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from typing import List, Optional
from pathlib import Path
from langchain_core.documents import Document
from langchain_community.vectorstores import Chroma
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from code_chatbot.chunker import StructuralChunker
from code_chatbot.merkle_tree import MerkleTree, ChangeSet
from code_chatbot.path_obfuscator import PathObfuscator
from code_chatbot.config import get_config
import shutil
import logging
logger = logging.getLogger(__name__)
# Global ChromaDB client cache to avoid "different settings" error
_chroma_clients = {}
def get_chroma_client(persist_directory: str):
"""Get or create a shared ChromaDB client for a given path."""
global _chroma_clients
if persist_directory not in _chroma_clients:
import chromadb
from chromadb.config import Settings
_chroma_clients[persist_directory] = chromadb.PersistentClient(
path=persist_directory,
settings=Settings(
anonymized_telemetry=False,
allow_reset=True
)
)
return _chroma_clients[persist_directory]
class Indexer:
"""
Indexes code files into a Vector Database.
Now uses StructuralChunker for semantic splitting.
"""
def __init__(self, persist_directory: str = "chroma_db", embedding_function=None, provider: str = "gemini", api_key: str = None):
self.persist_directory = persist_directory
self.provider = provider
# Load configuration
self.config = get_config()
# Initialize Structural Chunker
self.chunker = StructuralChunker(max_tokens=self.config.chunking.max_chunk_tokens)
# Initialize Merkle tree for change detection
self.merkle_tree = MerkleTree(ignore_patterns=self.config.indexing.ignore_patterns)
# Initialize path obfuscator if enabled
self.path_obfuscator: Optional[PathObfuscator] = None
if self.config.privacy.enable_path_obfuscation:
self.path_obfuscator = PathObfuscator(
secret_key=self.config.privacy.obfuscation_key,
mapping_file=self.config.privacy.obfuscation_mapping_file
)
logger.info("Path obfuscation enabled")
# Setup Embeddings - supports Gemini (API) and local HuggingFace
if embedding_function:
self.embedding_function = embedding_function
else:
if provider == "local" or provider == "huggingface":
# Use local embeddings - NO RATE LIMITS!
from langchain_huggingface import HuggingFaceEmbeddings
self.embedding_function = HuggingFaceEmbeddings(
model_name="all-MiniLM-L6-v2", # Fast & good quality
model_kwargs={'device': 'cpu'},
encode_kwargs={'normalize_embeddings': True}
)
logger.info("Using LOCAL embeddings (no rate limits)")
elif provider == "gemini":
api_key = api_key or os.getenv("GOOGLE_API_KEY")
if not api_key:
raise ValueError("Google API Key is required for Gemini Embeddings")
self.embedding_function = GoogleGenerativeAIEmbeddings(
model="models/gemini-embedding-001",
google_api_key=api_key
)
logger.info("Using Gemini embeddings (API rate limits apply)")
else:
raise ValueError(f"Unsupported embedding provider: {provider}. Use 'local', 'huggingface', or 'gemini'.")
def clear_collection(self, collection_name: str = "codebase"):
"""
Safely clears a collection from the vector database.
"""
try:
client = get_chroma_client(self.persist_directory)
try:
client.delete_collection(collection_name)
logger.info(f"Deleted collection '{collection_name}'")
except ValueError:
# Collection doesn't exist
pass
except Exception as e:
logger.warning(f"Failed to clear collection: {e}")
def index_documents(self, documents: List[Document], collection_name: str = "codebase", vector_db_type: str = "chroma"):
"""
Splits documents structurally and generates embeddings.
Supports 'chroma' and 'faiss'.
"""
if not documents:
logger.warning("No documents to index.")
return
all_chunks = []
for doc in documents:
# chunker.chunk returns List[Document]
file_chunks = self.chunker.chunk(doc.page_content, doc.metadata["file_path"])
all_chunks.extend(file_chunks)
if not all_chunks:
pass
# Create/Update Vector # Filter out complex metadata and potential None values that slip through
from langchain_community.vectorstores.utils import filter_complex_metadata
# Ensure metadata is clean
for doc in all_chunks:
# Double check for None values in metadata values and remove them
doc.metadata = {k:v for k,v in doc.metadata.items() if v is not None}
all_chunks = filter_complex_metadata(all_chunks)
if vector_db_type == "chroma":
# Use shared client to avoid "different settings" error
chroma_client = get_chroma_client(self.persist_directory)
vectordb = Chroma(
client=chroma_client,
embedding_function=self.embedding_function,
collection_name=collection_name
)
elif vector_db_type == "faiss":
from langchain_community.vectorstores import FAISS
# FAISS is in-memory by default, we'll save it to disk later
vectordb = None # We build it in the loop
elif vector_db_type == "qdrant":
vectordb = None # Built in bulk later
else:
raise ValueError(f"Unsupported Vector DB: {vector_db_type}")
# Batch processing - smaller batches to avoid rate limits
batch_size = 20 # Reduced for free tier rate limits
total_chunks = len(all_chunks)
logger.info(f"Indexing {total_chunks} chunks in batches of {batch_size}...")
