mirror of
https://github.com/mudler/LocalAI.git
synced 2024-12-22 05:57:50 +00:00
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import os
|
|
|
|
import weaviate
|
|
|
|
from llama_index import ServiceContext, VectorStoreIndex, StorageContext
|
|
from llama_index.llms import LocalAI
|
|
from llama_index.vector_stores import WeaviateVectorStore
|
|
from llama_index.storage.storage_context import StorageContext
|
|
|
|
# Weaviate client setup
|
|
client = weaviate.Client("http://weviate.default")
|
|
|
|
# Weaviate vector store setup
|
|
vector_store = WeaviateVectorStore(weaviate_client=client, index_name="AIChroma")
|
|
|
|
# Storage context setup
|
|
storage_context = StorageContext.from_defaults(vector_store=vector_store)
|
|
|
|
# LocalAI setup
|
|
llm = LocalAI(temperature=0, model_name="gpt-3.5-turbo", api_base="http://local-ai.default", api_key="stub")
|
|
llm.globally_use_chat_completions = True;
|
|
|
|
# Service context setup
|
|
service_context = ServiceContext.from_defaults(llm=llm, embed_model="local")
|
|
|
|
# Load index from stored vectors
|
|
index = VectorStoreIndex.from_vector_store(
|
|
vector_store,
|
|
storage_context=storage_context,
|
|
service_context=service_context
|
|
)
|
|
|
|
# Query engine setup
|
|
query_engine = index.as_query_engine(similarity_top_k=1, vector_store_query_mode="hybrid")
|
|
|
|
# Query example
|
|
response = query_engine.query("What is LocalAI?")
|
|
print(response) |