Pre-release
This release is a pre-release and may not be stable for production use.
Llama-index with InterSystems IRIS
Llama-index with support for InterSystems IRIS
Install
pip install llama-iris
Example
import os
from dotenv import load_dotenv
from llama_index import SimpleDirectoryReader, StorageContext, ServiceContext
from llama_index.indices.vector_store import VectorStoreIndex
import openai
from llama_iris import IRISVectorStore
load_dotenv(override=True)
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
vector_store = IRISVectorStore.from_params(
connection_string=CONNECTION_STRING,
table_name="paul_graham_essay",
embed_dim=1536, # openai embedding dimension
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
show_progress=True,
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do?")
import textwrap
print(textwrap.fill(str(response), 100))
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