add_texts

Adds text documents to the vector store with optional metadata.

Embedded
Python SDK
JS/TS

add_texts(
    texts: Iterable[str],
    metadatas: Optional[List[dict]] = None,
    *,
    ids: Optional[List[str]] = None,
    **kwargs: Any
) -> List[str]

Parameters

Parameter	Type	Description
`texts`	`Iterable[str]`	Iterable of text strings to add
`metadatas`	`Optional[List[dict]]`	(Optional) List of metadata dictionaries for each text
`ids`	`Optional[List[str]]`	(Optional) List of IDs for the texts (auto-generated if not provided)
`**kwargs`	`Any`	Additional keyword arguments (currently unused)

Returns

List[str]: List of IDs for the added texts

Example Usage

# Add texts with auto-generated IDs
texts = ["Document about AI", "Document about ML", "Document about NLP"]
ids = store.add_texts(texts)
print(f"Added {len(ids)} documents")

# Add texts with metadata and custom IDs
texts = ["Python tutorial", "JavaScript guide"]
metadatas = [
    {"language": "python", "level": "beginner"},
    {"language": "javascript", "level": "intermediate"}
]
custom_ids = ["doc1", "doc2"]
ids = store.add_texts(texts, metadatas=metadatas, ids=custom_ids)

add_texts(
    texts: Iterable[str],
    metadatas: Optional[List[dict]] = None,
    *,
    ids: Optional[List[str]] = None,
    **kwargs: Any
) -> List[str]

Parameters

Parameter	Type	Description
`texts`	`Iterable[str]`	Iterable of text strings to add
`metadatas`	`Optional[List[dict]]`	(Optional) List of metadata dictionaries for each text
`ids`	`Optional[List[str]]`	(Optional) List of IDs for the texts (auto-generated if not provided)
`**kwargs`	`Any`	Additional keyword arguments

Returns

List[str]: List of IDs for the added texts

The Python SDK supports passing pre-computed embeddings via kwargs for cases where you want to skip the embedding step.

Example Usage

texts = ["Document about AI", "Document about ML"]
metadatas = [{"topic": "ai"}, {"topic": "ml"}]
ids = store.add_texts(texts, metadatas=metadatas)

addTexts(
    texts: string[],
    metadatas?: Record<string, any>[],
    options?: { ids?: string[] }
): Promise<string[]>

Parameters

Parameter	Type	Description
`texts`	`string[]`	Array of text strings to add
`metadatas`	`Record<string, any>[]`	(Optional) Array of metadata objects for each text
`options`	`object`	(Optional) Options object with optional `ids` array

Returns

Promise<string[]>: Array of IDs for the added texts

The JS/TS SDK also provides addVectors() for adding pre-computed embedding vectors directly, bypassing the embedding step.

Example Usage

const texts = ["Document about AI", "Document about ML"];
const metadatas = [{ topic: "ai" }, { topic: "ml" }];
const ids = await store.addTexts(texts, metadatas);

Exceptions

ValueError

Throws if length of ids doesn’t match length of texts

Async

Python async variants

The Embedded and Python SDK provide async versions of this method prefixed with a:

# aadd_texts — async variant
ids = await store.aadd_texts(texts, metadatas=metadatas)

JS/TS methods are natively async — all signatures above already return Promise<...>. No separate async variant is needed.

Introduction

CyborgVectorStore

Types

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Returns

Example Usage

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Exceptions

Async

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Async