#!/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


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) -> 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

        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():
                documents.append(
                    {
                        "page_content": content,
                        "metadata": {
                            "source": metadata.name,
                            "category": "overview",
                            "file": "SKILL.md",
                            "type": "documentation",
                            "version": metadata.version,
                        },
                    }
                )

        # 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()

                documents.append(
                    {
                        "page_content": ref_content,
                        "metadata": {
                            "source": metadata.name,
                            "category": category,
                            "file": ref_file.name,
                            "type": "reference",
                            "version": metadata.version,
                        },
                    }
                )

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

    def package(self, skill_dir: Path, output_path: Path) -> 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

        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
        metadata = SkillMetadata(
            name=skill_dir.name,
            description=f"LangChain documents for {skill_dir.name}",
            version="1.0.0",
        )

        # Generate LangChain documents
        documents_json = self.format_skill_md(skill_dir, metadata)

        # 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 = """
# Example: Load into LangChain

from langchain.schema import Document
import json

# Load documents
with open("{path}") 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")
""".format(
            path=package_path.name
        )

        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