Add docstrings for Clickhouse class methods
diff --git a/.gitignore b/.gitignore
index c24d6e3f564984..aed12c91c6a3b3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,6 +116,7 @@ celerybeat.pid
 .env
 .envrc
 .venv*
+venv*
 env/
 ENV/
 env.bak/
diff --git a/libs/community/langchain_community/vectorstores/clickhouse.py b/libs/community/langchain_community/vectorstores/clickhouse.py
index 816ccd2aacab59..bc541084547876 100644
--- a/libs/community/langchain_community/vectorstores/clickhouse.py
+++ b/libs/community/langchain_community/vectorstores/clickhouse.py
@@ -211,12 +211,48 @@ def __init__(
 
     @property
     def embeddings(self) -> Embeddings:
+        """Provides access to the embedding mechanism used by the Clickhouse instance.
+
+        This property allows direct access to the embedding function or model being
+        used by the Clickhouse instance to convert text documents into embedding vectors
+        for vector similarity search.
+
+        Returns:
+            The `Embeddings` instance associated with this Clickhouse instance.
+        """
         return self.embedding_function
 
     def escape_str(self, value: str) -> str:
+        """Escape special characters in a string for Clickhouse SQL queries.
+
+        This method is used internally to prepare strings for safe insertion
+        into SQL queries by escaping special characters that might otherwise
+        interfere with the query syntax.
+
+        Args:
+            value: The string to be escaped.
+
+        Returns:
+            The escaped string, safe for insertion into SQL queries.
+        """
         return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
 
     def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
+        """Construct an SQL query for inserting data into the Clickhouse database.
+
+        This method formats and constructs an SQL `INSERT` query string using the
+        provided transaction data and column names. It is utilized internally during
+        the process of batch insertion of documents and their embeddings into the
+        database.
+
+        Args:
+            transac: iterable of tuples, representing a row of data to be inserted.
+            column_names: iterable of strings representing the names of the columns
+                into which data will be inserted.
+
+        Returns:
+            A string containing the constructed SQL `INSERT` query.
+        """
         ks = ",".join(column_names)
         _data = []
         for n in transac:
@@ -231,6 +267,17 @@ def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> s
         return i_str
 
     def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
+        """Execute an SQL query to insert data into the Clickhouse database.
+
+        This method performs the actual insertion of data into the database by
+        executing the SQL query constructed by `_build_insert_sql`. It's a critical
+        step in adding new documents and their associated data into the vector store.
+
+        Args:
+            transac:iterable of tuples, representing a row of data to be inserted.
+            column_names: An iterable of strings representing the names of the columns
+                into which data will be inserted.
+        """
         _insert_query = self._build_insert_sql(transac, column_names)
         self.client.command(_insert_query)
 
@@ -345,6 +392,21 @@ def __repr__(self) -> str:
     def _build_query_sql(
         self, q_emb: List[float], topk: int, where_str: Optional[str] = None
     ) -> str:
+        """Construct an SQL query for performing a similarity search.
+
+        This internal method generates an SQL query for finding the top-k most similar
+        vectors in the database to a given query vector.It allows for optional filtering
+        conditions to be applied via a WHERE clause.
+
+        Args:
+            q_emb: The query vector as a list of floats.
+            topk: The number of top similar items to retrieve.
+            where_str: opt str representing additional WHERE conditions for the query
+                Defaults to None.
+
+        Returns:
+            A string containing the SQL query for the similarity search.
+        """
         q_emb_str = ",".join(map(str, q_emb))
         if where_str:
             where_str = f"PREWHERE {where_str}"

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