DA-767: Modified: Rename CouchbaseVectorStore to CouchbaseSearchVectorStore

Author: AayushTyagi1

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

@@ -140,7 +140,7 @@ from couchbase.cluster import Cluster
 from couchbase.options import ClusterOptions
 from couchbase.auth import PasswordAuthenticator
 from couchbase.diagnostics import PingState, ServiceType
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 from langchain_openai import OpenAIEmbeddings, ChatOpenAI
 import time
 import json
@@ -380,7 +380,7 @@ class CouchbaseStorage(RAGStorage):
             self.index_name = os.getenv('INDEX_NAME', 'vector_search_crew')
 
             # Initialize vector store
-            self.vector_store = CouchbaseVectorStore(
+            self.vector_store = CouchbaseSearchVectorStore(
                 cluster=self.cluster,
                 bucket_name=self.bucket_name,
                 scope_name=self.scope_name,

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

@@ -93,7 +93,7 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import RunnablePassthrough
 from langchain_couchbase.cache import CouchbaseCache
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
 from tqdm import tqdm
 ```
@@ -404,7 +404,7 @@ The vector store is set up to manage the embeddings created in the previous step
 
 ```python
 try:
-    vector_store = CouchbaseVectorStore(
+    vector_store = CouchbaseSearchVectorStore(
         cluster=cluster,
         bucket_name=CB_BUCKET_NAME,
         scope_name=SCOPE_NAME,
@@ -495,7 +495,7 @@ except Exception as e:
 # Perform Semantic Search
 Semantic search in Couchbase involves converting queries and documents into vector representations using an embeddings model. These vectors capture the semantic meaning of the text and are stored directly in Couchbase. When a query is made, Couchbase performs a similarity search by comparing the query vector against the stored document vectors. The similarity metric used for this comparison is configurable, allowing flexibility in how the relevance of documents is determined. Common metrics include cosine similarity, Euclidean distance, or dot product, but other metrics can be implemented based on specific use cases. Different embedding models like BERT, Word2Vec, or GloVe can also be used depending on the application's needs, with the vectors generated by these models stored and searched within Couchbase itself.
 
-In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
+In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseSearchVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
 
 
 ```python

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

@@ -97,7 +97,7 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts.chat import ChatPromptTemplate
 from langchain_core.runnables import RunnablePassthrough
 from langchain_couchbase.cache import CouchbaseCache
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 from tqdm import tqdm
 ```
 
@@ -386,7 +386,7 @@ A vector store is where we'll keep our embeddings. Unlike the FTS index, which i
 
 ```python
 try:
-    vector_store = CouchbaseVectorStore(
+    vector_store = CouchbaseSearchVectorStore(
         cluster=cluster,
         bucket_name=CB_BUCKET_NAME,
         scope_name=SCOPE_NAME,
@@ -554,7 +554,7 @@ except Exception as e:
 # Perform Semantic Search
 Semantic search in Couchbase involves converting queries and documents into vector representations using an embeddings model. These vectors capture the semantic meaning of the text and are stored directly in Couchbase. When a query is made, Couchbase performs a similarity search by comparing the query vector against the stored document vectors. The similarity metric used for this comparison is configurable, allowing flexibility in how the relevance of documents is determined. Common metrics include cosine similarity, Euclidean distance, or dot product, but other metrics can be implemented based on specific use cases. Different embedding models like BERT, Word2Vec, or GloVe can also be used depending on the application's needs, with the vectors generated by these models stored and searched within Couchbase itself.
 
-In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
+In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseSearchVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
 
 
 ```python

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

@@ -98,7 +98,7 @@ from langchain_core.prompts.chat import (ChatPromptTemplate,
                                          SystemMessagePromptTemplate)
 from langchain_core.runnables import RunnablePassthrough
 from langchain_couchbase.cache import CouchbaseCache
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 from langchain_openai import OpenAIEmbeddings
 ```
 
@@ -392,7 +392,7 @@ A vector store is where we'll keep our embeddings. Unlike the FTS index, which i
 
 ```python
 try:
-    vector_store = CouchbaseVectorStore(
+    vector_store = CouchbaseSearchVectorStore(
         cluster=cluster,
         bucket_name=CB_BUCKET_NAME,
         scope_name=SCOPE_NAME,
@@ -540,7 +540,7 @@ except Exception as e:
 # Perform Semantic Search
 Semantic search in Couchbase involves converting queries and documents into vector representations using an embeddings model. These vectors capture the semantic meaning of the text and are stored directly in Couchbase. When a query is made, Couchbase performs a similarity search by comparing the query vector against the stored document vectors. The similarity metric used for this comparison is configurable, allowing flexibility in how the relevance of documents is determined. 
 
