import json
import hashlib
from uuid import uuid4
from typing import List, Dict, Optional, Union, Any
from pydantic import Field

from llama_index.core.schema import ImageNode
from llama_index.core.schema import QueryBundle
from llama_index.core import Document as LlamaIndexDocument
from llama_index.core.schema import BaseNode, TextNode, RelatedNodeInfo
from llama_index.core.graph_stores.types import (
    Relation,
    EntityNode,
    ChunkNode,
)

from evoagentx.core.base_config import BaseModule
from evoagentx.core.logging import logger

DEAFULT_EXCLUDED = ['file_name', 'file_type', 'file_size', 'page_count', 'creation_date', 
                        'last_modified_date', 'language', 'word_count', 'custom_fields', 'hash_doc', 
                        'graph_node', # for graph store 
                    ]

class DocumentMetadata(BaseModule):
    """
    This class ensures type safety and validation for metadata associated with a document,
    such as file information, creation date, and custom fields.
    """

    file_name: Optional[str] = Field(default=None, description="The name of the document file, excluding the path.")
    file_path: Optional[str] = Field(default=None, description="The file path or URL where the document is stored.")
    file_type: Optional[str] = Field(default=None, description="The type of the document (e.g., '.pdf', '.docx', '.md', '.txt').")
    file_size: Optional[int] = Field(default=None, description="The size of the document.")
    page_count: Optional[int] = Field(default=None, description="The number of pages in the document, if applicable (e.g., for PDFs).")
    creation_date: Optional[str] = Field(default=None, description="The creation date and time of the document.")
    last_modified_date: Optional[str] = Field(default=None, description="The last modified date and time of the document.")
    language: Optional[str] = Field(default=None, description="The primary language of the document (e.g., 'en', 'zh').")
    word_count: Optional[int] = Field(default=None, description="The number of words in the document, calculated during initialization.")
    custom_fields: Dict[str, Any] = Field(default_factory=dict, description="A dictionary for storing additional user-defined metadata.")
    hash_doc: Optional[str] = Field(default=None, description="The hash code of this Document for deduplication")


class GraphNodeData(BaseModule):
    # graph support
    # Basic
    label: Optional[str] = Field(default="entity", description="The label name of the 'LabelNode', 'EntityNode', 'Relation' in llama_index node.")
    
    # Relation
    node_class_name: Optional[str] = Field(default=None, description="The class name of the source llama_index node.")
    properties: Optional[Dict] = Field(default_factory=dict, description="Represents all information from the Node.")
    
    # Entity Node
    node_name: Optional[str] = Field(default=None, description="Entity name of each node.")
    source_id: Optional[str] = Field(default=None, description="Source node ID.")
    target_id: Optional[str] = Field(default=None, description="Target node ID.")

    # Chunk Node
    text: Optional[str] = Field(default=None, description="The text stored in the ChunkNode.")
    id_: Optional[str] = Field(default=None, description="ChunkNode id.")

class ChunkMetadata(DocumentMetadata):
    """
    This class holds metadata for a chunk, including its relationship to the parent document,
    chunking parameters, and retrieval-related information.
    """

