diff --git a/libs/partners/chroma/langchain_chroma/vectorstores.py b/libs/partners/chroma/langchain_chroma/vectorstores.py
index 227da2d230f..2b966f662f2 100644
--- a/libs/partners/chroma/langchain_chroma/vectorstores.py
+++ b/libs/partners/chroma/langchain_chroma/vectorstores.py
@@ -79,11 +79,14 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
     X = np.array(X)
     Y = np.array(Y)
     if X.shape[1] != Y.shape[1]:
-        raise ValueError(
+        msg = (
             "Number of columns in X and Y must be the same. X has shape"
             f"{X.shape} "
             f"and Y has shape {Y.shape}."
         )
+        raise ValueError(
+            msg,
+        )
 
     X_norm = np.linalg.norm(X, axis=1)
     Y_norm = np.linalg.norm(Y, axis=1)
@@ -285,7 +288,7 @@ class Chroma(VectorStore):
         collection_metadata: Optional[dict] = None,
         client: Optional[chromadb.ClientAPI] = None,
         relevance_score_fn: Optional[Callable[[float], float]] = None,
-        create_collection_if_not_exists: Optional[bool] = True,
+        create_collection_if_not_exists: Optional[bool] = True,  # noqa: FBT002
     ) -> None:
         """Initialize with a Chroma client.
 
@@ -351,10 +354,13 @@ class Chroma(VectorStore):
     def _collection(self) -> chromadb.Collection:
         """Returns the underlying Chroma collection or throws an exception."""
         if self._chroma_collection is None:
-            raise ValueError(
+            msg = (
                 "Chroma collection not initialized. "
                 "Use `reset_collection` to re-create and initialize the collection. "
             )
+            raise ValueError(
+                msg,
+            )
         return self._chroma_collection
 
     @property
@@ -392,10 +398,10 @@ class Chroma(VectorStore):
         """
         return self._collection.query(
             query_texts=query_texts,
-            query_embeddings=query_embeddings,  # type: ignore
+            query_embeddings=query_embeddings,  # type: ignore[arg-type]
             n_results=n_results,
-            where=where,  # type: ignore
-            where_document=where_document,  # type: ignore
+            where=where,  # type: ignore[arg-type]
+            where_document=where_document,  # type: ignore[arg-type]
             **kwargs,
         )
 
@@ -432,11 +438,12 @@ class Chroma(VectorStore):
         if ids is None:
             ids = [str(uuid.uuid4()) for _ in uris]
         else:
-            ids = [id if id is not None else str(uuid.uuid4()) for id in ids]
+            ids = [id_ if id_ is not None else str(uuid.uuid4()) for id_ in ids]
         embeddings = None
         # Set embeddings
         if self._embedding_function is not None and hasattr(
-            self._embedding_function, "embed_image"
+            self._embedding_function,
+            "embed_image",
         ):
             embeddings = self._embedding_function.embed_image(uris=uris)
         if metadatas:
@@ -461,8 +468,8 @@ class Chroma(VectorStore):
                 ids_with_metadata = [ids[idx] for idx in non_empty_ids]
                 try:
                     self._collection.upsert(
-                        metadatas=metadatas,  # type: ignore
-                        embeddings=embeddings_with_metadatas,  # type: ignore
+                        metadatas=metadatas,  # type: ignore[arg-type]
+                        embeddings=embeddings_with_metadatas,  # type: ignore[arg-type]
                         documents=images_with_metadatas,
                         ids=ids_with_metadata,
                     )
@@ -473,8 +480,7 @@ class Chroma(VectorStore):
                             "langchain_community.vectorstores.utils.filter_complex_metadata."
                         )
                         raise ValueError(e.args[0] + "\n\n" + msg)
-                    else:
-                        raise e
+                    raise e
             if empty_ids:
                 images_without_metadatas = [b64_texts[j] for j in empty_ids]
                 embeddings_without_metadatas = (
@@ -519,7 +525,7 @@ class Chroma(VectorStore):
         if ids is None:
             ids = [str(uuid.uuid4()) for _ in texts]
         else:
-            ids = [id if id is not None else str(uuid.uuid4()) for id in ids]
+            ids = [id_ if id_ is not None else str(uuid.uuid4()) for id_ in ids]
 
