🐛 FIX: Formatting

Author: arre-ankit

examples/agent/agent.run.stream.py

@@ -33,26 +33,26 @@ def main():
             model="openai:gpt-4.1-mini",
             instructions="You are a helpful assistant that help users summarize text.",
             input=[{"role": "user", "content": "Who is an AI Engineer?"}],
-            api_key=api_key
+            api_key=api_key,
         )
 
         # Convert the stream to a stream runner - equivalent to getRunner(stream)
         runner = get_runner(response)
 
         # Event-like handling in Python
         # Method 1: Using iterator pattern (Python equivalent of event listeners)
-        
+
         # Equivalent to runner.on('connect', ...)
         print("Stream started.\n")
 
         try:
             # Equivalent to runner.on('content', content => {...})
             for content in runner.text_generator():
                 print(content, end="", flush=True)
-            
+
             # Equivalent to runner.on('end', ...)
             print("\nStream ended.")
-            
+
         except Exception as error:
             # Equivalent to runner.on('error', error => {...})
             print(f"Error: {error}")

examples/chunker/chunker.py

@@ -2,9 +2,9 @@
 Example demonstrating how to chunk text content using Langbase.
 """
 
+import json
 import os
 import pathlib
-import json
 
 from dotenv import load_dotenv
 

examples/embed/embed.py

@@ -1,7 +1,8 @@
 # Experimental upcoming beta AI primitve.
 # Please refer to the documentation for more information: https://langbase.com/docs for more information.
-import os
 import json
+import os
+
 from dotenv import load_dotenv
 
 from langbase import Langbase

examples/memory/memory.docs.delete.py

@@ -25,7 +25,9 @@ def main():
 
     # Delete the document
     try:
-        response = lb.memories.documents.delete(memory_name=memory_name, document_name=document_id)
+        response = lb.memories.documents.delete(
+            memory_name=memory_name, document_name=document_id
+        )
 
         print(
             f"Document '{document_id}' deleted successfully from memory '{memory_name}'"

examples/memory/memory.docs.list.py

@@ -24,9 +24,7 @@ def main():
 
     # List documents in the memory
     try:
-        response = lb.memories.documents.list(
-            memory_name=memory_name
-        )
+        response = lb.memories.documents.list(memory_name=memory_name)
 
         print(f"Documents in memory '{memory_name}':")
         print(json.dumps(response, indent=2))

examples/memory/memory.docs.retry-embed.py

@@ -21,11 +21,13 @@ def main():
 
     # Memory name to retry embedding for
     memory_name = "product-knowledge"  # Replace with your memory name
-    document_name="name.txt" # Replace with document name
+    document_name = "name.txt"  # Replace with document name
 
     # Retry embedding for failed documents
     try:
-        response = lb.memories.documents.embeddings.retry(memory_name=memory_name,document_name=document_name)
+        response = lb.memories.documents.embeddings.retry(
+            memory_name=memory_name, document_name=document_name
+        )
 
         print(f"Retry embedding initiated for memory '{memory_name}'")
         print(json.dumps(response, indent=2))

examples/memory/memory.docs.upload.py

@@ -29,9 +29,9 @@ def main():
         response1 = lb.memories.documents.upload(
             memory_name=memory_name,
             document_name="intro.txt",
-            document=content1.encode('utf-8'),  # Convert string to bytes
+            document=content1.encode("utf-8"),  # Convert string to bytes
             content_type="text/plain",
-            meta={"source": "documentation", "section": "introduction"}
+            meta={"source": "documentation", "section": "introduction"},
         )
         print("Document 1 uploaded successfully!")
         print(f"Status: {response1.status_code}")

examples/memory/memory.retrieve.py

@@ -29,11 +29,7 @@ def main():
     try:
         response = lb.memories.retrieve(
             query=query,
-            memory=[
-                {
-                    "name": memory_name
-                }
-            ],
+            memory=[{"name": memory_name}],
             top_k=5,  # Number of relevant memories to retrieve
         )
 

examples/pipes/pipes.create.py

@@ -30,7 +30,7 @@ def main():
                     "content": "You are a helpful assistant that summarizes text clearly and concisely.",
                 }
             ],
-            upsert=True
+            upsert=True,
         )
 
         print("Pipe created successfully!")

examples/pipes/pipes.run.py

@@ -20,18 +20,12 @@ def main():
     # Initialize the client
     lb = Langbase(api_key=langbase_api_key)
 
-
     # Run the pipe with explicit stream=False
     try:
         response = lb.pipes.run(
-            name="summary-agent", 
-            messages=[
-                {
-                    "role": "user",
-                    "content": "Who is an AI Engineer?"
-                }
-            ], 
-            stream=False
+            name="summary-agent",
+            messages=[{"role": "user", "content": "Who is an AI Engineer?"}],
+            stream=False,
         )
 
         # Print the entire response as is