    doc_id: Optional[str] = Field(default=None, description="The unique identifier of the parent document.")
    corpus_id: Optional[str] = Field(default=None, description="The unique identifier of the Corpus(Indexing).")
    chunk_size: Optional[int] = Field(default=None, description="The size of the chunk in characters, if applicable.")
    chunk_overlap: Optional[int] = Field(default=None, description="The number of overlapping characters between adjacent chunks.")
    chunk_index: Optional[int] = Field(default=None, description="The index of the chunk within the parent document.")
    chunking_strategy: Optional[str] = Field(default=None, description="The strategy used to create the chunk (e.g., 'simple', 'semantic', 'tree').")
    similarity_score: Optional[float] = Field(default=None, description="Similarity score from retrieval.")
    # graph support
    graph_node: Optional[GraphNodeData] = Field(default=None, description="The properties of all types of graph nodes.")
    # LongTermMemory
    content: Optional[str] = Field(default=None, description="the content of the message, will be dumps by 'dumps' from json lib.")
    memory_id: Optional[str] = Field(default=None, description="Unique identifier for memory entries.")
    agent: Optional[str] = Field(default=None, description="The sender of the message.")
    msg_type: Optional[str] = Field(default=None, description="The type of the message (e.g., 'request', 'response').")
    prompt: Optional[Union[str, List[dict]]] = Field(default=None, description="The prompt used to generate the message.")
    next_actions: Optional[List[str]] = Field(default=None, description="The following actions after the message.")
    wf_task: Optional[str] = Field(default=None, description="The name of a task in the workflow.")
    wf_task_desc: Optional[str] = Field(default=None, description="The description of a task in the workflow.")
    message_id: Optional[str] = Field(default=None, description="Unique identifier for the message.")
    action: Optional[str] = Field(default=None, description="the trigger of the message, normally set as the action name.")
    wf_goal: Optional[str] = Field(default=None, description="the goal of the whole workflow.")
    timestamp: Optional[str] = Field(default=None, description="the timestame of the message. ")

class IndexMetadata(BaseModule):
    corpus_id: str = Field(..., description="Identifier for the corpus")
    index_type: str = Field(..., description="Type of index (e.g., 'vector', 'graph', 'summary', 'tree')")
    collection_name: Optional[str] = Field(default="default_collection", description="Vector store collection name or FAISS file path")
    dimension: Optional[int] = Field(default=1536, description="Vector dimension")
    vector_db_type: Optional[str] = Field(default=None, description="Vector database type (e.g., 'faiss', 'qdrant', 'chroma')")
    graph_db_type: Optional[str] = Field(default=None, description="Graph database type (e.g., 'neo4j')")
    embedding_model_name: Optional[str] = Field(default=None, description="")
    date: Optional[str] = Field(default=None, description="Creation or last update date")


class Document(BaseModule):
    """A custom document class for managing documents in the RAG pipeline.

    Attributes:
        text (str): The full content of the document.
        doc_id (str): Unique identifier for the document.
        metadata (DocumentMetadata): Metadata including file info, creation date, etc.
        source (str): Source of the document (e.g., file path or URL).
        llama_doc (LlamaIndexDocument): Underlying LlamaIndex Document object.
    """

    def __init__(
        self,
        text: str,
        metadata: Optional[Union[Dict, DocumentMetadata]] = None,
        embedding: Optional[List[float]] = None,
        doc_id: Optional[str] = None,
        excluded_embed_metadata_keys: List[str] = DEAFULT_EXCLUDED,
        excluded_llm_metadata_keys: List[str] = DEAFULT_EXCLUDED,
        relationships: Dict[str, RelatedNodeInfo] = {}, 
        metadata_template: str = '{key}: {value}', 
        metadata_separator: str = '\n',
        text_template: str = '{metadata_str}\n\n{content}'
    ):
        metadata = (
            DocumentMetadata.model_validate(metadata) if isinstance(metadata, dict) else metadata or DocumentMetadata()
        )
        
        super().__init__(
            text=text.strip(),
            doc_id=doc_id or str(uuid4()),
            metadata=metadata,
            embedding=embedding,
            excluded_embed_metadata_keys=list(set(DEAFULT_EXCLUDED + excluded_embed_metadata_keys)),
            excluded_llm_metadata_keys=list(set(DEAFULT_EXCLUDED + excluded_llm_metadata_keys)),
            relationships=relationships,
            metadata_template=metadata_template,
            metadata_separator=metadata_separator,
            text_template=text_template,
        )
        self.metadata.word_count = len(self.text.split())

    def to_llama_document(self) -> LlamaIndexDocument:
        """Convert to LlamaIndex Document."""
        return LlamaIndexDocument(
            text=self.text,
            metadata=self.metadata.model_dump(),
            id_=self.doc_id,
            embedding=self.embedding,
            excluded_llm_metadata_keys=self.excluded_llm_metadata_keys,
            excluded_embed_metadata_keys=self.excluded_embed_metadata_keys,
            relationships=self.relationships,
            metadata_template=self.metadata_template,
            metadata_separator=self.metadata_separator,
            text_template=self.text_template,
        )