         embeddings = None
         texts = list(texts)
@@ -549,8 +555,8 @@ class Chroma(VectorStore):
                 ids_with_metadata = [ids[idx] for idx in non_empty_ids]
                 try:
                     self._collection.upsert(
-                        metadatas=metadatas,  # type: ignore
-                        embeddings=embeddings_with_metadatas,  # type: ignore
+                        metadatas=metadatas,  # type: ignore[arg-type]
+                        embeddings=embeddings_with_metadatas,  # type: ignore[arg-type]
                         documents=texts_with_metadatas,
                         ids=ids_with_metadata,
                     )
@@ -561,8 +567,7 @@ class Chroma(VectorStore):
                             "langchain_community.vectorstores.utils.filter_complex_metadata."
                         )
                         raise ValueError(e.args[0] + "\n\n" + msg)
-                    else:
-                        raise e
+                    raise e
             if empty_ids:
                 texts_without_metadatas = [texts[j] for j in empty_ids]
                 embeddings_without_metadatas = (
@@ -570,13 +575,13 @@ class Chroma(VectorStore):
                 )
                 ids_without_metadatas = [ids[j] for j in empty_ids]
                 self._collection.upsert(
-                    embeddings=embeddings_without_metadatas,  # type: ignore
+                    embeddings=embeddings_without_metadatas,  # type: ignore[arg-type]
                     documents=texts_without_metadatas,
                     ids=ids_without_metadatas,
                 )
         else:
             self._collection.upsert(
-                embeddings=embeddings,  # type: ignore
+                embeddings=embeddings,  # type: ignore[arg-type]
                 documents=texts,
                 ids=ids,
             )
@@ -586,7 +591,7 @@ class Chroma(VectorStore):
         self,
         query: str,
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         **kwargs: Any,
     ) -> list[Document]:
         """Run similarity search with Chroma.
@@ -601,7 +606,10 @@ class Chroma(VectorStore):
             List of documents most similar to the query text.
         """
         docs_and_scores = self.similarity_search_with_score(
-            query, k, filter=filter, **kwargs
+            query,
+            k,
+            filter=filter,
+            **kwargs,
         )
         return [doc for doc, _ in docs_and_scores]
 
@@ -609,7 +617,7 @@ class Chroma(VectorStore):
         self,
         embedding: list[float],
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         where_document: Optional[dict[str, str]] = None,
         **kwargs: Any,
     ) -> list[Document]:
@@ -639,7 +647,7 @@ class Chroma(VectorStore):
         self,
         embedding: list[float],
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         where_document: Optional[dict[str, str]] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
@@ -670,7 +678,7 @@ class Chroma(VectorStore):
         self,
         query: str,
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         where_document: Optional[dict[str, str]] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
@@ -712,7 +720,7 @@ class Chroma(VectorStore):
         self,
         query: str,
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         where_document: Optional[dict[str, str]] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, np.ndarray]]:
@@ -780,22 +788,24 @@ class Chroma(VectorStore):
 
         if distance == "cosine":
             return self._cosine_relevance_score_fn
-        elif distance == "l2":
+        if distance == "l2":
             return self._euclidean_relevance_score_fn
-        elif distance == "ip":
+        if distance == "ip":
             return self._max_inner_product_relevance_score_fn
-        else:
-            raise ValueError(
-                "No supported normalization function"
-                f" for distance metric of type: {distance}."
-                "Consider providing relevance_score_fn to Chroma constructor."
-            )
+        msg = (
+            "No supported normalization function"
+            f" for distance metric of type: {distance}."
+            "Consider providing relevance_score_fn to Chroma constructor."
+        )
+        raise ValueError(
+            msg,
+        )
 
     def similarity_search_by_image(
         self,
         uri: str,
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         **kwargs: Any,
     ) -> list[Document]:
         """Search for similar images based on the given image URI.
@@ -817,29 +827,29 @@ class Chroma(VectorStore):
             ValueError: If the embedding function does not support image embeddings.
         """
         if self._embedding_function is None or not hasattr(
-            self._embedding_function, "embed_image"
+            self._embedding_function,
+            "embed_image",
         ):
-            raise ValueError("The embedding function must support image embedding.")
+            msg = "The embedding function must support image embedding."
+            raise ValueError(msg)
 
