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import os
from dotenv import load_dotenv
from pinecone import Pinecone, ServerlessSpec
from llama_index.core import (SimpleDirectoryReader,Document, VectorStoreIndex, StorageContext, load_index_from_storage)
from llama_index.core.node_parser import SemanticSplitterNodeParser
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.readers.file import CSVReader
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.settings import Settings
from llama_index.llms.groq import Groq
load_dotenv()
embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
llm = Groq(
model="llama-3.1-8b-instant",
api_key=os.getenv("GROQ_API_KEY"),
max_tokens=500,
temperature=0.1
)
Settings.embed_model = embed_model
Settings.llm = llm
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
index_name = os.getenv("PINECONE_INDEX")
def get_vector_store():
pinecone_index = pc.Index(index_name)
return PineconeVectorStore(pinecone_index=pinecone_index)
def get_storage_context(for_rebuild=False):
vector_store = get_vector_store()
persist_dir = "./storage"
if for_rebuild or not os.path.exists(persist_dir):
return StorageContext.from_defaults(vector_store=vector_store)
else:
return StorageContext.from_defaults(
vector_store=vector_store,
persist_dir=persist_dir
)
def get_and_chunk_documents():
try:
file_extractor = {".csv": CSVReader()}
documents = SimpleDirectoryReader(
"../knowledge_base",
file_extractor=file_extractor
).load_data()
print(f"📖 Loaded {len(documents)} documents")
node_parser = SemanticSplitterNodeParser(
buffer_size=1,
breakpoint_percentile_threshold=95,
embed_model=embed_model
)
nodes = node_parser.get_nodes_from_documents(documents)
print(f"📄 Created {len(nodes)} document chunks")
return nodes
except Exception as e:
print(f"❌ Error loading documents: {e}")
return []
def get_index():
try:
storage_context = get_storage_context()
return load_index_from_storage(storage_context)
except Exception as e:
print(f"⚠️ Local storage not found, creating index from existing Pinecone data...")
try:
vector_store = get_vector_store()
storage_context = get_storage_context()
index = VectorStoreIndex.from_vector_store(
vector_store=vector_store,
storage_context=storage_context
)
return index
except Exception as e2:
print(f"❌ Error creating index from vector store: {e2}")
return None
def check_index_status():
try:
pinecone_index = pc.Index(index_name)
stats = pinecone_index.describe_index_stats()
vector_count = stats.get('total_vector_count', 0)
if vector_count > 0:
print(f"✅ Index found with {vector_count} vectors")
return True
else:
print("❌ Index exists but is empty")
return False
except Exception as e:
print(f"❌ Error checking index: {e}")
return False
def clear_pinecone_index():
"""Delete all vectors from Pinecone index"""
try:
pinecone_index = pc.Index(index_name)
stats = pinecone_index.describe_index_stats()
vector_count = stats.get('total_vector_count', 0)
print(f"🗑️ Current vectors in index: {vector_count}")
if vector_count > 0:
pinecone_index.delete(delete_all=True)
print("✅ All vectors deleted from Pinecone index")
else:
print("ℹ️ Index is already empty")
return True
except Exception as e:
print(f"❌ Error clearing index: {e}")
return False
def rebuild_index():
"""Clear old data and rebuild index with new CSV processing"""
try:
print("🔄 Starting index rebuild process...")
if not clear_pinecone_index():
print("❌ Failed to clear index, aborting rebuild")
return None
import shutil
if os.path.exists("./storage"):
shutil.rmtree("./storage")
print("🗑️ Cleared local storage")
nodes = get_and_chunk_documents()
if not nodes:
print("❌ No nodes created, cannot rebuild index")
return None
storage_context = get_storage_context(for_rebuild=True)
index = VectorStoreIndex(nodes, storage_context=storage_context)
index.storage_context.persist(persist_dir="./storage")
print(f"✅ Index rebuilt successfully with {len(nodes)} nodes")
return index
except Exception as e:
print(f"❌ Error rebuilding index: {e}")
return None |