feat: google genai integration with tool block

llama-index-integrations/llms/llama-index-llms-google-genai/llama_index/llms/google_genai/base.py

@@ -6,6 +6,7 @@
 import typing
 from typing import (
     TYPE_CHECKING,
+    cast,
     Any,
     AsyncGenerator,
     Dict,
@@ -38,6 +39,7 @@
     MessageRole,
     ThinkingBlock,
     TextBlock,
+    ToolCallBlock,
 )
 from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr
 from llama_index.core.callbacks import CallbackManager
@@ -376,7 +378,6 @@ def _stream_chat(
 
         def gen() -> ChatResponseGen:
             content = ""
-            existing_tool_calls = []
             thoughts = ""
             for r in response:
                 if not r.candidates:
@@ -390,14 +391,11 @@ def gen() -> ChatResponseGen:
                     else:
                         content += content_delta
                 llama_resp = chat_from_gemini_response(r)
-                existing_tool_calls.extend(
-                    llama_resp.message.additional_kwargs.get("tool_calls", [])
-                )
-                llama_resp.delta = content_delta
-                llama_resp.message.blocks = [TextBlock(text=content)]
-                llama_resp.message.blocks.append(ThinkingBlock(content=thoughts))
-                llama_resp.message.additional_kwargs["tool_calls"] = existing_tool_calls
-                yield llama_resp
+                if content:
+                    llama_resp.message.blocks.append(TextBlock(text=content))
+                if thoughts:
+                    llama_resp.message.blocks.append(ThinkingBlock(content=thoughts))
+                    yield llama_resp
 
             if self.use_file_api:
                 asyncio.run(
@@ -429,7 +427,6 @@ async def _astream_chat(
 
         async def gen() -> ChatResponseAsyncGen:
             content = ""
-            existing_tool_calls = []
             thoughts = ""
             async for r in await chat.send_message_stream(
                 next_msg.parts if isinstance(next_msg, types.Content) else next_msg
@@ -448,19 +445,15 @@ async def gen() -> ChatResponseAsyncGen:
                                 else:
                                     content += content_delta
                             llama_resp = chat_from_gemini_response(r)
-                            existing_tool_calls.extend(
-                                llama_resp.message.additional_kwargs.get(
-                                    "tool_calls", []
-                                )
-                            )
                             llama_resp.delta = content_delta
-                            llama_resp.message.blocks = [TextBlock(text=content)]
-                            llama_resp.message.blocks.append(
-                                ThinkingBlock(content=thoughts)
-                            )
-                            llama_resp.message.additional_kwargs["tool_calls"] = (
-                                existing_tool_calls
-                            )
+                            if content:
+                                llama_resp.message.blocks.append(
+                                    TextBlock(text=content)
+                                )
+                            if thoughts:
+                                llama_resp.message.blocks.append(
+                                    ThinkingBlock(content=thoughts)
+                                )
                             yield llama_resp
 
             if self.use_file_api:
@@ -551,7 +544,11 @@ def get_tool_calls_from_response(
         **kwargs: Any,
     ) -> List[ToolSelection]:
         """Predict and call the tool."""
-        tool_calls = response.message.additional_kwargs.get("tool_calls", [])
+        tool_calls = [
+            block
+            for block in response.message.blocks
+            if isinstance(block, ToolCallBlock)
+        ]
 
         if len(tool_calls) < 1:
             if error_on_no_tool_call:
@@ -565,9 +562,9 @@ def get_tool_calls_from_response(
         for tool_call in tool_calls:
             tool_selections.append(
                 ToolSelection(
-                    tool_id=tool_call["name"],
-                    tool_name=tool_call["name"],
-                    tool_kwargs=tool_call["args"],
+                    tool_id=tool_call.tool_name,
+                    tool_name=tool_call.tool_name,
+                    tool_kwargs=cast(Dict[str, Any], tool_call.tool_kwargs),
                 )
             )
 

llama-index-integrations/llms/llama-index-llms-google-genai/llama_index/llms/google_genai/utils.py

