a203fc0f7c
* Rename AzureChatOpenAI to LCAzureChatOpenAI * Provide vanilla ChatOpenAI and AzureChatOpenAI * Remove the highest accuracy, lowest cost criteria These criteria are unnecessary. The users, not pipeline creators, should choose which LLM to use. Furthermore, it's cumbersome to input this information, really degrades user experience. * Remove the LLM selection in simple reasoning pipeline * Provide a dedicated stream method to generate the output * Return placeholder message to chat if the text is empty
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
import json
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from pathlib import Path
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from unittest.mock import patch
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import pytest
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from index import ReaderIndexingPipeline
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from openai.resources.embeddings import Embeddings
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from openai.types.chat.chat_completion import ChatCompletion
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from kotaemon.llms import LCAzureChatOpenAI
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with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
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openai_embedding = json.load(f)
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_openai_chat_completion_response = ChatCompletion.parse_obj(
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{
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"id": "chatcmpl-7qyuw6Q1CFCpcKsMdFkmUPUa7JP2x",
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"object": "chat.completion",
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"created": 1692338378,
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"model": "gpt-35-turbo",
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"system_fingerprint": None,
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"choices": [
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{
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"index": 0,
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"finish_reason": "stop",
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"message": {
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"role": "assistant",
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"content": "Hello! How can I assist you today?",
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"function_call": None,
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"tool_calls": None,
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},
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}
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],
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"usage": {"completion_tokens": 9, "prompt_tokens": 10, "total_tokens": 19},
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}
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)
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@pytest.fixture(scope="function")
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def mock_openai_embedding(monkeypatch):
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monkeypatch.setattr(Embeddings, "create", lambda *args, **kwargs: openai_embedding)
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@patch(
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"openai.resources.chat.completions.Completions.create",
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side_effect=lambda *args, **kwargs: _openai_chat_completion_response,
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)
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def test_ingest_pipeline(patch, mock_openai_embedding, tmp_path):
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indexing_pipeline = ReaderIndexingPipeline(
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storage_path=tmp_path,
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)
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indexing_pipeline.indexing_vector_pipeline.embedding.openai_api_key = "some-key"
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input_file_path = Path(__file__).parent / "resources/dummy.pdf"
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# call ingestion pipeline
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indexing_pipeline(input_file_path, force_reindex=True)
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retrieving_pipeline = indexing_pipeline.to_retrieving_pipeline()
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results = retrieving_pipeline("This is a query")
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assert len(results) == 1
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# create llm
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llm = LCAzureChatOpenAI(
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openai_api_base="https://test.openai.azure.com/",
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openai_api_key="some-key",
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openai_api_version="2023-03-15-preview",
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deployment_name="gpt35turbo",
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temperature=0,
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)
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qa_pipeline = indexing_pipeline.to_qa_pipeline(llm=llm, openai_api_key="some-key")
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response = qa_pipeline("Summarize this document.")
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assert response
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