    @classmethod
    def from_llama_document(cls, llama_doc: LlamaIndexDocument) -> "Document":
        """Create Document from LlamaIndex Document."""
        metadata = DocumentMetadata.model_validate(llama_doc.metadata)
        return cls(
            text=llama_doc.text,
            metadata=metadata,
            doc_id=llama_doc.id_,
            embedding=llama_doc.embedding,
            excluded_llm_metadata_keys=llama_doc.excluded_llm_metadata_keys,
            excluded_embed_metadata_keys=llama_doc.excluded_llm_metadata_keys,
            relationships=llama_doc.relationships,
            metadata_template=llama_doc.metadata_template,
            metadata_separator=llama_doc.metadata_separator,
            text_template=llama_doc.text_template
        )

    def set_embedding(self, embedding: List[float]):
        """Set the embedding vector for the Document."""
        self.embedding = embedding

    def compute_hash(self) -> str:
        """Compute a hash of the document text for deduplication."""
        return hashlib.sha256(self.text.encode()).hexdigest()

    def get_fragment(self, max_length: int = 100) -> str:
        """Return a fragment of the document text."""
        return (self.text[:max_length] + "...") if len(self.text) > max_length else self.text

    def to_dict(self) -> Dict:
        """Convert document to dictionary for serialization."""
        return {
            "doc_id": self.doc_id,
            "text": self.text,
            "metadata": self.metadata.model_dump(),
            "embedding": self.embedding,
            "excluded_embed_metadata_keys": self.excluded_embed_metadata_keys,
            "excluded_llm_metadata_keys": self.excluded_llm_metadata_keys,
            "relationships": {str(k): v for k, v in self.relationships.items()},
            "metadata_template": self.metadata_template,
            "metadata_separator": self.metadata_separator,
            "text_template": self.text_template,
        }

    def to_json(self, indent: int = 2) -> str:
        """Convert document to JSON string."""
        return json.dumps(self.to_dict(), indent=indent, ensure_ascii=False)

    def __str__(self) -> str:
        return (
            f"Document(id={self.doc_id}, embedding={self.embedding}, metadata={self.metadata.model_dump()}"
            f"fragment={self.get_fragment(max_length=300)})"
        )

    def __repr__(self) -> str:
        return (
            f"Document(doc_id={self.doc_id}, embedding={self.embedding}, metadata={self.metadata.model_dump()},"
            f"fragment={self.get_fragment(max_length=300)})"
        )

class TextChunk(BaseModule):
    """A single chunk of a document for RAG processing.

    Attributes:
        text (str): The content of the chunk.
        doc_id (str): ID of the parent document.
        chunk_id (str): Unique identifier for the chunk.
        metadata (ChunkMetadata): Metadata including chunk size, embedding, etc.
        llama_node (BaseNode): Underlying LlamaIndex Node object.
    """

    def __init__(
        self,
        text: str = "",
        chunk_id: Optional[str] = None,
        embedding: Optional[List[float]] = None,
        start_char_idx: Optional[int] = None,
        end_char_idx: Optional[int] = None,
        excluded_embed_metadata_keys: List[str] = DEAFULT_EXCLUDED,
        excluded_llm_metadata_keys: List[str] = DEAFULT_EXCLUDED,
        text_template: str = '{metadata_str}\n\n{content}',
        relationships: Dict[str, RelatedNodeInfo] = {}, 
        metadata: Optional[Union[Dict, ChunkMetadata]] = None,
    ):
        metadata = (
            ChunkMetadata.model_validate(metadata) if isinstance(metadata, dict) else metadata or ChunkMetadata()
        )
        super().__init__(
            text=text.strip(),
            chunk_id=chunk_id or str(uuid4()),
            embedding=embedding,
            start_char_idx=start_char_idx,
            end_char_idx=end_char_idx,
            excluded_embed_metadata_keys=list(set(DEAFULT_EXCLUDED + excluded_embed_metadata_keys)),
            excluded_llm_metadata_keys=list(set(DEAFULT_EXCLUDED + excluded_llm_metadata_keys)),
            text_template=text_template,
            relationships=relationships,
            metadata=metadata,
        )
        self.metadata.word_count = len(self.text.split())