@@ -1,16 +1,9 @@
 import asyncio
+import json
 import logging
 from collections.abc import Sequence
 from io import BytesIO
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Union,
-    Optional,
-    Type,
-    Tuple,
-)
+from typing import TYPE_CHECKING, Any, Dict, Union, Optional, Type, Tuple, cast
 import typing
 
 import google.genai.types as types
@@ -29,6 +22,7 @@
     DocumentBlock,
     VideoBlock,
     ThinkingBlock,
+    ToolCallBlock,
 )
 from llama_index.core.program.utils import _repair_incomplete_json
 from tenacity import (
@@ -188,16 +182,33 @@ def chat_from_gemini_response(
                 )
                 additional_kwargs["thought_signatures"].append(part.thought_signature)
             if part.function_call:
-                if "tool_calls" not in additional_kwargs:
-                    additional_kwargs["tool_calls"] = []
-                additional_kwargs["tool_calls"].append(
-                    {
-                        "id": part.function_call.id if part.function_call.id else "",
-                        "name": part.function_call.name,
-                        "args": part.function_call.args,
-                        "thought_signature": part.thought_signature,
-                    }
+                if (
+                    part.thought_signature
+                    not in additional_kwargs["thought_signatures"]
+                ):
+                    additional_kwargs["thought_signatures"].append(
+                        part.thought_signature
+                    )
+                content_blocks.append(
+                    ToolCallBlock(
+                        tool_call_id=part.function_call.id or "",
+                        tool_name=part.function_call.name or "",
+                        tool_kwargs=part.function_call.args or {},
+                    )
                 )
+            if part.function_response:
+                # follow the same pattern as for transforming a chatmessage into a gemini message: if it's a function response, package it alone and return it
+                additional_kwargs["tool_call_id"] = part.function_response.id
+                role = ROLES_FROM_GEMINI[top_candidate.content.role]
+                print("RESPONSE", json.dumps(part.function_response.response))
+                return ChatResponse(
+                    message=ChatMessage(
+                        role=role, content=json.dumps(part.function_response.response)
+                    ),
+                    raw=raw,
+                    additional_kwargs=additional_kwargs,
+                )
+
     if thought_tokens:
         thinking_blocks = [
             i
@@ -271,6 +282,7 @@ async def chat_message_to_gemini(
     message: ChatMessage, use_file_api: bool = False, client: Optional[Client] = None
 ) -> Union[types.Content, types.File]:
     """Convert ChatMessages to Gemini-specific history, including ImageDocuments."""
+    unique_tool_calls = []
     parts = []
     part = None
     for index, block in enumerate(message.blocks):
@@ -326,6 +338,11 @@ async def chat_message_to_gemini(
                 part.thought_signature = block.additional_information.get(
                     "thought_signature", None
                 )
+        elif isinstance(block, ToolCallBlock):
+            part = types.Part.from_function_call(
+                name=block.tool_name, args=cast(Dict[str, Any], block.tool_kwargs)
+            )
+            unique_tool_calls.append((block.tool_name, str(block.tool_kwargs)))
         else:
             msg = f"Unsupported content block type: {type(block).__name__}"
             raise ValueError(msg)
@@ -343,15 +360,20 @@ async def chat_message_to_gemini(
 
     for tool_call in message.additional_kwargs.get("tool_calls", []):
         if isinstance(tool_call, dict):
-            part = types.Part.from_function_call(
-                name=tool_call.get("name"), args=tool_call.get("args")
-            )
-            part.thought_signature = tool_call.get("thought_signature")
+            if (
+                tool_call.get("name", ""),
+                str(tool_call.get("args", {})),
+            ) not in unique_tool_calls:
+                part = types.Part.from_function_call(
+                    name=tool_call.get("name", ""), args=tool_call.get("args", {})
+                )
+                part.thought_signature = tool_call.get("thought_signature")
         else:
-            part = types.Part.from_function_call(
-                name=tool_call.name, args=tool_call.args
-            )
-            part.thought_signature = tool_call.thought_signature
+            if (tool_call.name, str(tool_call.args)) not in unique_tool_calls:
+                part = types.Part.from_function_call(
+                    name=tool_call.name, args=tool_call.args
+                )
+                part.thought_signature = tool_call.thought_signature
         parts.append(part)
 
