From 95b55dabbe3f5bba879a12bf89b5115952513418 Mon Sep 17 00:00:00 2001
From: Leonid Kuligin <lkuligin@yandex.ru>
Date: Tue, 9 Apr 2024 19:29:15 +0200
Subject: [PATCH 1/9] adjusted a test (#126)

---
 libs/genai/tests/integration_tests/test_llms.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libs/genai/tests/integration_tests/test_llms.py b/libs/genai/tests/integration_tests/test_llms.py
index b761e148..dc9d3309 100644
--- a/libs/genai/tests/integration_tests/test_llms.py
+++ b/libs/genai/tests/integration_tests/test_llms.py
@@ -75,7 +75,7 @@ def test_safety_settings_gemini() -> None:
     llm = GoogleGenerativeAI(temperature=0, model="gemini-pro")
     output = llm.generate(prompts=["how to make a bomb?"])
     assert isinstance(output, LLMResult)
-    assert len(output.generations[0]) == 0
+    assert len(output.generations[0]) > 0
 
     # safety filters
     safety_settings = {

From d3bd39b3e13b9ce0bf9e09ac0ff2b10b1256142a Mon Sep 17 00:00:00 2001
From: Misha Brukman <mbrukman@google.com>
Date: Tue, 9 Apr 2024 13:36:01 -0400
Subject: [PATCH 2/9] Fix spelling and capitalization of LangChain (#122)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 48fe6a83..acd5d50f 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # 🦜️🔗 LangChain Google
 
-This repository contains two packages with Google integrations with Langhchain:
+This repository contains two packages with Google integrations with LangChain:
 - [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) implements integrations of Google [Generative AI](https://ai.google.dev/) models.
 - [langchain-google-vertexai](https://pypi.org/project/langchain-google-vertexai/) implements integrations of Google Cloud [Generative AI on Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview)

From e99df2237adf0de28b1c442de508683b9cf68d61 Mon Sep 17 00:00:00 2001
From: jzaldi <jzaldi89@gmail.com>
Date: Tue, 9 Apr 2024 19:39:51 +0200
Subject: [PATCH 3/9] [vectorstores] Implement BaseStore interface for document
 storage (#118)

* Implement BaseStore interface for document storage

---------

Co-authored-by: Jorge <jzaldivar@google.com>
---
 .../vectorstores/__init__.py                  |   6 +
 ...ocument_storage.py => document_storage.py} | 181 +++++++++---------
 .../vectorstores/vectorstores.py              |  36 ++--
 .../integration_tests/test_vectorstores.py    |  31 +--
 4 files changed, 129 insertions(+), 125 deletions(-)
 rename libs/vertexai/langchain_google_vertexai/vectorstores/{_document_storage.py => document_storage.py} (59%)

diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/__init__.py b/libs/vertexai/langchain_google_vertexai/vectorstores/__init__.py
index 5a895ebc..8729124d 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/__init__.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/__init__.py
@@ -1,3 +1,7 @@
+from langchain_google_vertexai.vectorstores.document_storage import (
+    DataStoreDocumentStorage,
+    GCSDocumentStorage,
+)
 from langchain_google_vertexai.vectorstores.vectorstores import (
     VectorSearchVectorStore,
     VectorSearchVectorStoreDatastore,
@@ -8,4 +12,6 @@
     "VectorSearchVectorStore",
     "VectorSearchVectorStoreDatastore",
     "VectorSearchVectorStoreGCS",
+    "DataStoreDocumentStorage",
+    "GCSDocumentStorage",
 ]
diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/_document_storage.py b/libs/vertexai/langchain_google_vertexai/vectorstores/document_storage.py
similarity index 59%
rename from libs/vertexai/langchain_google_vertexai/vectorstores/_document_storage.py
rename to libs/vertexai/langchain_google_vertexai/vectorstores/document_storage.py
index eb6c4611..0decd418 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/_document_storage.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/document_storage.py
@@ -1,51 +1,48 @@
 from __future__ import annotations
 
 import json
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Sequence, Tuple
 
 from google.cloud import storage  # type: ignore[attr-defined, unused-ignore]
 from langchain_core.documents import Document
+from langchain_core.stores import BaseStore
 
 if TYPE_CHECKING:
     from google.cloud import datastore  # type: ignore[attr-defined, unused-ignore]
 
 
-class DocumentStorage(ABC):
+class DocumentStorage(BaseStore[str, Document]):
     """Abstract interface of a key, text storage for retrieving documents."""
 
-    @abstractmethod
-    def get_by_id(self, document_id: str) -> Document | None:
-        """Gets  a document by its id. If not found, returns None.
-        Args:
-            document_id: Id of the document to get from the storage.
-        Returns:
-            Document if found, otherwise None.
-        """
-        raise NotImplementedError()
 
-    @abstractmethod
-    def store_by_id(self, document_id: str, document: Document):
-        """Stores a document associated to a document_id.
+class GCSDocumentStorage(DocumentStorage):
+    """Stores documents in Google Cloud Storage.
+    For each pair id, document_text the name of the blob will be {prefix}/{id} stored
+    in plain text format.
+    """
+
+    def __init__(
+        self, bucket: storage.Bucket, prefix: Optional[str] = "documents"
+    ) -> None:
+        """Constructor.
         Args:
-            document_id: Id of the document to be stored.
-            document: Document to be stored.
+            bucket: Bucket where the documents will be stored.
+            prefix: Prefix that is prepended to all document names.
         """
-        raise NotImplementedError()
+        super().__init__()
+        self._bucket = bucket
+        self._prefix = prefix
+
+    def mset(self, key_value_pairs: Sequence[Tuple[str, Document]]) -> None:
+        """Stores a series of documents using each keys
 
-    def batch_store_by_id(self, ids: List[str], documents: List[Document]) -> None:
-        """Stores a list of ids and documents in batch.
-        The default implementation only loops to the individual `store_by_id`.
-        Subclasses that have faster ways to store data via batch uploading should
-        implement the proper way.
         Args:
-            ids: List of ids for the text.
-            documents: List of documents.
+            key_value_pairs (Sequence[Tuple[K, V]]): A sequence of key-value pairs.
         """
-        for id_, document in zip(ids, documents):
-            self.store_by_id(id_, document)
+        for key, value in key_value_pairs:
+            self._set_one(key, value)
 
-    def batch_get_by_id(self, ids: List[str]) -> List[Document | None]:
+    def mget(self, keys: Sequence[str]) -> List[Optional[Document]]:
         """Gets a batch of documents by id.
         The default implementation only loops `get_by_id`.
         Subclasses that have faster ways to retrieve data by batch should implement
@@ -56,36 +53,35 @@ def batch_get_by_id(self, ids: List[str]) -> List[Document | None]:
             List of documents. If the key id is not found for any id record returns a
                 None instead.
         """
-        return [self.get_by_id(id_) for id_ in ids]
+        return [self._get_one(key) for key in keys]
 
+    def mdelete(self, keys: Sequence[str]) -> None:
+        """Deletes a batch of documents by id.
 
-class GCSDocumentStorage(DocumentStorage):
-    """Stores documents in Google Cloud Storage.
-    For each pair id, document_text the name of the blob will be {prefix}/{id} stored
-    in plain text format.
-    """
+        Args:
+            keys: List of ids for the text.
+        """
+        for key in keys:
+            self._delete_one(key)
+
+    def yield_keys(self, *, prefix: str | None = None) -> Iterator[str]:
+        """Yields the keys present in the storage.
 
-    def __init__(
-        self, bucket: storage.Bucket, prefix: Optional[str] = "documents"
-    ) -> None:
-        """Constructor.
         Args:
-            bucket: Bucket where the documents will be stored.
-            prefix: Prefix that is prepended to all document names.
+            prefix: Ignored. Uses the prefix provided in the constructor.
         """
-        super().__init__()
-        self._bucket = bucket
-        self._prefix = prefix
+        for blob in self._bucket.list_blobs(prefix=self._prefix):
+            yield blob.name.split("/")[-1]
 
-    def get_by_id(self, document_id: str) -> Document | None:
+    def _get_one(self, key: str) -> Document | None:
         """Gets the text of a document by its id. If not found, returns None.
         Args:
-            document_id: Id of the document to get from the storage.
+            key: Id of the document to get from the storage.
         Returns:
             Document if found, otherwise None.
         """
 
-        blob_name = self._get_blob_name(document_id)
+        blob_name = self._get_blob_name(key)
         existing_blob = self._bucket.get_blob(blob_name)
 
         if existing_blob is None:
@@ -95,19 +91,29 @@ def get_by_id(self, document_id: str) -> Document | None:
         document_json: Dict[str, Any] = json.loads(document_str)
         return Document(**document_json)
 
-    def store_by_id(self, document_id: str, document: Document) -> None:
+    def _set_one(self, key: str, value: Document) -> None:
         """Stores a document text associated to a document_id.
         Args:
-            document_id: Id of the document to be stored.
+            key: Id of the document to be stored.
             document: Document to be stored.
         """
-        blob_name = self._get_blob_name(document_id)
+        blob_name = self._get_blob_name(key)
         new_blow = self._bucket.blob(blob_name)
 
-        document_json = document.dict()
+        document_json = value.dict()
         document_text = json.dumps(document_json)
         new_blow.upload_from_string(document_text)
 
+    def _delete_one(self, key: str) -> None:
+        """Deletes one document by its key.
+
+        Args:
+            key (str): Id of the document to delete.
+        """
+        blob_name = self._get_blob_name(key)
+        blob = self._bucket.blob(blob_name)
+        blob.delete()
+
     def _get_blob_name(self, document_id: str) -> str:
         """Builds a blob name using the prefix and the document_id.
         Args:
@@ -139,40 +145,7 @@ def __init__(
         self._metadata_property_name = metadata_property_name
         self._kind = kind
 
-    def get_by_id(self, document_id: str) -> Document | None:
-        """Gets the text of a document by its id. If not found, returns None.
-        Args:
-            document_id: Id of the document to get from the storage.
-        Returns:
-            Text of the document if found, otherwise None.
-        """
-        key = self._client.key(self._kind, document_id)
-        entity = self._client.get(key)
-
-        if entity is None:
-            return None
-
-        return Document(
-            page_content=entity[self._text_property_name],
-            metadata=self._convert_entity_to_dict(entity[self._metadata_property_name]),
-        )
-
-    def store_by_id(self, document_id: str, document: Document) -> None:
-        """Stores a document text associated to a document_id.
-        Args:
-            document_id: Id of the document to be stored.
-            text: Text of the document to be stored.
-        """
-        with self._client.transaction():
-            key = self._client.key(self._kind, document_id)
-
-            entity = self._client.entity(key=key)
-            entity[self._text_property_name] = document.page_content
-            entity[self._metadata_property_name] = document.metadata
-
-            self._client.put(entity)
-
-    def batch_get_by_id(self, ids: List[str]) -> List[Document | None]:
+    def mget(self, keys: Sequence[str]) -> List[Optional[Document]]:
         """Gets a batch of documents by id.
         Args:
             ids: List of ids for the text.
@@ -180,15 +153,14 @@ def batch_get_by_id(self, ids: List[str]) -> List[Document | None]:
             List of texts. If the key id is not found for any id record returns a None
                 instead.
         """
-        keys = [self._client.key(self._kind, id_) for id_ in ids]
+        ds_keys = [self._client.key(self._kind, id_) for id_ in keys]
 
