File size: 1,490 Bytes
ee00031 f367ea8 ee00031 f367ea8 ee00031 f367ea8 ee00031 f367ea8 ee00031 f367ea8 ee00031 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone, ServerlessSpec
from google import genai
from langchain.embeddings.base import Embeddings
import os
from dotenv import load_dotenv
load_dotenv()
# Init client (once)
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
index_name = "rag-chatbot" # Matches your dashboard name
# Create if not exists (idempotent; only runs first time)
if index_name not in pc.list_indexes().names():
pc.create_index(
name=index_name,
dimension=3072,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1")
)
def create_retriever(chunks, embeddings):
# Connect to the index
index = pc.Index(index_name)
# Get index stats to check for existing namespaces/vectors
stats = index.describe_index_stats()
# If there are any namespaces (indicating vectors exist somewhere), delete all in the default namespace
if 'namespaces' in stats and len(stats['namespaces']) > 0:
index.delete(delete_all=True, namespace="")
vector_store = PineconeVectorStore.from_documents(
chunks, embeddings, index_name=index_name, namespace=""
)
return vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
def load_retriever(embeddings):
vector_store = PineconeVectorStore.from_existing_index(index_name, embeddings, namespace="")
return vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5}) |