[Feature] Add support for hybrid search for pinecone vector database (#1259)
This commit is contained in:
Deshraj Yadav
@@ -25,4 +25,4 @@ app = App()
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url = "https://drive.google.com/drive/u/0/folders/xxx-xxx"
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app.add(url, data_type="google_drive")
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```
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```
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@@ -5,34 +5,35 @@ title: Overview
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Embedchain comes with built-in support for various data sources. We handle the complexity of loading unstructured data from these data sources, allowing you to easily customize your app through a user-friendly interface.
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<CardGroup cols={4}>
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<Card title="📰 PDF file" href="/components/data-sources/pdf-file"></Card>
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<Card title="📊 CSV file" href="/components/data-sources/csv"></Card>
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<Card title="📃 JSON file" href="/components/data-sources/json"></Card>
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<Card title="📝 Text" href="/components/data-sources/text"></Card>
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<Card title="📁 Directory/ Folder" href="/components/data-sources/directory"></Card>
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<Card title="🌐 HTML Web page" href="/components/data-sources/web-page"></Card>
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<Card title="📽️ Youtube Channel" href="/components/data-sources/youtube-channel"></Card>
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<Card title="📺 Youtube Video" href="/components/data-sources/youtube-video"></Card>
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<Card title="📚 Docs website" href="/components/data-sources/docs-site"></Card>
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<Card title="📝 MDX file" href="/components/data-sources/mdx"></Card>
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<Card title="📄 DOCX file" href="/components/data-sources/docx"></Card>
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<Card title="📓 Notion" href="/components/data-sources/notion"></Card>
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<Card title="🗺️ Sitemap" href="/components/data-sources/sitemap"></Card>
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<Card title="🧾 XML file" href="/components/data-sources/xml"></Card>
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<Card title="❓💬 Q&A pair" href="/components/data-sources/qna"></Card>
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<Card title="🙌 OpenAPI" href="/components/data-sources/openapi"></Card>
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<Card title="📬 Gmail" href="/components/data-sources/gmail"></Card>
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<Card title="📝 Github" href="/components/data-sources/github"></Card>
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<Card title="🐘 Postgres" href="/components/data-sources/postgres"></Card>
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<Card title="🐬 MySQL" href="/components/data-sources/mysql"></Card>
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<Card title="🤖 Slack" href="/components/data-sources/slack"></Card>
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<Card title="💬 Discord" href="/components/data-sources/discord"></Card>
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<Card title="🗨️ Discourse" href="/components/data-sources/discourse"></Card>
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<Card title="📝 Substack" href="/components/data-sources/substack"></Card>
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<Card title="🐝 Beehiiv" href="/components/data-sources/beehiiv"></Card>
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<Card title="💾 Dropbox" href="/components/data-sources/dropbox"></Card>
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<Card title="🖼️ Image" href="/components/data-sources/image"></Card>
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<Card title="⚙️ Custom" href="/components/data-sources/custom"></Card>
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<Card title="PDF file" href="/components/data-sources/pdf-file"></Card>
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<Card title="CSV file" href="/components/data-sources/csv"></Card>
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<Card title="JSON file" href="/components/data-sources/json"></Card>
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<Card title="Text" href="/components/data-sources/text"></Card>
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<Card title="Directory" href="/components/data-sources/directory"></Card>
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<Card title="Web page" href="/components/data-sources/web-page"></Card>
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<Card title="Youtube Channel" href="/components/data-sources/youtube-channel"></Card>
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<Card title="Youtube Video" href="/components/data-sources/youtube-video"></Card>
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<Card title="Docs website" href="/components/data-sources/docs-site"></Card>
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<Card title="MDX file" href="/components/data-sources/mdx"></Card>
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<Card title="DOCX file" href="/components/data-sources/docx"></Card>
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<Card title="Notion" href="/components/data-sources/notion"></Card>
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<Card title="Sitemap" href="/components/data-sources/sitemap"></Card>
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<Card title="XML file" href="/components/data-sources/xml"></Card>
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<Card title="Q&A pair" href="/components/data-sources/qna"></Card>
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<Card title="OpenAPI" href="/components/data-sources/openapi"></Card>
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<Card title="Gmail" href="/components/data-sources/gmail"></Card>
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<Card title="Google Drive" href="/components/data-sources/google-drive"></Card>
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<Card title="GitHub" href="/components/data-sources/github"></Card>
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<Card title="Postgres" href="/components/data-sources/postgres"></Card>
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<Card title="MySQL" href="/components/data-sources/mysql"></Card>
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<Card title="Slack" href="/components/data-sources/slack"></Card>
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<Card title="Discord" href="/components/data-sources/discord"></Card>
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<Card title="Discourse" href="/components/data-sources/discourse"></Card>
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<Card title="Substack" href="/components/data-sources/substack"></Card>
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<Card title="Beehiiv" href="/components/data-sources/beehiiv"></Card>
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<Card title="Dropbox" href="/components/data-sources/dropbox"></Card>
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<Card title="Image" href="/components/data-sources/image"></Card>
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<Card title="Custom" href="/components/data-sources/custom"></Card>
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</CardGroup>
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<br/ >
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@@ -17,242 +17,4 @@ Utilizing a vector database alongside Embedchain is a seamless process. All you
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<Card title="Weaviate" href="#weaviate"></Card>
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</CardGroup>
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## ChromaDB
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load chroma configuration from yaml file
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app = App.from_config(config_path="config1.yaml")
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```
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```yaml config1.yaml
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vectordb:
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provider: chroma
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config:
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collection_name: 'my-collection'
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dir: db
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allow_reset: true
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```
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```yaml config2.yaml
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vectordb:
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provider: chroma
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config:
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collection_name: 'my-collection'
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host: localhost
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port: 5200
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allow_reset: true
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```
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</CodeGroup>
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## Elasticsearch
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Install related dependencies using the following command:
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```bash
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pip install --upgrade 'embedchain[elasticsearch]'
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```
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<Note>
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You can configure the Elasticsearch connection by providing either `es_url` or `cloud_id`. If you are using the Elasticsearch Service on Elastic Cloud, you can find the `cloud_id` on the [Elastic Cloud dashboard](https://cloud.elastic.co/deployments).
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</Note>
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You can authorize the connection to Elasticsearch by providing either `basic_auth`, `api_key`, or `bearer_auth`.
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load elasticsearch configuration from yaml file
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app = App.from_config(config_path="config.yaml")
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```
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```yaml config.yaml
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vectordb:
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provider: elasticsearch
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config:
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collection_name: 'es-index'
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cloud_id: 'deployment-name:xxxx'
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basic_auth:
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- elastic
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- <your_password>
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verify_certs: false
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```
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</CodeGroup>
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## OpenSearch
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Install related dependencies using the following command:
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```bash
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pip install --upgrade 'embedchain[opensearch]'
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```
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load opensearch configuration from yaml file
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app = App.from_config(config_path="config.yaml")
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```
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```yaml config.yaml
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vectordb:
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provider: opensearch
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config:
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collection_name: 'my-app'
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opensearch_url: 'https://localhost:9200'
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http_auth:
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- admin
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- admin
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vector_dimension: 1536
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use_ssl: false
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verify_certs: false
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```
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</CodeGroup>
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## Zilliz
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Install related dependencies using the following command:
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```bash
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pip install --upgrade 'embedchain[milvus]'
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```
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Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN` which you can find it on their [cloud platform](https://cloud.zilliz.com/).
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
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os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'
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# load zilliz configuration from yaml file
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app = App.from_config(config_path="config.yaml")
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```
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```yaml config.yaml
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vectordb:
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provider: zilliz
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config:
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collection_name: 'zilliz_app'
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uri: https://xxxx.api.gcp-region.zillizcloud.com
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token: xxx
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vector_dim: 1536
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metric_type: L2
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```
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</CodeGroup>
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## LanceDB
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_Coming soon_
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## Pinecone
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Install pinecone related dependencies using the following command:
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```bash
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pip install --upgrade 'embedchain[pinecone]'
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```
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In order to use Pinecone as vector database, set the environment variable `PINECONE_API_KEY` which you can find on [Pinecone dashboard](https://app.pinecone.io/).
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load pinecone configuration from yaml file
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app = App.from_config(config_path="pod_config.yaml")
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# or
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app = App.from_config(config_path="serverless_config.yaml")
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```
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```yaml pod_config.yaml
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vectordb:
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provider: pinecone
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config:
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metric: cosine
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vector_dimension: 1536
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index_name: my-pinecone-index
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pod_config:
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environment: gcp-starter
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metadata_config:
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indexed:
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- "url"
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- "hash"
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```
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```yaml serverless_config.yaml
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vectordb:
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provider: pinecone
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config:
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metric: cosine
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vector_dimension: 1536
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index_name: my-pinecone-index
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serverless_config:
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cloud: aws
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region: us-west-2
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```
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</CodeGroup>
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<br />
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<Note>
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You can find more information about Pinecone configuration [here](https://docs.pinecone.io/docs/manage-indexes#create-a-pod-based-index).
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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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</Note>
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## Qdrant
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In order to use Qdrant as a vector database, set the environment variables `QDRANT_URL` and `QDRANT_API_KEY` which you can find on [Qdrant Dashboard](https://cloud.qdrant.io/).