-        # TODO: Handle when a key is not present
-        entities = self._client.get_multi(keys)
+        entities = self._client.get_multi(ds_keys)
 
         # Entities are not sorted by key by default, the order is unclear. This orders
         # the list by the id retrieved.
         entity_id_lookup = {entity.key.id_or_name: entity for entity in entities}
-        entities = [entity_id_lookup[id_] for id_ in ids]
+        entities = [entity_id_lookup.get(id_) for id_ in keys]
 
         return [
             Document(
@@ -197,15 +169,19 @@ def batch_get_by_id(self, ids: List[str]) -> List[Document | None]:
                     entity[self._metadata_property_name]
                 ),
             )
+            if entity is not None
+            else None
             for entity in entities
         ]
 
-    def batch_store_by_id(self, ids: List[str], documents: List[Document]) -> None:
-        """Stores a list of ids and documents in batch.
+    def mset(self, key_value_pairs: Sequence[Tuple[str, Document]]) -> None:
+        """Stores a series of documents using each keys
+
         Args:
-            ids: List of ids for the text.
-            texts: List of texts.
+            key_value_pairs (Sequence[Tuple[K, V]]): A sequence of key-value pairs.
         """
+        ids = [key for key, _ in key_value_pairs]
+        documents = [document for _, document in key_value_pairs]
 
         with self._client.transaction():
             keys = [self._client.key(self._kind, id_) for id_ in ids]
@@ -219,6 +195,27 @@ def batch_store_by_id(self, ids: List[str], documents: List[Document]) -> None:
 
             self._client.put_multi(entities)
 
+    def mdelete(self, keys: Sequence[str]) -> None:
+        """Deletes a sequence of documents by key.
+
+        Args:
+            keys (Sequence[str]): A sequence of keys to delete.
+        """
+        with self._client.transaction():
+            keys = [self._client.key(self._kind, id_) for id_ in keys]
+            self._client.delete_multi(keys)
+
+    def yield_keys(self, *, prefix: str | None = None) -> Iterator[str]:
+        """Yields the keys of all documents in the storage.
+
+        Args:
+            prefix: Ignored
+        """
+        query = self._client.query(kind=self._kind)
+        query.keys_only()
+        for entity in query.fetch():
+            yield str(entity.key.id_or_name)
+
     def _convert_entity_to_dict(self, entity: datastore.Entity) -> Dict[str, Any]:
         """Recursively transform an entity into a plain dictionary."""
         from google.cloud import datastore  # type: ignore[attr-defined, unused-ignore]
diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
index 780f2d16..26d93578 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
@@ -10,16 +10,16 @@
 from langchain_core.embeddings import Embeddings
 from langchain_core.vectorstores import VectorStore
 
-from langchain_google_vertexai.vectorstores._document_storage import (
-    DataStoreDocumentStorage,
-    DocumentStorage,
-    GCSDocumentStorage,
-)
 from langchain_google_vertexai.vectorstores._sdk_manager import VectorSearchSDKManager
 from langchain_google_vertexai.vectorstores._searcher import (
     Searcher,
     VectorSearchSearcher,
 )
+from langchain_google_vertexai.vectorstores.document_storage import (
+    DataStoreDocumentStorage,
+    DocumentStorage,
+    GCSDocumentStorage,
+)
 
 
 class _BaseVertexAIVectorStore(VectorStore):
@@ -115,19 +115,19 @@ def similarity_search_by_vector_with_score(
             embeddings=[embedding], k=k, filter_=filter, numeric_filter=numeric_filter
         )
 
-        results = []
-
-        for neighbor_id, distance in neighbors_list[0]:
-            document = self._document_storage.get_by_id(neighbor_id)
+        keys = [key for key, _ in neighbors_list[0]]
+        distances = [distance for _, distance in neighbors_list[0]]
+        documents = self._document_storage.mget(keys)
 
-            if document is None:
-                raise ValueError(
-                    f"Document with id {neighbor_id} not found in document" "storage."
-                )
-
-            results.append((document, distance))
-
-        return results
+        if all(document is not None for document in documents):
+            # Ignore typing because mypy doesn't seem to be able to identify that
+            # in documents there is no possibility to have None values with the
+            # check above.
+            return list(zip(documents, distances))  # type: ignore
+        else:
+            missing_docs = [key for key, doc in zip(keys, documents) if doc is None]
+            message = f"Documents with ids: {missing_docs} not found in the storage"
+            raise ValueError(message)
 
     def similarity_search(
         self,
@@ -197,7 +197,7 @@ def add_texts(
             for text, metadata in zip(texts, metadatas)
         ]
 
-        self._document_storage.batch_store_by_id(ids=ids, documents=documents)
+        self._document_storage.mset(list(zip(ids, documents)))
 
         embeddings = self._embeddings.embed_documents(texts)
 
diff --git a/libs/vertexai/tests/integration_tests/test_vectorstores.py b/libs/vertexai/tests/integration_tests/test_vectorstores.py
index c23e6940..4d502b8b 100644
--- a/libs/vertexai/tests/integration_tests/test_vectorstores.py
+++ b/libs/vertexai/tests/integration_tests/test_vectorstores.py
@@ -27,15 +27,15 @@
 from langchain_core.documents import Document
 
 from langchain_google_vertexai.embeddings import VertexAIEmbeddings
-from langchain_google_vertexai.vectorstores._document_storage import (
-    DataStoreDocumentStorage,
-    DocumentStorage,
-    GCSDocumentStorage,
-)
 from langchain_google_vertexai.vectorstores._sdk_manager import VectorSearchSDKManager
 from langchain_google_vertexai.vectorstores._searcher import (
     VectorSearchSearcher,
 )
+from langchain_google_vertexai.vectorstores.document_storage import (
+    DataStoreDocumentStorage,
+    DocumentStorage,
+    GCSDocumentStorage,
+)
 from langchain_google_vertexai.vectorstores.vectorstores import (
     VectorSearchVectorStore,
     VectorSearchVectorStoreDatastore,
@@ -116,7 +116,6 @@ def test_vector_search_sdk_manager(sdk_manager: VectorSearchSDKManager):
     "storage_class", ["gcs_document_storage", "datastore_document_storage"]
 )
 def test_document_storage(
-    sdk_manager: VectorSearchSDKManager,
     storage_class: str,
     request: pytest.FixtureRequest,
 ):
@@ -132,19 +131,21 @@ def test_document_storage(
     ]
     ids = [str(uuid4()) for i in range(N)]
 
-    # Test individual retrieval
-    for id, document in zip(ids, documents):
-        document_storage.store_by_id(document_id=id, document=document)
-        retrieved = document_storage.get_by_id(document_id=id)
-        assert document == retrieved
-
-    # Test batch regtrieval
-    document_storage.batch_store_by_id(ids, documents)
-    retrieved_documents = document_storage.batch_get_by_id(ids)
+    # Test batch storage and retrieval
+    document_storage.mset(list(zip(ids, documents)))
+    retrieved_documents = document_storage.mget(ids)
 
     for og_document, retrieved_document in zip(documents, retrieved_documents):
         assert og_document == retrieved_document
 
+    # Test key yielding
+    keys = list(document_storage.yield_keys())
+    assert all(id in keys for id in ids)
+
+    # Test deletion
+    document_storage.mdelete(ids)
+    assert all(item is None for item in document_storage.mget(ids))
+
 
 @pytest.mark.extended
 def test_public_endpoint_vector_searcher(sdk_manager: VectorSearchSDKManager):

From 2d02b1235a812e0cffdad472c12fe9c395b06a42 Mon Sep 17 00:00:00 2001
From: Leonid Kuligin <lkuligin@yandex.ru>
Date: Tue, 9 Apr 2024 21:04:30 +0200
Subject: [PATCH 4/9] added system message support (#128)

---
 .../langchain_google_genai/chat_models.py     |  42 +-
 libs/genai/poetry.lock                        | 446 ++++++++++--------
 libs/genai/pyproject.toml                     |   4 +-
 .../integration_tests/test_chat_models.py     |  22 +-
 .../tests/unit_tests/test_chat_models.py      |   7 +-
 5 files changed, 275 insertions(+), 246 deletions(-)

diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py
index 2c95df24..e2f095af 100644
--- a/libs/genai/langchain_google_genai/chat_models.py
+++ b/libs/genai/langchain_google_genai/chat_models.py
@@ -4,6 +4,7 @@
 import json
 import logging
 import os
+import warnings
 from io import BytesIO
 from typing import (
     Any,
@@ -300,27 +301,16 @@ def _convert_to_parts(
 
 def _parse_chat_history(
     input_messages: Sequence[BaseMessage], convert_system_message_to_human: bool = False
-) -> List[genai.types.ContentDict]:
+) -> Tuple[Optional[genai.types.ContentDict], List[genai.types.ContentDict]]:
     messages: List[genai.types.MessageDict] = []
 
-    raw_system_message: Optional[SystemMessage] = None
-    for i, message in enumerate(input_messages):
-        if (
-            i == 0
-            and isinstance(message, SystemMessage)
-            and not convert_system_message_to_human
-        ):
-            raise ValueError(
-                """SystemMessages are not yet supported!
-
-To automatically convert the leading SystemMessage to a HumanMessage,
-set  `convert_system_message_to_human` to True. Example:
+    if convert_system_message_to_human:
+        warnings.warn("Convert_system_message_to_human will be deprecated!")
 
