#!/usr/bin/env python3
"""
LangChain Adaptor

Implements LangChain Document format for RAG pipelines.
Converts Skill Seekers documentation into LangChain-compatible Document objects.
"""

import json
from pathlib import Path
from typing import Any

from .base import SkillAdaptor, SkillMetadata
from skill_seekers.cli.arguments.common import DEFAULT_CHUNK_TOKENS, DEFAULT_CHUNK_OVERLAP_TOKENS


class LangChainAdaptor(SkillAdaptor):
    """
    LangChain platform adaptor.

    Handles:
    - LangChain Document format (page_content + metadata)
    - JSON packaging with array of documents
    - No upload (users import directly into code)
    - Optimized for RAG/vector store ingestion
    """

    PLATFORM = "langchain"
    PLATFORM_NAME = "LangChain (RAG Framework)"
    DEFAULT_API_ENDPOINT = None  # No upload endpoint

    def format_skill_md(
        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
    ) -> str:
        """
        Format skill as JSON array of LangChain Documents.

        Converts SKILL.md and all references/*.md into LangChain Document format:
        {
          "page_content": "...",
          "metadata": {"source": "...", "category": "...", ...}
        }

        Args:
            skill_dir: Path to skill directory
            metadata: Skill metadata
            enable_chunking: Enable intelligent chunking for large documents
            **kwargs: Additional chunking parameters (chunk_max_tokens, preserve_code_blocks)

        Returns:
            JSON string containing array of LangChain Documents
        """
        documents = []

        # Convert SKILL.md (main documentation)
        skill_md_path = skill_dir / "SKILL.md"
        if skill_md_path.exists():
            content = self._read_existing_content(skill_dir)
            if content.strip():
                doc_metadata = {
                    "source": metadata.name,
                    "category": "overview",
                    "file": "SKILL.md",
                    "type": "documentation",
                    "version": metadata.version,
                    "doc_version": metadata.doc_version,
                }

                # Chunk if enabled
                chunks = self._maybe_chunk_content(
                    content,
                    doc_metadata,
                    enable_chunking=enable_chunking,
                    chunk_max_tokens=kwargs.get("chunk_max_tokens", DEFAULT_CHUNK_TOKENS),
                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
                    source_file="SKILL.md",
                    chunk_overlap_tokens=kwargs.get(
                        "chunk_overlap_tokens", DEFAULT_CHUNK_OVERLAP_TOKENS
                    ),
                )

                # Add all chunks to documents
                for chunk_text, chunk_meta in chunks:
                    documents.append({"page_content": chunk_text, "metadata": chunk_meta})

        # Convert all reference files using base helper method
        for ref_file, ref_content in self._iterate_references(skill_dir):
            if ref_content.strip():
                # Derive category from filename
                category = ref_file.stem.replace("_", " ").lower()

                doc_metadata = {
                    "source": metadata.name,
                    "category": category,
                    "file": ref_file.name,
                    "type": "reference",
                    "version": metadata.version,
                    "doc_version": metadata.doc_version,
                }

                # Chunk if enabled
                chunks = self._maybe_chunk_content(
                    ref_content,
                    doc_metadata,
                    enable_chunking=enable_chunking,
                    chunk_max_tokens=kwargs.get("chunk_max_tokens", DEFAULT_CHUNK_TOKENS),
                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
                    source_file=ref_file.name,
                    chunk_overlap_tokens=kwargs.get(
                        "chunk_overlap_tokens", DEFAULT_CHUNK_OVERLAP_TOKENS
                    ),
                )

                # Add all chunks to documents
                for chunk_text, chunk_meta in chunks:
                    documents.append({"page_content": chunk_text, "metadata": chunk_meta})

        # Return as formatted JSON
        return json.dumps(documents, indent=2, ensure_ascii=False)

    def package(
        self,
        skill_dir: Path,
        output_path: Path,
        enable_chunking: bool = False,
        chunk_max_tokens: int = DEFAULT_CHUNK_TOKENS,
        preserve_code_blocks: bool = True,
        chunk_overlap_tokens: int = DEFAULT_CHUNK_OVERLAP_TOKENS,
    ) -> Path:
        """
        Package skill into JSON file for LangChain.

        Creates a JSON file containing an array of LangChain Documents ready
        for ingestion into vector stores (Chroma, Pinecone, etc.)

