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embedchain/docs/api-reference/app/search.mdx

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.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

<ParamField path="query" type="str"> Question </ParamField> <ParamField path="num_documents" type="int" optional> Number of relevant documents to fetch. Defaults to `3` </ParamField> <ParamField path="where" type="dict" optional> Key value pair for metadata filtering. </ParamField> <ParamField path="raw_filter" type="dict" optional> Pass raw filter query based on your vector database. Currently, `raw_filter` param is only supported for Pinecone vector database. </ParamField>

Returns

<ResponseField name="answer" type="dict"> Return list of dictionaries that contain the relevant chunk and their source information. </ResponseField>

Usage

Basic

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

python
from embedchain import App

app = App()
app.add("https://www.forbes.com/profile/elon-musk")

context = app.search("What is the net worth of Elon?", num_documents=2)
print(context)

Advanced

Metadata filtering using where params

Here is an advanced example of search() API with metadata filtering on pinecone database:

python
import os

from embedchain import App

os.environ["PINECONE_API_KEY"] = "xxx"

config = {
    "vectordb": {
        "provider": "pinecone",
        "config": {
            "metric": "dotproduct",
            "vector_dimension": 1536,
            "index_name": "ec-test",
            "serverless_config": {"cloud": "aws", "region": "us-west-2"},
        },
    }
}

app = App.from_config(config=config)

app.add("https://www.forbes.com/profile/bill-gates", metadata={"type": "forbes", "person": "gates"})
app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"type": "wiki", "person": "gates"})

results = app.search("What is the net worth of Bill Gates?", where={"person": "gates"})
print("Num of search results: ", len(results))

Metadata filtering using raw_filter params

Following is an example of metadata filtering by passing the raw filter query that pinecone vector database follows:

python
import os

from embedchain import App

os.environ["PINECONE_API_KEY"] = "xxx"

config = {
    "vectordb": {
        "provider": "pinecone",
        "config": {
            "metric": "dotproduct",
            "vector_dimension": 1536,
            "index_name": "ec-test",
            "serverless_config": {"cloud": "aws", "region": "us-west-2"},
        },
    }
}

app = App.from_config(config=config)

app.add("https://www.forbes.com/profile/bill-gates", metadata={"year": 2022, "person": "gates"})
app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"year": 2024, "person": "gates"})

print("Filter with person: gates and year > 2023")
raw_filter = {"$and": [{"person": "gates"}, {"year": {"$gt": 2023}}]}
results = app.search("What is the net worth of Bill Gates?", raw_filter=raw_filter)
print("Num of search results: ", len(results))