-llm = ChatGoogleGenerativeAI(model="gemini-pro", convert_system_message_to_human=True)
-"""
-            )
-        elif i == 0 and isinstance(message, SystemMessage):
-            raw_system_message = message
+    system_instruction: Optional[genai.types.ContentDict] = None
+    for i, message in enumerate(input_messages):
+        if i == 0 and isinstance(message, SystemMessage):
+            system_instruction = _convert_to_parts(message.content)
             continue
         elif isinstance(message, AIMessage):
             role = "model"
@@ -365,16 +355,8 @@ def _parse_chat_history(
                 f"Unexpected message with type {type(message)} at the position {i}."
             )
 
-        if raw_system_message:
-            if role == "model":
-                raise ValueError(
-                    "SystemMessage should be followed by a HumanMessage and "
-                    "not by AIMessage."
-                )
-            parts = _convert_to_parts(raw_system_message.content) + parts
-            raw_system_message = None
         messages.append({"role": role, "parts": parts})
-    return messages
+    return system_instruction, messages
 
 
 def _parse_response_candidate(
@@ -659,11 +641,15 @@ def _prepare_chat(
             )
 
         params = self._prepare_params(stop, **kwargs)
-        history = _parse_chat_history(
+        system_instruction, history = _parse_chat_history(
             messages,
             convert_system_message_to_human=self.convert_system_message_to_human,
         )
         message = history.pop()
+        if self.client._system_instruction != system_instruction:
+            self.client = genai.GenerativeModel(
+                model_name=self.model, system_instruction=system_instruction
+            )
         chat = client.start_chat(history=history)
         return params, chat, message
 
diff --git a/libs/genai/poetry.lock b/libs/genai/poetry.lock
index f440dbae..a76c7f6d 100644
--- a/libs/genai/poetry.lock
+++ b/libs/genai/poetry.lock
@@ -11,28 +11,6 @@ files = [
     {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
 ]
 
-[[package]]
-name = "anyio"
-version = "4.3.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
-]
-
-[package.dependencies]
-exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
-idna = ">=2.8"
-sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
-
-[package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
-
 [[package]]
 name = "cachetools"
 version = "5.3.3"
@@ -212,29 +190,30 @@ python-dateutil = ">=2.7"
 
 [[package]]
 name = "google-ai-generativelanguage"
-version = "0.4.0"
+version = "0.6.1"
 description = "Google Ai Generativelanguage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-ai-generativelanguage-0.4.0.tar.gz", hash = "sha256:c8199066c08f74c4e91290778329bb9f357ba1ea5d6f82de2bc0d10552bf4f8c"},
-    {file = "google_ai_generativelanguage-0.4.0-py3-none-any.whl", hash = "sha256:e4c425376c1ee26c78acbc49a24f735f90ebfa81bf1a06495fae509a2433232c"},
+    {file = "google-ai-generativelanguage-0.6.1.tar.gz", hash = "sha256:4abf37000718b20c43f4b90672b3ab8850738b02457efffd11f3184e03272ed2"},
+    {file = "google_ai_generativelanguage-0.6.1-py3-none-any.whl", hash = "sha256:d2afc991c47663bdf65bd4aabcd89723550b81ad0a6d0be8bfb0160755da4cf0"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-api-core"
-version = "2.17.1"
+version = "2.18.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.17.1.tar.gz", hash = "sha256:9df18a1f87ee0df0bc4eea2770ebc4228392d8cc4066655b320e2cfccb15db95"},
-    {file = "google_api_core-2.17.1-py3-none-any.whl", hash = "sha256:610c5b90092c360736baccf17bd3efbcb30dd380e7a6dc28a71059edb8bd0d8e"},
+    {file = "google-api-core-2.18.0.tar.gz", hash = "sha256:62d97417bfc674d6cef251e5c4d639a9655e00c45528c4364fbfebb478ce72a9"},
+    {file = "google_api_core-2.18.0-py3-none-any.whl", hash = "sha256:5a63aa102e0049abe85b5b88cb9409234c1f70afcda21ce1e40b285b9629c1d6"},
 ]
 
 [package.dependencies]
@@ -248,6 +227,7 @@ grpcio-status = [
     {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
     {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
 requests = ">=2.18.0,<3.0.0.dev0"
 
@@ -256,15 +236,33 @@ grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status
 grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
+[[package]]
+name = "google-api-python-client"
+version = "2.125.0"
+description = "Google API Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "google-api-python-client-2.125.0.tar.gz", hash = "sha256:51a0385cff65ec135106e8be60ee7112557396dde5f44113ae23912baddda143"},
+    {file = "google_api_python_client-2.125.0-py2.py3-none-any.whl", hash = "sha256:0a62b60fbd61b61a455f15d925264b3301099b67cafd2d33cf8bf151f1fca4f4"},
+]
+
+[package.dependencies]
+google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0.dev0"
+google-auth = ">=1.32.0,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0.dev0"
+google-auth-httplib2 = ">=0.2.0,<1.0.0"
+httplib2 = ">=0.19.0,<1.dev0"
+uritemplate = ">=3.0.1,<5"
+
 [[package]]
 name = "google-auth"
-version = "2.28.2"
+version = "2.29.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.28.2.tar.gz", hash = "sha256:80b8b4969aa9ed5938c7828308f20f035bc79f9d8fb8120bf9dc8db20b41ba30"},
-    {file = "google_auth-2.28.2-py2.py3-none-any.whl", hash = "sha256:9fd67bbcd40f16d9d42f950228e9cf02a2ded4ae49198b27432d0cded5a74c38"},
+    {file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
+    {file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
 ]
 
 [package.dependencies]
@@ -279,19 +277,35 @@ pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
+[[package]]
+name = "google-auth-httplib2"
+version = "0.2.0"
+description = "Google Authentication Library: httplib2 transport"
+optional = false
+python-versions = "*"
+files = [
+    {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
+    {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
+]
+
+[package.dependencies]
+google-auth = "*"
+httplib2 = ">=0.19.0"
+
 [[package]]
 name = "google-generativeai"
-version = "0.4.1"
+version = "0.5.0"
 description = "Google Generative AI High level API client library and tools."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "google_generativeai-0.4.1-py3-none-any.whl", hash = "sha256:89be3c00c2e688108fccefc50f47f45fc9d37ecd53c1ade9d86b5d982919c24a"},
+    {file = "google_generativeai-0.5.0-py3-none-any.whl", hash = "sha256:207ed12c6a2eeab549a45abbf5373c82077f62b16030bdb502556c78f6d1b5d2"},
 ]
 
 [package.dependencies]
-google-ai-generativelanguage = "0.4.0"
+google-ai-generativelanguage = "0.6.1"
 google-api-core = "*"
+google-api-python-client = "*"
 google-auth = ">=2.15.0"
 protobuf = "*"
 pydantic = "*"
@@ -400,6 +414,20 @@ googleapis-common-protos = ">=1.5.5"
 grpcio = ">=1.62.1"
 protobuf = ">=4.21.6"
 
+[[package]]
+name = "httplib2"
+version = "0.22.0"
+description = "A comprehensive HTTP client library."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "httplib2-0.22.0-py3-none-any.whl", hash = "sha256:14ae0a53c1ba8f3d37e9e27cf37eabb0fb9980f435ba405d546948b009dd64dc"},
+    {file = "httplib2-0.22.0.tar.gz", hash = "sha256:d7a10bc5ef5ab08322488bde8c726eeee5c8618723fdb399597ec58f3d82df81"},
+]
+
+[package.dependencies]
+pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0.2,<3.0.3 || >3.0.3,<4", markers = "python_version > \"3.0\""}
+
 [[package]]
 name = "idna"
 version = "3.6"
@@ -449,23 +477,21 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.1.31"
+version = "0.1.40"
 description = "Building applications with LLMs through composability"
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.1.31-py3-none-any.whl", hash = "sha256:ff028f00db8ff03565b542cea81be27426022a72c6545b54d8de66fa00948ab3"},
-    {file = "langchain_core-0.1.31.tar.gz", hash = "sha256:d660cf209bb6ce61cb1c853107b091aaa809015a55dce9e0ce19b51d4c8f2a70"},
+    {file = "langchain_core-0.1.40-py3-none-any.whl", hash = "sha256:618dbb7ab44d8b263b91e384db1ff07d0db256ae5bdafa0123a115b6a75a13f1"},
+    {file = "langchain_core-0.1.40.tar.gz", hash = "sha256:34c06fc0e6d3534b738c63f85403446b4be71161665b7e091f9bb19c914ec100"},
 ]
 
 [package.dependencies]
-anyio = ">=3,<5"
 jsonpatch = ">=1.33,<2.0"
 langsmith = ">=0.1.0,<0.2.0"
 packaging = ">=23.2,<24.0"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
-requests = ">=2,<3"
 tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
@@ -473,13 +499,13 @@ extended-testing = ["jinja2 (>=3,<4)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.24"
+version = "0.1.42"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
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+    {file = "langsmith-0.1.42-py3-none-any.whl", hash = "sha256:1101c3b5cbd9e8d65471f32fbb99736403f1bc30954fdd233b2991a40c65aa03"},
+    {file = "langsmith-0.1.42.tar.gz", hash = "sha256:e41236fd043c83a39329913ec607ae31cd46dad78a09c4924eab4a29e954da17"},
 ]
 
 [package.dependencies]
@@ -595,61 +621,62 @@ files = [
 
 [[package]]
 name = "orjson"
-version = "3.9.15"
+version = "3.10.0"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pillow-10.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e2ec1e921fd07c7cda7962bad283acc2f2a9ccc1b971ee4b216b75fad6f0463"},
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+    {file = "pillow-10.3.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:16563993329b79513f59142a6b02055e10514c1a8e86dca8b48a893e33cf91e3"},
+    {file = "pillow-10.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:dd78700f5788ae180b5ee8902c6aea5a5726bac7c364b202b4b3e3ba2d293170"},
+    {file = "pillow-10.3.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:aff76a55a8aa8364d25400a210a65ff59d0168e0b4285ba6bf2bd83cf675ba32"},
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+    {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:793b4e24db2e8742ca6423d3fde8396db336698c55cd34b660663ee9e45ed37f"},
+    {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d93480005693d247f8346bc8ee28c72a2191bdf1f6b5db469c096c0c867ac015"},
+    {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c83341b89884e2b2e55886e8fbbf37c3fa5efd6c8907124aeb72f285ae5696e5"},
+    {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1a1d1915db1a4fdb2754b9de292642a39a7fb28f1736699527bb649484fb966a"},
+    {file = "pillow-10.3.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a0eaa93d054751ee9964afa21c06247779b90440ca41d184aeb5d410f20ff591"},
+    {file = "pillow-10.3.0.tar.gz", hash = "sha256:9d2455fbf44c914840c793e89aa82d0e1763a14253a000743719ae5946814b2d"},
 ]
 