-In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
+In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseSearchVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
 
 
 ```python

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

@@ -96,7 +96,7 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import RunnablePassthrough
 from langchain_couchbase.cache import CouchbaseCache
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 ```
 
 # Setup Logging
@@ -386,7 +386,7 @@ The vector store is set up to manage the embeddings created in the previous step
 
 ```python
 try:
-    vector_store = CouchbaseVectorStore(
+    vector_store = CouchbaseSearchVectorStore(
         cluster=cluster,
         bucket_name=CB_BUCKET_NAME,
         scope_name=SCOPE_NAME,
@@ -531,7 +531,7 @@ except Exception as e:
 # Perform Semantic Search
 Semantic search in Couchbase involves converting queries and documents into vector representations using an embeddings model. These vectors capture the semantic meaning of the text and are stored directly in Couchbase. When a query is made, Couchbase performs a similarity search by comparing the query vector against the stored document vectors. The similarity metric used for this comparison is configurable, allowing flexibility in how the relevance of documents is determined. 
 
-In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
+In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseSearchVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
 
 
 ```python

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

@@ -98,7 +98,7 @@ from couchbase.options import ClusterOptions
 from datasets import load_dataset
 from dotenv import load_dotenv
 from crewai.tools import tool
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 
 from crewai import Agent, Crew, Process, Task
@@ -440,7 +440,7 @@ A vector store is where we'll keep our embeddings. Unlike the FTS index, which i
 
 ```python
 # Setup vector store
-vector_store = CouchbaseVectorStore(
+vector_store = CouchbaseSearchVectorStore(
     cluster=cluster,
     bucket_name=CB_BUCKET_NAME,
     scope_name=SCOPE_NAME,

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

@@ -97,7 +97,7 @@ from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.prompts.chat import ChatPromptTemplate
 from langchain_core.runnables import RunnablePassthrough
 from langchain_couchbase.cache import CouchbaseCache
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 ```
 
 # Setup Logging
@@ -394,7 +394,7 @@ A vector store is where we'll keep our embeddings. Unlike the FTS index, which i
 
 ```python
 try:
-    vector_store = CouchbaseVectorStore(
+    vector_store = CouchbaseSearchVectorStore(
         cluster=cluster,
         bucket_name=CB_BUCKET_NAME,
         scope_name=SCOPE_NAME,
@@ -559,7 +559,7 @@ except Exception as e:
 ## Perform Semantic Search
 Semantic search in Couchbase involves converting queries and documents into vector representations using an embeddings model. These vectors capture the semantic meaning of the text and are stored directly in Couchbase. When a query is made, Couchbase performs a similarity search by comparing the query vector against the stored document vectors. The similarity metric used for this comparison is configurable, allowing flexibility in how the relevance of documents is determined.
 
-In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
+In the provided code, the search process begins by recording the start time, followed by executing the similarity_search_with_score method of the CouchbaseSearchVectorStore. This method searches Couchbase for the most relevant documents based on the vector similarity to the query. The search results include the document content and a similarity score that reflects how closely each document aligns with the query in the defined semantic space. The time taken to perform this search is then calculated and logged, and the results are displayed, showing the most relevant documents along with their similarity scores. This approach leverages Couchbase as both a storage and retrieval engine for vector data, enabling efficient and scalable semantic searches. The integration of vector storage and search capabilities within Couchbase allows for sophisticated semantic search operations without relying on external services for vector storage or comparison.
 
 ### Note on Retry Mechanism
 The search implementation includes a retry mechanism to handle rate limiting and API errors gracefully. If a rate limit error (HTTP 429) is encountered, the system will automatically retry the request up to 3 times with exponential backoff, waiting 2 seconds initially and doubling the wait time between each retry. This helps manage API usage limits while maintaining service reliability. For other types of errors, such as payment requirements or general failures, appropriate error messages and troubleshooting steps are provided to help diagnose and resolve the issue.