anthropic[minor]: Adds streaming tool call support for Anthropic (#22687)

Preserves string content chunks for non tool call requests for
convenience.

One thing - Anthropic events look like this:

```
RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start')
RawContentBlockDeltaEvent(delta=TextDelta(text='<thinking>\nThe', type='text_delta'), index=0, type='content_block_delta')
RawContentBlockDeltaEvent(delta=TextDelta(text=' provide', type='text_delta'), index=0, type='content_block_delta')
...
RawContentBlockStartEvent(content_block=ToolUseBlock(id='toolu_01GJ6x2ddcMG3psDNNe4eDqb', input={}, name='get_weather', type='tool_use'), index=1, type='content_block_start')
RawContentBlockDeltaEvent(delta=InputJsonDelta(partial_json='', type='input_json_delta'), index=1, type='content_block_delta')
```

Note that `delta` has a `type` field. With this implementation, I'm
dropping it because `merge_list` behavior will concatenate strings.

We currently have `index` as a special field when merging lists, would
it be worth adding `type` too?

If so, what do we set as a context block chunk? `text` vs.
`text_delta`/`tool_use` vs `input_json_delta`?

CC @ccurme @efriis @baskaryan

libs/partners/anthropic/tests/integration_tests/test_chat_models.py

@@ -146,6 +146,55 @@ async def test_abatch_tags() -> None:
         assert isinstance(token.content, str)
 
 
+async def test_async_tool_use() -> None:
+    llm = ChatAnthropic(  # type: ignore[call-arg]
+        model=MODEL_NAME,
+    )
+
+    llm_with_tools = llm.bind_tools(
+        [
+            {
+                "name": "get_weather",
+                "description": "Get weather report for a city",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {"location": {"type": "string"}},
+                },
+            }
+        ]
+    )
+    response = await llm_with_tools.ainvoke("what's the weather in san francisco, ca")
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, list)
+    assert isinstance(response.tool_calls, list)
+    assert len(response.tool_calls) == 1
+    tool_call = response.tool_calls[0]
+    assert tool_call["name"] == "get_weather"
+    assert isinstance(tool_call["args"], dict)
+    assert "location" in tool_call["args"]
+
+    # Test streaming
+    first = True
+    chunks = []  # type: ignore
+    async for chunk in llm_with_tools.astream(
+        "what's the weather in san francisco, ca"
+    ):
+        chunks = chunks + [chunk]
+        if first:
+            gathered = chunk
+            first = False
+        else:
+            gathered = gathered + chunk  # type: ignore
+    assert len(chunks) > 1
+    assert isinstance(gathered, AIMessageChunk)
+    assert isinstance(gathered.tool_call_chunks, list)
+    assert len(gathered.tool_call_chunks) == 1
+    tool_call_chunk = gathered.tool_call_chunks[0]
+    assert tool_call_chunk["name"] == "get_weather"
+    assert isinstance(tool_call_chunk["args"], str)
+    assert "location" in json.loads(tool_call_chunk["args"])
+
+
 def test_batch() -> None:
     """Test batch tokens from ChatAnthropicMessages."""
     llm = ChatAnthropicMessages(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]
@@ -313,7 +362,7 @@ async def test_astreaming() -> None:
 
 def test_tool_use() -> None:
     llm = ChatAnthropic(  # type: ignore[call-arg]
-        model="claude-3-opus-20240229",
+        model=MODEL_NAME,
     )
 
     llm_with_tools = llm.bind_tools(
@@ -339,20 +388,55 @@ def test_tool_use() -> None:
     assert "location" in tool_call["args"]
 
     # Test streaming
+    input = "how are you? what's the weather in san francisco, ca"
     first = True
-    for chunk in llm_with_tools.stream("what's the weather in san francisco, ca"):
+    chunks = []  # type: ignore
+    for chunk in llm_with_tools.stream(input):
+        chunks = chunks + [chunk]
         if first:
             gathered = chunk
             first = False
         else:
             gathered = gathered + chunk  # type: ignore
+    assert len(chunks) > 1
+    assert isinstance(gathered.content, list)
+    assert len(gathered.content) == 2
+    tool_use_block = None
+    for content_block in gathered.content:
+        assert isinstance(content_block, dict)
+        if content_block["type"] == "tool_use":
+            tool_use_block = content_block
+            break
+    assert tool_use_block is not None
+    assert tool_use_block["name"] == "get_weather"
+    assert "location" in json.loads(tool_use_block["partial_json"])
     assert isinstance(gathered, AIMessageChunk)
-    assert isinstance(gathered.tool_call_chunks, list)
-    assert len(gathered.tool_call_chunks) == 1
-    tool_call_chunk = gathered.tool_call_chunks[0]
-    assert tool_call_chunk["name"] == "get_weather"
-    assert isinstance(tool_call_chunk["args"], str)
-    assert "location" in json.loads(tool_call_chunk["args"])
+    assert isinstance(gathered.tool_calls, list)
+    assert len(gathered.tool_calls) == 1
+    tool_call = gathered.tool_calls[0]
+    assert tool_call["name"] == "get_weather"
+    assert isinstance(tool_call["args"], dict)
+    assert "location" in tool_call["args"]
+    assert tool_call["id"] is not None
+
+    # Test passing response back to model
+    stream = llm_with_tools.stream(
+        [
+            input,
+            gathered,
+            ToolMessage(content="sunny and warm", tool_call_id=tool_call["id"]),
+        ]
+    )
+    chunks = []  # type: ignore
+    first = True
+    for chunk in stream:
+        chunks = chunks + [chunk]
+        if first:
+            gathered = chunk
+            first = False
+        else:
+            gathered = gathered + chunk  # type: ignore
+    assert len(chunks) > 1
 
 
 def test_anthropic_with_empty_text_block() -> None:
@@ -428,7 +512,7 @@ class GetWeather(BaseModel):
 @pytest.mark.parametrize("tool_choice", ["GetWeather", "auto", "any"])
 def test_anthropic_bind_tools_tool_choice(tool_choice: str) -> None:
     chat_model = ChatAnthropic(  # type: ignore[call-arg]
-        model="claude-3-sonnet-20240229",
+        model=MODEL_NAME,
     )
     chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice=tool_choice)
     response = chat_model_with_tools.invoke("what's the weather in ny and la")