using ingestion pipeline for chromadb (#87)

templates/components/vectordbs/python/chroma/generate.py

-from dotenv import load_dotenv
-
-load_dotenv()
-
-import os
-import logging
-from llama_index.core.storage import StorageContext
-from llama_index.core.indices import VectorStoreIndex
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-from app.engine.vectordb import get_vector_store
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index")
-    # load the documents and create the index
-    documents = get_documents()
-    store = get_vector_store()
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info("Successfully created embeddings in the ChromaDB")
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/chroma/index.py

-import logging
-
-from llama_index.core.indices import VectorStoreIndex
-from app.engine.vectordb import get_vector_store
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to ChromaDB..")
-    store = get_vector_store()
-    index = VectorStoreIndex.from_vector_store(store, use_async=False)
-    logger.info("Finished connecting to ChromaDB.")
-    return index