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load qdrant configuration from yaml file
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app = App.from_config(config_path="config.yaml")
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```
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```yaml config.yaml
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vectordb:
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provider: qdrant
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config:
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collection_name: my_qdrant_index
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```
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</CodeGroup>
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## Weaviate
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In order to use Weaviate as a vector database, set the environment variables `WEAVIATE_ENDPOINT` and `WEAVIATE_API_KEY` which you can find on [Weaviate dashboard](https://console.weaviate.cloud/dashboard).
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load weaviate configuration from yaml file
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app = App.from_config(config_path="config.yaml")
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```
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```yaml config.yaml
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vectordb:
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provider: weaviate
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config:
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collection_name: my_weaviate_index
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```
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</CodeGroup>
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<Snippet file="missing-vector-db-tip.mdx" />
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35
docs/components/vector-databases/chromadb.mdx
Normal file
35
docs/components/vector-databases/chromadb.mdx
Normal file
@@ -0,0 +1,35 @@
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---
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title: ChromaDB
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---
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load chroma configuration from yaml file
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app = App.from_config(config_path="config1.yaml")
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```
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```yaml config1.yaml
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vectordb:
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provider: chroma
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config:
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collection_name: 'my-collection'
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dir: db
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allow_reset: true
|
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```
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```yaml config2.yaml
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vectordb:
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provider: chroma
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config:
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collection_name: 'my-collection'
|
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host: localhost
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port: 5200
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allow_reset: true
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```
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</CodeGroup>
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<Snippet file="missing-vector-db-tip.mdx" />
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39
docs/components/vector-databases/elasticsearch.mdx
Normal file
39
docs/components/vector-databases/elasticsearch.mdx
Normal file
@@ -0,0 +1,39 @@
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---
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title: Elasticsearch
|
||||
---
|
||||
|
||||
Install related dependencies using the following command:
|
||||
|
||||
```bash
|
||||
pip install --upgrade 'embedchain[elasticsearch]'
|
||||
```
|
||||
|
||||
<Note>
|
||||
You can configure the Elasticsearch connection by providing either `es_url` or `cloud_id`. If you are using the Elasticsearch Service on Elastic Cloud, you can find the `cloud_id` on the [Elastic Cloud dashboard](https://cloud.elastic.co/deployments).
|
||||
</Note>
|
||||
|
||||
You can authorize the connection to Elasticsearch by providing either `basic_auth`, `api_key`, or `bearer_auth`.
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python main.py
|
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from embedchain import App
|
||||
|
||||
# load elasticsearch configuration from yaml file
|
||||
app = App.from_config(config_path="config.yaml")
|
||||
```
|
||||
|
||||
```yaml config.yaml
|
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vectordb:
|
||||
provider: elasticsearch
|
||||
config:
|
||||
collection_name: 'es-index'
|
||||
cloud_id: 'deployment-name:xxxx'
|
||||
basic_auth:
|
||||
- elastic
|
||||
- <your_password>
|
||||
verify_certs: false
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
36
docs/components/vector-databases/opensearch.mdx
Normal file
36
docs/components/vector-databases/opensearch.mdx
Normal file
@@ -0,0 +1,36 @@
|
||||
---
|
||||
title: OpenSearch
|
||||
---
|
||||
|
||||
Install related dependencies using the following command:
|
||||
|
||||
```bash
|
||||
pip install --upgrade 'embedchain[opensearch]'
|
||||
```
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python main.py
|
||||
from embedchain import App
|
||||
|
||||
# load opensearch configuration from yaml file
|
||||
app = App.from_config(config_path="config.yaml")
|
||||
```
|
||||
|
||||
```yaml config.yaml
|
||||
vectordb:
|
||||
provider: opensearch
|
||||
config:
|
||||
collection_name: 'my-app'
|
||||
opensearch_url: 'https://localhost:9200'
|
||||
http_auth:
|
||||
- admin
|
||||
- admin
|
||||
vector_dimension: 1536
|
||||
use_ssl: false
|
||||
verify_certs: false
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
106
docs/components/vector-databases/pinecone.mdx
Normal file
106
docs/components/vector-databases/pinecone.mdx
Normal file
@@ -0,0 +1,106 @@
|
||||
---
|
||||
title: Pinecone
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Install pinecone related dependencies using the following command:
|
||||
|
||||
```bash
|
||||
pip install --upgrade 'embedchain[pinecone]'
|
||||
```
|
||||
|
||||
In order to use Pinecone as vector database, set the environment variable `PINECONE_API_KEY` which you can find on [Pinecone dashboard](https://app.pinecone.io/).
