diff --git a/libs/community/langchain_google_community/__init__.py b/libs/community/langchain_google_community/__init__.py
index 0a1d7015..8d8b6c65 100644
--- a/libs/community/langchain_google_community/__init__.py
+++ b/libs/community/langchain_google_community/__init__.py
@@ -3,6 +3,9 @@
 from langchain_google_community.bq_storage_vectorstores.bigquery import (
     BigQueryVectorStore,
 )
+from langchain_google_community.bq_storage_vectorstores.bruteforce import (
+    BruteForceBQVectorStore,
+)
 from langchain_google_community.bq_storage_vectorstores.featurestore import (
     VertexFSVectorStore,
 )
@@ -40,6 +43,7 @@
     "BigQueryLoader",
     "BigQueryVectorStore",
     "BigQueryVectorSearch",
+    "BruteForceBQVectorStore",
     "CloudVisionLoader",
     "CloudVisionParser",
     "DocAIParser",
diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/bruteforce.py b/libs/community/langchain_google_community/bq_storage_vectorstores/bruteforce.py
index b352c3ee..19e4c894 100644
--- a/libs/community/langchain_google_community/bq_storage_vectorstores/bruteforce.py
+++ b/libs/community/langchain_google_community/bq_storage_vectorstores/bruteforce.py
@@ -1,6 +1,9 @@
 import numpy as np
+import pandas as pd
+from pydantic import Field
 from typing import Any, Dict, List, Optional, Union
 from langchain_core.documents import Document
+from langchain_core.pydantic_v1 import root_validator
 from langchain_google_community.bq_storage_vectorstores._base import (
     BaseBigQueryVectorStore,
 )
@@ -10,16 +13,52 @@
 
 
 class BruteForceBQVectorStore(BaseBigQueryVectorStore):
+    """
+    A Bruteforce local vector store that source the data from a BigQuery Table
 
-    def sync_data(self):
-        self._df = self._query_table_to_df()
-        self._vectors = np.array(
-            self._df[self.embedding_field].tolist()
+    This class is particularly indicated for quick small local prototyping.
+
+    Attributes:
+        embedding: Embedding model for generating and comparing embeddings.
+        project_id: Google Cloud Project ID where BigQuery resources are located.
+        dataset_name: BigQuery dataset name.
+        table_name: BigQuery table name.
+        location: BigQuery region/location.
+        content_field: Name of the column storing document content (default: "content").
+        embedding_field: Name of the column storing text embeddings (default:
+            "embedding").
+        doc_id_field: Name of the column storing document IDs (default: "doc_id").
+        credentials: Optional Google Cloud credentials object.
+    """
+
+    _df: pd.DataFrame
+    _vectors: np.array
+    _vectors_transpose: np.array
+    _df_records: List[Dict]
+
+    @root_validator(pre=False, skip_on_failure=True)
+    def initialize_bf_vector_index(cls, values: dict) -> dict:
+        values["_df"] = cls._query_table_to_df(values)
+        values["_vectors"] =  np.array(
+            values["_df"][values["embedding_field"]].tolist()
         )
-        self._vectors_transpose = self._vectors.T
-        self._df_records = self._df.drop(
-            columns=[self.embedding_field]
+        values["_vectors_transpose"] = values["_vectors"].T
+        values["_df_records"] = values["_df"].drop(
+            columns=[values["embedding_field"]]
         ).to_dict("records")
+        return values
+
+
+    def sync_data(self):
+        pass
+        # self._df = self._query_table_to_df()
+        # self._vectors = np.array(
+        #     self._df[self.embedding_field].tolist()
+        # )
+        # self._vectors_transpose = self._vectors.T
+        # self._df_records = self._df.drop(
+        #     columns=[self.embedding_field]
+        # ).to_dict("records")
 
     def _similarity_search_by_vectors_with_scores_and_embeddings(
         self,
@@ -73,11 +112,14 @@ def _similarity_search_by_vectors_with_scores_and_embeddings(
                     document=doc, filter=filter
                 ):
                     continue
+                metadata = doc.copy()
+                del metadata[self.content_field]
+
                 query_docs.append(
                     [
                         Document(
-                            page_content=doc[self._vector_store.content_field], 
-                            metadata=doc
+                            page_content=doc[self.content_field], 
+                            metadata=metadata
                         ), 
                         doc_score, 
                         embedding
@@ -85,18 +127,33 @@ def _similarity_search_by_vectors_with_scores_and_embeddings(
                 )
             documents.append(query_docs)
         return documents
-
-    def _query_table_to_df(self):
+        
+    # def _query_table_to_df(self) -> pd.DataFrame:
+    #     from google.cloud import bigquery
+    #     table = self._full_table_id
+    #     query = f"SELECT * FROM {table}"
+    #     # Create a query job to read the data
+    #     self._logger.info(f"Reading data from {table}. It might take a few minutes...")
+    #     job_config = bigquery.QueryJobConfig(
+    #         use_query_cache=True,
+    #         priority=bigquery.QueryPriority.INTERACTIVE
+    #     )
+    #     query_job = self._bq_client.query(query, job_config=job_config)
+    #     df = query_job.to_dataframe()
+    #     return df
+    
+    @staticmethod
+    def _query_table_to_df(values) -> pd.DataFrame:
         from google.cloud import bigquery
-        table = self._full_table_id
+        table = values["_full_table_id"]
         query = f"SELECT * FROM {table}"
         # Create a query job to read the data
-        self._logger.info(f"Reading data from {table}. It might take a few minutes...")
+        values["_logger"].info(f"Reading data from {table}. It might take a few minutes...")
         job_config = bigquery.QueryJobConfig(
             use_query_cache=True,
             priority=bigquery.QueryPriority.INTERACTIVE
         )
-        query_job = self._bq_client.query(query, job_config=job_config)
+        query_job = values["_bq_client"].query(query, job_config=job_config)
         df = query_job.to_dataframe()
         return df