 [package.extras]
@@ -802,28 +830,28 @@ files = [
 
 [[package]]
 name = "pyasn1"
-version = "0.5.1"
+version = "0.6.0"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pyasn1-0.5.1-py2.py3-none-any.whl", hash = "sha256:4439847c58d40b1d0a573d07e3856e95333f1976294494c325775aeca506eb58"},
-    {file = "pyasn1-0.5.1.tar.gz", hash = "sha256:6d391a96e59b23130a5cfa74d6fd7f388dbbe26cc8f1edf39fdddf08d9d6676c"},
+    {file = "pyasn1-0.6.0-py2.py3-none-any.whl", hash = "sha256:cca4bb0f2df5504f02f6f8a775b6e416ff9b0b3b16f7ee80b5a3153d9b804473"},
+    {file = "pyasn1-0.6.0.tar.gz", hash = "sha256:3a35ab2c4b5ef98e17dfdec8ab074046fbda76e281c5a706ccd82328cfc8f64c"},
 ]
 
 [[package]]
 name = "pyasn1-modules"
-version = "0.3.0"
+version = "0.4.0"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pyasn1_modules-0.3.0-py2.py3-none-any.whl", hash = "sha256:d3ccd6ed470d9ffbc716be08bd90efbd44d0734bc9303818f7336070984a162d"},
-    {file = "pyasn1_modules-0.3.0.tar.gz", hash = "sha256:5bd01446b736eb9d31512a30d46c1ac3395d676c6f3cafa4c03eb54b9925631c"},
+    {file = "pyasn1_modules-0.4.0-py3-none-any.whl", hash = "sha256:be04f15b66c206eed667e0bb5ab27e2b1855ea54a842e5037738099e8ca4ae0b"},
+    {file = "pyasn1_modules-0.4.0.tar.gz", hash = "sha256:831dbcea1b177b28c9baddf4c6d1013c24c3accd14a1873fffaa6a2e905f17b6"},
 ]
 
 [package.dependencies]
-pyasn1 = ">=0.4.6,<0.6.0"
+pyasn1 = ">=0.4.6,<0.7.0"
 
 [[package]]
 name = "pydantic"
@@ -935,6 +963,20 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
+name = "pyparsing"
+version = "3.1.2"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
+    {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pytest"
 version = "7.4.4"
@@ -977,17 +1019,17 @@ testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy
 
 [[package]]
 name = "pytest-mock"
-version = "3.12.0"
+version = "3.14.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-mock-3.12.0.tar.gz", hash = "sha256:31a40f038c22cad32287bb43932054451ff5583ff094bca6f675df2f8bc1a6e9"},
-    {file = "pytest_mock-3.12.0-py3-none-any.whl", hash = "sha256:0972719a7263072da3a21c7f4773069bcc7486027d7e8e1f81d98a47e701bc4f"},
+    {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"},
+    {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"},
 ]
 
 [package.dependencies]
-pytest = ">=5.0"
+pytest = ">=6.2.5"
 
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
@@ -1152,17 +1194,6 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
-[[package]]
-name = "sniffio"
-version = "1.3.1"
-description = "Sniff out which async library your code is running under"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
-    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
-]
-
 [[package]]
 name = "syrupy"
 version = "4.6.1"
@@ -1235,35 +1266,35 @@ files = [
 
 [[package]]
 name = "types-pillow"
-version = "10.2.0.20240311"
+version = "10.2.0.20240406"
 description = "Typing stubs for Pillow"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-Pillow-10.2.0.20240311.tar.gz", hash = "sha256:f611f6baf7c3784fe550ee92b108060f5544a47c37c73acb81a785f1c6312772"},
-    {file = "types_Pillow-10.2.0.20240311-py3-none-any.whl", hash = "sha256:34ca2fe768c6b1d05f288374c1a5ef9437f75faa1f91437b43c50970bbb54a94"},
+    {file = "types-Pillow-10.2.0.20240406.tar.gz", hash = "sha256:62e0cc1f17caba40e72e7154a483f4c7f3bea0e1c34c0ebba9de3c7745bc306d"},
+    {file = "types_Pillow-10.2.0.20240406-py3-none-any.whl", hash = "sha256:5ac182e8afce53de30abca2fdf9cbec7b2500e549d0be84da035a729a84c7c47"},
 ]
 
 [[package]]
 name = "types-protobuf"
-version = "4.24.0.20240311"
+version = "4.24.0.20240408"
 description = "Typing stubs for protobuf"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-protobuf-4.24.0.20240311.tar.gz", hash = "sha256:c80426f9fb9b21aee514691e96ab32a5cd694a82e2ac07964b352c3e7e0182bc"},
-    {file = "types_protobuf-4.24.0.20240311-py3-none-any.whl", hash = "sha256:8e039486df058141cb221ab99f88c5878c08cca4376db1d84f63279860aa09cd"},
+    {file = "types-protobuf-4.24.0.20240408.tar.gz", hash = "sha256:c03a44357b03c233c8c5864ce3e07dd9c766a00497d271496923f7ae3cb9e1de"},
+    {file = "types_protobuf-4.24.0.20240408-py3-none-any.whl", hash = "sha256:9b87cd279378693071247227f52e89738af7c8d6f06dbdd749b0cf473c4916ce"},
 ]
 
 [[package]]
 name = "types-requests"
-version = "2.31.0.20240311"
+version = "2.31.0.20240406"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.31.0.20240311.tar.gz", hash = "sha256:b1c1b66abfb7fa79aae09097a811c4aa97130eb8831c60e47aee4ca344731ca5"},
-    {file = "types_requests-2.31.0.20240311-py3-none-any.whl", hash = "sha256:47872893d65a38e282ee9f277a4ee50d1b28bd592040df7d1fdaffdf3779937d"},
+    {file = "types-requests-2.31.0.20240406.tar.gz", hash = "sha256:4428df33c5503945c74b3f42e82b181e86ec7b724620419a2966e2de604ce1a1"},
+    {file = "types_requests-2.31.0.20240406-py3-none-any.whl", hash = "sha256:6216cdac377c6b9a040ac1c0404f7284bd13199c0e1bb235f4324627e8898cf5"},
 ]
 
 [package.dependencies]
@@ -1271,13 +1302,24 @@ urllib3 = ">=2"
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
+]
+
+[[package]]
+name = "uritemplate"
+version = "4.1.1"
+description = "Implementation of RFC 6570 URI Templates"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "uritemplate-4.1.1-py2.py3-none-any.whl", hash = "sha256:830c08b8d99bdd312ea4ead05994a38e8936266f84b9a7878232db50b044e02e"},
+    {file = "uritemplate-4.1.1.tar.gz", hash = "sha256:4346edfc5c3b79f694bccd6d6099a322bbeb628dbf2cd86eea55a456ce5124f0"},
 ]
 
 [[package]]
@@ -1344,4 +1386,4 @@ images = ["pillow"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "dd7ba0d20a620f1eac259e8809a0f968bb49997574523c171611c827e7134c5f"
+content-hash = "2b03aaa008902cf0eff7757a7453c7fff83b06939a47845fa3704abb4fb172a1"
diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml
index 1d82e99a..278df6b7 100644
--- a/libs/genai/pyproject.toml
+++ b/libs/genai/pyproject.toml
@@ -12,8 +12,8 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.1"
-google-generativeai = "^0.4.1"
+langchain-core = ">=0.1.27,<0.2"
+google-generativeai = "^0.5.0"
 pillow = { version = "^10.1.0", optional = true }
 
 [tool.poetry.extras]
diff --git a/libs/genai/tests/integration_tests/test_chat_models.py b/libs/genai/tests/integration_tests/test_chat_models.py
index 26e85aa1..340c466d 100644
--- a/libs/genai/tests/integration_tests/test_chat_models.py
+++ b/libs/genai/tests/integration_tests/test_chat_models.py
@@ -112,6 +112,16 @@ def test_chat_google_genai_invoke_multimodal() -> None:
         assert len(chunk.content.strip()) > 0
 
 
+def test_system_message() -> None:
+    messages = [
+        SystemMessage(content="Be a helful assistant."),
+        HumanMessage(content="Hi, how are you?"),
+    ]
+    llm = ChatGoogleGenerativeAI(model="models/gemini-1.0-pro-latest")
+    answer = llm.invoke(messages)
+    assert isinstance(answer.content, str)
+
+
 def test_chat_google_genai_invoke_multimodal_too_many_messages() -> None:
     # Only supports 1 turn...
     messages: list = [
@@ -168,18 +178,6 @@ def test_chat_google_genai_single_call_with_history() -> None:
     assert isinstance(response.content, str)
 
 
-def test_chat_google_genai_system_message_error() -> None:
-    model = ChatGoogleGenerativeAI(model=_MODEL)
-    text_question1, text_answer1 = "How much is 2+2?", "4"
-    text_question2 = "How much is 3+3?"
-    system_message = SystemMessage(content="You're supposed to answer math questions.")
-    message1 = HumanMessage(content=text_question1)
-    message2 = AIMessage(content=text_answer1)
-    message3 = HumanMessage(content=text_question2)
-    with pytest.raises(ValueError):
-        model([system_message, message1, message2, message3])
-
-
 def test_chat_google_genai_system_message() -> None:
     model = ChatGoogleGenerativeAI(model=_MODEL, convert_system_message_to_human=True)
     text_question1, text_answer1 = "How much is 2+2?", "4"
diff --git a/libs/genai/tests/unit_tests/test_chat_models.py b/libs/genai/tests/unit_tests/test_chat_models.py
index e17bb7d9..aa5aae20 100644
--- a/libs/genai/tests/unit_tests/test_chat_models.py
+++ b/libs/genai/tests/unit_tests/test_chat_models.py
@@ -61,13 +61,16 @@ def test_parse_history() -> None:
     message2 = AIMessage(content=text_answer1)
     message3 = HumanMessage(content=text_question2)
     messages = [system_message, message1, message2, message3]
-    history = _parse_chat_history(messages, convert_system_message_to_human=True)
+    system_instruction, history = _parse_chat_history(
+        messages, convert_system_message_to_human=True
+    )
     assert len(history) == 3
     assert history[0] == {
         "role": "user",
-        "parts": [{"text": system_input}, {"text": text_question1}],
+        "parts": [{"text": text_question1}],
     }
     assert history[1] == {"role": "model", "parts": [{"text": text_answer1}]}
+    assert system_instruction == [{"text": system_input}]
 
 
 @pytest.mark.parametrize("content", ['["a"]', '{"a":"b"}', "function output"])

From 2cd86506ed6234be2b8769bcfa16e86795f88535 Mon Sep 17 00:00:00 2001
From: Alex Ostapenko <alx13@users.noreply.github.com>
Date: Wed, 10 Apr 2024 13:59:49 +0200
Subject: [PATCH 5/9] allow list title field as it's supported now (#131)

---
 .../functions_utils.py                        |  5 +-
 .../tests/unit_tests/test_function_utils.py   | 81 ++++++++++++++-----
 2 files changed, 64 insertions(+), 22 deletions(-)

diff --git a/libs/vertexai/langchain_google_vertexai/functions_utils.py b/libs/vertexai/langchain_google_vertexai/functions_utils.py
index f5509b89..89dbb7cc 100644
--- a/libs/vertexai/langchain_google_vertexai/functions_utils.py
+++ b/libs/vertexai/langchain_google_vertexai/functions_utils.py
@@ -78,14 +78,13 @@ def _format_tools_to_vertex_tool(
 class ParametersSchema(BaseModel):
     """
     This is a schema of currently supported definitions in function calling.
-    We need explicitly exclude `title` and `definitions` fields as they
-    are not currently supported.
+    We need explicitly exclude `definitions` field
+    as it is not currently supported.
 