         # Obtain image embedding
         # Assuming embed_image returns a single embedding
         image_embedding = self._embedding_function.embed_image(uris=[uri])
 
         # Perform similarity search based on the obtained embedding
-        results = self.similarity_search_by_vector(
+        return self.similarity_search_by_vector(
             embedding=image_embedding,
             k=k,
             filter=filter,
             **kwargs,
         )
 
-        return results
-
     def similarity_search_by_image_with_relevance_score(
         self,
         uri: str,
         k: int = DEFAULT_K,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         """Search for similar images based on the given image URI.
@@ -861,31 +871,31 @@ class Chroma(VectorStore):
             ValueError: If the embedding function does not support image embeddings.
         """
         if self._embedding_function is None or not hasattr(
-            self._embedding_function, "embed_image"
+            self._embedding_function,
+            "embed_image",
         ):
-            raise ValueError("The embedding function must support image embedding.")
+            msg = "The embedding function must support image embedding."
+            raise ValueError(msg)
 
         # Obtain image embedding
         # Assuming embed_image returns a single embedding
         image_embedding = self._embedding_function.embed_image(uris=[uri])
 
         # Perform similarity search based on the obtained embedding
-        results = self.similarity_search_by_vector_with_relevance_scores(
+        return self.similarity_search_by_vector_with_relevance_scores(
             embedding=image_embedding,
             k=k,
             filter=filter,
             **kwargs,
         )
 
-        return results
-
     def max_marginal_relevance_search_by_vector(
         self,
         embedding: list[float],
         k: int = DEFAULT_K,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         where_document: Optional[dict[str, str]] = None,
         **kwargs: Any,
     ) -> list[Document]:
@@ -928,8 +938,7 @@ class Chroma(VectorStore):
 
         candidates = _results_to_docs(results)
 
-        selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
-        return selected_results
+        return [r for i, r in enumerate(candidates) if i in mmr_selected]
 
     def max_marginal_relevance_search(
         self,
@@ -937,7 +946,7 @@ class Chroma(VectorStore):
         k: int = DEFAULT_K,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: Optional[dict[str, str]] = None,
+        filter: Optional[dict[str, str]] = None,  # noqa: A002
         where_document: Optional[dict[str, str]] = None,
         **kwargs: Any,
     ) -> list[Document]:
@@ -966,8 +975,9 @@ class Chroma(VectorStore):
             ValueError: If the embedding function is not provided.
         """
         if self._embedding_function is None:
+            msg = "For MMR search, you must specify an embedding function on creation."
             raise ValueError(
-                "For MMR search, you must specify an embedding function on creation."
+                msg,
             )
 
         embedding = self._embedding_function.embed_query(query)
@@ -1032,7 +1042,7 @@ class Chroma(VectorStore):
         if include is not None:
             kwargs["include"] = include
 
-        return self._collection.get(**kwargs)  # type: ignore
+        return self._collection.get(**kwargs)  # type: ignore[arg-type, return-value]
 
     def get_by_ids(self, ids: Sequence[str], /) -> list[Document]:
         """Get documents by their IDs.
@@ -1062,7 +1072,9 @@ class Chroma(VectorStore):
         return [
             Document(page_content=doc, metadata=meta, id=doc_id)
             for doc, meta, doc_id in zip(
-                results["documents"], results["metadatas"], results["ids"]
+                results["documents"],
+                results["metadatas"],
+                results["ids"],
             )
         ]
 
@@ -1075,7 +1087,6 @@ class Chroma(VectorStore):
         """
         return self.update_documents([document_id], [document])
 
-    # type: ignore
     def update_documents(self, ids: list[str], documents: list[Document]) -> None:
         """Update a document in the collection.
 
