Skip to content

Awadb

AwaDBVectorStore #

Bases: VectorStore

AwaDB vector store.

In this vector store, embeddings are stored within a AwaDB table.

During query time, the index uses AwaDB to query for the top k most similar nodes.

Examples:

pip install llama-index-vector-stores-awadb

from llama_index.vector_stores.awadb import AwaDBVectorStore

vector_store = AwaDBVectorStore(table_name="llamaindex")
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py 
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
class AwaDBVectorStore(VectorStore):
    """AwaDB vector store.

    In this vector store, embeddings are stored within a AwaDB table.

    During query time, the index uses AwaDB to query for the top
    k most similar nodes.

    Examples:
        `pip install llama-index-vector-stores-awadb`

        ```python
        from llama_index.vector_stores.awadb import AwaDBVectorStore

        vector_store = AwaDBVectorStore(table_name="llamaindex")
        ```

    """

    flat_metadata: bool = True
    stores_text: bool = True
    DEFAULT_TABLE_NAME = "llamaindex_awadb"

    @property
    def client(self) -> Any:
        """Get AwaDB client."""
        return self.awadb_client

    def __init__(
        self,
        table_name: str = DEFAULT_TABLE_NAME,
        log_and_data_dir: Optional[str] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize with AwaDB client.
           If table_name is not specified,
           a random table name of `DEFAULT_TABLE_NAME + last segment of uuid`
           would be created automatically.

        Args:
            table_name: Name of the table created, default DEFAULT_TABLE_NAME.
            log_and_data_dir: Optional the root directory of log and data.
            kwargs: Any possible extend parameters in the future.

        Returns:
            None.
        """
        import_err_msg = "`awadb` package not found, please run `pip install awadb`"
        try:
            import awadb
        except ImportError:
            raise ImportError(import_err_msg)
        if log_and_data_dir is not None:
            self.awadb_client = awadb.Client(log_and_data_dir)
        else:
            self.awadb_client = awadb.Client()

        if table_name == self.DEFAULT_TABLE_NAME:
            table_name += "_"
            table_name += str(uuid.uuid4()).split("-")[-1]

        self.awadb_client.Create(table_name)

    def add(
        self,
        nodes: List[BaseNode],
        **add_kwargs: Any,
    ) -> List[str]:
        """Add nodes to AwaDB.

        Args:
            nodes: List[BaseNode]: list of nodes with embeddings

        Returns:
            Added node ids
        """
        if not self.awadb_client:
            raise ValueError("AwaDB client not initialized")

        embeddings = []
        metadatas = []
        ids = []
        texts = []
        for node in nodes:
            embeddings.append(node.get_embedding())
            metadatas.append(
                node_to_metadata_dict(
                    node, remove_text=True, flat_metadata=self.flat_metadata
                )
            )
            ids.append(node.node_id)
            texts.append(node.get_content(metadata_mode=MetadataMode.NONE) or "")

        self.awadb_client.AddTexts(
            "embedding_text",
            "text_embedding",
            texts,
            embeddings,
            metadatas,
            is_duplicate_texts=False,
            ids=ids,
        )

        return ids

    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
        """Delete nodes using with ref_doc_id.

        Args:
            ref_doc_id (str): The doc_id of the document to delete.

        Returns:
            None
        """
        if len(ref_doc_id) == 0:
            return
        ids: List[str] = []
        ids.append(ref_doc_id)
        self.awadb_client.Delete(ids)

    def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
        """Query index for top k most similar nodes.

        Args:
            query : vector store query

        Returns:
            VectorStoreQueryResult: Query results
        """
        meta_filters = {}
        if query.filters is not None:
            for filter in query.filters.legacy_filters():
                meta_filters[filter.key] = filter.value

        not_include_fields: Set[str] = {"text_embedding"}
        results = self.awadb_client.Search(
            query=query.query_embedding,
            topn=query.similarity_top_k,
            meta_filter=meta_filters,
            not_include_fields=not_include_fields,
        )

        nodes = []
        similarities = []
        ids = []

        for item_detail in results[0]["ResultItems"]:
            content = ""
            meta_data = {}
            node_id = ""
            for item_key in item_detail:
                if item_key == "embedding_text":
                    content = item_detail[item_key]
                    continue
                elif item_key == "_id":
                    node_id = item_detail[item_key]
                    ids.append(node_id)
                    continue
                elif item_key == "score":
                    similarities.append(item_detail[item_key])
                    continue
                meta_data[item_key] = item_detail[item_key]

