Make ktem official (#134)

* Move kotaemon and ktem into same folder

* Update docs

* Update CI

* Resolve mypy, isorts

* Re-allow test pdf files

libs/ktem/khapptests/test_qa.py

@@ -0,0 +1,72 @@
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+from index import ReaderIndexingPipeline
+from kotaemon.llms import AzureChatOpenAI
+from openai.resources.embeddings import Embeddings
+from openai.types.chat.chat_completion import ChatCompletion
+
+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(
+        openai_api_base="https://test.openai.azure.com/",
+        openai_api_key="some-key",
+        openai_api_version="2023-03-15-preview",
+        deployment_name="gpt35turbo",
+        temperature=0,
+    )
+    qa_pipeline = indexing_pipeline.to_qa_pipeline(llm=llm, openai_api_key="some-key")
+    response = qa_pipeline("Summarize this document.")
+    assert response