🔍 search - Embedchain

.search() enables you to uncover the most pertinent context by performing a semantic search across your data sources based on a given query. Refer to the function signature below:

Parameters

str

Question

int

Number of relevant documents to fetch. Defaults to 3

Returns

dict

Return list of dictionaries that contain the relevant chunk and their source information.

Usage

Refer to the following example on how to use the search api:

Code example

from embedchain import App

# Initialize app
app = App()

# Add data source
app.add("https://www.forbes.com/profile/elon-musk")

# Get relevant context using semantic search
context = app.search("What is the net worth of Elon?", num_documents=2)
print(context)
# Context:
# [
#     {
#         'context': 'Elon Musk PROFILEElon MuskCEO, Tesla$221.9BReal Time Net Worth ...',
#         'metadata': {
#             'source': 'https://www.forbes.com/profile/elon-musk',
#             'document_id': 'some_document_id',
#             'score': 0.404,
#         }
#     },
#     {
#         'context': 'company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH ...',
#         'metadata': {
#             'source': 'https://www.forbes.com/profile/elon-musk',
#             'document_id': 'some_document_id',
#             'score': 0.435,
#         }
#     }
# ]

​Parameters

​Returns

​Usage

Parameters

Returns

Usage