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Overview

Install pinecone related dependencies using the following command:

pip install --upgrade 'pinecone-client pinecone-text'

In order to use Pinecone as vector database, set the environment variable PINECONE_API_KEY which you can find on Pinecone dashboard.


You can find more information about Pinecone configuration here. You can also optionally provide index_name as a config param in yaml file to specify the index name. If not provided, the index name will be {collection_name}-{vector_dimension}.

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Usage

Here is an example of how you can do hybrid search using Pinecone as a vector database through Embedchain.

import os

from embedchain import App

config = {
    'app': {
        "config": {
            "id": "ec-docs-hybrid-search"
        }
    },
    'vectordb': {
        'provider': 'pinecone',
        'config': {
            'metric': 'dotproduct',
            'vector_dimension': 1536,
            'index_name': 'my-index',
            'serverless_config': {
                'cloud': 'aws',
                'region': 'us-west-2'
            },
            'hybrid_search': True, # Remember to set this for hybrid search
        }
    }
}

# Initialize app
app = App.from_config(config=config)

# Add documents
app.add("/path/to/file.pdf", data_type="pdf_file", namespace="my-namespace")

# Query
app.query("<YOUR QUESTION HERE>", namespace="my-namespace")

# Chat
app.chat("<YOUR QUESTION HERE>", namespace="my-namespace")

Under the hood, Embedchain fetches the relevant chunks from the documents you added by doing hybrid search on the pinecone index. If you have questions on how pinecone hybrid search works, please refer to their offical documentation here.

If you can't find specific feature or run into issues, please feel free to reach out through one of the following channels.

Slack

Let us know on our slack community

Discord

Let us know on discord community

GitHub

Open an issue on our GitHub

Schedule a call

Schedule a call with Embedchain founder

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