            try:
                node = metadata_dict_to_node(meta_data)
                node.set_content(content)
            except Exception:
                # NOTE: deprecated legacy logic for backward compatibility
                metadata, node_info, relationships = legacy_metadata_dict_to_node(
                    meta_data
                )

                node = TextNode(
                    text=content,
                    id_=node_id,
                    metadata=metadata,
                    start_char_idx=node_info.get("start", None),
                    end_char_idx=node_info.get("end", None),
                    relationships=relationships,
                )

            nodes.append(node)

        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

client property #

client: Any

Get AwaDB client.

add #

add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]

Add nodes to AwaDB.

Parameters:

Name Type Description Default
nodes List[BaseNode]

List[BaseNode]: list of nodes with embeddings

 required

Returns:

Type Description
List[str]

Added node ids
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py 
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
def add(
    self,
    nodes: List[BaseNode],
    **add_kwargs: Any,
) -> List[str]:
    """Add nodes to AwaDB.

    Args:
        nodes: List[BaseNode]: list of nodes with embeddings

    Returns:
        Added node ids
    """
    if not self.awadb_client:
        raise ValueError("AwaDB client not initialized")

    embeddings = []
    metadatas = []
    ids = []
    texts = []
    for node in nodes:
        embeddings.append(node.get_embedding())
        metadatas.append(
            node_to_metadata_dict(
                node, remove_text=True, flat_metadata=self.flat_metadata
            )
        )
        ids.append(node.node_id)
        texts.append(node.get_content(metadata_mode=MetadataMode.NONE) or "")

    self.awadb_client.AddTexts(
        "embedding_text",
        "text_embedding",
        texts,
        embeddings,
        metadatas,
        is_duplicate_texts=False,
        ids=ids,
    )

    return ids

delete #

delete(ref_doc_id: str, **delete_kwargs: Any) -> None

Delete nodes using with ref_doc_id.

Parameters:

Name Type Description Default
ref_doc_id str

The doc_id of the document to delete.

 required

Returns:

Type Description
None

None
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py 
131
132
133
134
135
136
137
138
139
140
141
142
143
144
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
    """Delete nodes using with ref_doc_id.

    Args:
        ref_doc_id (str): The doc_id of the document to delete.

    Returns:
        None
    """
    if len(ref_doc_id) == 0:
        return
    ids: List[str] = []
    ids.append(ref_doc_id)
    self.awadb_client.Delete(ids)

query #

query(query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters:

Name Type Description Default
query

vector store query

 required

Returns:

Name Type Description
VectorStoreQueryResult VectorStoreQueryResult

Query results
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py 
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
    """Query index for top k most similar nodes.

    Args:
        query : vector store query

    Returns:
        VectorStoreQueryResult: Query results
    """
    meta_filters = {}
    if query.filters is not None:
        for filter in query.filters.legacy_filters():
            meta_filters[filter.key] = filter.value

    not_include_fields: Set[str] = {"text_embedding"}
    results = self.awadb_client.Search(
        query=query.query_embedding,
        topn=query.similarity_top_k,
        meta_filter=meta_filters,
        not_include_fields=not_include_fields,
    )

    nodes = []
    similarities = []
    ids = []

    for item_detail in results[0]["ResultItems"]:
        content = ""
        meta_data = {}
        node_id = ""
        for item_key in item_detail:
            if item_key == "embedding_text":
                content = item_detail[item_key]
                continue
            elif item_key == "_id":
                node_id = item_detail[item_key]
                ids.append(node_id)
                continue
            elif item_key == "score":
                similarities.append(item_detail[item_key])
                continue
            meta_data[item_key] = item_detail[item_key]

        try:
            node = metadata_dict_to_node(meta_data)
            node.set_content(content)
        except Exception:
            # NOTE: deprecated legacy logic for backward compatibility
            metadata, node_info, relationships = legacy_metadata_dict_to_node(
                meta_data
            )

            node = TextNode(
                text=content,
                id_=node_id,
                metadata=metadata,
                start_char_idx=node_info.get("start", None),
                end_char_idx=node_info.get("end", None),
                relationships=relationships,
            )

        nodes.append(node)

    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)