From ae5fe63843bf469d36ad9e5dc250e7fd69749879 Mon Sep 17 00:00:00 2001
From: Logan <logan.markewich@live.com>
Date: Thu, 4 Apr 2024 18:38:53 -0600
Subject: [PATCH] add anthropic tool calling (#12591)
* add anthropic tool calling
* update docs
---
.../docs/examples/agent/anthropic_agent.ipynb | 414 ++++++++++++++++++
docs/mkdocs.yml | 1 +
.../llama_index/llms/anthropic/base.py | 170 ++++++-
.../llama_index/llms/anthropic/utils.py | 52 ++-
.../llama-index-llms-anthropic/pyproject.toml | 4 +-
5 files changed, 624 insertions(+), 17 deletions(-)
create mode 100644 docs/docs/examples/agent/anthropic_agent.ipynb
diff --git a/docs/docs/examples/agent/anthropic_agent.ipynb b/docs/docs/examples/agent/anthropic_agent.ipynb
new file mode 100644
index 000000000..4ff125a99
--- /dev/null
+++ b/docs/docs/examples/agent/anthropic_agent.ipynb
@@ -0,0 +1,414 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "24103c51",
+ "metadata": {},
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/agent/mistral_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99cea58c-48bc-4af6-8358-df9695659983",
+ "metadata": {},
+ "source": [
+ "# Function Calling Anthropic Agent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "673df1fe-eb6c-46ea-9a73-a96e7ae7942e",
+ "metadata": {},
+ "source": [
+ "This notebook shows you how to use our Anthropic agent, powered by function calling capabilities.\n",
+ "\n",
+ "**NOTE:** Only claude-3 models support function calling using Anthropic's API."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54b7bc2e-606f-411a-9490-fcfab9236dfc",
+ "metadata": {},
+ "source": [
+ "## Initial Setup "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23e80e5b-aaee-4f23-b338-7ae62b08141f",
+ "metadata": {},
+ "source": [
+ "Let's start by importing some simple building blocks. \n",
+ "\n",
+ "The main thing we need is:\n",
+ "1. the Anthropic API (using our own `llama_index` LLM class)\n",
+ "2. a place to keep conversation history \n",
+ "3. a definition for tools that our agent can use."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "41101795",
+ "metadata": {},
+ "source": [
+ "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4985c578",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index-llms-anthropic\n",
+ "%pip install llama-index-embeddings-openai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c61c873d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install llama-index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d47283b-025e-4874-88ed-76245b22f82e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.llms.anthropic import Anthropic\n",
+ "from llama_index.core.tools import FunctionTool\n",
+ "\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6fe08eb1-e638-4c00-9103-5c305bfacccf",
+ "metadata": {},
+ "source": [
+ "Let's define some very simple calculator tools for our agent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3dd3c4a6-f3e0-46f9-ad3b-7ba57d1bc992",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def multiply(a: int, b: int) -> int:\n",
+ " \"\"\"Multiple two integers and returns the result integer\"\"\"\n",
+ " return a * b\n",
+ "\n",
+ "\n",
+ "multiply_tool = FunctionTool.from_defaults(fn=multiply)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bfcfb78b-7d4f-48d9-8d4c-ffcded23e7ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(a: int, b: int) -> int:\n",
+ " \"\"\"Add two integers and returns the result integer\"\"\"\n",
+ " return a + b\n",
+ "\n",
+ "\n",
+ "add_tool = FunctionTool.from_defaults(fn=add)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eeac7d4c-58fd-42a5-9da9-c258375c61a0",
+ "metadata": {},
+ "source": [
+ "Make sure your ANTHROPIC_API_KEY is set. Otherwise explicitly specify the `api_key` parameter."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4becf171-6632-42e5-bdec-918a00934696",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = Anthropic(model=\"claude-3-opus-20240229\", api_key=\"sk-ant-...\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "707d30b8-6405-4187-a9ed-6146dcc42167",
+ "metadata": {},
+ "source": [
+ "## Initialize Anthropic Agent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "798ca3fd-6711-4c0c-a853-d868dd14b484",
+ "metadata": {},
+ "source": [
+ "Here we initialize a simple Mistral agent with calculator functions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38ab3938-1138-43ea-b085-f430b42f5377",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.core.agent import AgentRunner\n",
+ "\n",
+ "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ " [multiply_tool, add_tool],\n",
+ " llm=llm,\n",
+ " verbose=True,\n",
+ " allow_parallel_tool_calls=False,\n",
+ ")\n",
+ "agent = AgentRunner(agent_worker)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "500cbee4",
+ "metadata": {},
+ "source": [
+ "### Chat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9450401d-769f-46e8-8bab-0f27f7362f5d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added user message to memory: What is (121 + 2) * 5?\n",
+ "=== Calling Function ===\n",
+ "Calling function: add with args: {\"a\": 121, \"b\": 2}\n",
+ "=== Calling Function ===\n",
+ "Calling function: multiply with args: {\"a\": 123, \"b\": 5}\n",
+ "assistant: Therefore, (121 + 2) * 5 = 615\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = agent.chat(\"What is (121 + 2) * 5?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "538bf32f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ToolOutput(content='123', tool_name='add', raw_input={'args': (), 'kwargs': {'a': 121, 'b': 2}}, raw_output=123), ToolOutput(content='615', tool_name='multiply', raw_input={'args': (), 'kwargs': {'a': 123, 'b': 5}}, raw_output=615)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# inspect sources\n",
