Merge branch 'main' of github.com:moiz-stri/langchain-google

libs/vertexai/README.md

@@ -22,6 +22,7 @@ llm.invoke("Sing a ballad of LangChain.")
 ```
 
 You can use other models, e.g. `chat-bison`:
+
 ```python
 from langchain_google_vertexai import ChatVertexAI
 
@@ -58,7 +59,6 @@ The value of `image_url` can be any of the following:
 - A local file path
 - A base64 encoded image (e.g., `data:image/png;base64,abcd124`)
 
-
 ## Embeddings
 
 You can use Google Cloud's embeddings models as:
@@ -71,24 +71,27 @@ embeddings.embed_query("hello, world!")
 ```
 
 ## LLMs
+
 You can use Google Cloud's generative AI models as Langchain LLMs:
 
 ```python
-from langchain.prompts import PromptTemplate
-from langchain_google_vertexai import VertexAI
+from langchain_core.prompts import PromptTemplate
+from langchain_google_vertexai import ChatVertexAI
 
 template = """Question: {question}
 
 Answer: Let's think step by step."""
 prompt = PromptTemplate.from_template(template)
 
+llm = ChatVertexAI(model_name="gemini-pro")
 chain = prompt | llm
 
-question = "Who was the president in the year Justin Beiber was born?"
+question = "Who was the president of the USA in 1994?"
 print(chain.invoke({"question": question}))
 ```
 
 You can use Gemini and Palm models, including code-generations ones:
+
 ```python
 from langchain_google_vertexai import VertexAI
 

libs/vertexai/langchain_google_vertexai/_base.py

@@ -27,6 +27,7 @@
 from langchain_google_vertexai._enums import HarmBlockThreshold, HarmCategory
 from langchain_google_vertexai._utils import (
     get_client_info,
+    get_user_agent,
     is_codey_model,
     is_gemini_model,
 )
@@ -142,6 +143,12 @@ def _default_params(self) -> Dict[str, Any]:
                 )
         return updated_params
 
+    @property
+    def _user_agent(self) -> str:
+        """Gets the User Agent."""
+        _, user_agent = get_user_agent(f"{type(self).__name__}_{self.model_name}")
+        return user_agent
+
     @classmethod
     def _init_vertexai(cls, values: Dict) -> None:
         vertexai.init(

libs/vertexai/langchain_google_vertexai/_image_utils.py

@@ -7,7 +7,7 @@
 from urllib.parse import urlparse
 
 import requests
-from google.cloud import storage  # type: ignore[attr-defined]
+from google.cloud import storage
 
 
 class ImageBytesLoader:

libs/vertexai/langchain_google_vertexai/_utils.py

@@ -3,12 +3,12 @@
 import dataclasses
 import re
 from importlib import metadata
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import google.api_core
 import proto  # type: ignore[import-untyped]
 from google.api_core.gapic_v1.client_info import ClientInfo
-from google.cloud import storage  # type: ignore[attr-defined]
+from google.cloud import storage  # type: ignore[attr-defined, unused-ignore]
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -45,7 +45,7 @@ def create_retry_decorator(
     return decorator
 
 
-def raise_vertex_import_error(minimum_expected_version: str = "1.38.0") -> None:
+def raise_vertex_import_error(minimum_expected_version: str = "1.44.0") -> None:
     """Raise ImportError related to Vertex SDK being not available.
 
     Args:
@@ -59,27 +59,43 @@ def raise_vertex_import_error(minimum_expected_version: str = "1.38.0") -> None:
     )
 
 
-def get_client_info(module: Optional[str] = None) -> "ClientInfo":
+def get_user_agent(module: Optional[str] = None) -> Tuple[str, str]:
     r"""Returns a custom user agent header.
 
     Args:
         module (Optional[str]):
             Optional. The module for a custom user agent header.
     Returns:
-        google.api_core.gapic_v1.client_info.ClientInfo
+        Tuple[str, str]
     """
-    langchain_version = metadata.version("langchain")
+    try:
+        langchain_version = metadata.version("langchain")
+    except metadata.PackageNotFoundError:
+        langchain_version = "0.0.0"
     client_library_version = (
         f"{langchain_version}-{module}" if module else langchain_version
     )
+    return client_library_version, f"langchain/{client_library_version}"
+
+
+def get_client_info(module: Optional[str] = None) -> "ClientInfo":
+    r"""Returns a client info object with a custom user agent header.
+
+    Args:
+        module (Optional[str]):
+            Optional. The module for a custom user agent header.
+    Returns:
+        google.api_core.gapic_v1.client_info.ClientInfo
+    """
+    client_library_version, user_agent = get_user_agent(module)
     return ClientInfo(
         client_library_version=client_library_version,
-        user_agent=f"langchain/{client_library_version}",
+        user_agent=user_agent,
     )
 
 
 def load_image_from_gcs(path: str, project: Optional[str] = None) -> Image:
-    """Loads im Image from GCS."""
+    """Loads an Image from GCS."""
     gcs_client = storage.Client(project=project)
     pieces = path.split("/")
     blobs = list(gcs_client.list_blobs(pieces[2], prefix="/".join(pieces[3:])))