import glob as globmod
import json
import os
import time

import chromadb
from chromadb.config import Settings
import config


# ── ChromaDB singleton ───────────────────────────────────────────────

_store = None

def _get_store():
    global _store
    if _store is None:
        _store = chromadb.PersistentClient(
            path=config.RAG_PERSIST_DIR,
            settings=Settings(anonymized_telemetry=False),
        )
    return _store

def _collection(name):
    """Get or create collection — uses ChromaDB's default ONNX embedding (all-MiniLM-L6-v2)."""
    return _get_store().get_or_create_collection(name=name)


# ── Tool schemas ─────────────────────────────────────────────────────

schema_store_knowledge = {
    "type": "function",
    "function": {
        "name": "store_knowledge",
        "description": (
            "Store one or more documents with arbitrary metadata into a RAG collection. "
            "Metadata is a free-form dict — choose meaningful keys for future filtering "
            "(e.g., restaurant, category, allergens, spice_level, taste_profile, price"
            ", customer_id, dietary)."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "collection": {
                    "type": "string",
                    "description": "Target collection name (must be defined in config)"
                },
                "documents": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "id": {"type": "string", "description": "Unique document ID"},
                            "text": {"type": "string", "description": "Document body text"},
                            "metadata": {
                                "type": "object",
                                "description": "Arbitrary key-value metadata",
                                "default": {}
                            }
                        },
                        "required": ["id", "text"]
                    },
                    "description": "List of documents to persist"
                }
            },
            "required": ["collection", "documents"]
        }
    }
}

schema_search_knowledge = {
    "type": "function",
    "function": {
        "name": "search_knowledge",
        "description": (
            "Semantically search a RAG collection. Optionally narrow with a "
            "metadata filter using ChromaDB where syntax. "
            "Examples: {'category': 'main_course'}, {'spice_level': {'$lte': 2}}, "
            "{'allergens': {'$contains': 'seafood'}}."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "collection": {
                    "type": "string",
                    "description": "Collection name to search in"
                },
                "query": {
                    "type": "string",
                    "description": "Natural-language search query"
                },
                "n_results": {
                    "type": "integer",
                    "description": "Max results to return (default 5)",
                    "default": 5
                },
                "filter": {
                    "type": "object",
                    "description": "Optional metadata filter dict",
                    "default": None
                }
            },
            "required": ["collection", "query"]
        }
    }
}

schema_create_collection = {
    "type": "function",
    "function": {
        "name": "create_collection",
        "description": (
            "Create a new RAG collection for a new topic/domain. Use a short, descriptive name "
            "with underscores (e.g., 'tanaman_hias', 'customer_profiles'). Optionally provide a description."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "name": {
                    "type": "string",
                    "description": "Collection name (lowercase, underscores for spaces)"
                },
                "description": {
                    "type": "string",
                    "description": "What this collection stores",
                    "default": ""
                }
            },
            "required": ["name"]
        }
    }
}

schema_delete_collection = {
    "type": "function",
    "function": {
        "name": "delete_collection",
        "description": "Permanently delete an entire RAG collection and all documents in it. Use with caution — this cannot be undone.",
        "parameters": {
            "type": "object",
            "properties": {
                "name": {
                    "type": "string",
                    "description": "Collection name to delete"
                }
            },
            "required": ["name"]
        }
    }
}

schema_list_collections = {
    "type": "function",
    "function": {
        "name": "list_collections",
        "description": "List all existing RAG collections with their document count and description.",
        "parameters": {"type": "object", "properties": {}}
    }
}

schema_inspect_collection = {
    "type": "function",
    "function": {
        "name": "inspect_collection",
        "description": (
            "Examine sample documents and metadata fields in a RAG collection. "
            "Always call this before search_knowledge to learn what metadata keys "
            "are available for filtering, then pass them in the filter parameter."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "collection": {
                    "type": "string",
                    "description": "Collection name to inspect"
                },
                "sample_size": {
                    "type": "integer",
                    "description": "Number of sample documents (default 3)",
                    "default": 3
                }
            },
            "required": ["collection"]
        }
    }
}