@@ -1089,24 +1100,27 @@ class Chroma(VectorStore):
         text = [document.page_content for document in documents]
         metadata = [document.metadata for document in documents]
         if self._embedding_function is None:
+            msg = "For update, you must specify an embedding function on creation."
             raise ValueError(
-                "For update, you must specify an embedding function on creation."
+                msg,
             )
         embeddings = self._embedding_function.embed_documents(text)
 
         if hasattr(
-            self._collection._client, "get_max_batch_size"
+            self._collection._client,  # noqa: SLF001
+            "get_max_batch_size",
         ) or hasattr(  # for Chroma 0.5.1 and above
-            self._collection._client, "max_batch_size"
+            self._collection._client,  # noqa: SLF001
+            "max_batch_size",
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches
 
             for batch in create_batches(
-                api=self._collection._client,
+                api=self._collection._client,  # noqa: SLF001
                 ids=ids,
-                metadatas=metadata,  # type: ignore
+                metadatas=metadata,  # type: ignore[arg-type]
                 documents=text,
-                embeddings=embeddings,  # type: ignore
+                embeddings=embeddings,  # type: ignore[arg-type]
             ):
                 self._collection.update(
                     ids=batch[0],
@@ -1117,9 +1131,9 @@ class Chroma(VectorStore):
         else:
             self._collection.update(
                 ids=ids,
-                embeddings=embeddings,  # type: ignore
+                embeddings=embeddings,  # type: ignore[arg-type]
                 documents=text,
-                metadatas=metadata,  # type: ignore
+                metadatas=metadata,  # type: ignore[arg-type]
             )
 
     @classmethod
@@ -1170,23 +1184,25 @@ class Chroma(VectorStore):
         if ids is None:
             ids = [str(uuid.uuid4()) for _ in texts]
         else:
-            ids = [id if id is not None else str(uuid.uuid4()) for id in ids]
+            ids = [id_ if id_ is not None else str(uuid.uuid4()) for id_ in ids]
         if hasattr(
-            chroma_collection._client, "get_max_batch_size"
+            chroma_collection._client,  # noqa: SLF001
+            "get_max_batch_size",
         ) or hasattr(  # for Chroma 0.5.1 and above
-            chroma_collection._client, "max_batch_size"
+            chroma_collection._client,  # noqa: SLF001
+            "max_batch_size",
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches
 
             for batch in create_batches(
-                api=chroma_collection._client,
+                api=chroma_collection._client,  # noqa: SLF001
                 ids=ids,
-                metadatas=metadatas,  # type: ignore
+                metadatas=metadatas,  # type: ignore[arg-type]
                 documents=texts,
             ):
                 chroma_collection.add_texts(
                     texts=batch[3] if batch[3] else [],
-                    metadatas=batch[2] if batch[2] else None,  # type: ignore
+                    metadatas=batch[2] if batch[2] else None,  # type: ignore[arg-type]
                     ids=batch[0],
                 )
         else:
diff --git a/libs/partners/chroma/pyproject.toml b/libs/partners/chroma/pyproject.toml
index 73825651b47..dc1d6a6d5e1 100644
--- a/libs/partners/chroma/pyproject.toml
+++ b/libs/partners/chroma/pyproject.toml
@@ -62,8 +62,52 @@ disallow_untyped_defs = true
 target-version = "py39"
 
