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import pipmaster as pm
from llama_index.core.llms import (
ChatMessage,
MessageRole,
ChatResponse,
)
from typing import List, Optional
from lightrag.utils import logger
# 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
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] = [],
chat_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))
response: ChatResponse = await model.achat(
messages=formatted_messages, **chat_kwargs
)
# 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")
# Use _get_text_embeddings for batch processing
embeddings = embed_model._get_text_embeddings(texts)
return np.array(embeddings)
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