init__(self, index_dir: str):
        self.llm = LLMAdapter()
        self.emb = EmbeddingsAdapter()
        self.index = FaissStore(dim=1536, index_dir=index_dir)   # 3072 for text-embedding-3-large

    def build_index(self, docs: List[Dict]):
        chunks = []
        for d in docs:
            chunks += chunk_text(d["text"], d["url"], d["title"])
        vecs = self.emb.embed([c.text for c in chunks])
        metas = [c.model_dump() for c in chunks]
        self.index.add(vecs, metas)
        self.index.save()

    def answer(self, question: str) -> Dict:
        qv = self.emb.embed([question])[0]
        hits = self.index.search(qv, k=6)
        ctx_blocks = []
        mapping = {}
        for i, (score, meta) in enumerate(hits, start=1):
            tag = f"S{i}"
            mapping[tag] = {"title": meta["title"], "url": meta["source"], "score": score}
            ctx_blocks.append(f"[{tag}] {meta['title']} — {meta['source']}\n{meta['text']}\n")
        messages = [
            {"role":"system","content": SYSTEM_ANSWER},
            {"role":"user","content": f"Question: {question}\n\nContext:\n" + "\n\n".join(ctx_blocks)}
        ]
        reply = self.llm.chat(messages)
        return {"answer": reply, "sources": mapping}

    def brief(self) -> Dict:
        seed = "company overview latest results kpis risks guidance"
        qv = self.emb.embed([seed])[0]
        hits = self.index.search(qv, k=8)
        ctx = "\n\n".join([h[1]["text"] for h in hits])
        messages = [
            {"role":"system","content": SYSTEM_ANSWER},
            {"role":"user","content": f"{BRIEF_PROMPT}\n\nContext:\n{ctx}"}
        ]
        reply = self.llm.chat(messages)
        src = {f"S{i+1}":{"title":h[1]["title"],"url":h[1]["source"],"score":h[0]} for i,h in enumerate(hits)}
        return {"brief": reply, "sources": src}