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from typing import List, Tuple
import chromadb
from chromadb.utils import embedding_functions
import os
from .dto.chunk_dto import ChunkDTO
from lpm_kernel.common.llm import LLMClient
from lpm_kernel.file_data.document_dto import DocumentDTO
from typing import List, Dict, Optional
from lpm_kernel.configs.logging import get_train_process_logger
logger = get_train_process_logger()
class EmbeddingService:
def __init__(self):
from lpm_kernel.file_data.chroma_utils import detect_embedding_model_dimension
from lpm_kernel.api.services.user_llm_config_service import UserLLMConfigService
chroma_path = os.getenv("CHROMA_PERSIST_DIRECTORY", "./data/chroma_db")
self.client = chromadb.PersistentClient(path=chroma_path)
self.llm_client = LLMClient()
# Get embedding model dimension from user config
try:
user_llm_config_service = UserLLMConfigService()
user_llm_config = user_llm_config_service.get_available_llm()
if user_llm_config and user_llm_config.embedding_model_name:
# Detect dimension based on model name
self.dimension = detect_embedding_model_dimension(user_llm_config.embedding_model_name)
logger.info(f"Detected embedding dimension: {self.dimension} for model: {user_llm_config.embedding_model_name}")
else:
# Default to OpenAI dimension if no config found
self.dimension = 1536
logger.info(f"No embedding model configured, using default dimension: {self.dimension}")
except Exception as e:
# Default to OpenAI dimension if error occurs
self.dimension = 1536
logger.error(f"Error detecting embedding dimension, using default: {self.dimension}. Error: {str(e)}", exc_info=True)
# Check for dimension mismatches in all collections first
collections_to_init = ["documents", "document_chunks"]
dimension_mismatch_detected = False
# First pass: check all collections for dimension mismatches
for collection_name in collections_to_init:
try:
collection = self.client.get_collection(name=collection_name)
if collection.metadata.get("dimension") != self.dimension:
logger.warning(f"Dimension mismatch in '{collection_name}' collection: {collection.metadata.get('dimension')} vs {self.dimension}")
dimension_mismatch_detected = True
except ValueError:
# Collection doesn't exist yet, will be created later
pass
# Handle dimension mismatch if detected in any collection
if dimension_mismatch_detected:
self._handle_dimension_mismatch()
# Second pass: create or get collections with the correct dimension
try:
self.document_collection = self.client.get_collection(name="documents")
# Verify dimension after possible reinitialization
doc_dimension = self.document_collection.metadata.get("dimension")
if doc_dimension != self.dimension:
logger.error(f"Collection 'documents' still has incorrect dimension after reinitialization: {doc_dimension} vs {self.dimension}")
# Try to reinitialize again if dimension is still incorrect
raise RuntimeError(f"Failed to set correct dimension for 'documents' collection: {doc_dimension} vs {self.dimension}")
except ValueError:
# Collection doesn't exist, create it with the correct dimension
try:
self.document_collection = self.client.create_collection(
name="documents", metadata={"hnsw:space": "cosine", "dimension": self.dimension}
)
logger.info(f"Created 'documents' collection with dimension {self.dimension}")
except Exception as e:
logger.error(f"Failed to create 'documents' collection: {str(e)}", exc_info=True)
raise RuntimeError(f"Failed to create 'documents' collection: {str(e)}")
try:
self.chunk_collection = self.client.get_collection(name="document_chunks")
# Verify dimension after possible reinitialization
chunk_dimension = self.chunk_collection.metadata.get("dimension")
if chunk_dimension != self.dimension:
logger.error(f"Collection 'document_chunks' still has incorrect dimension after reinitialization: {chunk_dimension} vs {self.dimension}")
# Try to reinitialize again if dimension is still incorrect
raise RuntimeError(f"Failed to set correct dimension for 'document_chunks' collection: {chunk_dimension} vs {self.dimension}")
except ValueError:
# Collection doesn't exist, create it with the correct dimension
try:
self.chunk_collection = self.client.create_collection(
name="document_chunks", metadata={"hnsw:space": "cosine", "dimension": self.dimension}
)
logger.info(f"Created 'document_chunks' collection with dimension {self.dimension}")
except Exception as e:
logger.error(f"Failed to create 'document_chunks' collection: {str(e)}", exc_info=True)
raise RuntimeError(f"Failed to create 'document_chunks' collection: {str(e)}")
def generate_document_embedding(self, document: DocumentDTO) -> List[float]:
"""Process document level embedding and store in ChromaDB"""