schema_ingest_files = {
    "type": "function",
    "function": {
        "name": "ingest_files",
        "description": (
            "Read one or more files (supports glob patterns like *.py or src/**/*.md) "
            "and store their content into a RAG collection. "
            "Optionally chunk files into smaller pieces by line count. "
            "Automatically extracts metadata: filename, path, extension, size, modification time."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "collection": {
                    "type": "string",
                    "description": "Target collection name (will be created if it doesn't exist)"
                },
                "paths": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "File paths or glob patterns (e.g., ['*.txt', 'src/**/*.py'])"
                },
                "chunk_size": {
                    "type": "integer",
                    "description": "Lines per chunk (0 = whole file as one document)",
                    "default": 0
                },
                "chunk_overlap": {
                    "type": "integer",
                    "description": "Line overlap between chunks (only used when chunk_size > 0)",
                    "default": 0
                },
                "recursive": {
                    "type": "boolean",
                    "description": "Search directories recursively when using glob patterns",
                    "default": True
                }
            },
            "required": ["collection", "paths"]
        }
    }
}


# ── Tool handlers ────────────────────────────────────────────────────

def _sanitize_meta(meta):
    """ChromaDB metadata only allows str/int/float/bool. Convert lists to JSON string, remove empty lists."""
    out = {}
    for k, v in meta.items():
        if isinstance(v, list):
            if len(v) == 0:
                continue
            out[k] = json.dumps(v, ensure_ascii=False)
        elif isinstance(v, (str, int, float, bool)):
            out[k] = v
        else:
            out[k] = str(v)
    return out

def store_knowledge(collection, documents):
    try:
        col = _collection(collection)
        ids, texts, metas = [], [], []
        for doc in documents:
            ids.append(doc["id"])
            texts.append(doc["text"])
            metas.append(_sanitize_meta(doc.get("metadata", {})))
        col.add(ids=ids, documents=texts, metadatas=metas)
        return f"Stored {len(documents)} document(s) in '{collection}'."
    except Exception as e:
        return f"Error: {e}"


def search_knowledge(collection, query, n_results=5, filter=None):
    try:
        col = _collection(collection)
        kw = {"query_texts": [query], "n_results": n_results}
        if filter:
            kw["where"] = filter
        r = col.query(**kw)
        if not r["ids"] or not r["ids"][0]:
            return "No results found."
        out = []
        for i in range(len(r["ids"][0])):
            did = r["ids"][0][i]
            txt = r["documents"][0][i]
            if len(txt) > 500:
                txt = txt[:500] + "..."
            meta = json.dumps(r["metadatas"][0][i], ensure_ascii=False) if r.get("metadatas") else "{}"
            dist = ""
            if r.get("distances"):
                dist = f" (score: {r['distances'][0][i]:.4f})"
            out.append(f"[{did}]{dist}\n  text: {txt}\n  metadata: {meta}")
        return "\n---\n".join(out)
    except Exception as e:
        return f"Error: {e}"


def create_collection(name, description=""):
    try:
        col = _get_store().get_or_create_collection(name=name)
        col.modify(metadata={"description": description})
        return f"Collection '{name}' is ready."
    except Exception as e:
        return f"Error: {e}"

def delete_collection(name):
    try:
        _get_store().delete_collection(name)
        return f"Deleted collection '{name}'."
    except Exception as e:
        return f"Error: {e}"

def list_collections():
    try:
        cols = _get_store().list_collections()
        if not cols:
            return "No collections exist yet."
        out = ["Available collections:"]
        for col in cols:
            meta = col.metadata or {}
            desc = meta.get("description", "")
            cnt = col.count()
            tag = f" ({desc})" if desc else ""
            out.append(f"- {col.name}{tag} [{cnt} docs]")
        return "\n".join(out)
    except Exception as e:
        return f"Error: {e}"


def inspect_collection(collection, sample_size=3):
    try:
        col = _collection(collection)
        cnt = col.count()
        if cnt == 0:
            return f"Collection '{collection}' is empty."
        n = min(sample_size, cnt)
        r = col.get(limit=n, include=["documents", "metadatas"])
        out = [f"Collection: {collection}  |  Total documents: {cnt}", f"Sample ({n}):"]
        for i in range(len(r["ids"])):
            txt = r["documents"][i]
            if len(txt) > 200:
                txt = txt[:200] + "..."
            meta = json.dumps(r["metadatas"][i], ensure_ascii=False) if r.get("metadatas") and r["metadatas"][i] else "(none)"
            out.append(f"\n  [{r['ids'][i]}]  text: {txt}  metadata: {meta}")
        keys = set()
        for m in r["metadatas"]:
            if m:
                keys.update(m.keys())
        if keys:
            out.append(f"\nMetadata keys: {', '.join(sorted(keys))}")
        return "\n".join(out)
    except Exception as e:
        return f"Error: {e}"


def ingest_files(collection, paths, chunk_size=0, chunk_overlap=0, recursive=True):
    try:
        col = _collection(collection)
        all_ids, all_texts, all_metas = [], [], []
        processed, skipped = 0, 0

