Generated markdown tutorials from Jupyter Notebooks

Generated from: couchbase-examples/vector-search-cookbook

Author: actions-user

tutorial/markdown/generated/vector-search-cookbook/CouchbaseStorage_Demo.md

@@ -59,11 +59,9 @@ To build our semantic search engine, we need a robust set of tools. The librarie
 
 
 ```python
-!pip install datasets langchain-couchbase langchain-openai
+!pip install datasets==3.5.0 langchain-couchbase==0.3.0 langchain-openai==0.3.13
 ```
 
-    [Output too long, omitted for brevity]
-
 # Importing Necessary Libraries
 The script starts by importing a series of libraries required for various tasks, including handling JSON, logging, time tracking, Couchbase connections, embedding generation, and dataset loading. These libraries provide essential functions for working with data, managing database connections, and processing machine learning models.
 
@@ -98,6 +96,10 @@ from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
 from tqdm import tqdm
 ```
 
+    /Users/aayush.tyagi/Documents/AI/vector-search-cookbook/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+      from .autonotebook import tqdm as notebook_tqdm
+
+
 # Setup Logging
 Logging is configured to track the progress of the script and capture any errors or warnings. This is crucial for debugging and understanding the flow of execution. The logging output includes timestamps, log levels (e.g., INFO, ERROR), and messages that describe what is happening in the script.
 
@@ -194,6 +196,7 @@ def setup_collection(cluster, bucket_name, scope_name, collection_name):
             logging.info(f"Collection '{collection_name}' already exists.Skipping creation.")
 
         collection = bucket.scope(scope_name).collection(collection_name)
+        time.sleep(2)  # Give the collection time to be ready for queries
 
         # Ensure primary index exists
         try:

tutorial/markdown/generated/vector-search-cookbook/RAG_with_Couchbase_and_AzureOpenAI.md

@@ -61,7 +61,7 @@ To build our semantic search engine, we need a robust set of tools. The librarie
 
 
 ```python
-%pip install --quiet datasets langchain-couchbase langchain-anthropic langchain-openai python-dotenv
+%pip install --quiet datasets==3.5.0 langchain-couchbase==0.3.0 langchain-anthropic==0.3.11 langchain-openai==0.3.13 python-dotenv==1.1.0
 ```
 
     Note: you may need to restart the kernel to use updated packages.
@@ -208,6 +208,7 @@ def setup_collection(cluster, bucket_name, scope_name, collection_name):
                 num_replicas=0
             )
             cluster.buckets().create_bucket(bucket_settings)
+            time.sleep(2)  # Wait for bucket creation to complete and become available
             bucket = cluster.bucket(bucket_name)
             logging.info(f"Bucket '{bucket_name}' created successfully.")
 

tutorial/markdown/generated/vector-search-cookbook/RAG_with_Couchbase_and_Bedrock.md

@@ -60,7 +60,7 @@ To build our semantic search engine, we need a robust set of tools. The librarie
 
 
 ```python
-%pip install --quiet datasets langchain-couchbase langchain-cohere python-dotenv
+%pip install --quiet datasets==3.5.0 langchain-couchbase==0.3.0 langchain-cohere==0.4.4 python-dotenv==1.1.0
 ```
 
     Note: you may need to restart the kernel to use updated packages.
@@ -205,6 +205,7 @@ def setup_collection(cluster, bucket_name, scope_name, collection_name):
                 num_replicas=0
             )
             cluster.buckets().create_bucket(bucket_settings)
+            time.sleep(2)  # Wait for bucket creation to complete and become available
             bucket = cluster.bucket(bucket_name)
             logging.info(f"Bucket '{bucket_name}' created successfully.")
 

tutorial/markdown/generated/vector-search-cookbook/RAG_with_Couchbase_and_Claude(by_Anthropic).md

@@ -56,7 +56,7 @@ To build our semantic search engine, we need a robust set of tools. The librarie
 
 
 ```python
-%pip install --quiet -U datasets langchain-couchbase langchain-openai python-dotenv smolagents ipywidgets
+%pip install --quiet -U datasets==3.5.0 langchain-couchbase==0.3.0 langchain-openai==0.3.13 python-dotenv==1.1.0 smolagents==1.13.0 ipywidgets==8.1.6
 ```
 
 # Importing Necessary Libraries
@@ -187,6 +187,7 @@ def setup_collection(cluster, bucket_name, scope_name, collection_name):
                 num_replicas=0
             )
             cluster.buckets().create_bucket(bucket_settings)
+            time.sleep(2)  # Wait for bucket creation to complete and become available
             bucket = cluster.bucket(bucket_name)
             logging.info(f"Bucket '{bucket_name}' created successfully.")
 
@@ -606,8 +607,11 @@ more uncomfortable than when the situation is going well, always that happened.
 Sporting side managed by Ruben Amorim, who will be in the United dugout at the weekend. City dominated the first 
 half in Lisbon, led thanks to Phil Foden's early effort and looked to be cruising. Instead, they conceded three 
 times in <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">11</span> minutes either side of half-time as Sporting eventually ran out <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span>-<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span> winners.
+
 ...
+
 ...
+
 Afterwards Guardiola was calm, so much so it was difficult to hear him in the news conference, a contrast to the 
 frustrated figure he cut on the touchline. He said: <span style="color: #008000; text-decoration-color: #008000">"It depends on us. The solution is bring the players back. We </span>
 <span style="color: #008000; text-decoration-color: #008000">have just one central defender fit, that is difficult. We are going to try next game - another opportunity and we </span>