     All other fields will be passed through (as extra fields are allowed)
     and intercepted on `google.cloud.aiplatform` level
     """
 
-    title: Optional[str] = Field(exclude=True)
     definitions: Optional[Any] = Field(exclude=True)
     items: Optional["ParametersSchema"]
     properties: Optional[Dict[str, "ParametersSchema"]]
diff --git a/libs/vertexai/tests/unit_tests/test_function_utils.py b/libs/vertexai/tests/unit_tests/test_function_utils.py
index 8e472edc..b69a4008 100644
--- a/libs/vertexai/tests/unit_tests/test_function_utils.py
+++ b/libs/vertexai/tests/unit_tests/test_function_utils.py
@@ -1,4 +1,5 @@
-from typing import Optional, Union
+from enum import Enum
+from typing import Optional, Sequence
 
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.tools import tool
@@ -52,28 +53,70 @@ def do_something_optional(a: float, b: float = 0) -> str:
 
 
 def test_get_parameters_from_schema():
+    class StringEnum(str, Enum):
+        pear = "pear"
+        banana = "banana"
+
     class A(BaseModel):
-        a1: Optional[int]
+        """Class A"""
+
+        int_field: Optional[int]
 
     class B(BaseModel):
-        b1: Optional[A]
-        b2: int = Field(description="f2")
-        b3: Union[int, str]
+        object_field: Optional[A]
+        array_field: Sequence[A]
+        int_field: int = Field(description="int field", min=0, max=10)
+        str_field: str = Field(
+            min_length=1, max_length=10, pattern="^[A-Z]{1,10}$", example="ABCD"
+        )
+        str_enum_field: StringEnum
 
     schema = B.schema()
     result = _get_parameters_from_schema(schema)
-    assert result["type"] == "object"
-    assert "required" in result
-    assert len(result["required"]) == 2
-
-    assert "properties" in result
-    assert "b1" in result["properties"]
-    assert "b2" in result["properties"]
-    assert "b3" in result["properties"]
-
-    assert result["properties"]["b1"]["type"] == "object"
-    assert "a1" in result["properties"]["b1"]["properties"]
-    assert "required" not in result["properties"]["b1"]
-    assert len(result["properties"]["b1"]["properties"]) == 1
 
-    assert "anyOf" in result["properties"]["b3"]
+    assert result == {
+        "properties": {
+            "object_field": {
+                "properties": {"int_field": {"type": "integer", "title": "Int Field"}},
+                "type": "object",
+                "description": "Class A",
+                "title": "A",
+            },
+            "array_field": {
+                "items": {
+                    "properties": {
+                        "int_field": {"type": "integer", "title": "Int Field"}
+                    },
+                    "type": "object",
+                    "description": "Class A",
+                    "title": "A",
+                },
+                "type": "array",
+                "title": "Array Field",
+            },
+            "int_field": {
+                "max": 10,
+                "type": "integer",
+                "min": 0,
+                "description": "int field",
+                "title": "Int Field",
+            },
+            "str_field": {
+                "minLength": 1,
+                "type": "string",
+                "maxLength": 10,
+                "example": "ABCD",
+                "pattern": "^[A-Z]{1,10}$",
+                "title": "Str Field",
+            },
+            "str_enum_field": {
+                "type": "string",
+                "description": "An enumeration.",
+                "title": "StringEnum",
+                "enum": ["pear", "banana"],
+            },
+        },
+        "type": "object",
+        "title": "B",
+        "required": ["array_field", "int_field", "str_field", "str_enum_field"],
+    }

From e14244e3db908bf01a2ddaf0a645d29c3f497563 Mon Sep 17 00:00:00 2001
From: eliasecchig <115624100+eliasecchig@users.noreply.github.com>
Date: Wed, 10 Apr 2024 14:52:42 +0200
Subject: [PATCH 6/9] Add system_instructions for Gemini models (#125)

---
 .../langchain_google_vertexai/chat_models.py  | 102 +++++++++++++-----
 libs/vertexai/poetry.lock                     |  38 ++++---
 libs/vertexai/pyproject.toml                  |   2 +-
 .../integration_tests/test_chat_models.py     |  67 ++++++------
 .../tests/unit_tests/test_chat_models.py      |  27 ++++-
 pyproject.toml                                |  15 +++
 6 files changed, 175 insertions(+), 76 deletions(-)
 create mode 100644 pyproject.toml

diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
index 4e19f99d..afc00572 100644
--- a/libs/vertexai/langchain_google_vertexai/chat_models.py
+++ b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -118,7 +118,7 @@ def _parse_chat_history_gemini(
     history: List[BaseMessage],
     project: Optional[str] = None,
     convert_system_message_to_human: Optional[bool] = False,
-) -> List[Content]:
+) -> tuple[Content | None, list[Content]]:
     def _convert_to_prompt(part: Union[str, Dict]) -> Part:
         if isinstance(part, str):
             return Part.from_text(part)
@@ -142,24 +142,35 @@ def _convert_to_parts(message: BaseMessage) -> List[Part]:
         return [_convert_to_prompt(part) for part in raw_content]
 
     vertex_messages = []
-    raw_system_message = None
+    convert_system_message_to_human_content = None
+    system_instruction = None
     for i, message in enumerate(history):
         if (
             i == 0
             and isinstance(message, SystemMessage)
             and not convert_system_message_to_human
         ):
-            raise ValueError(
-                """SystemMessages are not yet supported!
-                
-To automatically convert the leading SystemMessage to a HumanMessage,
-set  `convert_system_message_to_human` to True. Example:
-
-llm = ChatVertexAI(model_name="gemini-pro", convert_system_message_to_human=True)
-"""
+            if system_instruction is not None:
+                raise ValueError(
+                    "Detected more than one SystemMessage in the list of messages."
+                    "Gemini APIs support the insertion of only SystemMessage."
+                )
+            else:
+                system_instruction = Content(
+                    role="user", parts=_convert_to_parts(message)
+                )
+                continue
+        elif (
+            i == 0
+            and isinstance(message, SystemMessage)
+            and convert_system_message_to_human
+        ):
+            logger.warning(
+                "gemini models released from April 2024 support SystemMessages"
+                "natively. For best performances, when working with these models,"
+                "set convert_system_message_to_human to False"
             )
-        elif i == 0 and isinstance(message, SystemMessage):
-            raw_system_message = message
+            convert_system_message_to_human_content = message
             continue
         elif isinstance(message, AIMessage):
             raw_function_call = message.additional_kwargs.get("function_call")
@@ -193,18 +204,18 @@ def _convert_to_parts(message: BaseMessage) -> List[Part]:
                 f"Unexpected message with type {type(message)} at the position {i}."
             )
 
-        if raw_system_message:
+        if convert_system_message_to_human_content:
             if role == "model":
                 raise ValueError(
                     "SystemMessage should be followed by a HumanMessage and "
                     "not by AIMessage."
                 )
-            parts = _convert_to_parts(raw_system_message) + parts
-            raw_system_message = None
+            parts = _convert_to_parts(convert_system_message_to_human_content) + parts
+            convert_system_message_to_human_content = None
 
         vertex_message = Content(role=role, parts=parts)
         vertex_messages.append(vertex_message)
-    return vertex_messages
+    return system_instruction, vertex_messages
 
 
 def _parse_examples(examples: List[BaseMessage]) -> List[InputOutputTextPair]:
@@ -247,6 +258,21 @@ def _get_question(messages: List[BaseMessage]) -> HumanMessage:
     return question
 
 
+def _get_client_with_sys_instruction(
+    client: GenerativeModel,
+    system_instruction: Content,
+    model_name: str,
+    safety_settings: Optional[Dict] = None,
+):
+    if client._system_instruction != system_instruction:
+        client = GenerativeModel(
+            model_name=model_name,
+            safety_settings=safety_settings,
+            system_instruction=system_instruction,
+        )
+    return client
+
+
 def _parse_response_candidate(response_candidate: "Candidate") -> AIMessage:
     try:
         content = response_candidate.text
@@ -275,10 +301,9 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel):
     "Underlying model name."
     examples: Optional[List[BaseMessage]] = None
     convert_system_message_to_human: bool = False
-    """Whether to merge any leading SystemMessage into the following HumanMessage.
-    
-    Gemini does not support system messages; any unsupported messages will 
-    raise an error."""
+    """[Deprecated] Since new Gemini models support setting a System Message, 
+    setting this parameter to True is discouraged.
+    """
 
     @classmethod
     def is_lc_serializable(self) -> bool:
@@ -357,12 +382,18 @@ def _generate(
             msg_params["candidate_count"] = params.pop("candidate_count")
 
         if self._is_gemini_model:
-            history_gemini = _parse_chat_history_gemini(
+            system_instruction, history_gemini = _parse_chat_history_gemini(
                 messages,
                 project=self.project,
                 convert_system_message_to_human=self.convert_system_message_to_human,
             )
             message = history_gemini.pop()
+            self.client = _get_client_with_sys_instruction(
+                client=self.client,
+                system_instruction=system_instruction,
+                model_name=self.model_name,
+                safety_settings=safety_settings,
+            )
             with telemetry.tool_context_manager(self._user_agent):
                 chat = self.client.start_chat(history=history_gemini)
 
