.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:

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Parameters

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query
str
Question
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num_documents
int
Number of relevant documents to fetch. Defaults to 3

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Returns

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

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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,
#         }
#     }
# ]