        Args:
            skill_dir: Path to skill directory
            output_path: Output path/filename for JSON file
            enable_chunking: Enable intelligent chunking for large documents
            chunk_max_tokens: Maximum tokens per chunk (default: 512)
            preserve_code_blocks: Preserve code blocks during chunking

        Returns:
            Path to created JSON file
        """
        skill_dir = Path(skill_dir)

        # Determine output filename using base helper method
        output_path = self._format_output_path(skill_dir, Path(output_path), "-langchain.json")
        output_path.parent.mkdir(parents=True, exist_ok=True)

        # Read metadata from SKILL.md frontmatter
        metadata = self._build_skill_metadata(skill_dir)

        # Generate LangChain documents with chunking
        documents_json = self.format_skill_md(
            skill_dir,
            metadata,
            enable_chunking=enable_chunking,
            chunk_max_tokens=chunk_max_tokens,
            preserve_code_blocks=preserve_code_blocks,
            chunk_overlap_tokens=chunk_overlap_tokens,
        )

        # Write to file
        output_path.write_text(documents_json, encoding="utf-8")

        print(f"\n✅ LangChain documents packaged successfully!")
        print(f"📦 Output: {output_path}")

        # Parse and show stats
        documents = json.loads(documents_json)
        print(f"📊 Total documents: {len(documents)}")

        # Show category breakdown
        categories = {}
        for doc in documents:
            cat = doc["metadata"].get("category", "unknown")
            categories[cat] = categories.get(cat, 0) + 1

        print("📁 Categories:")
        for cat, count in sorted(categories.items()):
            print(f"   - {cat}: {count}")

        return output_path

    def upload(self, package_path: Path, _api_key: str, **_kwargs) -> dict[str, Any]:
        """
        LangChain format does not support direct upload.

        Users should import the JSON file into their LangChain code:

        ```python
        from langchain.schema import Document
        import json

        # Load documents
        with open("skill-langchain.json") as f:
            docs_data = json.load(f)

        # Convert to LangChain Documents
        documents = [
            Document(page_content=doc["page_content"], metadata=doc["metadata"])
            for doc in docs_data
        ]

        # Use with vector store
        from langchain.vectorstores import Chroma
        from langchain.embeddings import OpenAIEmbeddings

        vectorstore = Chroma.from_documents(documents, OpenAIEmbeddings())
        ```

        Args:
            package_path: Path to JSON file
            api_key: Not used
            **kwargs: Not used

        Returns:
            Result indicating no upload capability
        """
        example_code = f"""
# Example: Load into LangChain

from langchain.schema import Document
import json

# Load documents
with open("{package_path.name}") as f:
    docs_data = json.load(f)

# Convert to LangChain Documents
documents = [
    Document(page_content=doc["page_content"], metadata=doc["metadata"])
    for doc in docs_data
]

# Use with vector store
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

vectorstore = Chroma.from_documents(documents, OpenAIEmbeddings())
retriever = vectorstore.as_retriever()

# Query
results = retriever.get_relevant_documents("your query here")
"""

        return {
            "success": False,
            "skill_id": None,
            "url": str(package_path.absolute()),
            "message": (
                f"LangChain documents packaged at: {package_path.absolute()}\n\n"
                "Load into your code:\n"
                f"{example_code}"
            ),
        }

    def validate_api_key(self, _api_key: str) -> bool:
        """
        LangChain format doesn't use API keys for packaging.

        Args:
            api_key: Not used

        Returns:
            Always False (no API needed for packaging)
        """
        return False

    def get_env_var_name(self) -> str:
        """
        No API key needed for LangChain packaging.

        Returns:
            Empty string
        """
        return ""

    def supports_enhancement(self) -> bool:
        """
        LangChain format doesn't support AI enhancement.

        Enhancement should be done before conversion using:
        skill-seekers enhance output/skill/ --mode LOCAL

        Returns:
            False
        """
        return False

    def enhance(self, _skill_dir: Path, _api_key: str) -> bool:
        """
        LangChain format doesn't support enhancement.

        Args:
            skill_dir: Not used
            api_key: Not used

        Returns:
            False
        """
        print("❌ LangChain format does not support enhancement")
        print("   Enhance before packaging:")
        print("   skill-seekers enhance output/skill/ --mode LOCAL")
        print("   skill-seekers package output/skill/ --target langchain")
        return False
