[Feat] Implement keyword search in Qdrant

This commit implements keyword search in Qdrant.

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

tests/unit/providers/vector_io/test_qdrant.py

@@ -145,3 +145,89 @@ async def test_qdrant_register_and_unregister_vector_db(
     await qdrant_adapter.unregister_vector_db(vector_db_id)
     assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
     assert len((await qdrant_adapter.client.get_collections()).collections) == 0
+
+
+# Keyword search tests
+async def test_query_chunks_keyword_search(qdrant_vec_index, sample_chunks, sample_embeddings):
+    """Test keyword search functionality in Qdrant."""
+    await qdrant_vec_index.add_chunks(sample_chunks, sample_embeddings)
+    query_string = "Sentence 5"
+    response = await qdrant_vec_index.query_keyword(query_string=query_string, k=3, score_threshold=0.0)
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) > 0, f"Expected some chunks, but got {len(response.chunks)}"
+
+    non_existent_query_str = "blablabla"
+    response_no_results = await qdrant_vec_index.query_keyword(
+        query_string=non_existent_query_str, k=1, score_threshold=0.0
+    )
+
+    assert isinstance(response_no_results, QueryChunksResponse)
+    assert len(response_no_results.chunks) == 0, f"Expected 0 results, but got {len(response_no_results.chunks)}"
+
+
+async def test_query_chunks_keyword_search_k_greater_than_results(qdrant_vec_index, sample_chunks, sample_embeddings):
+    """Test keyword search when k is greater than available results."""
+    await qdrant_vec_index.add_chunks(sample_chunks, sample_embeddings)
+
+    query_str = "Sentence 1 from document 0"  # Should match only one chunk
+    response = await qdrant_vec_index.query_keyword(k=5, score_threshold=0.0, query_string=query_str)
+
+    assert isinstance(response, QueryChunksResponse)
+    assert 0 < len(response.chunks) < 5, f"Expected results between [1, 4], got {len(response.chunks)}"
+    assert any("Sentence 1 from document 0" in chunk.content for chunk in response.chunks), "Expected chunk not found"
+
+
+async def test_query_chunks_keyword_search_score_threshold(qdrant_vec_index, sample_chunks, sample_embeddings):
+    """Test keyword search with score threshold filtering."""
+    await qdrant_vec_index.add_chunks(sample_chunks, sample_embeddings)
+
+    query_string = "Sentence 5"
+
+    # Test with low threshold (should return results)
+    response_low_threshold = await qdrant_vec_index.query_keyword(query_string=query_string, k=3, score_threshold=0.0)
+    assert len(response_low_threshold.chunks) > 0
+
+    # Test with negative threshold (should return results since scores are 0.0)
+    response_negative_threshold = await qdrant_vec_index.query_keyword(
+        query_string=query_string, k=3, score_threshold=-1.0
+    )
+    assert len(response_negative_threshold.chunks) > 0
+
+
+async def test_query_chunks_keyword_search_edge_cases(qdrant_vec_index, sample_chunks, sample_embeddings):
+    """Test keyword search edge cases."""
+    await qdrant_vec_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Test with empty string
+    response_empty = await qdrant_vec_index.query_keyword(query_string="", k=3, score_threshold=0.0)
+    assert isinstance(response_empty, QueryChunksResponse)
+
+    # Test with very long query string
+    long_query = "a" * 1000
+    response_long = await qdrant_vec_index.query_keyword(query_string=long_query, k=3, score_threshold=0.0)
+    assert isinstance(response_long, QueryChunksResponse)
+
+    # Test with special characters
+    special_query = "!@#$%^&*()_+-=[]{}|;':\",./<>?"
+    response_special = await qdrant_vec_index.query_keyword(query_string=special_query, k=3, score_threshold=0.0)
+    assert isinstance(response_special, QueryChunksResponse)
+
+
+async def test_query_chunks_keyword_search_metadata_preservation(
+    qdrant_vec_index, sample_chunks_with_metadata, sample_embeddings_with_metadata
+):
+    """Test that keyword search preserves chunk metadata."""
+    await qdrant_vec_index.add_chunks(sample_chunks_with_metadata, sample_embeddings_with_metadata)
+
+    query_string = "Sentence 0"
+    response = await qdrant_vec_index.query_keyword(query_string=query_string, k=2, score_threshold=0.0)
+
+    assert len(response.chunks) > 0
+    for chunk in response.chunks:
+        # Check that metadata is preserved
+        assert hasattr(chunk, "metadata") or hasattr(chunk, "chunk_metadata")
+        if hasattr(chunk, "chunk_metadata") and chunk.chunk_metadata:
+            assert chunk.chunk_metadata.document_id is not None
+            assert chunk.chunk_metadata.chunk_id is not None
+            assert chunk.chunk_metadata.source is not None