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Kdbai rest compatible #14511

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merged 5 commits into from
Jul 2, 2024
Merged

Kdbai rest compatible #14511

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d7b38d4
Adding changes for REST compatibility
mshawFD Jul 1, 2024
4e2bc89
Merge branch 'run-llama:main' into KDBAI-REST-compatible
mshawFD Jul 1, 2024
b7baac2
Merge branch 'run-llama:main' into KDBAI-REST-compatible
mshawFD Jul 2, 2024
ef77bce
format changes
mshawFD Jul 2, 2024
cee781f
vbump
logan-markewich Jul 2, 2024
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46 changes: 23 additions & 23 deletions ...ons/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,10 +84,10 @@ def __init__(

if hybrid_search:
if sparse_encoder is None:
if kdbai.version("kdbai_client") >= "1.2.0":
self._sparse_encoder = default_sparse_encoder_v2
else:
if isinstance(self._table, kdbai.Table):
self._sparse_encoder = default_sparse_encoder_v1
elif isinstance(self._table, kdbai.TablePyKx):
self._sparse_encoder = default_sparse_encoder_v2
else:
self._sparse_encoder = sparse_encoder

Expand Down Expand Up @@ -129,17 +129,17 @@ def add(
df = pd.DataFrame()
docs = []

if kdbai.version("kdbai_client") >= "1.2.0":
if isinstance(self._table, kdbai.Table):
schema = self._table.schema()["columns"]
elif isinstance(self._table, kdbai.TablePyKx):
schema = self._table.schema["schema"]["c"]
types = self._table.schema["schema"]["t"].decode("utf-8")
else:
schema = self._table.schema()["columns"]

if self.hybrid_search:
if kdbai.version("kdbai_client") >= "1.2.0":
schema = [item for item in schema if item != "sparseVectors"]
else:
if isinstance(self._table, kdbai.Table):
schema = [item for item in schema if item["name"] != "sparseVectors"]
elif isinstance(self._table, kdbai.TablePyKx):
schema = [item for item in schema if item != "sparseVectors"]

try:
for node in nodes:
Expand All @@ -154,7 +154,17 @@ def add(

# handle extra columns
if len(schema) > len(DEFAULT_COLUMN_NAMES):
if kdbai.version("kdbai_client") >= "1.2.0":
if isinstance(self._table, kdbai.Table):
for column in schema[len(DEFAULT_COLUMN_NAMES) :]:
try:
doc[column["name"]] = convert_metadata_col_v1(
column, node.metadata[column["name"]]
)
except Exception as e:
logger.error(
f"Error writing column {column['name']} as type {column['pytype']}: {e}."
)
elif isinstance(self._table, kdbai.TablePyKx):
for column_name, column_type in zip(
schema[len(DEFAULT_COLUMN_NAMES) :],
types[len(DEFAULT_COLUMN_NAMES) :],
Expand All @@ -167,16 +177,6 @@ def add(
logger.error(
f"Error writing column {column_name} as qtype {column_type}: {e}."
)
else:
for column in schema[len(DEFAULT_COLUMN_NAMES) :]:
try:
doc[column["name"]] = convert_metadata_col_v1(
column, node.metadata[column["name"]]
)
except Exception as e:
logger.error(
f"Error writing column {column['name']} as type {column['pytype']}: {e}."
)

docs.append(doc)

Expand Down Expand Up @@ -216,10 +216,10 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
if self.hybrid_search:
alpha = query.alpha if query.alpha is not None else 0.5

if kdbai.version("kdbai_client") >= "1.2.0":
sparse_vectors = [self._sparse_encoder([query.query_str])]
else:
if isinstance(self._table, kdbai.Table):
sparse_vectors = self._sparse_encoder([query.query_str])
elif isinstance(self._table, kdbai.TablePyKx):
sparse_vectors = [self._sparse_encoder([query.query_str])]

results = self._table.hybrid_search(
dense_vectors=[query.query_embedding],
Expand Down
2 changes: 1 addition & 1 deletion llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ exclude = ["**/BUILD"]
license = "MIT"
name = "llama-index-vector-stores-kdbai"
readme = "README.md"
version = "0.1.7"
version = "0.1.8"

[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
Expand Down
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