forked from phoenix/litellm-mirror
d57be47b0f
* ci(config.yml): add a 'check_code_quality' step Addresses https://github.com/BerriAI/litellm/issues/5991 * ci(config.yml): check why circle ci doesn't pick up this test * ci(config.yml): fix to run 'check_code_quality' tests * fix(__init__.py): fix unprotected import * fix(__init__.py): don't remove unused imports * build(ruff.toml): update ruff.toml to ignore unused imports * fix: fix: ruff + pyright - fix linting + type-checking errors * fix: fix linting errors * fix(lago.py): fix module init error * fix: fix linting errors * ci(config.yml): cd into correct dir for checks * fix(proxy_server.py): fix linting error * fix(utils.py): fix bare except causes ruff linting errors * fix: ruff - fix remaining linting errors * fix(clickhouse.py): use standard logging object * fix(__init__.py): fix unprotected import * fix: ruff - fix linting errors * fix: fix linting errors * ci(config.yml): cleanup code qa step (formatting handled in local_testing) * fix(_health_endpoints.py): fix ruff linting errors * ci(config.yml): just use ruff in check_code_quality pipeline for now * build(custom_guardrail.py): include missing file * style(embedding_handler.py): fix ruff check
36 lines
1 KiB
Python
36 lines
1 KiB
Python
import os, dotenv
|
|
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv()
|
|
|
|
from llama_index.llms import AzureOpenAI
|
|
from llama_index.embeddings import AzureOpenAIEmbedding
|
|
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
|
|
|
|
llm = AzureOpenAI(
|
|
engine="azure-gpt-3.5",
|
|
temperature=0.0,
|
|
azure_endpoint="http://0.0.0.0:4000",
|
|
api_key="sk-1234",
|
|
api_version="2023-07-01-preview",
|
|
)
|
|
|
|
embed_model = AzureOpenAIEmbedding(
|
|
deployment_name="azure-embedding-model",
|
|
azure_endpoint="http://0.0.0.0:4000",
|
|
api_key="sk-1234",
|
|
api_version="2023-07-01-preview",
|
|
)
|
|
|
|
|
|
# response = llm.complete("The sky is a beautiful blue and")
|
|
# print(response)
|
|
|
|
documents = SimpleDirectoryReader("llama_index_data").load_data()
|
|
service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
|
|
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
|
|
|
|
query_engine = index.as_query_engine()
|
|
response = query_engine.query("What did the author do growing up?")
|
|
print(response)
|