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Yes, you can use
ServiceContext when creating an instance of
VectorStoreIndex. The
ServiceContext object contains abstractions such as the LLM and chunk size. When creating a
VectorStoreIndex, you can pass the
ServiceContext as an argument.
Here is an example of how you can do it:
from llama_index import ServiceContext, LLMPredictor
from llama_index.storage import StorageContext
from llama_index.vector_stores import PineconeVectorStore
from llama_index.langchain_helpers.text_splitter import TokenTextSplitter
from llama_index.llms import OpenAI
# define node parser and LLM
chunk_size = 1024
llm = OpenAI(temperature=0, model="gpt-4", streaming=True)
service_context = ServiceContext.from_defaults(chunk_size=chunk_size, llm=llm)
Then, you can use the
service_context when creating the
VectorStoreIndex:
from llama_index.indices.vector_store.base import VectorStoreIndex
index = VectorStoreIndex([], service_context=service_context)
Please note that the
VectorStoreIndex also accepts other parameters like
storage_context,
use_async,
store_nodes_override,
show_progress, and
kwargs. Make sure to provide these parameters if necessary.
For more details, you can refer to the
LlamaIndex documentation ().
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