Skip to content

Vector

LlamaIndex data structures.

VectorStoreIndex #

Bases: BaseIndex[IndexDict]

Vector Store Index.

Parameters:

Name Type Description Default
use_async bool

Whether to use asynchronous calls. Defaults to False.

False
show_progress bool

Whether to show tqdm progress bars. Defaults to False.

False
store_nodes_override bool

set to True to always store Node objects in index store and document store even if vector store keeps text. Defaults to False

False
Source code in llama-index-core/llama_index/core/indices/vector_store/base.py
 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
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
class VectorStoreIndex(BaseIndex[IndexDict]):
    """
    Vector Store Index.

    Args:
        use_async (bool): Whether to use asynchronous calls. Defaults to False.
        show_progress (bool): Whether to show tqdm progress bars. Defaults to False.
        store_nodes_override (bool): set to True to always store Node objects in index
            store and document store even if vector store keeps text. Defaults to False
    """

    index_struct_cls = IndexDict

    def __init__(
        self,
        nodes: Optional[Sequence[BaseNode]] = None,
        # vector store index params
        use_async: bool = False,
        store_nodes_override: bool = False,
        embed_model: Optional[EmbedType] = None,
        insert_batch_size: int = 2048,
        # parent class params
        objects: Optional[Sequence[IndexNode]] = None,
        index_struct: Optional[IndexDict] = None,
        storage_context: Optional[StorageContext] = None,
        callback_manager: Optional[CallbackManager] = None,
        transformations: Optional[List[TransformComponent]] = None,
        show_progress: bool = False,
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        self._use_async = use_async
        self._store_nodes_override = store_nodes_override
        self._embed_model = (
            resolve_embed_model(embed_model, callback_manager=callback_manager)
            if embed_model
            else Settings.embed_model
        )

        self._insert_batch_size = insert_batch_size
        super().__init__(
            nodes=nodes,
            index_struct=index_struct,
            storage_context=storage_context,
            show_progress=show_progress,
            objects=objects,
            callback_manager=callback_manager,
            transformations=transformations,
            **kwargs,
        )

    @classmethod
    def from_vector_store(
        cls,
        vector_store: BasePydanticVectorStore,
        embed_model: Optional[EmbedType] = None,
        **kwargs: Any,
    ) -> "VectorStoreIndex":
        if not vector_store.stores_text:
            raise ValueError(
                "Cannot initialize from a vector store that does not store text."
            )

        kwargs.pop("storage_context", None)
        storage_context = StorageContext.from_defaults(vector_store=vector_store)

        return cls(
            nodes=[],
            embed_model=embed_model,
            storage_context=storage_context,
            **kwargs,
        )

    @property
    def vector_store(self) -> BasePydanticVectorStore:
        return self._vector_store

    def as_retriever(self, **kwargs: Any) -> BaseRetriever:
        # NOTE: lazy import
        from llama_index.core.indices.vector_store.retrievers import (
            VectorIndexRetriever,
        )

        return VectorIndexRetriever(
            self,
            node_ids=list(self.index_struct.nodes_dict.values()),
            callback_manager=self._callback_manager,
            object_map=self._object_map,
            **kwargs,
        )

    def _get_node_with_embedding(
        self,
        nodes: Sequence[BaseNode],
        show_progress: bool = False,
    ) -> List[BaseNode]:
        """
        Get tuples of id, node, and embedding.

        Allows us to store these nodes in a vector store.
        Embeddings are called in batches.

        """
        id_to_embed_map = embed_nodes(
            nodes, self._embed_model, show_progress=show_progress
        )

        results = []
        for node in nodes:
            embedding = id_to_embed_map[node.node_id]
            result = node.model_copy()
            result.embedding = embedding
            results.append(result)
        return results

    async def _aget_node_with_embedding(
        self,
        nodes: Sequence[BaseNode],
        show_progress: bool = False,
    ) -> List[BaseNode]:
        """
        Asynchronously get tuples of id, node, and embedding.

        Allows us to store these nodes in a vector store.
        Embeddings are called in batches.