 [tool.ruff.lint]
-select = ["E", "F", "I", "T201", "D", "UP", "S"]
-ignore = [ "UP007", ]
+select = [
+    "A",      # flake8-builtins
+    "ASYNC",  # flake8-async
+    "C4",     # flake8-comprehensions
+    "COM",    # flake8-commas
+    "D",      # pydocstyle
+    "DOC",    # pydoclint
+    "E",      # pycodestyle error
+    "EM",     # flake8-errmsg
+    "F",      # pyflakes
+    "FA",     # flake8-future-annotations
+    "FBT",    # flake8-boolean-trap
+    "FLY",    # flake8-flynt
+    "I",      # isort
+    "ICN",    # flake8-import-conventions
+    "INT",    # flake8-gettext
+    "ISC",    # isort-comprehensions
+    "PGH",    # pygrep-hooks
+    "PIE",    # flake8-pie
+    "PERF",   # flake8-perf
+    "PYI",    # flake8-pyi
+    "Q",      # flake8-quotes
+    "RET",    # flake8-return
+    "RSE",    # flake8-rst-docstrings
+    "RUF",    # ruff
+    "S",      # flake8-bandit
+    "SLF",    # flake8-self
+    "SLOT",   # flake8-slots
+    "SIM",    # flake8-simplify
+    "T10",    # flake8-debugger
+    "T20",    # flake8-print
+    "TID",    # flake8-tidy-imports
+    "UP",     # pyupgrade
+    "W",      # pycodestyle warning
+    "YTT",    # flake8-2020
+]
+ignore = [
+    "D100",    # Missing docstring in public module
+    "D101",    # Missing docstring in public class
+    "D102",    # Missing docstring in public method
+    "D103",    # Missing docstring in public function
+    "D104",    # Missing docstring in public package
+    "D105",    # Missing docstring in magic method
+    "D107",    # Missing docstring in __init__
+    "UP007",   # pyupgrade: non-pep604-annotation-union
+]
 
 [tool.coverage.run]
 omit = ["tests/*"]
@@ -84,6 +128,7 @@ convention = "google"
 
 [tool.ruff.lint.extend-per-file-ignores]
 "tests/**/*.py" = [
-    "S101", # Tests need assertions
-    "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
+    "S101",   # Tests need assertions
+    "S311",   # Standard pseudo-random generators are not suitable for cryptographic purposes
+    "SLF001", # Private member access in tests
 ]
\ No newline at end of file
diff --git a/libs/partners/chroma/scripts/check_imports.py b/libs/partners/chroma/scripts/check_imports.py
index a7fcbc5ca63..b850913e53e 100644
--- a/libs/partners/chroma/scripts/check_imports.py
+++ b/libs/partners/chroma/scripts/check_imports.py
@@ -10,7 +10,7 @@ if __name__ == "__main__":
     for file in files:
         try:
             SourceFileLoader("x", file).load_module()
-        except Exception:
+        except Exception:  # noqa: PERF203
             has_failure = True
             print(file)  # noqa: T201
             traceback.print_exc()
diff --git a/libs/partners/chroma/tests/integration_tests/fake_embeddings.py b/libs/partners/chroma/tests/integration_tests/fake_embeddings.py
index e04f295782b..9f318b60cfe 100644
--- a/libs/partners/chroma/tests/integration_tests/fake_embeddings.py
+++ b/libs/partners/chroma/tests/integration_tests/fake_embeddings.py
@@ -44,7 +44,7 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
             if text not in self.known_texts:
                 self.known_texts.append(text)
             vector = [1.0] * (self.dimensionality - 1) + [
-                float(self.known_texts.index(text))
+                float(self.known_texts.index(text)),
             ]
             out_vectors.append(vector)
         return out_vectors
diff --git a/libs/partners/chroma/tests/integration_tests/test_compile.py b/libs/partners/chroma/tests/integration_tests/test_compile.py
index 5196f4c0f8c..5c812af3144 100644
--- a/libs/partners/chroma/tests/integration_tests/test_compile.py
+++ b/libs/partners/chroma/tests/integration_tests/test_compile.py
@@ -4,4 +4,3 @@ import pytest  # type: ignore[import-not-found]
 @pytest.mark.compile
 def test_placeholder() -> None:
     """Used for compiling integration tests without running any real tests."""
-    pass
diff --git a/libs/partners/chroma/tests/integration_tests/test_vectorstores.py b/libs/partners/chroma/tests/integration_tests/test_vectorstores.py
index a42ba95254c..f5794c3875c 100644
--- a/libs/partners/chroma/tests/integration_tests/test_vectorstores.py
+++ b/libs/partners/chroma/tests/integration_tests/test_vectorstores.py
@@ -28,8 +28,8 @@ class MyEmbeddingFunction:
     def __init__(self, fak: Fak):
         self.fak = fak
 
