Jaguar
JaguarVectorStore #
Bases: BasePydanticVectorStore
Jaguar vector store.
See http://www.jaguardb.com See http://github.com/fserv/jaguar-sdk
Examples:
pip install llama-index-vector-stores-jaguar
from llama_index.vector_stores.jaguar import JaguarVectorStore
vectorstore = JaguarVectorStore(
pod = 'vdb',
store = 'mystore',
vector_index = 'v',
vector_type = 'cosine_fraction_float',
vector_dimension = 1536,
url='http://192.168.8.88:8080/fwww/',
)
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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add #
add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]
Add nodes to index.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
nodes |
List[BaseNode]
|
List[BaseNode]: list of nodes with embeddings |
required |
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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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 |
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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query #
query(query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult
Query index for top k most similar nodes.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query |
VectorStoreQuery
|
VectorStoreQuery object |
required |
kwargs |
Any
|
may contain 'where', 'metadata_fields', 'args', 'fetch_k' |
{}
|
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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load_documents #
load_documents(embedding: List[float], k: int, **kwargs: Any) -> List[Document]
Query index to load top k most similar documents.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
embedding |
List[float]
|
a list of floats |
required |
k |
int
|
topK number |
required |
kwargs |
Any
|
may contain 'where', 'metadata_fields', 'args', 'fetch_k' |
{}
|
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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create #
create(metadata_fields: str, text_size: int) -> None
create the vector store on the backend database.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
metadata_fields |
str
|
exrta metadata columns and types |
required |
Returns: True if successful; False if not successful
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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add_text #
add_text(text: str, embedding: List[float], metadata: Optional[dict] = None, **kwargs: Any) -> str
Add texts through the embeddings and add to the vectorstore.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
texts |
text string to add to the jaguar vector store. |
required | |
embedding |
List[float]
|
embedding vector of the text, list of floats |
required |
metadata |
Optional[dict]
|
{'file_path': '../data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2023-12-24', 'last_modified_date': '2023-12-24', 'last_accessed_date': '2023-12-28'} |
None
|
kwargs |
Any
|
vector_index=name_of_vector_index file_column=name_of_file_column metadata={...} |
{}
|
Returns:
Type | Description |
---|---|
str
|
id from adding the text into the vectorstore |
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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similarity_search_with_score #
similarity_search_with_score(embedding: Optional[List[float]], k: int = 3, form: str = 'node', **kwargs: Any) -> Union[Tuple[List[TextNode], List[str], List[float]], List[Document]]
Return nodes most similar to query embedding, along with ids and scores.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
embedding |
Optional[List[float]]
|
embedding of text to look up. |
required |
k |
int
|
Number of nodes to return. Defaults to 3. |
3
|
form |
str
|
if "node", return Tuple[List[TextNode], List[str], List[float]] if "doc", return List[Document] |
'node'
|
kwargs |
Any
|
may have where, metadata_fields, args, fetch_k |
{}
|
Returns: Tuple(list of nodes, list of ids, list of similaity scores)
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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is_anomalous #
is_anomalous(node: BaseNode, **kwargs: Any) -> bool
Detect if given text is anomalous from the dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query |
Text to detect if it is anomaly |
required |
Returns: True or False
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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run #
run(query: str, withFile: bool = False) -> dict
Run any query statement in jaguardb.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query |
str
|
query statement to jaguardb |
required |
Returns: None for invalid token, or json result string
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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count #
count() -> int
Count records of a store in jaguardb.
Args: no args Returns: (int) number of records in pod store
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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clear #
clear() -> None
Delete all records in jaguardb.
Args: No args Returns: None
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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drop #
drop() -> None
Drop or remove a store in jaguardb.
Args: no args Returns: None
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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login #
login(jaguar_api_key: Optional[str] = '') -> bool
Login to jaguar server with a jaguar_api_key or let self._jag find a key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
optional |
jaguar_api_key (str
|
API key of user to jaguardb server |
required |
Returns: True if successful; False if not successful
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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logout #
logout() -> None
Logout to cleanup resources.
Args: no args Returns: None
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py
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