        """
        id_to_embed_map = await async_embed_nodes(
            nodes=nodes,
            embed_model=self._embed_model,
            show_progress=show_progress,
        )

        results = []
        for node in nodes:
            embedding = id_to_embed_map[node.node_id]
            result = node.model_copy()
            result.embedding = embedding
            results.append(result)
        return results

    async def _async_add_nodes_to_index(
        self,
        index_struct: IndexDict,
        nodes: Sequence[BaseNode],
        show_progress: bool = False,
        **insert_kwargs: Any,
    ) -> None:
        """Asynchronously add nodes to index."""
        if not nodes:
            return

        for nodes_batch in iter_batch(nodes, self._insert_batch_size):
            nodes_batch = await self._aget_node_with_embedding(
                nodes_batch, show_progress
            )
            new_ids = await self._vector_store.async_add(nodes_batch, **insert_kwargs)

            # if the vector store doesn't store text, we need to add the nodes to the
            # index struct and document store
            if not self._vector_store.stores_text or self._store_nodes_override:
                for node, new_id in zip(nodes_batch, new_ids):
                    # NOTE: remove embedding from node to avoid duplication
                    node_without_embedding = node.model_copy()
                    node_without_embedding.embedding = None

                    index_struct.add_node(node_without_embedding, text_id=new_id)
                    self._docstore.add_documents(
                        [node_without_embedding], allow_update=True
                    )
            else:
                # NOTE: if the vector store keeps text,
                # we only need to add image and index nodes
                for node, new_id in zip(nodes_batch, new_ids):
                    if isinstance(node, (ImageNode, IndexNode)):
                        # NOTE: remove embedding from node to avoid duplication
                        node_without_embedding = node.model_copy()
                        node_without_embedding.embedding = None

                        index_struct.add_node(node_without_embedding, text_id=new_id)
                        self._docstore.add_documents(
                            [node_without_embedding], allow_update=True
                        )

    def _add_nodes_to_index(
        self,
        index_struct: IndexDict,
        nodes: Sequence[BaseNode],
        show_progress: bool = False,
        **insert_kwargs: Any,
    ) -> None:
        """Add document to index."""
        if not nodes:
            return

        for nodes_batch in iter_batch(nodes, self._insert_batch_size):
            nodes_batch = self._get_node_with_embedding(nodes_batch, show_progress)
            new_ids = self._vector_store.add(nodes_batch, **insert_kwargs)

            if not self._vector_store.stores_text or self._store_nodes_override:
                # NOTE: if the vector store doesn't store text,
                # we need to add the nodes to the index struct and document store
                for node, new_id in zip(nodes_batch, new_ids):
                    # NOTE: remove embedding from node to avoid duplication
                    node_without_embedding = node.model_copy()
                    node_without_embedding.embedding = None

                    index_struct.add_node(node_without_embedding, text_id=new_id)
                    self._docstore.add_documents(
                        [node_without_embedding], allow_update=True
                    )
            else:
                # NOTE: if the vector store keeps text,
                # we only need to add image and index nodes
                for node, new_id in zip(nodes_batch, new_ids):
                    if isinstance(node, (ImageNode, IndexNode)):
                        # NOTE: remove embedding from node to avoid duplication
                        node_without_embedding = node.model_copy()
                        node_without_embedding.embedding = None

                        index_struct.add_node(node_without_embedding, text_id=new_id)
                        self._docstore.add_documents(
                            [node_without_embedding], allow_update=True
                        )

    def _build_index_from_nodes(
        self,
        nodes: Sequence[BaseNode],
        **insert_kwargs: Any,
    ) -> IndexDict:
        """Build index from nodes."""
        index_struct = self.index_struct_cls()
        if self._use_async:
            tasks = [
                self._async_add_nodes_to_index(
                    index_struct,
                    nodes,
                    show_progress=self._show_progress,
                    **insert_kwargs,
                )
            ]
            run_async_tasks(tasks)
        else:
            self._add_nodes_to_index(
                index_struct,
                nodes,
                show_progress=self._show_progress,
                **insert_kwargs,
            )
        return index_struct

    def build_index_from_nodes(
        self,
        nodes: Sequence[BaseNode],
        **insert_kwargs: Any,
    ) -> IndexDict:
        """
        Build the index from nodes.

        NOTE: Overrides BaseIndex.build_index_from_nodes.
            VectorStoreIndex only stores nodes in document store
            if vector store does not store text
        """
        # raise an error if even one node has no content
        if any(
            node.get_content(metadata_mode=MetadataMode.EMBED) == "" for node in nodes
        ):
            raise ValueError(
                "Cannot build index from nodes with no content. "
                "Please ensure all nodes have content."
            )

        return self._build_index_from_nodes(nodes, **insert_kwargs)

    def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
        """Insert a document."""
        self._add_nodes_to_index(self._index_struct, nodes, **insert_kwargs)

    def insert_nodes(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
        """
        Insert nodes.

