diff --git a/langchain/vectorstores/chroma.py b/langchain/vectorstores/chroma.py
index 7d29dbe5d72..19617947048 100644
--- a/langchain/vectorstores/chroma.py
+++ b/langchain/vectorstores/chroma.py
@@ -128,6 +128,7 @@ class Chroma(VectorStore):
         self,
         query: str,
         k: int = 4,
+        find_highest_possible_k: Optional[bool] = True,
         filter: Optional[Dict[str, str]] = None,
         **kwargs: Any,
     ) -> List[Document]:
@@ -136,18 +137,32 @@ class Chroma(VectorStore):
         Args:
             query (str): Query text to search for.
             k (int): Number of results to return. Defaults to 4.
+            find_highest_possible_k (Optional[bool], True): If True, will iteratively lower k
+                until there are enough items in the vectorstore to not raise an Error.
             filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
 
         Returns:
             List[Document]: List of documents most similar to the query text.
         """
-        docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
-        return [doc for doc, _ in docs_and_scores]
+        def _similarity_search(k: int):
+            docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
+            return [doc for doc, _ in docs_and_scores]
 
+        if not find_highest_possible_k:
+            return _similarity_search(k=k)
+
+        # Iteratively lower k until an error isn't raised by Chroma
+        for try_k in range(k, 0, -1):
+            try:
+                return _similarity_search(k=try_k)
+            except chromadb.errors.NotEnoughElementsException:
+                continue
+        
     def similarity_search_by_vector(
         self,
         embedding: List[float],
         k: int = 4,
+        find_highest_possible_k: Optional[bool] = True,
         filter: Optional[Dict[str, str]] = None,
         **kwargs: Any,
     ) -> List[Document]:
@@ -155,18 +170,32 @@ class Chroma(VectorStore):
         Args:
             embedding: Embedding to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
+            find_highest_possible_k (Optional[bool], True): If True, will iteratively lower k
+                until there are enough items in the vectorstore to not raise an Error.
         Returns:
             List of Documents most similar to the query vector.
         """
-        results = self._collection.query(
-            query_embeddings=embedding, n_results=k, where=filter
-        )
-        return _results_to_docs(results)
+        def _similarity_search(k: int):
+            results = self._collection.query(
+                query_embeddings=embedding, n_results=k, where=filter
+            )
+            return _results_to_docs(results)
+
+        if not find_highest_possible_k:
+            return _similarity_search(k=k)
+        
+        # Iteratively lower k until an error isn't raised by Chroma
+        for try_k in range(k, 0, -1):
+            try:
+                return _similarity_search(k=try_k)
+            except chromadb.errors.NotEnoughElementsException:
+                continue
 
     def similarity_search_with_score(
         self,
         query: str,
         k: int = 4,
+        find_highest_possible_k: Optional[bool] = True,
         filter: Optional[Dict[str, str]] = None,
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
@@ -175,23 +204,36 @@ class Chroma(VectorStore):
         Args:
             query (str): Query text to search for.
             k (int): Number of results to return. Defaults to 4.
+            find_highest_possible_k (Optional[bool], True): If True, will iteratively lower k
+                until there are enough items in the vectorstore to not raise an Error.
             filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
 
         Returns:
             List[Tuple[Document, float]]: List of documents most similar to the query
                 text with distance in float.
         """
-        if self._embedding_function is None:
-            results = self._collection.query(
-                query_texts=[query], n_results=k, where=filter
-            )
-        else:
-            query_embedding = self._embedding_function.embed_query(query)
-            results = self._collection.query(
-                query_embeddings=[query_embedding], n_results=k, where=filter
-            )
+        def _similarity_search(k: int):
+            if self._embedding_function is None:
+                results = self._collection.query(
+                    query_texts=[query], n_results=k, where=filter
+                )
+            else:
+                query_embedding = self._embedding_function.embed_query(query)
+                results = self._collection.query(
+                    query_embeddings=[query_embedding], n_results=k, where=filter
+                )
 
-        return _results_to_docs_and_scores(results)
+            return _results_to_docs_and_scores(results)
+
+        if not find_highest_possible_k:
+            return _similarity_search(k=k)
+        
+        # Iteratively lower k until an error isn't raised by Chroma
+        for try_k in range(k, 0, -1):
+            try:
+                return _similarity_search(k=try_k)
+            except chromadb.errors.NotEnoughElementsException:
+                continue
 
     def max_marginal_relevance_search_by_vector(
         self,