     # the tool call id is the name of the tool

llama-index-integrations/llms/llama-index-llms-google-genai/pyproject.toml

@@ -27,7 +27,7 @@ dev = [
 
 [project]
 name = "llama-index-llms-google-genai"
-version = "0.6.2"
+version = "0.7.0"
 description = "llama-index llms google genai integration"
 authors = [{name = "Your Name", email = "[email protected]"}]
 requires-python = ">=3.9,<4.0"
@@ -36,7 +36,7 @@ license = "MIT"
 dependencies = [
     "pillow>=10.2.0",
     "google-genai>=1.24.0,<2",
-    "llama-index-core>=0.14.3,<0.15",
+    "llama-index-core>=0.14.5,<0.15",
 ]
 
 [tool.codespell]

llama-index-integrations/llms/llama-index-llms-google-genai/tests/test_llms_google_genai.py

@@ -11,6 +11,7 @@
     TextBlock,
     VideoBlock,
     ThinkingBlock,
+    ToolCallBlock,
 )
 from llama_index.core.llms.llm import ToolSelection
 from llama_index.core.program.function_program import get_function_tool
@@ -564,8 +565,16 @@ def test_tool_required_integration(llm: GoogleGenAI) -> None:
         tools=[search_tool],
         tool_required=True,
     )
-    assert response.message.additional_kwargs.get("tool_calls") is not None
-    assert len(response.message.additional_kwargs["tool_calls"]) > 0
+    assert (
+        len(
+            [
+                block
+                for block in response.message.blocks
+                if isinstance(block, ToolCallBlock)
+            ]
+        )
+        > 0
+    )
 
