> ## Documentation Index
> Fetch the complete documentation index at: https://docs.embedchain.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LanceDB

## Install Embedchain with LanceDB

Install Embedchain, LanceDB and  related dependencies using the following command:

```bash theme={null}
pip install "embedchain[lancedb]"
```

LanceDB is a developer-friendly, open source database for AI. From hyper scalable vector search and advanced retrieval for RAG, to streaming training data and interactive exploration of large scale AI datasets.
In order to use LanceDB as vector database, not need to set any key for local use.

### With OPENAI

<CodeGroup>
  ```python main.py theme={null}
  import os
  from embedchain import App

  # set OPENAI_API_KEY as env variable
  os.environ["OPENAI_API_KEY"] = "sk-xxx"

  # create Embedchain App and set config
  app = App.from_config(config={
      "vectordb": {
          "provider": "lancedb",
              "config": {
                  "collection_name": "lancedb-index"
              }
          }
      }
  )

  # add data source and start query in
  app.add("https://www.forbes.com/profile/elon-musk")

  # query continuously
  while(True):
      question = input("Enter question: ")
      if question in ['q', 'exit', 'quit']:
          break
      answer = app.query(question)
      print(answer)
  ```
</CodeGroup>

### With Local LLM

<CodeGroup>
  ```python main.py theme={null}
  from embedchain import Pipeline as App

  # config for Embedchain App
  config = {
    'llm': {
      'provider': 'huggingface',
      'config': {
        'model': 'mistralai/Mistral-7B-v0.1',
        'temperature': 0.1,
        'max_tokens': 250,
        'top_p': 0.1,
        'stream': True
      }
    },
    'embedder': {
      'provider': 'huggingface',
      'config': {
        'model': 'sentence-transformers/all-mpnet-base-v2'
      }
    },
    'vectordb': { 
      'provider': 'lancedb', 
      'config': { 
        'collection_name': 'lancedb-index' 
      } 
    }
  }

  app = App.from_config(config=config)

  # add data source and start query in
  app.add("https://www.tesla.com/ns_videos/2022-tesla-impact-report.pdf")

  # query continuously
  while(True):
      question = input("Enter question: ")
      if question in ['q', 'exit', 'quit']:
          break
      answer = app.query(question)
      print(answer)
  ```
</CodeGroup>

<Snippet file="missing-vector-db-tip.mdx" />