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Creates a CyborgVectorStore instance from a list of texts.
Embedded
Python SDK
JS/TS
@classmethod
from_texts(
cls,
texts: List[str],
embedding: Union[str, Embeddings, SentenceTransformer],
metadatas: Optional[List[dict]] = None,
*,
ids: Optional[List[str]] = None,
**kwargs: Any
) -> CyborgVectorStore
Parameters
| Parameter | Type | Description |
|---|
texts | List[str] | List of text strings to add to the store |
embedding | Union[str, Embeddings, SentenceTransformer] | Embedding model or model name |
metadatas | Optional[List[dict]] | (Optional) List of metadata dictionaries for each text |
**kwargs | Any | Additional arguments passed to constructor and add_texts |
Keyword Arguments
| Parameter | Type | Description |
|---|
ids | List[str] | (Optional) IDs for the texts |
index_name | str | Name of the index (default: “langchain_index”) |
index_key | bytes | 32-byte encryption key (required) |
api_key | str | API key for CyborgDB (required) |
index_location | DBConfig | Index storage location (required) |
config_location | DBConfig | Config storage location (required) |
index_type | str | Index type (default: “ivfflat”) |
metric | str | Distance metric (default: “cosine”) |
Returns
CyborgVectorStore: Initialized vector store with texts addedExample Usage
from cyborgdb_core import DBConfig
# Create store from texts
texts = [
"Introduction to Python",
"Advanced Python techniques",
"Python for data science"
]
metadatas = [
{"chapter": 1},
{"chapter": 2},
{"chapter": 3}
]
store = CyborgVectorStore.from_texts(
texts=texts,
embedding="sentence-transformers/all-MiniLM-L6-v2",
metadatas=metadatas,
index_name="python_docs",
index_key=CyborgVectorStore.generate_key(save=True),
api_key="your-api-key",
index_location=DBConfig("s3", bucket="my-bucket"),
config_location=DBConfig("s3", bucket="my-bucket")
)
@classmethod
from_texts(
cls,
texts: List[str],
embedding: Union[str, Embeddings, SentenceTransformer],
metadatas: Optional[List[dict]] = None,
*,
ids: Optional[List[str]] = None,
**kwargs: Any
) -> CyborgVectorStore
Parameters
| Parameter | Type | Description |
|---|
texts | List[str] | List of text strings to add to the store |
embedding | Union[str, Embeddings, SentenceTransformer] | Embedding model or model name |
metadatas | Optional[List[dict]] | (Optional) List of metadata dictionaries for each text |
**kwargs | Any | Additional arguments passed to constructor and add_texts |
Keyword Arguments
| Parameter | Type | Description |
|---|
ids | List[str] | (Optional) IDs for the texts |
index_name | str | Name of the index (default: “langchain_index”) |
index_key | bytes | 32-byte encryption key (required) |
api_key | str | API key for CyborgDB (required) |
base_url | str | Base URL of CyborgDB microservice (required) |
verify_ssl | bool | (Optional) SSL verification |
index_type | str | Index type (default: “ivfflat”) |
metric | str | Distance metric (default: “cosine”) |
Returns
CyborgVectorStore: Initialized vector store with texts addedThe embeddings parameter can optionally accept pre-computed embedding vectors instead of a model, for cases where you generate embeddings externally.
Example Usage
store = CyborgVectorStore.from_texts(
texts=["Introduction to Python", "Advanced Python techniques"],
embedding="sentence-transformers/all-MiniLM-L6-v2",
metadatas=[{"chapter": 1}, {"chapter": 2}],
index_name="python_docs",
index_key=CyborgVectorStore.generate_key(save=True),
api_key="your-api-key",
base_url="http://localhost:8000"
)
static fromTexts(
texts: string[],
metadatas: Record<string, any>[],
embeddings: EmbeddingsInterface,
config: CyborgVectorStoreConfig
): Promise<CyborgVectorStore>
Parameters
| Parameter | Type | Description |
|---|
texts | string[] | Array of text strings to add to the store |
metadatas | Record<string, any>[] | Array of metadata objects for each text |
embeddings | EmbeddingsInterface | LangChain embeddings instance |
config | CyborgVectorStoreConfig | Configuration object with baseUrl, apiKey, indexName, indexKey, etc. |
Returns
Promise<CyborgVectorStore>: Initialized vector store with texts addedExample Usage
import { CyborgVectorStore } from 'cyborgdb';
import { OpenAIEmbeddings } from '@langchain/openai';
const store = await CyborgVectorStore.fromTexts(
["Introduction to Python", "Advanced Python techniques"],
[{ chapter: 1 }, { chapter: 2 }],
new OpenAIEmbeddings(),
{
baseUrl: "http://localhost:8000",
apiKey: "your-api-key",
indexName: "python_docs",
indexKey: CyborgVectorStore.generateKey()
}
);
Exceptions
- Throws if index_key is not provided
