From 17d4aca53829f1947c3de9d2587972d082b6d758 Mon Sep 17 00:00:00 2001
From: lxobr <lazar@topoteretes.com>
Date: Tue, 5 Nov 2024 10:05:14 +0100
Subject: [PATCH 1/2] feat: add simple python example

---
 examples/python/simple_example.py | 39 +++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 examples/python/simple_example.py

diff --git a/examples/python/simple_example.py b/examples/python/simple_example.py
new file mode 100644
index 000000000..9b64142ed
--- /dev/null
+++ b/examples/python/simple_example.py
@@ -0,0 +1,39 @@
+import cognee
+import asyncio
+from cognee.api.v1.search import SearchType
+
+# Prerequisites:
+# 1. Copy `.env.template` and rename it to `.env`.
+# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
+#    LLM_API_KEY = "your_key_here"
+# 3. (Optional) To minimize setup effort, set `VECTOR_DB_PROVIDER="lancedb"` in `.env".
+
+async def main():
+    # Create a clean slate for cognee -- reset data and system state
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+
+    # cognee knowledge graph will be created based on this text
+    text = """
+    Natural language processing (NLP) is an interdisciplinary
+    subfield of computer science and information retrieval.
+    """
+
+    # Add the text, and make it available for cognify
+    await cognee.add(text)
+
+    # Use LLMs and cognee to create knowledge graph
+    await cognee.cognify()
+
+    # Query cognee for insights on the added text
+    search_results = await cognee.search(
+        SearchType.INSIGHTS,
+        {'query': 'Tell me about NLP'}
+    )
+
+    # Display search results
+    for result_text in search_results:
+        print(result_text)
+
+if __name__ == '__main__':
+    asyncio.run(main())

From 6c395d7d7f5c100a37499a8b0dfec9e9ef811036 Mon Sep 17 00:00:00 2001
From: lxobr <lazar@topoteretes.com>
Date: Tue, 5 Nov 2024 10:05:43 +0100
Subject: [PATCH 2/2] docs: update README simple example

---
 README.md | 36 ++++++++++++++++++++++++++----------
 1 file changed, 26 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 6d3a89b73..28c07fab4 100644
--- a/README.md
+++ b/README.md
@@ -96,24 +96,40 @@ DB_PASSWORD=cognee
 
 ### Simple example
 
-Run the default cognee pipeline:
+First, copy `.env.template` to `.env` and add your OpenAI API key to the LLM_API_KEY field. 
 
-```
+Optionally, set `VECTOR_DB_PROVIDER="lancedb"` in `.env` to simplify setup.
+
+This script will run the default pipeline:
+
+```python
 import cognee
+import asyncio
+from cognee.api.v1.search import SearchType
 
-text = """Natural language processing (NLP) is an interdisciplinary
-       subfield of computer science and information retrieval"""
+async def main():
+    await cognee.prune.prune_data()  # Reset cognee data
+    await cognee.prune.prune_system(metadata=True)  # Reset cognee system state
 
-await cognee.add(text) # Add a new piece of information
+    text = """
+    Natural language processing (NLP) is an interdisciplinary
+    subfield of computer science and information retrieval.
+    """
 
-await cognee.cognify() # Use LLMs and cognee to create a knowledge graph
+    await cognee.add(text)  # Add text to cognee
+    await cognee.cognify()  # Use LLMs and cognee to create knowledge graph
 
-search_results = await cognee.search("INSIGHTS", {'query': 'NLP'}) # Query cognee for the insights
+    search_results = await cognee.search(  # Search cognee for insights
+        SearchType.INSIGHTS,
+        {'query': 'Tell me about NLP'}
+    )
 
-for result in search_results:
-    do_something_with_result(result)
+    for result_text in search_results:  # Display results
+        print(result_text)
+
+asyncio.run(main())
 ```
-
+A version of this example is here: `examples/pyton/simple_example.py`
 
 ### Create your own memory store