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python main.py
|
||||
from embedchain import App
|
||||
|
||||
# Load pinecone configuration from yaml file
|
||||
app = App.from_config(config_path="pod_config.yaml")
|
||||
# Or
|
||||
app = App.from_config(config_path="serverless_config.yaml")
|
||||
```
|
||||
|
||||
```yaml pod_config.yaml
|
||||
vectordb:
|
||||
provider: pinecone
|
||||
config:
|
||||
metric: cosine
|
||||
vector_dimension: 1536
|
||||
index_name: my-pinecone-index
|
||||
pod_config:
|
||||
environment: gcp-starter
|
||||
metadata_config:
|
||||
indexed:
|
||||
- "url"
|
||||
- "hash"
|
||||
```
|
||||
|
||||
```yaml serverless_config.yaml
|
||||
vectordb:
|
||||
provider: pinecone
|
||||
config:
|
||||
metric: cosine
|
||||
vector_dimension: 1536
|
||||
index_name: my-pinecone-index
|
||||
serverless_config:
|
||||
cloud: aws
|
||||
region: us-west-2
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
<br />
|
||||
<Note>
|
||||
You can find more information about Pinecone configuration [here](https://docs.pinecone.io/docs/manage-indexes#create-a-pod-based-index).
|
||||
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}`.
|
||||
</Note>
|
||||
|
||||
## Usage
|
||||
|
||||
### Hybrid search
|
||||
|
||||
Here is an example of how you can do hybrid search using Pinecone as a vector database through Embedchain.
|
||||
|
||||
```python
|
||||
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")
|
||||
```
|
||||
|
||||
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](https://docs.pinecone.io/docs/hybrid-search).
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
23
docs/components/vector-databases/qdrant.mdx
Normal file
23
docs/components/vector-databases/qdrant.mdx
Normal file
@@ -0,0 +1,23 @@
|
||||
---
|
||||
title: Qdrant
|
||||
---
|
||||
|
||||
In order to use Qdrant as a vector database, set the environment variables `QDRANT_URL` and `QDRANT_API_KEY` which you can find on [Qdrant Dashboard](https://cloud.qdrant.io/).
|
||||
|
||||
<CodeGroup>
|
||||
```python main.py
|
||||
from embedchain import App
|
||||
|
||||
# load qdrant configuration from yaml file
|
||||
app = App.from_config(config_path="config.yaml")
|
||||
```
|
||||
|
||||
```yaml config.yaml
|
||||
vectordb:
|
||||
provider: qdrant
|
||||
config:
|
||||
collection_name: my_qdrant_index
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
24
docs/components/vector-databases/weaviate.mdx
Normal file
24
docs/components/vector-databases/weaviate.mdx
Normal file
@@ -0,0 +1,24 @@
|
||||
---
|
||||
title: Weaviate
|
||||
---
|
||||
|
||||
|
||||
In order to use Weaviate as a vector database, set the environment variables `WEAVIATE_ENDPOINT` and `WEAVIATE_API_KEY` which you can find on [Weaviate dashboard](https://console.weaviate.cloud/dashboard).
|
||||
|
||||
<CodeGroup>
|
||||
```python main.py
|
||||
from embedchain import App
|
||||
|
||||
# load weaviate configuration from yaml file
|
||||
app = App.from_config(config_path="config.yaml")
|
||||
```
|
||||
|
||||
```yaml config.yaml
|
||||
vectordb:
|
||||
provider: weaviate
|
||||
config:
|
||||
collection_name: my_weaviate_index
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
39
docs/components/vector-databases/zilliz.mdx
Normal file
39
docs/components/vector-databases/zilliz.mdx
Normal file
@@ -0,0 +1,39 @@
|
||||
---
|
||||
title: Zilliz
|
||||
---
|
||||
|
||||
Install related dependencies using the following command:
|
||||
|
||||
```bash
|
||||
pip install --upgrade 'embedchain[milvus]'
|
||||
```
|
||||
|
||||
Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN` which you can find it on their [cloud platform](https://cloud.zilliz.com/).
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python main.py
|
||||
import os
|
||||
from embedchain import App
|
||||
|
||||
os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
|
||||
os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'
|
||||
|
||||
# load zilliz configuration from yaml file
|
||||
app = App.from_config(config_path="config.yaml")
|
||||
```
|
||||
|
||||
```yaml config.yaml
|
||||
vectordb:
|
||||
provider: zilliz
|
||||
config:
|
||||
collection_name: 'zilliz_app'
|
||||
uri: https://xxxx.api.gcp-region.zillizcloud.com
|
||||
token: xxx
|
||||
vector_dim: 1536
|
||||
metric_type: L2
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
Reference in New Issue
Block a user