        # Expand glob patterns into real file paths
        file_set = set()
        for p in paths:
            expanded = globmod.glob(p, recursive=recursive)
            if expanded:
                file_set.update(expanded)
            else:
                # Maybe it's a literal path that doesn't look like a glob
                if os.path.isfile(p):
                    file_set.add(p)
                else:
                    skipped += 1

        if not file_set:
            return "No matching files found."

        for fpath in sorted(file_set):
            if not os.path.isfile(fpath):
                skipped += 1
                continue

            ext = os.path.splitext(fpath)[1].lower()
            stat = os.stat(fpath)
            base_meta = {
                "filename": os.path.basename(fpath),
                "path": os.path.relpath(fpath),
                "extension": ext,
                "size": stat.st_size,
                "mtime": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(stat.st_mtime)),
            }
            base_name = os.path.splitext(os.path.basename(fpath))[0]

            # ── read content ──────────────────────────────────────────
            if ext in (".xlsx", ".xlsm"):
                try:
                    import openpyxl
                except ImportError:
                    skipped += 1
                    continue

                wb = openpyxl.load_workbook(fpath, read_only=True, data_only=True)
                for sheet_name in wb.sheetnames:
                    ws = wb[sheet_name]
                    rows = []
                    for row in ws.iter_rows(values_only=True):
                        vals = [str(c) if c is not None else "" for c in row]
                        rows.append("\t".join(vals))
                    lines = rows
                    content = "\n".join(lines)
                    if not content.strip():
                        continue

                    sheet_meta = dict(base_meta)
                    sheet_meta["sheet"] = sheet_name

                    if chunk_size > 0:
                        n_lines = len(lines)
                        cid = 0
                        start = 0
                        while start < n_lines:
                            end = min(start + chunk_size, n_lines)
                            chunk_text = "\n".join(lines[start:end])
                            doc_id = f"{base_name}_{sheet_name}_chunk_{cid}"
                            meta = dict(sheet_meta)
                            meta["chunk_index"] = cid
                            meta["chunk_lines"] = end - start
                            meta["chunk_start_line"] = start + 1
                            all_ids.append(doc_id)
                            all_texts.append(chunk_text)
                            all_metas.append(_sanitize_meta(meta))
                            cid += 1
                            step = chunk_size - chunk_overlap
                            start += step if step > 0 else 1
                        processed += 1
                    else:
                        doc_id = f"{base_name}_{sheet_name}"
                        all_ids.append(doc_id)
                        all_texts.append(content)
                        all_metas.append(_sanitize_meta(sheet_meta))
                        processed += 1
                wb.close()
            else:
                # Plain-text files
                try:
                    with open(fpath, "r", encoding="utf-8", errors="replace") as f:
                        lines = f.readlines()
                except Exception:
                    skipped += 1
                    continue

                content = "".join(lines)
                if not content.strip():
                    skipped += 1
                    continue

                if chunk_size > 0:
                    n_lines = len(lines)
                    cid = 0
                    start = 0
                    while start < n_lines:
                        end = min(start + chunk_size, n_lines)
                        chunk_text = "".join(lines[start:end])
                        doc_id = f"{base_name}_chunk_{cid}"
                        meta = dict(base_meta)
                        meta["chunk_index"] = cid
                        meta["chunk_lines"] = end - start
                        meta["chunk_start_line"] = start + 1
                        all_ids.append(doc_id)
                        all_texts.append(chunk_text)
                        all_metas.append(_sanitize_meta(meta))
                        cid += 1
                        step = chunk_size - chunk_overlap
                        start += step if step > 0 else 1
                    processed += 1
                else:
                    doc_id = base_name
                    all_ids.append(doc_id)
                    all_texts.append(content)
                    all_metas.append(_sanitize_meta(base_meta))
                    processed += 1

        if all_ids:
            col.add(ids=all_ids, documents=all_texts, metadatas=all_metas)

        parts = [f"Ingested {processed} file(s) into '{collection}'"]
        if processed > 0:
            parts.append(f"({len(all_ids)} document(s) total)")
        if skipped > 0:
            parts.append(f"({skipped} file(s) skipped)")
        return " ".join(parts)

    except Exception as e:
        return f"Error: {e}"