diff --git a/libs/langchain/langchain/retrievers/web_research.py b/libs/langchain/langchain/retrievers/web_research.py
index a5ad41007f..008df92e0b 100644
--- a/libs/langchain/langchain/retrievers/web_research.py
+++ b/libs/langchain/langchain/retrievers/web_research.py
@@ -35,7 +35,7 @@ class SearchQueries(BaseModel):
 DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(
     input_variables=["question"],
     template="""<<SYS>> \n You are an assistant tasked with improving Google search 
-    results. \n <</SYS>> \n\n [INST] Generate FIVE Google search queries that 
+    results. \n <</SYS>> \n\n [INST] Generate THREE Google search queries that 
     are similar to this question. The output should be a numbered list of questions 
     and each should have a question mark at the end: \n\n {question} [/INST]""",
 )
@@ -43,7 +43,7 @@ DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(
 DEFAULT_SEARCH_PROMPT = PromptTemplate(
     input_variables=["question"],
     template="""You are an assistant tasked with improving Google search 
-    results. Generate FIVE Google search queries that are similar to
+    results. Generate THREE Google search queries that are similar to
     this question. The output should be a numbered list of questions and each
     should have a question mark at the end: {question}""",
 )
@@ -73,7 +73,6 @@ class WebResearchRetriever(BaseRetriever):
     )
     llm_chain: LLMChain
     search: GoogleSearchAPIWrapper = Field(..., description="Google Search API Wrapper")
-    max_splits_per_doc: int = Field(100, description="Maximum splits per document")
     num_search_results: int = Field(1, description="Number of pages per Google search")
     text_splitter: RecursiveCharacterTextSplitter = Field(
         RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50),
@@ -90,10 +89,9 @@ class WebResearchRetriever(BaseRetriever):
         llm: BaseLLM,
         search: GoogleSearchAPIWrapper,
         prompt: Optional[BasePromptTemplate] = None,
-        max_splits_per_doc: int = 100,
         num_search_results: int = 1,
         text_splitter: RecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(
-            chunk_size=1500, chunk_overlap=50
+            chunk_size=1500, chunk_overlap=150
         ),
     ) -> "WebResearchRetriever":
         """Initialize from llm using default template.
@@ -103,7 +101,6 @@ class WebResearchRetriever(BaseRetriever):
             llm: llm for search question generation
             search: GoogleSearchAPIWrapper
             prompt: prompt to generating search questions
-            max_splits_per_doc: Maximum splits per document to keep
             num_search_results: Number of pages per Google search
             text_splitter: Text splitter for splitting web pages into chunks
 
@@ -131,14 +128,30 @@ class WebResearchRetriever(BaseRetriever):
             vectorstore=vectorstore,
             llm_chain=llm_chain,
             search=search,
-            max_splits_per_doc=max_splits_per_doc,
             num_search_results=num_search_results,
             text_splitter=text_splitter,
         )
 
+    def clean_search_query(self, query: str) -> str:
+        # Some search tools (e.g., Google) will
+        # fail to return results if query has a
+        # leading digit: 1. "LangCh..."
+        # Check if the first character is a digit
+        if query[0].isdigit():
+            # Find the position of the first quote
+            first_quote_pos = query.find('"')
+            if first_quote_pos != -1:
+                # Extract the part of the string after the quote
+                query = query[first_quote_pos + 1 :]
+                # Remove the trailing quote if present
+                if query.endswith('"'):
+                    query = query[:-1]
+        return query.strip()
+
     def search_tool(self, query: str, num_search_results: int = 1) -> List[dict]:
         """Returns num_serch_results pages per Google search."""
-        result = self.search.results(query, num_search_results)
+        query_clean = self.clean_search_query(query)
+        result = self.search.results(query_clean, num_search_results)
         return result
 
     def _get_relevant_documents(