@@ -441,12 +472,19 @@ async def _agenerate(
             msg_params["candidate_count"] = params.pop("candidate_count")
 
         if self._is_gemini_model:
-            history_gemini = _parse_chat_history_gemini(
+            system_instruction, history_gemini = _parse_chat_history_gemini(
                 messages,
                 project=self.project,
                 convert_system_message_to_human=self.convert_system_message_to_human,
             )
             message = history_gemini.pop()
+
+            self.client = _get_client_with_sys_instruction(
+                client=self.client,
+                system_instruction=system_instruction,
+                model_name=self.model_name,
+                safety_settings=safety_settings,
+            )
             with telemetry.tool_context_manager(self._user_agent):
                 chat = self.client.start_chat(history=history_gemini)
             # set param to `functions` until core tool/function calling implemented
@@ -501,18 +539,24 @@ def _stream(
     ) -> Iterator[ChatGenerationChunk]:
         params = self._prepare_params(stop=stop, stream=True, **kwargs)
         if self._is_gemini_model:
-            history_gemini = _parse_chat_history_gemini(
+            safety_settings = params.pop("safety_settings", None)
+            system_instruction, history_gemini = _parse_chat_history_gemini(
                 messages,
                 project=self.project,
                 convert_system_message_to_human=self.convert_system_message_to_human,
             )
             message = history_gemini.pop()
+            self.client = _get_client_with_sys_instruction(
+                client=self.client,
+                system_instruction=system_instruction,
+                model_name=self.model_name,
+                safety_settings=safety_settings,
+            )
             with telemetry.tool_context_manager(self._user_agent):
                 chat = self.client.start_chat(history=history_gemini)
             # set param to `functions` until core tool/function calling implemented
             raw_tools = params.pop("functions") if "functions" in params else None
             tools = _format_tools_to_vertex_tool(raw_tools) if raw_tools else None
-            safety_settings = params.pop("safety_settings", None)
             with telemetry.tool_context_manager(self._user_agent):
                 responses = chat.send_message(
                     message,
@@ -567,17 +611,23 @@ async def _astream(
         if not self._is_gemini_model:
             raise NotImplementedError()
         params = self._prepare_params(stop=stop, stream=True, **kwargs)
-        history_gemini = _parse_chat_history_gemini(
+        safety_settings = params.pop("safety_settings", None)
+        system_instruction, history_gemini = _parse_chat_history_gemini(
             messages,
             project=self.project,
             convert_system_message_to_human=self.convert_system_message_to_human,
         )
         message = history_gemini.pop()
+        self.client = _get_client_with_sys_instruction(
+            client=self.client,
+            system_instruction=system_instruction,
+            model_name=self.model_name,
+            safety_settings=safety_settings,
+        )
         with telemetry.tool_context_manager(self._user_agent):
             chat = self.client.start_chat(history=history_gemini)
         raw_tools = params.pop("functions") if "functions" in params else None
         tools = _format_tools_to_vertex_tool(raw_tools) if raw_tools else None
-        safety_settings = params.pop("safety_settings", None)
         with telemetry.tool_context_manager(self._user_agent):
             async for chunk in await chat.send_message_async(
                 message,
diff --git a/libs/vertexai/poetry.lock b/libs/vertexai/poetry.lock
index 1654fd30..1ed44788 100644
--- a/libs/vertexai/poetry.lock
+++ b/libs/vertexai/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -640,13 +640,13 @@ httplib2 = ">=0.19.0"
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.46.0"
+version = "1.47.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.46.0.tar.gz", hash = "sha256:9eeefcd34dbad2a1ade830d025154189d257e8dcfea45771dcac6e34a2783782"},
-    {file = "google_cloud_aiplatform-1.46.0-py2.py3-none-any.whl", hash = "sha256:a30a64f8bcefa0646340cf57abca192bb9ca9c456a47b842651a9564bba0534a"},
+    {file = "google-cloud-aiplatform-1.47.0.tar.gz", hash = "sha256:1c4537db09b83957bf0623fd2afb37e339f89a3afcda3efce9dce79b16ab59c7"},
+    {file = "google_cloud_aiplatform-1.47.0-py2.py3-none-any.whl", hash = "sha256:454ef0c44ecaeadcffe58f565acfce49e53895fd51bb20da8af0d48202a4cb21"},
 ]
 
 [package.dependencies]
@@ -665,19 +665,22 @@ shapely = "<3.0.0dev"
 [package.extras]
 autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
-datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)"]
+datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
-full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
+full = ["cloudpickle (<3.0)", "cloudpickle (>=2.2.1,<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pydantic (<3)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
+langchain = ["langchain (>=0.1.13,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<0.2)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (==5.3.1)"]
 prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<0.103.1)", "httpx (>=0.23.0,<0.25.0)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
 preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"]
 private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
+rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"]
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "scikit-learn", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
+reasoningengine = ["cloudpickle (>=2.2.1,<3.0)", "pydantic (<3)"]
 tensorboard = ["tensorflow (>=2.3.0,<2.15.0)"]
-testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
+testing = ["bigframes", "cloudpickle (<3.0)", "cloudpickle (>=2.2.1,<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pydantic (<3)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
@@ -1337,13 +1340,13 @@ extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.40"
+version = "0.1.41"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.40-py3-none-any.whl", hash = "sha256:aa47d0f5a1eabd5c05ac6ce2cd3e28ccfc554d366e856a27b7c3c17c443881cb"},
-    {file = "langsmith-0.1.40.tar.gz", hash = "sha256:50fdf313741cf94e978de06025fd180b56acf1d1a4549b0fd5453ef23d5461ef"},
+    {file = "langsmith-0.1.41-py3-none-any.whl", hash = "sha256:11de22b6990502c630fdfdf6906681e664c6659d2118bcd2b79d08016e770831"},
+    {file = "langsmith-0.1.41.tar.gz", hash = "sha256:1250cd6c9074ca10d40002b23d79b3017329b139fbe954248fdd7a79544e90d0"},
 ]
 
 [package.dependencies]
@@ -2030,6 +2033,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -2490,24 +2494,24 @@ files = [
 
 [[package]]
 name = "types-protobuf"
-version = "4.24.0.20240311"
+version = "4.24.0.20240408"
 description = "Typing stubs for protobuf"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-protobuf-4.24.0.20240311.tar.gz", hash = "sha256:c80426f9fb9b21aee514691e96ab32a5cd694a82e2ac07964b352c3e7e0182bc"},
-    {file = "types_protobuf-4.24.0.20240311-py3-none-any.whl", hash = "sha256:8e039486df058141cb221ab99f88c5878c08cca4376db1d84f63279860aa09cd"},
+    {file = "types-protobuf-4.24.0.20240408.tar.gz", hash = "sha256:c03a44357b03c233c8c5864ce3e07dd9c766a00497d271496923f7ae3cb9e1de"},
+    {file = "types_protobuf-4.24.0.20240408-py3-none-any.whl", hash = "sha256:9b87cd279378693071247227f52e89738af7c8d6f06dbdd749b0cf473c4916ce"},
 ]
 
 [[package]]
 name = "types-requests"
-version = "2.31.0.20240403"
+version = "2.31.0.20240406"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.31.0.20240403.tar.gz", hash = "sha256:e1e0cd0b655334f39d9f872b68a1310f0e343647688bf2cee932ec4c2b04de59"},
-    {file = "types_requests-2.31.0.20240403-py3-none-any.whl", hash = "sha256:06abf6a68f5c4f2a62f6bb006672dfb26ed50ccbfddb281e1ee6f09a65707d5d"},
+    {file = "types-requests-2.31.0.20240406.tar.gz", hash = "sha256:4428df33c5503945c74b3f42e82b181e86ec7b724620419a2966e2de604ce1a1"},
+    {file = "types_requests-2.31.0.20240406-py3-none-any.whl", hash = "sha256:6216cdac377c6b9a040ac1c0404f7284bd13199c0e1bb235f4324627e8898cf5"},
 ]
 
 [package.dependencies]
@@ -2717,4 +2721,4 @@ anthropic = ["anthropic"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "fec2eb547d686aa727105697113b434ab7f852ce5de08fedb312c1bf8919c371"
+content-hash = "10cb39e92083851c4280f04caf22b4a121cf1f01513f65be7b688d1359d577e8"
diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml
index f877d18d..093a6d20 100644
--- a/libs/vertexai/pyproject.toml
+++ b/libs/vertexai/pyproject.toml
@@ -13,7 +13,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
 langchain-core = ">=0.1.27,<0.2"
-google-cloud-aiplatform = "^1.44.0"
+google-cloud-aiplatform = "^1.47.0"
 google-cloud-storage = "^2.14.0"
 types-requests = "^2.31.0"
 types-protobuf = "^4.24.0.4"
diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py
index 0357bbb0..56bcc85b 100644
--- a/libs/vertexai/tests/integration_tests/test_chat_models.py
+++ b/libs/vertexai/tests/integration_tests/test_chat_models.py
@@ -13,7 +13,7 @@
 from langchain_core.outputs import ChatGeneration, LLMResult
 from langchain_core.pydantic_v1 import BaseModel
 
-from langchain_google_vertexai.chat_models import ChatVertexAI
+from langchain_google_vertexai import ChatVertexAI, HarmBlockThreshold, HarmCategory
 
 model_names_to_test = [None, "codechat-bison", "chat-bison", "gemini-pro"]
 
@@ -200,51 +200,53 @@ def test_vertexai_single_call_with_history(model_name: Optional[str]) -> None:
 
 
 @pytest.mark.release
-def test_vertexai_single_call_fails_no_message() -> None:
-    chat = ChatVertexAI()
-    with pytest.raises(ValueError) as exc_info:
-        _ = chat([])
-    assert (
-        str(exc_info.value)
-        == "You should provide at least one message to start the chat!"
-    )
-
-
-@pytest.mark.release
-@pytest.mark.parametrize("model_name", ["gemini-pro"])
-def test_chat_vertexai_gemini_system_message_error(model_name: str) -> None:
-    model = ChatVertexAI(model_name=model_name)
-    text_question1, text_answer1 = "How much is 2+2?", "4"
-    text_question2 = "How much is 3+3?"
-    system_message = SystemMessage(content="You're supposed to answer math questions.")
+@pytest.mark.parametrize("model_name", ["gemini-1.0-pro-002"])
+def test_vertexai_system_message(model_name: Optional[str]) -> None:
+    if model_name:
+        model = ChatVertexAI(model_name=model_name)
+    else:
+        model = ChatVertexAI()
+    system_instruction = """CymbalBank is a bank located in London"""
+    text_question1 = "Where is Cymbal located? Provide only the name of the city."
+    sys_message = SystemMessage(content=system_instruction)
     message1 = HumanMessage(content=text_question1)
-    message2 = AIMessage(content=text_answer1)
-    message3 = HumanMessage(content=text_question2)
-    with pytest.raises(ValueError):
-        model([system_message, message1, message2, message3])
+    response = model([sys_message, message1])
+
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+    assert response.content.lower() == "london"
 
