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"""Weaviate-specific serializers for LlamaIndex data structures.
Contain conversion to and from dataclasses that LlamaIndex uses.
"""
import json
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Optional, TypeVar, cast
from gpt_index.data_structs.data_structs import IndexStruct, Node
from gpt_index.readers.weaviate.utils import (
get_by_id,
parse_get_response,
validate_client,
)
from gpt_index.utils import get_new_id
IS = TypeVar("IS", bound=IndexStruct)
class BaseWeaviateIndexStruct(Generic[IS]):
"""Base Weaviate index struct."""
@classmethod
@abstractmethod
def _class_name(cls, class_prefix: str) -> str:
"""Return class name."""
@classmethod
def _get_common_properties(cls) -> List[Dict]:
"""Get common properties."""
return [
{
"dataType": ["string"],
"description": "Text property",
"name": "text",
},
{
"dataType": ["string"],
"description": "Document id",
"name": "doc_id",
},
{
"dataType": ["string"],
"description": "extra_info (in JSON)",
"name": "extra_info",
},
]
@classmethod
@abstractmethod
def _get_properties(cls) -> List[Dict]:
"""Get properties specific to each index struct.
Used in creating schema.
"""
@classmethod
def _get_by_id(cls, client: Any, object_id: str, class_prefix: str) -> Dict:
"""Get entry by id."""
validate_client(client)
class_name = cls._class_name(class_prefix)
properties = cls._get_common_properties() + cls._get_properties()
prop_names = [p["name"] for p in properties]
entry = get_by_id(client, object_id, class_name, prop_names)
return entry
@classmethod
def create_schema(cls, client: Any, class_prefix: str) -> None:
"""Create schema."""
validate_client(client)
# first check if schema exists
schema = client.schema.get()
classes = schema["classes"]
existing_class_names = {c["class"] for c in classes}
# if schema already exists, don't create
class_name = cls._class_name(class_prefix)
if class_name in existing_class_names:
return
# get common properties
properties = cls._get_common_properties()
# get specific properties
properties.extend(cls._get_properties())
class_obj = {
"class": cls._class_name(class_prefix), # <= note the capital "A".
"description": f"Class for {class_name}",
"properties": properties,
}
client.schema.create_class(class_obj)
@classmethod
@abstractmethod
def _entry_to_gpt_index(cls, entry: Dict) -> IS:
"""Convert to LlamaIndex list."""
@classmethod
def to_gpt_index_list(
cls,
client: Any,
class_prefix: str,
vector: Optional[List[float]] = None,
object_limit: Optional[int] = None,
) -> List[IS]:
"""Convert to LlamaIndex list."""
validate_client(client)
class_name = cls._class_name(class_prefix)
properties = cls._get_common_properties() + cls._get_properties()
prop_names = [p["name"] for p in properties]
query = client.query.get(class_name, prop_names).with_additional(
["id", "vector"]
)
if vector is not None:
query = query.with_near_vector(
{
"vector": vector,
}
)
if object_limit is not None:
query = query.with_limit(object_limit)
query_result = query.do()
parsed_result = parse_get_response(query_result)
entries = parsed_result[class_name]
results: List[IS] = []
for entry in entries:
results.append(cls._entry_to_gpt_index(entry))
return results
@classmethod
@abstractmethod
def _from_gpt_index(
cls, client: Any, index: IS, class_prefix: str, batch: Optional[Any] = None
) -> str:
"""Convert from LlamaIndex."""
@classmethod
def from_gpt_index(cls, client: Any, index: IS, class_prefix: str) -> str:
"""Convert from LlamaIndex."""
validate_client(client)
index_id = cls._from_gpt_index(client, index, class_prefix)
client.batch.flush()
return index_id
class WeaviateNode(BaseWeaviateIndexStruct[Node]):
"""Weaviate node."""
@classmethod
def _class_name(cls, class_prefix: str) -> str:
"""Return class name."""
return f"{class_prefix}_Node"
@classmethod
def _get_properties(cls) -> List[Dict]:
"""Create schema."""
return [
{
"dataType": ["int"],
"description": "The index of the Node",
"name": "index",
},
{
"dataType": ["int[]"],
"description": "The child_indices of the Node",
"name": "child_indices",
},
{
"dataType": ["string"],
"description": "The ref_doc_id of the Node",
"name": "ref_doc_id",
},
{
"dataType": ["string"],
"description": "node_info (in JSON)",
"name": "node_info",
},
]
@classmethod
def _entry_to_gpt_index(cls, entry: Dict) -> Node:
"""Convert to LlamaIndex list."""
extra_info_str = entry["extra_info"]
if extra_info_str == "":
extra_info = None
else:
extra_info = json.loads(extra_info_str)
node_info_str = entry["node_info"]
if node_info_str == "":
node_info = None
else:
node_info = json.loads(node_info_str)
return Node(
text=entry["text"],
doc_id=entry["doc_id"],
index=int(entry["index"]),
child_indices=entry["child_indices"],
ref_doc_id=entry["ref_doc_id"],
embedding=entry["_additional"]["vector"],
extra_info=extra_info,
node_info=node_info,
)
@classmethod
def _from_gpt_index(
cls, client: Any, node: Node, class_prefix: str, batch: Optional[Any] = None
) -> str:
"""Convert from LlamaIndex."""
node_dict = node.to_dict()
vector = node_dict.pop("embedding")
extra_info = node_dict.pop("extra_info")
# json-serialize the extra_info
extra_info_str = ""
if extra_info is not None:
extra_info_str = json.dumps(extra_info)
node_dict["extra_info"] = extra_info_str
# json-serialize the node_info
node_info = node_dict.pop("node_info")
node_info_str = ""
if node_info is not None:
node_info_str = json.dumps(node_info)
node_dict["node_info"] = node_info_str
# TODO: account for existing nodes that are stored
node_id = get_new_id(set())
class_name = cls._class_name(class_prefix)
# if batch object is provided (via a contexxt manager), use that instead
if batch is not None:
batch.add_data_object(node_dict, class_name, node_id, vector)
else:
client.batch.add_data_object(node_dict, class_name, node_id, vector)
return node_id
@classmethod
def delete_document(cls, client: Any, ref_doc_id: str, class_prefix: str) -> None:
"""Delete entry."""
validate_client(client)
# make sure that each entry
class_name = cls._class_name(class_prefix)
where_filter = {
"path": ["ref_doc_id"],
"operator": "Equal",
"valueString": ref_doc_id,
}
query = (
client.query.get(class_name)
.with_additional(["id"])
.with_where(where_filter)
)
query_result = query.do()
parsed_result = parse_get_response(query_result)
entries = parsed_result[class_name]
for entry in entries:
client.data_object.delete(entry["_additional"]["id"], class_name)
@classmethod
def from_gpt_index_batch(
cls, client: Any, nodes: List[Node], class_prefix: str
) -> List[str]:
"""Convert from gpt index."""
from weaviate import Client # noqa: F401
client = cast(Client, client)
validate_client(client)
index_ids = []
with client.batch as batch:
for node in nodes:
index_id = cls._from_gpt_index(client, node, class_prefix, batch=batch)
index_ids.append(index_id)
return index_ids
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