google-gemini · MarkDaoust · Jul 8, 2024 · Apr 25, 2024 · Apr 26, 2024 · Apr 29, 2024
diff --git a/google/generativeai/answer.py b/google/generativeai/answer.py
@@ -82,7 +82,7 @@ def to_answer_style(x: AnswerStyleOptions) -> AnswerStyle:
 def _make_grounding_passages(source: GroundingPassagesOptions) -> protos.GroundingPassages:
     """
     Converts the `source` into a `protos.GroundingPassage`. A `GroundingPassages` contains a list of
-    `protos.GroundingPassage` objects, which each contain a `protos.Contant` and a string `id`.
+    `protos.GroundingPassage` objects, which each contain a `protos.Content` and a string `id`.
 
     Args:
         source: `Content` or a `GroundingPassagesOptions` that will be converted to protos.GroundingPassages.
@@ -191,7 +191,7 @@ def _make_generate_answer_request(
             single question to answer. For multi-turn queries, this is a repeated field that contains
             conversation history and the last `Content` in the list containing the question.
         inline_passages: Grounding passages (a list of `Content`-like objects or `(id, content)` pairs,
-            or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retreiver`,
+            or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retriever`,
             one must be set, but not both.
         semantic_retriever: A Corpus, Document, or `protos.SemanticRetrieverConfig` to use for grounding. Exclusive with
              `inline_passages`, one must be set, but not both.
@@ -267,7 +267,7 @@ def generate_answer(
     >>> my_corpus = retriever.get_corpus('my_corpus')
     >>> genai.generate_answer(
     ...     content=question,
-    ...     semantic_retreiver=my_corpus
+    ...     semantic_retriever=my_corpus
     ... )
 
 
@@ -276,7 +276,7 @@ def generate_answer(
         contents: The question to be answered by the model, grounded in the
                 provided source.
         inline_passages: Grounding passages (a list of `Content`-like objects or (id, content) pairs,
-            or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retreiver`,
+            or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retriever`,
             one must be set, but not both.
         semantic_retriever: A Corpus, Document, or `protos.SemanticRetrieverConfig` to use for grounding. Exclusive with
              `inline_passages`, one must be set, but not both.
@@ -330,7 +330,7 @@ async def generate_answer_async(
         contents: The question to be answered by the model, grounded in the
                 provided source.
         inline_passages: Grounding passages (a list of `Content`-like objects or (id, content) pairs,
-            or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retreiver`,
+            or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retriever`,
             one must be set, but not both.
         semantic_retriever: A Corpus, Document, or `protos.SemanticRetrieverConfig` to use for grounding. Exclusive with
              `inline_passages`, one must be set, but not both.

diff --git a/google/generativeai/generative_models.py b/google/generativeai/generative_models.py
@@ -139,7 +139,7 @@ def _prepare_request(
         """Creates a `protos.GenerateContentRequest` from raw inputs."""
         if hasattr(self, "_cached_content") and any([self._system_instruction, tools, tool_config]):
             raise ValueError(
-                "`tools`, `tool_config`, `system_instruction` cannot be set on a model instantinated with `cached_content` as its context."
+                "`tools`, `tool_config`, `system_instruction` cannot be set on a model instantiated with `cached_content` as its context."
             )
 
         tools_lib = self._get_tools_lib(tools)

diff --git a/google/generativeai/models.py b/google/generativeai/models.py
@@ -397,7 +397,7 @@ def update_tuned_model(
     client: glm.ModelServiceClient | None = None,
     request_options: helper_types.RequestOptionsType | None = None,
 ) -> model_types.TunedModel:
-    """Calls the API to puch updates to a specified tuned model where only certain attributes are updatable."""
+    """Calls the API to push updates to a specified tuned model where only certain attributes are updatable."""
 
     if request_options is None:
         request_options = {}

diff --git a/google/generativeai/notebook/flag_def.py b/google/generativeai/notebook/flag_def.py
@@ -160,7 +160,7 @@ def _has_non_default_value(
 
     Args:
       namespace: The Namespace that is populated by ArgumentParser.
-      dest: The attribute in the Namespacde to be populated.
+      dest: The attribute in the Namespace to be populated.
       has_default: "None" is a valid default value so we use an additional
         `has_default` boolean to indicate that `default_value` is present.
       default_value: The default value to use when `has_default` is True.

diff --git a/google/generativeai/notebook/lib/llmfn_post_process_cmds.py b/google/generativeai/notebook/lib/llmfn_post_process_cmds.py
@@ -29,7 +29,7 @@
 def _convert_view_to_output_row(
     row: llmfn_output_row.LLMFnOutputRowView,
 ) -> llmfn_output_row.LLMFnOutputRow:
-    """Convenience method to conert a LLMFnOutputRowView to LLMFnOutputRow.
+    """Convenience method to convert a LLMFnOutputRowView to LLMFnOutputRow.
 
     If `row` is already a LLMFnOutputRow, return as-is for efficiency.
     This could potentially break encapsulation as it could let code to modify

diff --git a/google/generativeai/types/retriever_types.py b/google/generativeai/types/retriever_types.py
@@ -1235,7 +1235,7 @@ def batch_update_chunks(
         Update multiple chunks within the same document.
 
         Args:
-            chunks: Data structure specifying which `Chunk`s to update and what the required updats are.
+            chunks: Data structure specifying which `Chunk`s to update and what the required updates are.
             request_options: Options for the request.
 
         Return: