2dd531114f
* Move kotaemon and ktem into same folder * Update docs * Update CI * Resolve mypy, isorts * Re-allow test pdf files
73 lines
2.3 KiB
Python
73 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 kotaemon.llms import AzureChatOpenAI
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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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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 = AzureChatOpenAI(
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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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