from tqdm import tqdm
import time
# FAISS handles batching poorly if we want to save incrementally, so we build a list first for FAISS or use from_documents
if vector_db_type == "faiss":
from langchain_community.vectorstores import FAISS
# For FAISS, it's faster to just do it all at once or in big batches
vectordb = FAISS.from_documents(all_chunks, self.embedding_function)
vectordb.save_local(folder_path=self.persist_directory, index_name=collection_name)
return vectordb
elif vector_db_type == "qdrant":
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
url = os.getenv("QDRANT_URL")
api_key = os.getenv("QDRANT_API_KEY")
if not url:
# Fallback to local
logger.info("No QDRANT_URL found, using local Qdrant memory/disk")
location = ":memory:" # or path
vectordb = QdrantVectorStore.from_documents(
documents=all_chunks,
embedding=self.embedding_function,
url=url,
api_key=api_key,
collection_name=collection_name,
prefer_grpc=True
)
return vectordb
# Loop for Chroma (existing logic)
for i in range(0, total_chunks, batch_size):
batch = all_chunks[i:i + batch_size]
# Retry logic for rate limits
max_retries = 5
for retry in range(max_retries):
try:
vectordb.add_documents(documents=batch)
logger.info(f"Indexed batch {i // batch_size + 1}/{(total_chunks + batch_size - 1) // batch_size}")
# Delay to avoid rate limits (free tier is ~15 req/min)
time.sleep(4) # 4 seconds between batches = ~15/min
break
except Exception as e:
error_str = str(e).lower()
if 'rate' in error_str or '429' in error_str or 'quota' in error_str or 'resource_exhausted' in error_str:
wait_time = 30 * (retry + 1) # 30s, 60s, 90s, 120s, 150s
logger.warning(f"Rate limit hit, waiting {wait_time}s... (retry {retry+1}/{max_retries})")
time.sleep(wait_time)
else:
logger.error(f"Error indexing batch {i}: {e}")
break
# PersistentClient auto-persists
logger.info(f"Indexed {len(all_chunks)} chunks into collection '{collection_name}' at {self.persist_directory}")
return vectordb
def get_retriever(self, collection_name: str = "codebase", k: int = 10, vector_db_type: str = "chroma"):
"""Get a retriever for the specified collection. Default k=10 for comprehensive results."""
logger.info(f"Creating retriever for collection '{collection_name}' from {self.persist_directory}")
if vector_db_type == "chroma":
# Use shared client to avoid "different settings" error
chroma_client = get_chroma_client(self.persist_directory)
# Load existing vector store
vector_store = Chroma(
client=chroma_client,
collection_name=collection_name,
embedding_function=self.embedding_function,
)
# Log collection info
try:
collection = vector_store._collection
count = collection.count()
logger.info(f"Collection '{collection_name}' has {count} documents")
except Exception as e:
logger.warning(f"Could not get collection count: {e}")
elif vector_db_type == "faiss":
from langchain_community.vectorstores import FAISS
try:
vector_store = FAISS.load_local(
folder_path=self.persist_directory,
embeddings=self.embedding_function,
index_name=collection_name,
allow_dangerous_deserialization=True # Codebase trust assumed for local use
)
logger.info(f"Loaded FAISS index from {self.persist_directory}")
except Exception as e:
logger.error(f"Failed to load FAISS index: {e}")
# Create empty store if failed? Or raise?
raise e
elif vector_db_type == "qdrant":
from langchain_qdrant import QdrantVectorStore
url = os.getenv("QDRANT_URL")
api_key = os.getenv("QDRANT_API_KEY")
vector_store = QdrantVectorStore(
client=None, # It will create one from url/api_key
collection_name=collection_name,
embedding=self.embedding_function,
url=url,
api_key=api_key,
)
logger.info(f"Connected to Qdrant at {url}")
else:
raise ValueError(f"Unsupported Vector DB: {vector_db_type}")
retriever = vector_store.as_retriever(search_kwargs={"k": k})
logger.info(f"Retriever created with k={k}")
return retriever
# Add incremental indexing methods to the Indexer class
from code_chatbot.incremental_indexing import add_incremental_indexing_methods
Indexer = add_incremental_indexing_methods(Indexer)
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