-    def __call__(self, input: Embeddable) -> list[list[float]]:
-        texts = cast(list[str], input)
+    def __call__(self, input_: Embeddable) -> list[list[float]]:
+        texts = cast(list[str], input_)
         return self.fak.embed_documents(texts=texts)
 
 
@@ -44,7 +44,9 @@ def test_chroma() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
     docsearch = Chroma.from_texts(
-        collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
     )
     output = docsearch.similarity_search("foo", k=1)
 
@@ -92,7 +94,9 @@ async def test_chroma_async() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
     docsearch = Chroma.from_texts(
-        collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
     )
     output = await docsearch.asimilarity_search("foo", k=1)
 
@@ -173,7 +177,7 @@ def test_chroma_with_metadatas_with_scores_and_ids() -> None:
     output = docsearch.similarity_search_with_score("foo", k=1)
     docsearch.delete_collection()
     assert output == [
-        (Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0)
+        (Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0),
     ]
 
 
@@ -211,11 +215,12 @@ def test_chroma_with_metadatas_with_scores_using_vector() -> None:
     )
     embedded_query = embeddings.embed_query("foo")
     output = docsearch.similarity_search_by_vector_with_relevance_scores(
-        embedding=embedded_query, k=1
+        embedding=embedded_query,
+        k=1,
     )
     docsearch.delete_collection()
     assert output == [
-        (Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0)
+        (Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0),
     ]
 
 
@@ -235,10 +240,10 @@ def test_chroma_search_filter() -> None:
     output2 = docsearch.similarity_search("far", k=1, filter={"first_letter": "b"})
     docsearch.delete_collection()
     assert output1 == [
-        Document(page_content="far", metadata={"first_letter": "f"}, id="id_0")
+        Document(page_content="far", metadata={"first_letter": "f"}, id="id_0"),
     ]
     assert output2 == [
-        Document(page_content="bar", metadata={"first_letter": "b"}, id="id_1")
+        Document(page_content="bar", metadata={"first_letter": "b"}, id="id_1"),
     ]
 
 
@@ -255,17 +260,21 @@ def test_chroma_search_filter_with_scores() -> None:
         ids=ids,
     )
     output1 = docsearch.similarity_search_with_score(
-        "far", k=1, filter={"first_letter": "f"}
+        "far",
+        k=1,
+        filter={"first_letter": "f"},
     )
     output2 = docsearch.similarity_search_with_score(
-        "far", k=1, filter={"first_letter": "b"}
+        "far",
+        k=1,
+        filter={"first_letter": "b"},
     )
     docsearch.delete_collection()
     assert output1 == [
-        (Document(page_content="far", metadata={"first_letter": "f"}, id="id_0"), 0.0)
+        (Document(page_content="far", metadata={"first_letter": "f"}, id="id_0"), 0.0),
     ]
     assert output2 == [
-        (Document(page_content="bar", metadata={"first_letter": "b"}, id="id_1"), 1.0)
+        (Document(page_content="bar", metadata={"first_letter": "b"}, id="id_1"), 1.0),
     ]
 
 
@@ -365,7 +374,9 @@ def test_chroma_mmr() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
     docsearch = Chroma.from_texts(
-        collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
     )
     output = docsearch.max_marginal_relevance_search("foo", k=1)
     docsearch.delete_collection()
@@ -379,7 +390,9 @@ def test_chroma_mmr_by_vector() -> None:
     texts = ["foo", "bar", "baz"]
     embeddings = FakeEmbeddings()
     docsearch = Chroma.from_texts(
-        collection_name="test_collection", texts=texts, embedding=embeddings
+        collection_name="test_collection",
+        texts=texts,
+        embedding=embeddings,
     )
     embedded_query = embeddings.embed_query("foo")
     output = docsearch.max_marginal_relevance_search_by_vector(embedded_query, k=1)
@@ -393,7 +406,9 @@ def test_chroma_with_include_parameter() -> None:
     """Test end to end construction and include parameter."""
     texts = ["foo", "bar", "baz"]
     docsearch = Chroma.from_texts(
-        collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
     )
     output1 = docsearch.get(include=["embeddings"])
     output2 = docsearch.get()
@@ -424,7 +439,7 @@ def test_chroma_update_document() -> None:
         embedding=embedding,
         ids=[document_id],
     )
-    old_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore
+    old_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore[index]
         docsearch._collection.peek()["ids"].index(document_id)
     ]
 
