feat: add DeepSearch multi-agent researcher with async jobs, vector store, and RAG chat

tests/unit/services/deepsearch/store.py

@@ -0,0 +1,52 @@
+# retoor <retoor@molodetz.nl>
+
+from devplacepy.services.deepsearch.embeddings import local_embed
+from devplacepy.services.deepsearch.store import Chunk, VectorStore
+
+
+def _chunks():
+    texts = [
+        "The transistor was invented at Bell Labs in nineteen forty seven.",
+        "Silicon is the primary semiconductor material used in chips.",
+        "Quantum tunnelling limits how small a transistor can become.",
+    ]
+    chunks = [
+        Chunk(uid=f"c{i}", text=text, url=f"https://s{i}.example", title=f"Source {i}")
+        for i, text in enumerate(texts)
+    ]
+    return chunks
+
+
+def test_add_and_count():
+    store = VectorStore("ds_store_test_count")
+    try:
+        chunks = _chunks()
+        vectors = local_embed([c.text for c in chunks]).vectors
+        store.add(chunks, vectors)
+        assert store.count() == 3
+    finally:
+        store.drop()
+
+
+def test_hybrid_search_returns_relevant_chunk():
+    store = VectorStore("ds_store_test_hybrid")
+    try:
+        chunks = _chunks()
+        vectors = local_embed([c.text for c in chunks]).vectors
+        store.add(chunks, vectors)
+        query = "where was the transistor invented"
+        query_vector = local_embed([query]).vectors[0]
+        results = store.hybrid_search(query, query_vector, top_k=2)
+        assert results
+        assert any("transistor" in r.text.lower() for r in results)
+    finally:
+        store.drop()
+
+
+def test_keyword_scores_rank_match_higher():
+    store = VectorStore("ds_store_test_keyword")
+    chunks = _chunks()
+    scores = store.keyword_scores("silicon semiconductor", chunks)
+    assert scores
+    best = max(scores, key=scores.get)
+    assert "silicon" in next(c.text for c in chunks if c.uid == best).lower()