     # Test with tool_required=False
     response = llm.chat_with_tools(
@@ -729,6 +738,10 @@ async def test_prepare_chat_params_more_than_2_tool_calls():
                 )
             ],
         ),
+        ChatMessage(
+            blocks=[ToolCallBlock(tool_name="get_available_tools", tool_kwargs={})],
+            role=MessageRole.ASSISTANT,
+        ),
         ChatMessage(
             content="Let me search for puppies.",
             role=MessageRole.ASSISTANT,
@@ -777,10 +790,11 @@ async def test_prepare_chat_params_more_than_2_tool_calls():
                     text="The user is asking me for a puppy, so I should search for puppies using the available tools.",
                     thought=True,
                 ),
+                types.Part.from_function_call(name="get_available_tools", args={}),
                 types.Part(text="Let me search for puppies."),
-                types.Part.from_function_call(name="tool_1", args=None),
-                types.Part.from_function_call(name="tool_2", args=None),
-                types.Part.from_function_call(name="tool_3", args=None),
+                types.Part.from_function_call(name="tool_1", args={}),
+                types.Part.from_function_call(name="tool_2", args={}),
+                types.Part.from_function_call(name="tool_3", args={}),
             ],
             role=MessageRole.MODEL,
         ),
@@ -872,6 +886,10 @@ def test_cached_content_in_response() -> None:
     mock_response.candidates[0].content.parts[0].text = "Test response"
     mock_response.candidates[0].content.parts[0].thought = False
     mock_response.candidates[0].content.parts[0].inline_data = None
+    mock_response.candidates[0].content.parts[0].function_call.id = ""
+    mock_response.candidates[0].content.parts[0].function_call.name = "hello"
+    mock_response.candidates[0].content.parts[0].function_call.args = {}
+    mock_response.candidates[0].content.parts[0].function_response = None
     mock_response.prompt_feedback = None
     mock_response.usage_metadata = None
     mock_response.function_calls = None
@@ -899,6 +917,10 @@ def test_cached_content_without_cached_content() -> None:
     mock_response.candidates[0].content.parts[0].text = "Test response"
     mock_response.candidates[0].content.parts[0].thought = False
     mock_response.candidates[0].content.parts[0].inline_data = None
+    mock_response.candidates[0].content.parts[0].function_call.id = ""
+    mock_response.candidates[0].content.parts[0].function_call.name = "hello"
+    mock_response.candidates[0].content.parts[0].function_call.args = {}
+    mock_response.candidates[0].content.parts[0].function_response = None
     mock_response.prompt_feedback = None
     mock_response.usage_metadata = None
     mock_response.function_calls = None
@@ -923,9 +945,15 @@ def test_thoughts_in_response() -> None:
     mock_response.candidates[0].content.parts[0].text = "This is a thought."
     mock_response.candidates[0].content.parts[0].inline_data = None
     mock_response.candidates[0].content.parts[0].thought = True
+    mock_response.candidates[0].content.parts[0].function_call.id = ""
+    mock_response.candidates[0].content.parts[0].function_call.name = "hello"
+    mock_response.candidates[0].content.parts[0].function_call.args = {}
     mock_response.candidates[0].content.parts[1].text = "This is not a thought."
     mock_response.candidates[0].content.parts[1].inline_data = None
     mock_response.candidates[0].content.parts[1].thought = None
+    mock_response.candidates[0].content.parts[1].function_call = None
+    mock_response.candidates[0].content.parts[1].function_response = None
+    mock_response.candidates[0].content.parts[0].function_response = None
     mock_response.candidates[0].content.parts[0].model_dump = MagicMock(return_value={})
     mock_response.candidates[0].content.parts[1].model_dump = MagicMock(return_value={})
     mock_response.prompt_feedback = None
@@ -967,6 +995,8 @@ def test_thoughts_without_thought_response() -> None:
     mock_response.candidates[0].content.parts[0].text = "This is not a thought."
     mock_response.candidates[0].content.parts[0].inline_data = None
     mock_response.candidates[0].content.parts[0].thought = None
+    mock_response.candidates[0].content.parts[0].function_call = None
+    mock_response.candidates[0].content.parts[0].function_response = None
     mock_response.prompt_feedback = None
     mock_response.usage_metadata = None
     mock_response.function_calls = None
@@ -1084,6 +1114,8 @@ def test_built_in_tool_in_response() -> None:
     ].text = "Test response with search results"
     mock_response.candidates[0].content.parts[0].inline_data = None
     mock_response.candidates[0].content.parts[0].thought = None
+    mock_response.candidates[0].content.parts[0].function_call = None
+    mock_response.candidates[0].content.parts[0].function_response = None
     mock_response.prompt_feedback = None
     mock_response.usage_metadata = MagicMock()
     mock_response.usage_metadata.model_dump.return_value = {
@@ -1523,6 +1555,8 @@ def test_code_execution_response_parts() -> None:
     )
     mock_text_part.inline_data = None
     mock_text_part.thought = None
+    mock_text_part.function_call = None
+    mock_text_part.function_response = None
 
     mock_code_part = MagicMock()
     mock_code_part.text = None
@@ -1532,6 +1566,8 @@ def test_code_execution_response_parts() -> None:
         "code": "def is_prime(n):\n    if n < 2:\n        return False\n    for i in range(2, int(n**0.5) + 1):\n        if n % i == 0:\n            return False\n    return True\n\nprimes = []\nn = 2\nwhile len(primes) < 50:\n    if is_prime(n):\n        primes.append(n)\n    n += 1\n\nprint(f'Sum of first 50 primes: {sum(primes)}')",
         "language": types.Language.PYTHON,
     }
+    mock_code_part.function_call = None
+    mock_code_part.function_response = None
 
     mock_result_part = MagicMock()
     mock_result_part.text = None
@@ -1541,11 +1577,15 @@ def test_code_execution_response_parts() -> None:
         "outcome": types.Outcome.OUTCOME_OK,
         "output": "Sum of first 50 primes: 5117",
     }
+    mock_result_part.function_call = None
+    mock_result_part.function_response = None
 
     mock_final_text_part = MagicMock()
     mock_final_text_part.text = "The sum of the first 50 prime numbers is 5117."
     mock_final_text_part.inline_data = None
     mock_final_text_part.thought = None
+    mock_final_text_part.function_call = None
+    mock_final_text_part.function_response = None
 
     mock_candidate.content.parts = [
         mock_text_part,