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import json
from pathlib import Path
from unittest.mock import patch
import pytest
from index import ReaderIndexingPipeline
from openai.resources.embeddings import Embeddings
from openai.types.chat.chat_completion import ChatCompletion
from kotaemon.llms import AzureChatOpenAI
with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
openai_embedding = json.load(f)
_openai_chat_completion_response = ChatCompletion.parse_obj(
{
"id": "chatcmpl-7qyuw6Q1CFCpcKsMdFkmUPUa7JP2x",
"object": "chat.completion",
"created": 1692338378,
"model": "gpt-35-turbo",
"system_fingerprint": None,
"choices": [
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "Hello! How can I assist you today?",
"function_call": None,
"tool_calls": None,
},
}
],
"usage": {"completion_tokens": 9, "prompt_tokens": 10, "total_tokens": 19},
}
)
@pytest.fixture(scope="function")
def mock_openai_embedding(monkeypatch):
monkeypatch.setattr(Embeddings, "create", lambda *args, **kwargs: openai_embedding)
@patch(
"openai.resources.chat.completions.Completions.create",
side_effect=lambda *args, **kwargs: _openai_chat_completion_response,
)
def test_ingest_pipeline(patch, mock_openai_embedding, tmp_path):
indexing_pipeline = ReaderIndexingPipeline(
storage_path=tmp_path,
)
indexing_pipeline.indexing_vector_pipeline.embedding.openai_api_key = "some-key"
input_file_path = Path(__file__).parent / "resources/dummy.pdf"
# call ingestion pipeline
indexing_pipeline(input_file_path, force_reindex=True)
retrieving_pipeline = indexing_pipeline.to_retrieving_pipeline()
results = retrieving_pipeline("This is a query")
assert len(results) == 1
# create llm
llm = AzureChatOpenAI(
api_key="dummy",
api_version="2024-05-01-preview",
azure_deployment="gpt-4o",
azure_endpoint="https://test.openai.azure.com/",
)
qa_pipeline = indexing_pipeline.to_qa_pipeline(llm=llm, openai_api_key="some-key")
response = qa_pipeline("Summarize this document.")
assert response
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