Remove LlamaIndex implementation from llm directory as per @MdNazishArmanShorthillsAI

- Deleted `lightrag/llm/llama_index_impl.py`
- Reorganization of the LlamaIndex wrapper location

lightrag/llm/llama_index_impl.py

@@ -1,214 +0,0 @@
-import pipmaster as pm
-from core.logging_config import setup_logger
-from llama_index.core.llms import (
-    ChatMessage,
-    MessageRole,
-    ChatResponse,
-)
-from typing import List, Optional
-
-# Install required dependencies
-if not pm.is_installed("llama-index"):
-    pm.install("llama-index")
-
-from llama_index.core.embeddings import BaseEmbedding
-from llama_index.core.settings import Settings as LlamaIndexSettings
-from tenacity import (
-    retry,
-    stop_after_attempt,
-    wait_exponential,
-    retry_if_exception_type,
-)
-from lightrag.utils import (
-    wrap_embedding_func_with_attrs,
-    locate_json_string_body_from_string,
-)
-from lightrag.exceptions import (
-    APIConnectionError,
-    RateLimitError,
-    APITimeoutError,
-)
-import numpy as np
-
-logger = setup_logger("lightrag.llm.llama_index_impl")
-
-
-def configure_llama_index(settings: LlamaIndexSettings = None, **kwargs):
-    """
-    Configure LlamaIndex settings.
-
-    Args:
-        settings: LlamaIndex Settings instance. If None, uses default settings.
-        **kwargs: Additional settings to override/configure
-    """
-    if settings is None:
-        settings = LlamaIndexSettings()
-
-    # Update settings with any provided kwargs
-    for key, value in kwargs.items():
-        if hasattr(settings, key):
-            setattr(settings, key, value)
-        else:
-            logger.warning(f"Unknown LlamaIndex setting: {key}")
-
-    # Set as global settings
-    LlamaIndexSettings.set_global(settings)
-    return settings
-
-
-def format_chat_messages(messages):
-    """Format chat messages into LlamaIndex format."""
-    formatted_messages = []
-
-    for msg in messages:
-        role = msg.get("role", "user")
-        content = msg.get("content", "")
-
-        if role == "system":
-            formatted_messages.append(
-                ChatMessage(role=MessageRole.SYSTEM, content=content)
-            )
-        elif role == "assistant":
-            formatted_messages.append(
-                ChatMessage(role=MessageRole.ASSISTANT, content=content)
-            )
-        elif role == "user":
-            formatted_messages.append(
-                ChatMessage(role=MessageRole.USER, content=content)
-            )
-        else:
-            logger.warning(f"Unknown role {role}, treating as user message")
-            formatted_messages.append(
-                ChatMessage(role=MessageRole.USER, content=content)
-            )
-
-    return formatted_messages
-
-
-@retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=60),
-    retry=retry_if_exception_type(
-        (RateLimitError, APIConnectionError, APITimeoutError)
-    ),
-)
-async def llama_index_complete_if_cache(
-    model: str,
-    prompt: str,
-    system_prompt: Optional[str] = None,
-    history_messages: List[dict] = [],
-    **kwargs,
-) -> str:
-    """Complete the prompt using LlamaIndex."""
-    try:
-        # Format messages for chat
-        formatted_messages = []
-
-        # Add system message if provided
-        if system_prompt:
-            formatted_messages.append(
-                ChatMessage(role=MessageRole.SYSTEM, content=system_prompt)
-            )
-
-        # Add history messages
-        for msg in history_messages:
-            formatted_messages.append(
-                ChatMessage(
-                    role=MessageRole.USER
-                    if msg["role"] == "user"
-                    else MessageRole.ASSISTANT,
-                    content=msg["content"],
-                )
-            )
-
-        # Add current prompt
-        formatted_messages.append(ChatMessage(role=MessageRole.USER, content=prompt))
-
-        # Get LLM instance from kwargs
-        if "llm_instance" not in kwargs:
-            raise ValueError("llm_instance must be provided in kwargs")
-        llm = kwargs["llm_instance"]
-
-        # Get response
-        response: ChatResponse = await llm.achat(messages=formatted_messages)
-
-        # In newer versions, the response is in message.content
-        content = response.message.content
-        return content
-
-    except Exception as e:
-        logger.error(f"Error in llama_index_complete_if_cache: {str(e)}")
-        raise
-
-
-async def llama_index_complete(
-    prompt,
-    system_prompt=None,
-    history_messages=None,
-    keyword_extraction=False,
-    settings: LlamaIndexSettings = None,
-    **kwargs,
-) -> str:
-    """
-    Main completion function for LlamaIndex
-
-    Args:
-        prompt: Input prompt
-        system_prompt: Optional system prompt
-        history_messages: Optional chat history
-        keyword_extraction: Whether to extract keywords from response
-        settings: Optional LlamaIndex settings
-        **kwargs: Additional arguments
-    """
-    if history_messages is None:
-        history_messages = []
-
-    keyword_extraction = kwargs.pop("keyword_extraction", None)
-    result = await llama_index_complete_if_cache(
-        kwargs.get("llm_instance"),
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        **kwargs,
-    )
-    if keyword_extraction:
-        return locate_json_string_body_from_string(result)
-    return result
-
-
-@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
-@retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=60),
-    retry=retry_if_exception_type(
-        (RateLimitError, APIConnectionError, APITimeoutError)
-    ),
-)
-async def llama_index_embed(
-    texts: list[str],
-    embed_model: BaseEmbedding = None,
-    settings: LlamaIndexSettings = None,
-    **kwargs,
-) -> np.ndarray:
-    """
-    Generate embeddings using LlamaIndex
-
-    Args:
-        texts: List of texts to embed
-        embed_model: LlamaIndex embedding model
-        settings: Optional LlamaIndex settings
-        **kwargs: Additional arguments
-    """
-    if settings:
-        configure_llama_index(settings)
-
-    if embed_model is None:
-        raise ValueError("embed_model must be provided")
-
-    # LlamaIndex's embed_query returns a list of floats
-    embeddings = []
-    for text in texts:
-        embedding = await embed_model.aembed_query(text)
-        embeddings.append(embedding)
-
-    return np.array(embeddings)