    def to_llama_node(self) -> Union[TextNode, Relation, EntityNode, ChunkNode]:
        """Convert to LlamaIndex Node."""
        relatiuonships = dict() 
        for k, v in self.relationships.items():
            relatiuonships[k] = v if isinstance(v, RelatedNodeInfo) else RelatedNodeInfo.from_dict(v)

        cls = TextNode
        if self.metadata.graph_node is not None:
            class_name = self.metadata.graph_node.node_class_name.lower()
            if class_name == "relation":
                cls = Relation(
                    label=self.metadata.graph_node.label,
                    source_id=self.metadata.graph_node.source_id,
                    target_id=self.metadata.graph_node.target_id,
                    properties={"metadata": json.dumps(self.metadata.graph_node.properties["metadata"])},
                )
            
            elif class_name == "entity":
                cls = EntityNode(
                    label=self.metadata.graph_node.label,
                    embedding=self.embedding,
                    name=self.metadata.graph_node.node_name,
                    properties={"triplet_source_id": self.metadata.graph_node.properties["triplet_source_id"]}
                )
                # cls.triplet_source_id

            else:
                NotImplementedError()
            return cls
        else:
            metadata = self.metadata.model_dump()
            if "class_name" in metadata:
                metadata.pop("class_name")
    
            return cls(
                text=self.text,
                metadata=metadata,
                id_=self.chunk_id,
                embedding=self.embedding,
                start_char_idx=self.start_char_idx,
                end_char_idx=self.end_char_idx,
                excluded_llm_metadata_keys=self.excluded_llm_metadata_keys,
                excluded_embed_metadata_keys=self.excluded_embed_metadata_keys,
                text_template=self.text_template,
                relationships=relatiuonships
            )

    @classmethod
    def from_llama_node(cls, node: Union[TextNode, Relation, EntityNode, ChunkNode]) -> "Chunk":
        """Create Chunk from LlamaIndex Node."""
        
        if isinstance(node, TextNode):
            return cls(
                chunk_id=node.id_,
                text=node.text,
                metadata=ChunkMetadata.model_validate(node.metadata),
                embedding=node.embedding,
                start_char_idx=getattr(node, "start_char_idx", None),
                end_char_idx=getattr(node, "end_char_idx", None),
                excluded_embed_metadata_keys=node.excluded_embed_metadata_keys,
                excluded_llm_metadata_keys=node.excluded_llm_metadata_keys,
                text_template=node.text_template,
                relationships=node.relationships
            )
        
        elif isinstance(node, Relation):
            if 'class_name' in node.properties:
                node.properties.pop('class_name')
            properties = node.properties if isinstance(node.properties, dict) else node.properties.model_dump()
            graph_node = GraphNodeData(
                node_class_name="relation",
                label=node.label,
                source_id=node.source_id,
                target_id=node.target_id,
                properties={"metadata": properties}
            )
            metadata= {"graph_node": graph_node}
            return cls(
                metadata=ChunkMetadata.model_validate(metadata)
            )
        
        elif isinstance(node, EntityNode):
            graph_node = GraphNodeData(
                node_class_name="entity",
                label=node.label,
                node_name=node.name,
                properties={"triplet_source_id": node.properties["triplet_source_id"]}
                # triplet_source_id=node.triplet_source_id
            )
            metadata= {"graph_node": graph_node}
            return cls(
                embedding=node.embedding,
                metadata=ChunkMetadata.model_validate(metadata)
            )

