[AUR-392, AUR-413, AUR-414] Define base vector store, and make use of ChromaVectorStore from llama_index. Indexing and retrieving vectors with vector store (#18)

Design the base interface of vector store, and apply it to the Chroma Vector Store (wrapped around llama_index's implementation). Provide the pipelines to populate and retrieve from vector store.

tests/test_indexing_retrieval.py

@@ -0,0 +1,59 @@
+import json
+from pathlib import Path
+
+import pytest
+from openai.api_resources.embedding import Embedding
+
+from kotaemon.documents.base import Document
+from kotaemon.embeddings.openai import AzureOpenAIEmbeddings
+from kotaemon.pipelines.indexing import IndexVectorStoreFromDocumentPipeline
+from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
+from kotaemon.vectorstores import ChromaVectorStore
+
+with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
+    openai_embedding = json.load(f)
+
+
+@pytest.fixture(scope="function")
+def mock_openai_embedding(monkeypatch):
+    monkeypatch.setattr(Embedding, "create", lambda *args, **kwargs: openai_embedding)
+
+
+def test_indexing(mock_openai_embedding, tmp_path):
+    db = ChromaVectorStore(path=str(tmp_path))
+    embedding = AzureOpenAIEmbeddings(
+        model="text-embedding-ada-002",
+        deployment="embedding-deployment",
+        openai_api_base="https://test.openai.azure.com/",
+        openai_api_key="some-key",
+    )
+
+    pipeline = IndexVectorStoreFromDocumentPipeline(
+        vector_store=db, embedding=embedding
+    )
+    assert pipeline.vector_store._collection.count() == 0, "Expected empty collection"
+    pipeline(text=Document(text="Hello world"))
+    assert pipeline.vector_store._collection.count() == 1, "Index 1 item"
+
+
+def test_retrieving(mock_openai_embedding, tmp_path):
+    db = ChromaVectorStore(path=str(tmp_path))
+    embedding = AzureOpenAIEmbeddings(
+        model="text-embedding-ada-002",
+        deployment="embedding-deployment",
+        openai_api_base="https://test.openai.azure.com/",
+        openai_api_key="some-key",
+    )
+
+    index_pipeline = IndexVectorStoreFromDocumentPipeline(
+        vector_store=db, embedding=embedding
+    )
+    retrieval_pipeline = RetrieveDocumentFromVectorStorePipeline(
+        vector_store=db, embedding=embedding
+    )
+
+    index_pipeline(text=Document(text="Hello world"))
+    output = retrieval_pipeline(text=["Hello world", "Hello world"])
+
+    assert len(output) == 2, "Expected 2 results"
+    assert output[0] == output[1], "Expected identical results"