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 textsExample 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 textsThe 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 textsThe 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
- Throws if length of ids doesn’t match length of texts
Async
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.