        elif isinstance(node, ChunkNode):
            graph_node = GraphNodeData(
                node_class_name="chunk",
                text=node.text,
                properties=node.properties,
                id_=node.id_,
            )
            metadata= {"graph_node": graph_node}
            return cls(
                embedding=node.embedding,
                metadata=ChunkMetadata.model_validate(metadata)
            )

    def get_fragment(self, max_length: int = 100) -> str:
        """Return a fragment of the chunk text."""
        return (self.text[:max_length] + "...") if len(self.text) > max_length else self.text

    def to_dict(self) -> Dict:
        """Convert chunk to dictionary for serialization."""
        relationships = dict() 
        for k, v in self.relationships.items():
            relationships[k] = v.to_dict() if isinstance(v, RelatedNodeInfo) else v
        self.relationships = relationships
        # return {"chunk_id": self.chunk_id,"text": self.text,"metadata": self.metadata.model_dump(),"embedding": self.embedding,"start_char_idx": self.start_char_idx,"end_char_idx": self.end_char_idx,"excluded_embed_metadata_keys": self.excluded_embed_metadata_keys,"excluded_llm_metadata_keys": self.excluded_llm_metadata_keys,"relationships": relatiuonships}
        return self.model_dump()
    
    def to_json(self, indent: int = 2) -> str:
        """Convert chunk to JSON string."""
        return self.model_dump_json(indent=indent).strip()

    def __str__(self) -> str:
        return (
            f"Chunk(id={self.chunk_id}, text={self.text}, "
            f"chunking_strategy={self.metadata.chunking_strategy}, "
            f"embedding={self.embedding}), "
            f"start_char_idx={self.start_char_idx}, "
            f"end_char_idx={self.end_char_idx}, "
            f"excluded_embed_metadata_keys={self.excluded_embed_metadata_keys},"
            f"excluded_llm_metadata_keys={self.excluded_llm_metadata_keys},"
            f"text_template={self.text_template},"
            f"metadata={self.metadata.model_dump()}"
        )

    def __repr__(self) -> str:
        return (
            f"Chunk(id={self.chunk_id}, text={self.text}, "
            f"chunking_strategy={self.metadata.chunking_strategy}, "
            f"embedding={self.embedding}), "
            f"start_char_idx={self.start_char_idx}, "
            f"end_char_idx={self.end_char_idx}, "
            f"excluded_embed_metadata_keys={self.excluded_embed_metadata_keys},"
            f"excluded_llm_metadata_keys={self.excluded_llm_metadata_keys},"
            f"text_template={self.text_template},"
            f"metadata={self.metadata.model_dump()}"
        )
    
# Backward compatibility alias
Chunk = TextChunk

class ImageChunk(BaseModule):
    """An image-based chunk with lazy loading.
    
    Attributes:
        image_path (str): Path to the image file.
        image_mimetype (Optional[str]): MIME type of the image.
        chunk_id (str): Unique identifier for the chunk.
        metadata (ChunkMetadata): Metadata including embedding, similarity scores, etc.
    """

    def __init__(
        self,
        image_path: str,
        image_mimetype: Optional[str] = None,
        chunk_id: Optional[str] = None,
        embedding: Optional[List[float]] = None,
        excluded_embed_metadata_keys: List[str] = DEAFULT_EXCLUDED,
        excluded_llm_metadata_keys: List[str] = DEAFULT_EXCLUDED,
        text_template: str = '{metadata_str}\n\n{content}',
        relationships: Dict[str, RelatedNodeInfo] = {},
        metadata: Optional[Union[Dict, ChunkMetadata]] = None,
    ):
        metadata = (
            ChunkMetadata.model_validate(metadata) if isinstance(metadata, dict) else metadata or ChunkMetadata()
        )
        super().__init__(
            image_path=image_path,
            image_mimetype=image_mimetype,
            chunk_id=chunk_id or str(uuid4()),
            embedding=embedding,
            excluded_embed_metadata_keys=list(set(DEAFULT_EXCLUDED + excluded_embed_metadata_keys)),
            excluded_llm_metadata_keys=list(set(DEAFULT_EXCLUDED + excluded_llm_metadata_keys)),
            text_template=text_template,
            relationships=relationships,
            metadata=metadata,
        )
        