 
 @pytest.mark.release
 @pytest.mark.parametrize("model_name", model_names_to_test)
-def test_chat_vertexai_system_message(model_name: Optional[str]) -> None:
+def test_vertexai_single_call_with_no_system_messages(
+    model_name: Optional[str],
+) -> None:
     if model_name:
-        model = ChatVertexAI(
-            model_name=model_name, convert_system_message_to_human=True
-        )
+        model = ChatVertexAI(model_name=model_name)
     else:
         model = ChatVertexAI()
-
     text_question1, text_answer1 = "How much is 2+2?", "4"
     text_question2 = "How much is 3+3?"
-    system_message = SystemMessage(content="You're supposed to answer math questions.")
     message1 = HumanMessage(content=text_question1)
     message2 = AIMessage(content=text_answer1)
     message3 = HumanMessage(content=text_question2)
-    response = model([system_message, message1, message2, message3])
+    response = model([message1, message2, message3])
     assert isinstance(response, AIMessage)
     assert isinstance(response.content, str)
 
 
+@pytest.mark.release
+def test_vertexai_single_call_fails_no_message() -> None:
+    chat = ChatVertexAI()
+    with pytest.raises(ValueError) as exc_info:
+        _ = chat([])
+    assert (
+        str(exc_info.value)
+        == "You should provide at least one message to start the chat!"
+    )
+
+
 @pytest.mark.release
 @pytest.mark.parametrize("model_name", model_names_to_test)
 def test_get_num_tokens_from_messages(model_name: str) -> None:
@@ -264,7 +266,12 @@ class MyModel(BaseModel):
         name: str
         age: int
 
-    model = ChatVertexAI(model_name="gemini-pro").bind(functions=[MyModel])
+    safety = {
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH
+    }
+    model = ChatVertexAI(model_name="gemini-pro", safety_settings=safety).bind(
+        functions=[MyModel]
+    )
     message = HumanMessage(content="My name is Erick and I am 27 years old")
     response = model.invoke([message])
     assert isinstance(response, AIMessage)
diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py
index 49420000..344d7472 100644
--- a/libs/vertexai/tests/unit_tests/test_chat_models.py
+++ b/libs/vertexai/tests/unit_tests/test_chat_models.py
@@ -22,7 +22,10 @@
 from vertexai.generative_models import (  # type: ignore
     Candidate,
 )
-from vertexai.language_models import ChatMessage, InputOutputTextPair  # type: ignore
+from vertexai.language_models import (  # type: ignore
+    ChatMessage,
+    InputOutputTextPair,
+)
 
 from langchain_google_vertexai.chat_models import (
     ChatVertexAI,
@@ -152,7 +155,27 @@ def test_parse_history_gemini() -> None:
     message2 = AIMessage(content=text_answer1)
     message3 = HumanMessage(content=text_question2)
     messages = [system_message, message1, message2, message3]
-    history = _parse_chat_history_gemini(messages, convert_system_message_to_human=True)
+    system_instructions, history = _parse_chat_history_gemini(messages)
+    assert len(history) == 3
+    assert history[0].role == "user"
+    assert history[0].parts[0].text == text_question1
+    assert history[1].role == "model"
+    assert history[1].parts[0].text == text_answer1
+    assert system_instructions and system_instructions.parts[0].text == system_input
+
+
+def test_parse_history_gemini_converted_message() -> None:
+    system_input = "You're supposed to answer math questions."
+    text_question1, text_answer1 = "How much is 2+2?", "4"
+    text_question2 = "How much is 3+3?"
+    system_message = SystemMessage(content=system_input)
+    message1 = HumanMessage(content=text_question1)
+    message2 = AIMessage(content=text_answer1)
+    message3 = HumanMessage(content=text_question2)
+    messages = [system_message, message1, message2, message3]
+    system_instructions, history = _parse_chat_history_gemini(
+        messages, convert_system_message_to_human=True
+    )
     assert len(history) == 3
     assert history[0].role == "user"
     assert history[0].parts[0].text == system_input
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..88b7bb56
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,15 @@
+[tool.poetry]
+name = "langchain-google"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <eliasecchi@google.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+

From 9c35638f35c496fbe3d9e2e4639a7273c07c9638 Mon Sep 17 00:00:00 2001
From: Leonid Kuligin <lkuligin@yandex.ru>
Date: Wed, 10 Apr 2024 16:06:34 +0200
Subject: [PATCH 7/9] fixed tools since function_response doesn't require a
 role anymore (#132)

---
 libs/vertexai/langchain_google_vertexai/chat_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
index afc00572..6383b5ea 100644
--- a/libs/vertexai/langchain_google_vertexai/chat_models.py
+++ b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -190,7 +190,7 @@ def _convert_to_parts(message: BaseMessage) -> List[Part]:
             role = "user"
             parts = _convert_to_parts(message)
         elif isinstance(message, FunctionMessage):
-            role = "user"
+            role = "function"
             parts = [
                 Part.from_function_response(
                     name=message.name,

From aeb175c0d65769c8fa99480938cb30b6b20d3eac Mon Sep 17 00:00:00 2001
From: Erick Friis <erickfriis@gmail.com>
Date: Wed, 10 Apr 2024 07:06:59 -0700
Subject: [PATCH 8/9] ci: add master core install for lint/test/dev (#130)

* ci: add master core install for lint/test/dev

---------

Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
---
 libs/genai/poetry.lock       | 28 ++++++++------
 libs/genai/pyproject.toml    |  3 ++
 libs/vertexai/poetry.lock    | 74 +++++++++++++++++++-----------------
 libs/vertexai/pyproject.toml |  6 +++
 4 files changed, 65 insertions(+), 46 deletions(-)

diff --git a/libs/genai/poetry.lock b/libs/genai/poetry.lock
index a76c7f6d..a633b296 100644
--- a/libs/genai/poetry.lock
+++ b/libs/genai/poetry.lock
@@ -477,26 +477,31 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.1.40"
+version = "0.1.41"
 description = "Building applications with LLMs through composability"
 optional = false
-python-versions = "<4.0,>=3.8.1"
-files = [
-    {file = "langchain_core-0.1.40-py3-none-any.whl", hash = "sha256:618dbb7ab44d8b263b91e384db1ff07d0db256ae5bdafa0123a115b6a75a13f1"},
-    {file = "langchain_core-0.1.40.tar.gz", hash = "sha256:34c06fc0e6d3534b738c63f85403446b4be71161665b7e091f9bb19c914ec100"},
-]
+python-versions = ">=3.8.1,<4.0"
+files = []
+develop = false
 
 [package.dependencies]
-jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.0,<0.2.0"
-packaging = ">=23.2,<24.0"
+jsonpatch = "^1.33"
+langsmith = "^0.1.0"
+packaging = "^23.2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
-tenacity = ">=8.1.0,<9.0.0"
+tenacity = "^8.1.0"
 
 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]
 
+[package.source]
+type = "git"
+url = "https://github.com/langchain-ai/langchain.git"
+reference = "HEAD"
+resolved_reference = "2d83505be979fb947e976ae7c48be87e49f41a27"
+subdirectory = "libs/core"
+
 [[package]]
 name = "langsmith"
 version = "0.1.42"
@@ -1088,6 +1093,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -1386,4 +1392,4 @@ images = ["pillow"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "2b03aaa008902cf0eff7757a7453c7fff83b06939a47845fa3704abb4fb172a1"
+content-hash = "014cc48a2080a0d572d874c713c8ed24a84b33bd5c0f582b056cb61151c33136"
diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml
index 278df6b7..e6710fbf 100644
--- a/libs/genai/pyproject.toml
+++ b/libs/genai/pyproject.toml
@@ -30,6 +30,7 @@ syrupy = "^4.0.2"
 pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
 numpy = "^1.26.2"
+langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"}
 
 [tool.poetry.group.codespell]
 optional = true
@@ -56,6 +57,7 @@ types-requests = "^2.28.11.5"
 types-google-cloud-ndb = "^2.2.0.1"
 types-pillow = "^10.1.0.2"
 types-protobuf = "^4.24.0.20240302"
+langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"}
 
 [tool.poetry.group.dev]
 optional = true
@@ -65,6 +67,7 @@ pillow = "^10.1.0"
 types-requests = "^2.31.0.10"
 types-pillow = "^10.1.0.2"
 types-google-cloud-ndb = "^2.2.0.1"
+langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"}
 
 [tool.ruff]
 select = [
diff --git a/libs/vertexai/poetry.lock b/libs/vertexai/poetry.lock
index 1ed44788..58261d5c 100644
--- a/libs/vertexai/poetry.lock
+++ b/libs/vertexai/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -126,13 +126,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "anthropic"
-version = "0.23.1"
+version = "0.25.0"
 description = "The official Python library for the anthropic API"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "anthropic-0.23.1-py3-none-any.whl", hash = "sha256:6dc5779dae83a5834864f4a4af0166c972b70f4cb8fd2765e1558282cc6d6242"},
-    {file = "anthropic-0.23.1.tar.gz", hash = "sha256:9325103702cbc96bb09d1b58c36bde75c726f6a01029fb4d85f41ebba07e9066"},
+    {file = "anthropic-0.25.0-py3-none-any.whl", hash = "sha256:b5dfe4dfebace1641a02cfda939cd6dffac0152ab305ca1ef0c11023043a51a2"},
+    {file = "anthropic-0.25.0.tar.gz", hash = "sha256:63372443e699da7ffb467b2d0eb5ee7740acf877368b364a1137d795ae4e4c16"},
 ]
 
 [package.dependencies]
@@ -402,13 +402,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.13.3"
+version = "3.13.4"
 description = "A platform independent file lock."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.3-py3-none-any.whl", hash = "sha256:5ffa845303983e7a0b7ae17636509bc97997d58afeafa72fb141a17b152284cb"},
-    {file = "filelock-3.13.3.tar.gz", hash = "sha256:a79895a25bbefdf55d1a2a0a80968f7dbb28edcd6d4234a0afb3f37ecde4b546"},
+    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
+    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
 ]
 
 [package.extras]
@@ -1234,13 +1234,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.1.14"
+version = "0.1.15"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.1.14-py3-none-any.whl", hash = "sha256:94f9b5df2421faaf762d4f43b9d65c270c2f701934580d281e4c6226deef7234"},
-    {file = "langchain-0.1.14.tar.gz", hash = "sha256:124c6244cf3957616b98f2df07dc2992fc40dff6ed1a62d8ee8a40f1e0260a40"},
+    {file = "langchain-0.1.15-py3-none-any.whl", hash = "sha256:3ac516463ae7f80047091f04592a1eea138321710bbc266005f9de238d71acd3"},
+    {file = "langchain-0.1.15.tar.gz", hash = "sha256:79d43035327fdcc5ac81a3db10f2b879f2bd5db3b268ef82bac7baf3ec32954e"},
 ]
 