@@ -441,7 +456,7 @@ def test_chroma_update_document() -> None:
     output = docsearch.similarity_search(updated_content, k=1)
 
     # Assert that the new embedding is correct
-    new_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore
+    new_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore[index]
         docsearch._collection.peek()["ids"].index(document_id)
     ]
 
@@ -449,7 +464,7 @@ def test_chroma_update_document() -> None:
 
     # Assert that the updated document is returned by the search
     assert output == [
-        Document(page_content=updated_content, metadata={"page": "0"}, id=document_id)
+        Document(page_content=updated_content, metadata={"page": "0"}, id=document_id),
     ]
 
     assert list(new_embedding) == list(embedding.embed_documents([updated_content])[0])
@@ -470,7 +485,9 @@ def test_chroma_update_document_with_id() -> None:
 
     # Create an instance of Document with initial content and metadata
     original_doc = Document(
-        page_content=initial_content, metadata={"page": "0"}, id=document_id
+        page_content=initial_content,
+        metadata={"page": "0"},
+        id=document_id,
     )
 
     # Initialize a Chroma instance with the original document
@@ -479,7 +496,7 @@ def test_chroma_update_document_with_id() -> None:
         documents=[original_doc],
         embedding=embedding,
     )
-    old_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore
+    old_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore[index]
         docsearch._collection.peek()["ids"].index(document_id)
     ]
 
@@ -488,7 +505,9 @@ def test_chroma_update_document_with_id() -> None:
 
     # Create a new Document instance with the updated content and the same id
     updated_doc = Document(
-        page_content=updated_content, metadata={"page": "0"}, id=document_id
+        page_content=updated_content,
+        metadata={"page": "0"},
+        id=document_id,
     )
 
     # Update the document in the Chroma instance
@@ -498,7 +517,7 @@ def test_chroma_update_document_with_id() -> None:
     output = docsearch.similarity_search(updated_content, k=1)
 
     # Assert that the new embedding is correct
-    new_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore
+    new_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore[index]
         docsearch._collection.peek()["ids"].index(document_id)
     ]
 
@@ -506,7 +525,7 @@ def test_chroma_update_document_with_id() -> None:
 
     # Assert that the updated document is returned by the search
     assert output == [
-        Document(page_content=updated_content, metadata={"page": "0"}, id=document_id)
+        Document(page_content=updated_content, metadata={"page": "0"}, id=document_id),
     ]
 
     assert list(new_embedding) == list(embedding.embed_documents([updated_content])[0])
@@ -568,7 +587,8 @@ def test_chroma_add_documents_mixed_metadata() -> None:
 
     assert actual_ids == ids
     assert sorted(search, key=lambda d: d.page_content) == sorted(
-        docs, key=lambda d: d.page_content
+        docs,
+        key=lambda d: d.page_content,
     )
 
 
@@ -582,9 +602,7 @@ def is_api_accessible(url: str) -> bool:
 
 def batch_support_chroma_version() -> bool:
     major, minor, patch = chromadb.__version__.split(".")
-    if int(major) == 0 and int(minor) >= 4 and int(patch) >= 10:
-        return True
-    return False
+    return bool(int(major) == 0 and int(minor) >= 4 and int(patch) >= 10)
 