        # Private cache for lazy loading
        self._cached_image = None

    def get_image(self):
        """Load PIL Image on-demand with caching."""
        if self._cached_image is None:
            from PIL import Image
            try:
                logger.debug(f"Loading image from path: {self.image_path}")
                if not self.image_path:
                    logger.error("Image path is None or empty!")
                    return None
                self._cached_image = Image.open(self.image_path)
                logger.debug(f"Successfully loaded image from {self.image_path}")
            except Exception as e:
                logger.error(f"Failed to load image from {self.image_path}: {str(e)}")
                return None
        return self._cached_image
    
    def get_image_bytes(self, format: str = "PNG") -> Optional[bytes]:
        """Get image as bytes for embedding or processing."""
        import io
        image = self.get_image()
        if image is None:
            return None
        
        img_bytes = io.BytesIO()
        image.save(img_bytes, format=format)
        return img_bytes.getvalue()

    def to_llama_node(self) -> ImageNode:
        """Convert to LlamaIndex ImageNode with on-demand image loading."""
        relationships = dict()
        for k, v in self.relationships.items():
            relationships[k] = v if isinstance(v, RelatedNodeInfo) else RelatedNodeInfo.from_dict(v)
        
        return ImageNode(
            image=None,
            image_path=self.image_path,
            image_mimetype=self.image_mimetype,
            metadata=self.metadata.model_dump(),
            id_=self.chunk_id,
            embedding=self.embedding,
            excluded_llm_metadata_keys=self.excluded_llm_metadata_keys,
            excluded_embed_metadata_keys=self.excluded_embed_metadata_keys,
            text_template=self.text_template,
            relationships=relationships
        )

    @classmethod
    def from_llama_node(cls, node: ImageNode) -> "ImageChunk":
        """Create ImageChunk from LlamaIndex ImageNode."""
        metadata = ChunkMetadata.model_validate(node.metadata)
        
        logger.debug(f"Creating ImageChunk from ImageNode - image_path: {node.image_path}")
        
        return cls(
            chunk_id=node.id_,
            image_path=node.image_path,
            image_mimetype=node.image_mimetype,
            metadata=metadata,
            embedding=node.embedding,
            excluded_embed_metadata_keys=node.excluded_embed_metadata_keys,
            excluded_llm_metadata_keys=node.excluded_llm_metadata_keys,
            text_template=node.text_template,
            relationships=node.relationships
        )

class Corpus(BaseModule):
    """A generic collection of document chunks for RAG processing.

    Attributes:
        corpus_id (str): The unique id for corpus.
        chunks (List[Union[TextChunk, ImageChunk]]): List of chunks in the corpus.
        chunk_index (Dict[str, Union[TextChunk, ImageChunk]]): Index of chunks by chunk_id for fast lookup.
        metadata (Optional[IndexMetadata]): the metadata for this corpus.
    """

    def __init__(self, chunks: Optional[List[Union[TextChunk, ImageChunk]]] = None, corpus_id: Optional[str] = None, 
                 metadata: Optional[Union[IndexMetadata, Dict]] = None):
        corpus_id = uuid4() if corpus_id is None else corpus_id
        chunks = [] if chunks is None else chunks
        chunk_index = {} if chunks is None else {chunk.chunk_id: chunk for chunk in chunks}
        
        if metadata is None:
            metadata = {}
        elif isinstance(metadata, IndexMetadata):
            metadata = metadata.model_dump()
        super().__init__(
            corpus_id=corpus_id,
            chunks=chunks,
            chunk_index=chunk_index,
            metadata=metadata
        )

    def to_llama_nodes(self) -> List[BaseNode]:
        """Convert to list of LlamaIndex Nodes."""
        if not self.chunks:
            self.chunks = []
        return [chunk.to_llama_node() for chunk in self.chunks]

    @classmethod
    def from_llama_nodes(cls, nodes: List[BaseNode]) -> "Corpus":
        """Create a Corpus from a list of LlamaIndex Nodes.

