LanceDB

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)

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)