update clickhouse integration docs

Author: npentrel

src/oss/javascript/integrations/vectorstores/clickhouse.mdx

@@ -9,12 +9,12 @@ description: "Integrate with the ClickHouse vector store using LangChain JavaScr
 Only available on Node.js.
 </Tip>
 
-[ClickHouse](https://clickhouse.com/) is a robust and open-source columnar database that is used for handling analytical queries and efficient storage, ClickHouse is designed to provide a powerful combination of vector search and analytics.
+[ClickHouse](https://clickhouse.com/) is an open-source columnar database for analytics that also supports vector search. For background on ClickHouse vector search (including approximate indexes), see [Exact and Approximate Vector Search](https://clickhouse.com/docs/engines/table-engines/mergetree-family/annindexes).
 
 ## Setup
 
-1. Launch a ClickHouse cluster. Refer to the [ClickHouse Installation Guide](https://clickhouse.com/docs/en/getting-started/install/) for details.
-2. After launching a ClickHouse cluster, retrieve the `Connection Details` from the cluster's `Actions` menu. You will need the host, port, username, and password.
+1. Launch a ClickHouse cluster. Refer to the [ClickHouse installation guide](https://clickhouse.com/docs/getting-started/install/) for details.
+2. After launching a ClickHouse cluster, retrieve the connection details. You will need the host, port, username, and password.
 3. Install the required Node.js peer dependency for ClickHouse in your workspace.
 
 You will need to install the following peer dependencies:
@@ -47,7 +47,9 @@ const vectorStore = await ClickHouseStore.fromTexts(
   new OpenAIEmbeddings(),
   {
     host: process.env.CLICKHOUSE_HOST || "localhost",
-    port: process.env.CLICKHOUSE_PORT || 8443,
+    port: process.env.CLICKHOUSE_PORT
+      ? Number.parseInt(process.env.CLICKHOUSE_PORT, 10)
+      : 8443,
     username: process.env.CLICKHOUSE_USER || "username",
     password: process.env.CLICKHOUSE_PASSWORD || "password",
     database: process.env.CLICKHOUSE_DATABASE || "default",
@@ -81,7 +83,9 @@ const vectorStore = await ClickHouseStore.fromExistingIndex(
   new OpenAIEmbeddings(),
   {
     host: process.env.CLICKHOUSE_HOST || "localhost",
-    port: process.env.CLICKHOUSE_PORT || 8443,
+    port: process.env.CLICKHOUSE_PORT
+      ? Number.parseInt(process.env.CLICKHOUSE_PORT, 10)
+      : 8443,
     username: process.env.CLICKHOUSE_USER || "username",
     password: process.env.CLICKHOUSE_PASSWORD || "password",
     database: process.env.CLICKHOUSE_DATABASE || "default",

src/oss/python/integrations/vectorstores/clickhouse.mdx

@@ -3,16 +3,16 @@ title: "ClickHouse integration"
 description: "Integrate with the ClickHouse vector store using LangChain Python."
 ---
 
-> [ClickHouse](https://clickhouse.com/) is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like `L2Distance`) as well as [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.
+> [ClickHouse](https://clickhouse.com/) is an open-source database for real-time apps and analytics with full SQL support. ClickHouse supports exact vector search (for example, using distance functions like `L2Distance`) and approximate vector search using vector similarity indexes (available in ClickHouse 25.8+). For details, see [Exact and Approximate Vector Search](https://clickhouse.com/docs/engines/table-engines/mergetree-family/annindexes).
 
-This notebook shows how to use functionality related to the `ClickHouse` vector store.
+This page shows how to use functionality related to the `ClickHouse` vector store.
 
 ## Setup
 
 First set up a local clickhouse server with docker:
 
 ```python
-! docker run -d -p 8123:8123 -p 9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 -e CLICKHOUSE_SKIP_USER_SETUP=1 clickhouse/clickhouse-server:25.7
+! docker run -d -p 8123:8123 -p 9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 -e CLICKHOUSE_SKIP_USER_SETUP=1 clickhouse/clickhouse-server:26.2
 ```
 
 You'll need to install `langchain-community` and `clickhouse-connect` to use this integration
@@ -153,9 +153,8 @@ Performing a simple similarity search can be done as follows:
 results = vector_store.similarity_search(
     "LangChain provides abstractions to make working with LLMs easy", k=2
 )
-for res in results:
-    page_content, metadata = res
-    print(f"* {page_content} [{metadata}]")
+for doc in results:
+    print(f"* {doc.page_content} [{doc.metadata}]")
 ```
 
 #### Similarity search with score
@@ -174,7 +173,7 @@ You can have direct access to ClickHouse SQL where statement. You can write `WHE
 
 **NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.
 
-If you custimized your `column_map` under your setting, you search with filter like this:
+If you customized your `column_map` in your settings, you can search with a filter like this:
 
 ```python
 meta = vector_store.metadata_column
@@ -195,14 +194,14 @@ There are a variety of other search methods that are not covered in this noteboo
 
 You can also transform the vector store into a retriever for easier usage in your chains.
 
-Here is how to transform your vector store into a retriever and then invoke the retreiever with a simple query and filter.
+Here is how to transform your vector store into a retriever and then invoke the retriever with a simple query and filter.
 
 ```python
 retriever = vector_store.as_retriever(
     search_type="similarity_score_threshold",
-    search_kwargs={"k": 1, "score_threshold": 0.5},
+    search_kwargs={"k": 1, "score_threshold": 0.5, "where_str": "metadata.source = 'news'"},
 )
-retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})
+retriever.invoke("Stealing from the bank is a crime")
 ```
 
 ## Usage for retrieval-augmented generation