        Args:
            nodes (List[BaseNode]): The LlamaIndex Nodes to convert.

        Returns:
            Corpus: A new Corpus instance.
        """
        chunks = []
        for node in nodes:
            if isinstance(node, ImageNode):
                chunks.append(ImageChunk.from_llama_node(node))
            else:
                # Default to TextChunk for TextNode and other node types
                chunks.append(TextChunk.from_llama_node(node))
        return cls(chunks)

    def add_chunk(self, batch_chunk: Union[TextChunk, ImageChunk, List[Union[TextChunk, ImageChunk]]]):
        """Add a batch chunk to the corpus and update index."""
        if not isinstance(batch_chunk, list):
            batch_chunk = [batch_chunk]

        for chunk in batch_chunk:
            self.chunks.append(chunk)
            self.chunk_index[chunk.chunk_id] = chunk

    def get_chunk(self, chunk_id: str) -> Optional[Union[TextChunk, ImageChunk]]:
        """Retrieve a chunk by its ID."""
        return self.chunk_index.get(chunk_id)

    def remove_chunk(self, chunk_id: str):
        """Remove a chunk by its ID."""
        self.chunks = [chunk for chunk in self.chunks if chunk.chunk_id != chunk_id]
        self.chunk_index.pop(chunk_id, None)

    def filter_by_doc_id(self, doc_id: str) -> List[Union[TextChunk, ImageChunk]]:
        """Filter chunks by parent document ID."""
        return [chunk for chunk in self.chunks if hasattr(chunk.metadata, 'doc_id') and chunk.metadata.doc_id == doc_id]

    def filter_by_similarity(self, threshold: float) -> List[Union[TextChunk, ImageChunk]]:
        """Filter chunks by similarity score."""
        return [chunk for chunk in self.chunks if chunk.metadata.similarity_score and chunk.metadata.similarity_score >= threshold]

    def sort_by_similarity(self, reverse: bool = True) -> List[Union[TextChunk, ImageChunk]]:
        """Sort chunks by similarity score (descending by default)."""
        return sorted(
            [chunk for chunk in self.chunks if chunk.metadata.similarity_score is not None],
            key=lambda x: x.metadata.similarity_score,
            reverse=reverse
        )

    def to_dict(self, round_trip=False) -> Dict:
        """Convert corpus to dictionary for serialization."""
        return [self.model_dump(round_trip=round_trip)]

    def to_json(self, indent: int = 2, round_trip=True) -> str:
        """Convert corpus to JSON string."""
        return json.dumps(self.to_dict(round_trip), indent=indent, ensure_ascii=False)

    def to_jsonl(self, output_path: str, indent: int = 0):
        with open(output_path, 'w', encoding='utf-8') as f:
            for chunk in self.chunks:
                json_str = chunk.to_json(indent=None)
                if '\n' in json_str:
                    # Log warning if JSON contains newlines, which breaks JSONL format
                    print(f"Chunk {chunk.chunk_id} contains newlines in JSON, which may break JSONL format.")
                f.write(json_str + '\n')

