import logging

logger = logging.getLogger("pr")

KNOWLEDGE_MESSAGE_MARKER = "[KNOWLEDGE_BASE_CONTEXT]"


def inject_knowledge_context(assistant, user_message):
    if not hasattr(assistant, "enhanced") or not assistant.enhanced:
        return

    messages = assistant.messages

    # Remove any existing knowledge context messages
    for i in range(len(messages) - 1, -1, -1):
        if messages[i].get("role") == "user" and KNOWLEDGE_MESSAGE_MARKER in messages[i].get(
            "content", ""
        ):
            del messages[i]
            logger.debug(f"Removed existing knowledge base message at index {i}")
            break

    try:
        # Search knowledge base with enhanced FTS + semantic search
        knowledge_results = assistant.enhanced.knowledge_store.search_entries(user_message, top_k=5)

        # Search conversation history for related content
        conversation_results = []
        if hasattr(assistant.enhanced, "conversation_memory"):
            history_results = assistant.enhanced.conversation_memory.search_conversations(
                user_message, limit=3
            )
            for conv in history_results:
                # Extract relevant messages from conversation
                conv_messages = assistant.enhanced.conversation_memory.get_conversation_messages(
                    conv["conversation_id"]
                )
                for msg in conv_messages[-5:]:  # Last 5 messages from each conversation
                    if msg["role"] == "user" and msg["content"] != user_message:
                        # Calculate relevance score
                        relevance = calculate_text_similarity(user_message, msg["content"])
                        if relevance > 0.3:  # Only include relevant matches
                            conversation_results.append(
                                {
                                    "content": msg["content"],
                                    "score": relevance,
                                    "source": f"Previous conversation: {conv['conversation_id'][:8]}",
                                }
                            )

        # Combine and sort results by relevance score
        all_results = []

        # Add knowledge base results
        for entry in knowledge_results:
            score = entry.metadata.get("search_score", 0.5)
            all_results.append(
                {
                    "content": entry.content,
                    "score": score,
                    "source": f"Knowledge Base ({entry.category})",
                    "type": "knowledge",
                }
            )

        # Add conversation results
        for conv in conversation_results:
            all_results.append(
                {
                    "content": conv["content"],
                    "score": conv["score"],
                    "source": conv["source"],
                    "type": "conversation",
                }
            )

        # Sort by score and take top 5
        all_results.sort(key=lambda x: x["score"], reverse=True)
        top_results = all_results[:5]

        if not top_results:
            logger.debug("No relevant knowledge or conversation matches found")
            return

        # Format context for LLM
        knowledge_parts = []
        for idx, result in enumerate(top_results, 1):
            content = result["content"]
            if len(content) > 1500:  # Shorter limit for multiple results
                content = content[:1500] + "..."

            score_indicator = f"({result['score']:.2f})" if result["score"] < 1.0 else "(exact)"
            knowledge_parts.append(
                f"Match {idx} {score_indicator} - {result['source']}:\n{content}"
            )

        knowledge_message_content = (
            f"{KNOWLEDGE_MESSAGE_MARKER}\nRelevant information from knowledge base and conversation history:\n\n"
            + "\n\n".join(knowledge_parts)
        )

        knowledge_message = {"role": "user", "content": knowledge_message_content}

        messages.append(knowledge_message)
        logger.debug(f"Injected enhanced context message with {len(top_results)} matches")

    except Exception as e:
        logger.error(f"Error injecting knowledge context: {e}")


def calculate_text_similarity(text1: str, text2: str) -> float:
    """Calculate similarity between two texts using word overlap and sequence matching."""
    import re

    # Normalize texts
    text1_lower = text1.lower()
    text2_lower = text2.lower()

    # Exact substring match gets highest score
    if text1_lower in text2_lower or text2_lower in text1_lower:
        return 1.0

    # Word-level similarity
    words1 = set(re.findall(r"\b\w+\b", text1_lower))
    words2 = set(re.findall(r"\b\w+\b", text2_lower))

    if not words1 or not words2:
        return 0.0

    intersection = words1 & words2
    union = words1 | words2

    word_similarity = len(intersection) / len(union)

    # Bonus for consecutive word sequences (partial sentences)
    consecutive_bonus = 0.0
    words1_list = list(words1)
    list(words2)

    for i in range(len(words1_list) - 1):
        for j in range(i + 2, min(i + 5, len(words1_list) + 1)):
            phrase = " ".join(words1_list[i:j])
            if phrase in text2_lower:
                consecutive_bonus += 0.1 * (j - i)

    total_similarity = min(1.0, word_similarity + consecutive_bonus)

    return total_similarity