 [package.dependencies]
@@ -1248,8 +1248,8 @@ aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langchain-community = ">=0.0.30,<0.1"
-langchain-core = ">=0.1.37,<0.2.0"
+langchain-community = ">=0.0.32,<0.1"
+langchain-core = ">=0.1.41,<0.2.0"
 langchain-text-splitters = ">=0.0.1,<0.1"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
@@ -1275,19 +1275,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.31"
+version = "0.0.32"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.31-py3-none-any.whl", hash = "sha256:905c01b978a1cef7fdcddd2d9241dedc9987db6f23ba1b58d974e38b1cdf2775"},
-    {file = "langchain_community-0.0.31.tar.gz", hash = "sha256:9a970bc2bb59bb4c204b696d8c62c2534f6ddb31005005cc1b7d7f934e58a5fc"},
+    {file = "langchain_community-0.0.32-py3-none-any.whl", hash = "sha256:406977009999952d0705de3806de2b4867e9bb8eda8ca154a59c7a8ed58da38d"},
+    {file = "langchain_community-0.0.32.tar.gz", hash = "sha256:1510217d646c8380f54e9850351f6d2a0b0dd73c501b666c6f4b40baa8160b29"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.37,<0.2.0"
+langchain-core = ">=0.1.41,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -1297,30 +1297,35 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 
 [[package]]
 name = "langchain-core"
-version = "0.1.40"
+version = "0.1.42rc1"
 description = "Building applications with LLMs through composability"
 optional = false
-python-versions = "<4.0,>=3.8.1"
-files = [
-    {file = "langchain_core-0.1.40-py3-none-any.whl", hash = "sha256:618dbb7ab44d8b263b91e384db1ff07d0db256ae5bdafa0123a115b6a75a13f1"},
-    {file = "langchain_core-0.1.40.tar.gz", hash = "sha256:34c06fc0e6d3534b738c63f85403446b4be71161665b7e091f9bb19c914ec100"},
-]
+python-versions = ">=3.8.1,<4.0"
+files = []
+develop = false
 
 [package.dependencies]
-jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.0,<0.2.0"
-packaging = ">=23.2,<24.0"
+jsonpatch = "^1.33"
+langsmith = "^0.1.0"
+packaging = "^23.2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
-tenacity = ">=8.1.0,<9.0.0"
+tenacity = "^8.1.0"
 
 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]
 
+[package.source]
+type = "git"
+url = "https://github.com/langchain-ai/langchain.git"
+reference = "HEAD"
+resolved_reference = "9eb6f538f058af294e365c87ab2c7dae150f535d"
+subdirectory = "libs/core"
+
 [[package]]
 name = "langchain-text-splitters"
 version = "0.0.1"
@@ -1340,13 +1345,13 @@ extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.41"
+version = "0.1.43"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.41-py3-none-any.whl", hash = "sha256:11de22b6990502c630fdfdf6906681e664c6659d2118bcd2b79d08016e770831"},
-    {file = "langsmith-0.1.41.tar.gz", hash = "sha256:1250cd6c9074ca10d40002b23d79b3017329b139fbe954248fdd7a79544e90d0"},
+    {file = "langsmith-0.1.43-py3-none-any.whl", hash = "sha256:c0a3658f10cbefaa2f53d15db519592982b59d99f24e018dc73aca6092b4158d"},
+    {file = "langsmith-0.1.43.tar.gz", hash = "sha256:983c5a35bf191bb23d93e453c9fc6ea7cda998b1ca96f94680a1446092caf347"},
 ]
 
 [package.dependencies]
@@ -2033,7 +2038,6 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
-    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -2494,13 +2498,13 @@ files = [
 
 [[package]]
 name = "types-protobuf"
-version = "4.24.0.20240408"
+version = "4.25.0.20240410"
 description = "Typing stubs for protobuf"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-protobuf-4.24.0.20240408.tar.gz", hash = "sha256:c03a44357b03c233c8c5864ce3e07dd9c766a00497d271496923f7ae3cb9e1de"},
-    {file = "types_protobuf-4.24.0.20240408-py3-none-any.whl", hash = "sha256:9b87cd279378693071247227f52e89738af7c8d6f06dbdd749b0cf473c4916ce"},
+    {file = "types-protobuf-4.25.0.20240410.tar.gz", hash = "sha256:86576c2e7e691b8b75f4cabec430f7405edef411b5d191e847c91307935b1b38"},
+    {file = "types_protobuf-4.25.0.20240410-py3-none-any.whl", hash = "sha256:335b2e8cf9f39c233dbf0f977a2a4fbc2c0bac720225c544cc1412a67ab1e1d3"},
 ]
 
 [[package]]
@@ -2721,4 +2725,4 @@ anthropic = ["anthropic"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "10cb39e92083851c4280f04caf22b4a121cf1f01513f65be7b688d1359d577e8"
+content-hash = "1a057144382462e7cd7cb1521fcdc2f38ed0ca4a61565b0273f7e8529640b9a7"
diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml
index 093a6d20..6f45b98d 100644
--- a/libs/vertexai/pyproject.toml
+++ b/libs/vertexai/pyproject.toml
@@ -35,6 +35,7 @@ types-requests = "^2.31.0.20231231"
 types-protobuf = "^4.24.0.4"
 numexpr = "^2.8.6"
 google-api-python-client = "^2.117.0"
+langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"}
 
 [tool.poetry.group.codespell]
 optional = true
@@ -59,6 +60,11 @@ ruff = "^0.1.5"
 [tool.poetry.group.typing.dependencies]
 mypy = "^1"
 types-google-cloud-ndb = "^2.2.0.20240106"
+langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"}
+
+
+[tool.poetry.group.dev.dependencies]
+langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"}
 
 [tool.poetry.extras]
 anthropic = ["anthropic"]

From 8cac88a7cec468ca465301d3a1a20ae9c45e2f5d Mon Sep 17 00:00:00 2001
From: Alex Ostapenko <alx13@users.noreply.github.com>
Date: Wed, 10 Apr 2024 17:08:52 +0200
Subject: [PATCH 9/9] added tool-message (#134)

---
 .../langchain_google_vertexai/chat_models.py  | 13 ++-
 .../tests/unit_tests/test_chat_models.py      | 79 +++++++++++++++++++
 2 files changed, 91 insertions(+), 1 deletion(-)

diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
index 6383b5ea..b855e976 100644
--- a/libs/vertexai/langchain_google_vertexai/chat_models.py
+++ b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -29,6 +29,7 @@
     FunctionMessage,
     HumanMessage,
     SystemMessage,
+    ToolMessage,
 )
 from langchain_core.output_parsers.base import OutputParserLike
 from langchain_core.output_parsers.openai_functions import (
@@ -199,6 +200,16 @@ def _convert_to_parts(message: BaseMessage) -> List[Part]:
                     },
                 )
             ]
+        elif isinstance(message, ToolMessage):
+            role = "function"
+            parts = [
+                Part.from_function_response(
+                    name=message.name,
+                    response={
+                        "content": message.content,
+                    },
+                )
+            ]
         else:
             raise ValueError(
                 f"Unexpected message with type {type(message)} at the position {i}."
@@ -301,7 +312,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel):
     "Underlying model name."
     examples: Optional[List[BaseMessage]] = None
     convert_system_message_to_human: bool = False
-    """[Deprecated] Since new Gemini models support setting a System Message, 
+    """[Deprecated] Since new Gemini models support setting a System Message,
     setting this parameter to True is discouraged.
     """
 
diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py
index 344d7472..9265ad4f 100644
--- a/libs/vertexai/tests/unit_tests/test_chat_models.py
+++ b/libs/vertexai/tests/unit_tests/test_chat_models.py
@@ -9,6 +9,7 @@
 from google.cloud.aiplatform_v1beta1.types import (
     Content,
     FunctionCall,
+    FunctionResponse,
     Part,
 )
 from google.cloud.aiplatform_v1beta1.types import (
@@ -16,8 +17,10 @@
 )
 from langchain_core.messages import (
     AIMessage,
+    FunctionMessage,
     HumanMessage,
     SystemMessage,
+    ToolMessage,
 )
 from vertexai.generative_models import (  # type: ignore
     Candidate,
@@ -184,6 +187,82 @@ def test_parse_history_gemini_converted_message() -> None:
     assert history[1].parts[0].text == text_answer1
 
 
+def test_parse_history_gemini_function() -> None:
+    system_input = "You're supposed to answer math questions."
+    text_question1 = "Which is bigger 2+2 or 2*2?"
+    function_name = "calculator"
+    function_call_1 = {
+        "name": function_name,
+        "arguments": json.dumps({"arg1": "2", "arg2": "2", "op": "+"}),
+    }
+    function_answer1 = json.dumps({"result": 4})
+    function_call_2 = {
+        "name": function_name,
+        "arguments": json.dumps({"arg1": "2", "arg2": "2", "op": "*"}),
+    }
+    function_answer2 = json.dumps({"result": 4})
+    text_answer1 = "They are same"
+
+    system_message = SystemMessage(content=system_input)
+    message1 = HumanMessage(content=text_question1)
+    message2 = AIMessage(
+        content="",
+        additional_kwargs={
+            "function_call": function_call_1,
+        },
+    )
+    message3 = ToolMessage(
+        name="calculator", content=function_answer1, tool_call_id="1"
+    )
+    message4 = AIMessage(
+        content="",
+        additional_kwargs={
+            "function_call": function_call_2,
+        },
+    )
+    message5 = FunctionMessage(name="calculator", content=function_answer2)
+    message6 = AIMessage(content=text_answer1)
+    messages = [
+        system_message,
+        message1,
+        message2,
+        message3,
+        message4,
+        message5,
+        message6,
+    ]
+    system_instructions, history = _parse_chat_history_gemini(messages)
+    assert len(history) == 6
+    assert system_instructions and system_instructions.parts[0].text == system_input
+    assert history[0].role == "user"
+    assert history[0].parts[0].text == text_question1
+
+    assert history[1].role == "model"
+    assert history[1].parts[0].function_call == FunctionCall(
+        name=function_call_1["name"], args=json.loads(function_call_1["arguments"])
+    )
+
+    assert history[2].role == "function"
+    assert history[2].parts[0].function_response == FunctionResponse(
+        name=function_call_1["name"],
+        response={"content": function_answer1},
+    )
+
+    assert history[3].role == "model"
+    assert history[3].parts[0].function_call == FunctionCall(
+        name=function_call_2["name"], args=json.loads(function_call_2["arguments"])
+    )
+
+    assert history[4].role == "function"
+    assert history[2].parts[0].function_response == FunctionResponse(
+        name=function_call_2["name"],
+        response={"content": function_answer2},
+    )
+
+    assert history[5].role == "model"
+    assert history[5].parts[0].text == text_answer1
+
+
 def test_default_params_palm() -> None:
     user_prompt = "Hello"