 
 @pytest.mark.requires("chromadb")
@@ -601,9 +619,9 @@ def test_chroma_large_batch() -> None:
     embedding_function = MyEmbeddingFunction(fak=Fak(size=255))
     col = client.get_or_create_collection(
         "my_collection",
-        embedding_function=embedding_function,  # type: ignore
+        embedding_function=embedding_function,  # type: ignore[arg-type]
     )
-    docs = ["This is a test document"] * (client.get_max_batch_size() + 100)  # type: ignore
+    docs = ["This is a test document"] * (client.get_max_batch_size() + 100)
     db = Chroma.from_texts(
         client=client,
         collection_name=col.name,
@@ -629,9 +647,9 @@ def test_chroma_large_batch_update() -> None:
     embedding_function = MyEmbeddingFunction(fak=Fak(size=255))
     col = client.get_or_create_collection(
         "my_collection",
-        embedding_function=embedding_function,  # type: ignore
+        embedding_function=embedding_function,  # type: ignore[arg-type]
     )
-    docs = ["This is a test document"] * (client.get_max_batch_size() + 100)  # type: ignore
+    docs = ["This is a test document"] * (client.get_max_batch_size() + 100)
     ids = [str(uuid.uuid4()) for _ in range(len(docs))]
     db = Chroma.from_texts(
         client=client,
@@ -642,11 +660,12 @@ def test_chroma_large_batch_update() -> None:
     )
     new_docs = [
         Document(
-            page_content="This is a new test document", metadata={"doc_id": f"{i}"}
+            page_content="This is a new test document",
+            metadata={"doc_id": f"{i}"},
         )
         for i in range(len(docs) - 10)
     ]
-    new_ids = [_id for _id in ids[: len(new_docs)]]
+    new_ids = list(ids[: len(new_docs)])
     db.update_documents(ids=new_ids, documents=new_docs)
 
     db.delete_collection()
@@ -658,14 +677,15 @@ def test_chroma_large_batch_update() -> None:
     reason="API not accessible",
 )
 @pytest.mark.skipif(
-    batch_support_chroma_version(), reason="ChromaDB version does not support batching"
+    batch_support_chroma_version(),
+    reason="ChromaDB version does not support batching",
 )
 def test_chroma_legacy_batching() -> None:
     client = chromadb.HttpClient()
     embedding_function = Fak(size=255)
     col = client.get_or_create_collection(
         "my_collection",
-        embedding_function=MyEmbeddingFunction,  # type: ignore
+        embedding_function=MyEmbeddingFunction,  # type: ignore[arg-type]
     )
     docs = ["This is a test document"] * 100
     db = Chroma.from_texts(
@@ -683,7 +703,9 @@ def test_create_collection_if_not_exist_default() -> None:
     """Tests existing behaviour without the new create_collection_if_not_exists flag."""
     texts = ["foo", "bar", "baz"]
     docsearch = Chroma.from_texts(
-        collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
     )
     assert docsearch._client.get_collection("test_collection") is not None
     docsearch.delete_collection()
@@ -798,7 +820,7 @@ def test_delete_where_clause(client: chromadb.ClientAPI) -> None:
         [
             Document(page_content="foo", metadata={"test": "bar"}),
             Document(page_content="bar", metadata={"test": "foo"}),
-        ]
+        ],
     )
     assert vectorstore._collection.count() == 2
     vectorstore.delete(where={"test": "bar"})
diff --git a/libs/partners/chroma/tests/unit_tests/test_standard.py b/libs/partners/chroma/tests/unit_tests/test_standard.py
index e933339a453..b2aa15d9a92 100644
--- a/libs/partners/chroma/tests/unit_tests/test_standard.py
+++ b/libs/partners/chroma/tests/unit_tests/test_standard.py
@@ -9,11 +9,10 @@ from langchain_chroma import Chroma
 
 class TestChromaStandard(VectorStoreIntegrationTests):
     @pytest.fixture()
-    def vectorstore(self) -> Generator[VectorStore, None, None]:  # type: ignore
+    def vectorstore(self) -> Generator[VectorStore, None, None]:  # type: ignore[override]
         """Get an empty vectorstore for unit tests."""
         store = Chroma(embedding_function=self.get_embeddings())
         try:
             yield store
         finally:
             store.delete_collection()
-            pass