        NOTE: overrides BaseIndex.insert_nodes.
            VectorStoreIndex only stores nodes in document store
            if vector store does not store text
        """
        for node in nodes:
            if isinstance(node, IndexNode):
                try:
                    node.dict()
                except ValueError:
                    self._object_map[node.index_id] = node.obj
                    node.obj = None

        with self._callback_manager.as_trace("insert_nodes"):
            self._insert(nodes, **insert_kwargs)
            self._storage_context.index_store.add_index_struct(self._index_struct)

    def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
        pass

    def delete_nodes(
        self,
        node_ids: List[str],
        delete_from_docstore: bool = False,
        **delete_kwargs: Any,
    ) -> None:
        """
        Delete a list of nodes from the index.

        Args:
            node_ids (List[str]): A list of node_ids from the nodes to delete

        """
        # delete nodes from vector store
        self._vector_store.delete_nodes(node_ids, **delete_kwargs)

        # delete from docstore only if needed
        if (
            not self._vector_store.stores_text or self._store_nodes_override
        ) and delete_from_docstore:
            for node_id in node_ids:
                self._docstore.delete_document(node_id, raise_error=False)

    def _delete_from_index_struct(self, ref_doc_id: str) -> None:
        # delete from index_struct only if needed
        if not self._vector_store.stores_text or self._store_nodes_override:
            ref_doc_info = self._docstore.get_ref_doc_info(ref_doc_id)
            if ref_doc_info is not None:
                for node_id in ref_doc_info.node_ids:
                    self._index_struct.delete(node_id)
                    self._vector_store.delete(node_id)

    def _delete_from_docstore(self, ref_doc_id: str) -> None:
        # delete from docstore only if needed
        if not self._vector_store.stores_text or self._store_nodes_override:
            self._docstore.delete_ref_doc(ref_doc_id, raise_error=False)

    def delete_ref_doc(
        self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
    ) -> None:
        """Delete a document and it's nodes by using ref_doc_id."""
        self._vector_store.delete(ref_doc_id, **delete_kwargs)
        self._delete_from_index_struct(ref_doc_id)
        if delete_from_docstore:
            self._delete_from_docstore(ref_doc_id)
        self._storage_context.index_store.add_index_struct(self._index_struct)

    async def _adelete_from_index_struct(self, ref_doc_id: str) -> None:
        """Delete from index_struct only if needed."""
        if not self._vector_store.stores_text or self._store_nodes_override:
            ref_doc_info = await self._docstore.aget_ref_doc_info(ref_doc_id)
            if ref_doc_info is not None:
                for node_id in ref_doc_info.node_ids:
                    self._index_struct.delete(node_id)
                    self._vector_store.delete(node_id)

    async def _adelete_from_docstore(self, ref_doc_id: str) -> None:
        """Delete from docstore only if needed."""
        if not self._vector_store.stores_text or self._store_nodes_override:
            await self._docstore.adelete_ref_doc(ref_doc_id, raise_error=False)

    async def adelete_ref_doc(
        self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
    ) -> None:
        """Delete a document and it's nodes by using ref_doc_id."""
        tasks = [
            self._vector_store.adelete(ref_doc_id, **delete_kwargs),
            self._adelete_from_index_struct(ref_doc_id),
        ]
        if delete_from_docstore:
            tasks.append(self._adelete_from_docstore(ref_doc_id))

        await asyncio.gather(*tasks)

        self._storage_context.index_store.add_index_struct(self._index_struct)

    @property
    def ref_doc_info(self) -> Dict[str, RefDocInfo]:
        """Retrieve a dict mapping of ingested documents and their nodes+metadata."""
        if not self._vector_store.stores_text or self._store_nodes_override:
            node_doc_ids = list(self.index_struct.nodes_dict.values())
            nodes = self.docstore.get_nodes(node_doc_ids)

            all_ref_doc_info = {}
            for node in nodes:
                ref_node = node.source_node
                if not ref_node:
                    continue

                ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id)
                if not ref_doc_info:
                    continue

                all_ref_doc_info[ref_node.node_id] = ref_doc_info
            return all_ref_doc_info
        else:
            raise NotImplementedError(
                "Vector store integrations that store text in the vector store are "
                "not supported by ref_doc_info yet."
            )

ref_doc_info property #

ref_doc_info: Dict[str, RefDocInfo]

Retrieve a dict mapping of ingested documents and their nodes+metadata.

build_index_from_nodes #

build_index_from_nodes(nodes: Sequence[BaseNode], **insert_kwargs: Any) -> IndexDict

Build the index from nodes.