try:
if not document.raw_content:
logger.warning(
f"Document {document.id} has no content to process embedding"
)
return None
# get embedding
logger.info(f"Generating embedding for document {document.id}")
embeddings = self.llm_client.get_embedding([document.raw_content])
if embeddings is None or len(embeddings) == 0:
logger.error(f"Failed to get embedding for document {document.id}")
return None
embedding = embeddings[0]
logger.info(f"Successfully got embedding for document {document.id}")
# store to ChromaDB
try:
logger.info(f"Storing embedding for document {document.id} in ChromaDB")
self.document_collection.add(
documents=[document.raw_content],
ids=[str(document.id)],
embeddings=[embedding.tolist()],
metadatas=[
{
"title": document.title or document.name,
"mime_type": document.mime_type,
"create_time": document.create_time.isoformat()
if document.create_time
else None,
"document_size": document.document_size,
"url": document.url,
}
],
)
logger.info(f"Successfully stored embedding for document {document.id}")
# verify embedding storage
result = self.document_collection.get(
ids=[str(document.id)], include=["embeddings"]
)
if not result or not result["embeddings"]:
logger.error(
f"Failed to verify embedding storage for document {document.id}"
)
return None
logger.info(f"Verified embedding storage for document {document.id}")
return embedding
except Exception as e:
logger.error(f"Error storing document embedding in ChromaDB: {str(e)}", exc_info=True)
return None
except Exception as e:
logger.error(f"Error processing document embedding: {str(e)}", exc_info=True)
raise
def generate_chunk_embeddings(self, chunks: List[ChunkDTO]) -> List[ChunkDTO]:
"""Process chunk level embeddings"""
"""
Store in ChromaDB, the structure is as follows:
documents=[c.content for c in unprocessed_chunks],
ids=[str(c.id) for c in unprocessed_chunks],
embeddings=embeddings.tolist(),
metadatas=[
{
"document_id": str(c.document_id),
"topic": c.topic or "",
"tags": ",".join(c.tags) if c.tags else "",
}
for c in unprocessed_chunks
],
"""
try:
unprocessed_chunks = [c for c in chunks if not c.has_embedding]
if not unprocessed_chunks:
logger.info("No unprocessed chunks found")
return chunks
logger.info(f"Processing embeddings for {len(unprocessed_chunks)} chunks")
contents = [c.content for c in unprocessed_chunks]
logger.info("Getting embeddings from LLM service... {}".format(contents))
embeddings = self.llm_client.get_embedding(contents)
if embeddings is None or len(embeddings) == 0:
logger.error("Failed to get embeddings from LLM service")
return chunks
logger.info(f"Successfully got embeddings with shape: {embeddings.shape}")
try:
logger.info("Adding embeddings to ChromaDB...")
self.chunk_collection.add(
documents=[c.content for c in unprocessed_chunks],
ids=[str(c.id) for c in unprocessed_chunks],
embeddings=embeddings.tolist(),
metadatas=[
{
"document_id": str(c.document_id),
"topic": c.topic or "",
"tags": ",".join(c.tags) if c.tags else "",
}
for c in unprocessed_chunks
],
)
logger.info("Successfully added embeddings to ChromaDB")
# verify embeddings storage
for chunk in unprocessed_chunks:
result = self.chunk_collection.get(
ids=[str(chunk.id)], include=["embeddings"]
)
if result and result["embeddings"]:
chunk.has_embedding = True
logger.info(f"Verified embedding for chunk {chunk.id}")
else:
logger.warning(
f"Failed to verify embedding for chunk {chunk.id}"
)
chunk.has_embedding = False
except Exception as e:
logger.error(f"Error storing embeddings in ChromaDB: {str(e)}", exc_info=True)
for chunk in unprocessed_chunks:
chunk.has_embedding = False
raise
return chunks
except Exception as e:
logger.error(f"Error processing chunk embeddings: {str(e)}", exc_info=True)
raise
def get_chunk_embedding_by_chunk_id(self, chunk_id: int) -> Optional[List[float]]:
"""Get the corresponding embedding vector by chunk_id
Args:
chunk_id (int): chunk ID
Returns:
List[float]: embedding vector, return None if not found
Raises:
ValueError: when chunk_id is invalid
Exception: other errors
"""
try:
if not isinstance(chunk_id, int) or chunk_id < 0:
raise ValueError("Invalid chunk_id")
# query from ChromaDB
result = self.chunk_collection.get(
ids=[str(chunk_id)], include=["embeddings"]
)
if not result or not result["embeddings"]:
logger.warning(f"No embedding found for chunk {chunk_id}")
return None
return result["embeddings"][0]
except Exception as e:
logger.error(f"Error getting embedding for chunk {chunk_id}: {str(e)}")
raise
def get_document_embedding_by_document_id(
self, document_id: int
) -> Optional[List[float]]:
"""Get the corresponding embedding vector by document_id
Args:
document_id (int): document ID
Returns:
List[float]: embedding vector, return None if not found
Raises:
ValueError: when document_id is invalid
Exception: other errors
"""
try:
if not isinstance(document_id, int) or document_id < 0:
raise ValueError("Invalid document_id")
# query from ChromaDB
result = self.document_collection.get(
ids=[str(document_id)], include=["embeddings"]
)
if not result or not result["embeddings"]:
logger.warning(f"No embedding found for document {document_id}")
return None
return result["embeddings"][0]
except Exception as e:
logger.error(
f"Error getting embedding for document {document_id}: {str(e)}"
)
raise
def _handle_dimension_mismatch(self):
"""
Handle dimension mismatch between current embedding model and ChromaDB collections
This method will reinitialize ChromaDB collections with the new dimension
"""
from lpm_kernel.file_data.chroma_utils import reinitialize_chroma_collections
logger.warning(f"Detected dimension mismatch in ChromaDB collections. Reinitializing with dimension {self.dimension}")
# Log the operation for better debugging
logger.info(f"Calling reinitialize_chroma_collections with dimension {self.dimension}")
try:
success = reinitialize_chroma_collections(self.dimension)
if success:
logger.info(f"Successfully reinitialized ChromaDB collections with dimension {self.dimension}")
# Refresh collection references
try:
self.document_collection = self.client.get_collection(name="documents")
self.chunk_collection = self.client.get_collection(name="document_chunks")
# Double-check dimensions after refresh
doc_dimension = self.document_collection.metadata.get("dimension")
chunk_dimension = self.chunk_collection.metadata.get("dimension")
if doc_dimension != self.dimension or chunk_dimension != self.dimension:
logger.error(f"Dimension mismatch after refresh: documents={doc_dimension}, chunks={chunk_dimension}, expected={self.dimension}")
raise RuntimeError(f"Failed to handle dimension mismatch: collections have incorrect dimensions after reinitialization")
except Exception as e:
logger.error(f"Error refreshing collection references: {str(e)}", exc_info=True)
raise RuntimeError(f"Failed to refresh ChromaDB collections after reinitialization: {str(e)}")
else:
logger.error("Failed to reinitialize ChromaDB collections")
raise RuntimeError("Failed to handle dimension mismatch in ChromaDB collections")
except Exception as e:
logger.error(f"Error during dimension mismatch handling: {str(e)}", exc_info=True)
raise RuntimeError(f"Failed to handle dimension mismatch in ChromaDB collections: {str(e)}")
def search_similar_chunks(
self, query: str, limit: int = 5
) -> List[Tuple[ChunkDTO, float]]:
"""Search similar chunks, return list of ChunkDTO objects and their similarity scores
Args:
query (str): query text
limit (int, optional): return result limit. Defaults to 5.
Returns:
List[Tuple[ChunkDTO, float]]: return list of (ChunkDTO, similarity score), sorted by similarity score in descending order
Raises:
ValueError: when query parameters are invalid
Exception: other errors
"""
try:
if not query or not query.strip():
raise ValueError("Query string cannot be empty")
if limit < 1:
raise ValueError("Limit must be positive")
# calculate query text embedding
query_embedding = self.llm_client.get_embedding([query])
if query_embedding is None or len(query_embedding) == 0:
raise Exception("Failed to generate embedding for query")
# query ChromaDB
results = self.chunk_collection.query(
query_embeddings=[query_embedding[0].tolist()],
n_results=limit,
include=["documents", "metadatas", "distances"],
)
if not results or not results["ids"]:
return []
# convert results to ChunkDTO objects
similar_chunks = []
for i in range(len(results["ids"])):
chunk_id = results["ids"][0][i] # ChromaDB returns nested lists
document_id = results["metadatas"][0][i]["document_id"]
content = results["documents"][0][i]
topic = results["metadatas"][0][i].get("topic", "")
tags = (
results["metadatas"][0][i].get("tags", "").split(",")
if results["metadatas"][0][i].get("tags")
else []
)
# calculate similarity score (ChromaDB returns distances, need to convert to similarity)
similarity_score = (
1 - results["distances"][0][i]
) # assume using Euclidean distance or cosine distance
chunk = ChunkDTO(
id=int(chunk_id),
document_id=int(document_id),
content=content,
topic=topic,
tags=tags,
has_embedding=True,
)
similar_chunks.append((chunk, similarity_score))
# sort by similarity score in descending order
similar_chunks.sort(key=lambda x: x[1], reverse=True)
return similar_chunks
except ValueError as ve:
logger.error(f"Invalid input parameters: {str(ve)}")
raise
except Exception as e:
logger.error(f"Error searching similar chunks: {str(e)}")
raise |