DriaAPIWrapper#
- class langchain_community.utilities.dria_index.DriaAPIWrapper(api_key: str, contract_id: str | None = None, top_n: int = 10)[source]#
Wrapper around Dria API.
This wrapper facilitates interactions with Dria’s vector search and retrieval services, including creating knowledge bases, inserting data, and fetching search results.
- Parameters:
api_key (str) –
contract_id (str | None) –
top_n (int) –
- api_key#
Your API key for accessing Dria.
- contract_id#
The contract ID of the knowledge base to interact with.
- top_n#
Number of top results to fetch for a search.
Methods
__init__(api_key[, contract_id, top_n])create_knowledge_base(name, description, ...)Create a new knowledge base.
insert_data(data)Insert data into the knowledge base.
query_with_vector(vector)Perform a vector-based query.
run(query)Method to handle both text-based searches and vector-based queries.
search(query)Perform a text-based search.
- __init__(api_key: str, contract_id: str | None = None, top_n: int = 10)[source]#
- Parameters:
api_key (str) –
contract_id (str | None) –
top_n (int) –
- create_knowledge_base(name: str, description: str, category: str, embedding: str) str[source]#
Create a new knowledge base.
- Parameters:
name (str) –
description (str) –
category (str) –
embedding (str) –
- Return type:
str
- insert_data(data: List[Dict[str, Any]]) str[source]#
Insert data into the knowledge base.
- Parameters:
data (List[Dict[str, Any]]) –
- Return type:
str
- query_with_vector(vector: List[float]) List[Dict[str, Any]][source]#
Perform a vector-based query.
- Parameters:
vector (List[float]) –
- Return type:
List[Dict[str, Any]]
- run(query: str | List[float]) List[Dict[str, Any]] | None[source]#
Method to handle both text-based searches and vector-based queries.
- Parameters:
query (str | List[float]) – A string for text-based search or a list of floats for
query. (vector-based) –
- Returns:
The search or query results from Dria.
- Return type:
List[Dict[str, Any]] | None