Overrides BaseIndex.build_index_from_nodes.

VectorStoreIndex only stores nodes in document store if vector store does not store text

Source code in llama-index-core/llama_index/core/indices/vector_store/base.py
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
def build_index_from_nodes(
    self,
    nodes: Sequence[BaseNode],
    **insert_kwargs: Any,
) -> IndexDict:
    """
    Build the index from nodes.

    NOTE: Overrides BaseIndex.build_index_from_nodes.
        VectorStoreIndex only stores nodes in document store
        if vector store does not store text
    """
    # raise an error if even one node has no content
    if any(
        node.get_content(metadata_mode=MetadataMode.EMBED) == "" for node in nodes
    ):
        raise ValueError(
            "Cannot build index from nodes with no content. "
            "Please ensure all nodes have content."
        )

    return self._build_index_from_nodes(nodes, **insert_kwargs)

insert_nodes #

insert_nodes(nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None

Insert nodes.

overrides BaseIndex.insert_nodes.

VectorStoreIndex only stores nodes in document store if vector store does not store text

Source code in llama-index-core/llama_index/core/indices/vector_store/base.py
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
def insert_nodes(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
    """
    Insert nodes.

    NOTE: overrides BaseIndex.insert_nodes.
        VectorStoreIndex only stores nodes in document store
        if vector store does not store text
    """
    for node in nodes:
        if isinstance(node, IndexNode):
            try:
                node.dict()
            except ValueError:
                self._object_map[node.index_id] = node.obj
                node.obj = None

    with self._callback_manager.as_trace("insert_nodes"):
        self._insert(nodes, **insert_kwargs)
        self._storage_context.index_store.add_index_struct(self._index_struct)

delete_nodes #

delete_nodes(node_ids: List[str], delete_from_docstore: bool = False, **delete_kwargs: Any) -> None

Delete a list of nodes from the index.

Parameters:

Name Type Description Default
node_ids List[str]

A list of node_ids from the nodes to delete

required
Source code in llama-index-core/llama_index/core/indices/vector_store/base.py
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
def delete_nodes(
    self,
    node_ids: List[str],
    delete_from_docstore: bool = False,
    **delete_kwargs: Any,
) -> None:
    """
    Delete a list of nodes from the index.

    Args:
        node_ids (List[str]): A list of node_ids from the nodes to delete

    """
    # delete nodes from vector store
    self._vector_store.delete_nodes(node_ids, **delete_kwargs)

    # delete from docstore only if needed
    if (
        not self._vector_store.stores_text or self._store_nodes_override
    ) and delete_from_docstore:
        for node_id in node_ids:
            self._docstore.delete_document(node_id, raise_error=False)

delete_ref_doc #

delete_ref_doc(ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any) -> None

Delete a document and it's nodes by using ref_doc_id.

Source code in llama-index-core/llama_index/core/indices/vector_store/base.py
373
374
375
376
377
378
379
380
381
def delete_ref_doc(
    self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
) -> None:
    """Delete a document and it's nodes by using ref_doc_id."""
    self._vector_store.delete(ref_doc_id, **delete_kwargs)
    self._delete_from_index_struct(ref_doc_id)
    if delete_from_docstore:
        self._delete_from_docstore(ref_doc_id)
    self._storage_context.index_store.add_index_struct(self._index_struct)

adelete_ref_doc async #

adelete_ref_doc(ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any) -> None

Delete a document and it's nodes by using ref_doc_id.

Source code in llama-index-core/llama_index/core/indices/vector_store/base.py
397
398
399
400
401
402
403
404
405
406
407
408
409
410
async def adelete_ref_doc(
    self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
) -> None:
    """Delete a document and it's nodes by using ref_doc_id."""
    tasks = [
        self._vector_store.adelete(ref_doc_id, **delete_kwargs),
        self._adelete_from_index_struct(ref_doc_id),
    ]
    if delete_from_docstore:
        tasks.append(self._adelete_from_docstore(ref_doc_id))

    await asyncio.gather(*tasks)

    self._storage_context.index_store.add_index_struct(self._index_struct)