diff --git a/docs/components/vector-databases/lancedb.mdx b/docs/components/vector-databases/lancedb.mdx
index f68d6de9..97af57df 100644
--- a/docs/components/vector-databases/lancedb.mdx
+++ b/docs/components/vector-databases/lancedb.mdx
@@ -13,7 +13,9 @@ 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
 import os
 from embedchain import App
@@ -21,7 +23,7 @@ from embedchain import App
 # set OPENAI_API_KEY as env variable
 os.environ["OPENAI_API_KEY"] = "sk-xxx"
 
-# Create Embedchain App and set config
+# create Embedchain App and set config
 app = App.from_config(config={
     "vectordb": {
         "provider": "lancedb",
@@ -32,7 +34,7 @@ app = App.from_config(config={
     }
 )
 
-# Add data source and start queryin
+# add data source and start query in
 app.add("https://www.forbes.com/profile/elon-musk")
 
 # query continuously
@@ -45,4 +47,54 @@ while(True):
 ```
 
 </CodeGroup>
+
+### With Local LLM 
+<CodeGroup>
+
+```python main.py
+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" />
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