    @classmethod
    def from_jsonl(cls, input_path: str, corpus_id: Optional[str] = None) -> "Corpus":
        chunks = []
        with open(input_path, 'r', encoding='utf-8') as f:
            for line in f:
                chunk_dict = json.loads(line.strip())
                metadata = ChunkMetadata.model_validate(chunk_dict["metadata"])
                chunk = Chunk(
                    chunk_id=chunk_dict["chunk_id"],
                    text=chunk_dict["text"],
                    metadata=metadata,
                    embedding=chunk_dict["embedding"],
                    start_char_idx=chunk_dict["start_char_idx"],
                    end_char_idx=chunk_dict["end_char_idx"],
                    excluded_embed_metadata_keys=chunk_dict["excluded_embed_metadata_keys"],
                    excluded_llm_metadata_keys=chunk_dict["excluded_llm_metadata_keys"],
                    relationships={
                        k: RelatedNodeInfo(**v) for k, v in chunk_dict["relationships"].items()
                    }
                )
                chunks.append(chunk)
        return cls(chunks=chunks, corpus_id=corpus_id)

    def __str__(self) -> str:
        stats = self.get_stats()
        return (
            f"Corpus(chunks={stats['chunk_count']}, unique_docs={stats['unique_docs']}, "
            f"avg_word_count={stats['avg_word_count']:.1f}, strategies={stats['strategies']})"
        )

    def __repr__(self) -> str:
        return f"Corpus(chunks={len(self.chunks)}, chunk_index_keys={list(self.chunk_index.keys())})"

    def __len__(self) -> int:
        return len(self.chunks)
    
    def get_stats(self) -> Dict[str, Any]:
        """Get statistics about the corpus."""
        if not self.chunks:
            return {
                'chunk_count': 0,
                'unique_docs': 0,
                'avg_word_count': 0.0,
                'strategies': set()
            }
        
        # Count unique documents
        unique_docs = set()
        total_word_count = 0
        strategies = set()
        
        for chunk in self.chunks:
            if hasattr(chunk.metadata, 'doc_id') and chunk.metadata.doc_id:
                unique_docs.add(chunk.metadata.doc_id)
            if hasattr(chunk.metadata, 'word_count') and chunk.metadata.word_count:
                total_word_count += chunk.metadata.word_count
            if hasattr(chunk.metadata, 'chunking_strategy') and chunk.metadata.chunking_strategy:
                strategies.add(chunk.metadata.chunking_strategy)
        
        avg_word_count = total_word_count / len(self.chunks) if self.chunks else 0.0
        
        return {
            'chunk_count': len(self.chunks),
            'unique_docs': len(unique_docs),
            'avg_word_count': avg_word_count,
            'strategies': strategies
        }
    

class Query(BaseModule):
    """Represents a retrieval query."""
    
    query_str: str = Field(description="The query string.")
    top_k: Optional[int] = Field(default=None, description="Number of top results to retrieve.")
    custom_embedding_strs: Optional[List[str]] = Field(default=None, description="The List to store additional strings need to be embed with the query.")
    similarity_cutoff: Optional[float] = Field(default=None, description="Minimum similarity score.")
    keyword_filters: Optional[List[str]] = Field(default=None, description="Keywords to filter results.")
    metadata_filters: Optional[Dict[str, Any]] = Field(default=None, description="Additional metadata filters.")

    @property
    def embedding_strs(self) -> List[str]:
        """Use custom embedding strs if specified, otherwise use query str."""
        if self.custom_embedding_strs is None:
            if len(self.query_str) == 0:
                return []
            return [self.query_str]
        else:
            return self.custom_embedding_strs

    def to_QueryBundle(self):
        return QueryBundle(
            query_str=self.query_str,
            custom_embedding_strs=self.custom_embedding_strs
        )

class RagResult(BaseModule):
    """Represents a generic retrieval result."""
    
    corpus: Corpus = Field(description="Retrieved chunks.")
    scores: List[float] = Field(description="Similarity scores for each chunk.")
    metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional result metadata.")
    
    def get_top_chunks(self, limit: int = None) -> List[Union[TextChunk, ImageChunk]]:
        """Get top chunks sorted by similarity score."""
        chunks = self.corpus.sort_by_similarity(reverse=True)
        return chunks[:limit] if limit else chunks