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@@ -13,7 +13,9 @@ pip install "embedchain[lancedb]"
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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.
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In order to use LanceDB as vector database, not need to set any key for local use.
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+### With OPENAI
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<CodeGroup>
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+
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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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@@ -21,7 +23,7 @@ from embedchain import App
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# set OPENAI_API_KEY as env variable
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os.environ["OPENAI_API_KEY"] = "sk-xxx"
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-# Create Embedchain App and set config
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+# create Embedchain App and set config
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app = App.from_config(config={
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"vectordb": {
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"provider": "lancedb",
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@@ -32,7 +34,7 @@ app = App.from_config(config={
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}
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)
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-# Add data source and start queryin
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+# add data source and start query in
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app.add("https://www.forbes.com/profile/elon-musk")
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# query continuously
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@@ -45,4 +47,54 @@ while(True):
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```
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</CodeGroup>
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+
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+### With Local LLM
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+<CodeGroup>
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+
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+```python main.py
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+from embedchain import Pipeline as App
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+
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+# config for Embedchain App
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+config = {
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+ 'llm': {
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+ 'provider': 'huggingface',
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+ 'config': {
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+ 'model': 'mistralai/Mistral-7B-v0.1',
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+ 'temperature': 0.1,
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+ 'max_tokens': 250,
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+ 'top_p': 0.1,
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+ 'stream': True
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+ }
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+ },
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+ 'embedder': {
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+ 'provider': 'huggingface',
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+ 'config': {
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+ 'model': 'sentence-transformers/all-mpnet-base-v2'
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+ }
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+ },
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+ 'vectordb': {
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+ 'provider': 'lancedb',
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+ 'config': {
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+ 'collection_name': 'lancedb-index'
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+ }
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+ }
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+}
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+
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+app = App.from_config(config=config)
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+
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+# add data source and start query in
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+app.add("https://www.tesla.com/ns_videos/2022-tesla-impact-report.pdf")
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+
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+# query continuously
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+while(True):
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+ question = input("Enter question: ")
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+ if question in ['q', 'exit', 'quit']:
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+ break
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+ answer = app.query(question)
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+ print(answer)
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+```
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+
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+</CodeGroup>
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+
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+
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<Snippet file="missing-vector-db-tip.mdx" />
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