+ "print(response.sources)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb33983c",
+ "metadata": {},
+ "source": [
+ "### Async Chat\n",
+ "\n",
+ "Also let's re-enable parallel function calling so that we can call two `multiply` operations simultaneously."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d1fc974",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added user message to memory: What is (121 * 3) + (5 * 8)?\n",
+ "=== Calling Function ===\n",
+ "Calling function: multiply with args: {\"a\": 121, \"b\": 3}\n",
+ "=== Calling Function ===\n",
+ "Calling function: multiply with args: {\"a\": 5, \"b\": 8}\n",
+ "=== Calling Function ===\n",
+ "Calling function: add with args: {\"a\": 363, \"b\": 40}\n",
+ "assistant: Therefore, the result of (121 * 3) + (5 * 8) is 403.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# enable parallel function calling\n",
+ "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ " [multiply_tool, add_tool],\n",
+ " llm=llm,\n",
+ " verbose=True,\n",
+ " allow_parallel_tool_calls=True,\n",
+ ")\n",
+ "agent = AgentRunner(agent_worker)\n",
+ "response = await agent.achat(\"What is (121 * 3) + (5 * 8)?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cabfdf01-8d63-43ff-b06e-a3059ede2ddf",
+ "metadata": {},
+ "source": [
+ "## Anthropic Agent over RAG Pipeline\n",
+ "\n",
+ "Build a Anthropic agent over a simple 10K document. We use OpenAI embeddings and claude-3-haiku-20240307 to construct the RAG pipeline, and pass it to the Anthropic Opus agent as a tool."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48120dd4-7f50-426f-bc7e-a903e090d32e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2024-04-04 18:12:42-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 1880483 (1.8M) [application/octet-stream]\n",
+ "Saving to: ‘data/10k/uber_2021.pdf’\n",
+ "\n",
+ "data/10k/uber_2021. 100%[===================>] 1.79M 6.09MB/s in 0.3s \n",
+ "\n",
+ "2024-04-04 18:12:43 (6.09 MB/s) - ‘data/10k/uber_2021.pdf’ saved [1880483/1880483]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir -p 'data/10k/'\n",
+ "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48c0cf98-3f10-4599-8437-d88dc89cefad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
+ "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n",
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.llms.anthropic import Anthropic\n",
+ "\n",
+ "embed_model = OpenAIEmbedding(api_key=\"sk-...\")\n",
+ "query_llm = Anthropic(model=\"claude-3-haiku-20240307\", api_key=\"sk-ant-...\")\n",
+ "\n",
+ "# load data\n",
+ "uber_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"./data/10k/uber_2021.pdf\"]\n",
+ ").load_data()\n",
+ "# build index\n",
+ "uber_index = VectorStoreIndex.from_documents(\n",
+ " uber_docs, embed_model=embed_model\n",
+ ")\n",
+ "uber_engine = uber_index.as_query_engine(similarity_top_k=3, llm=query_llm)\n",
+ "query_engine_tool = QueryEngineTool(\n",
+ " query_engine=uber_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"uber_10k\",\n",
+ " description=(\n",
+ " \"Provides information about Uber financials for year 2021. \"\n",
+ " \"Use a detailed plain text question as input to the tool.\"\n",
+ " ),\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ebfdaf80-e5e1-4c60-b556-20558da3d5e3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.core.agent import AgentRunner\n",
+ "\n",
+ "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ " [query_engine_tool], llm=llm, verbose=True\n",
+ ")\n",
+ "agent = AgentRunner(agent_worker)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "58c53f2a-0a3f-4abe-b8b6-97a974ec7546",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added user message to memory: Tell me both the risk factors and tailwinds for Uber?\n",
+ "=== Calling Function ===\n",
+ "Calling function: uber_10k with args: {\"input\": \"What were some of the key risk factors and tailwinds mentioned for Uber's business in 2021?\"}\n",
+ "assistant: In summary, some of the key risk factors Uber faced in 2021 included regulatory challenges, IP protection, staying competitive with new technologies, seasonality and forecasting challenges due to COVID-19, and risks of international expansion. However, Uber also benefited from tailwinds like accelerated growth in food delivery due to the pandemic and adapting well to new remote work arrangements.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = agent.chat(\"Tell me both the risk factors and tailwinds for Uber?\")\n",
+ "print(str(response))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index b3ba86175..1e45a1b6b 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -79,6 +79,7 @@ nav:
- ./examples/agent/agent_runner/agent_around_query_pipeline_with_HyDE_for_PDFs.ipynb
- ./examples/agent/mistral_agent.ipynb
- ./examples/agent/openai_agent_tool_call_parser.ipynb
+ - ./examples/agent/anthropic_agent.ipynb
- Callbacks:
- ./examples/callbacks/HoneyHiveLlamaIndexTracer.ipynb
- ./examples/callbacks/PromptLayerHandler.ipynb
diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py
index 58d223d47..3e159d405 100644
--- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py
+++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py
@@ -1,5 +1,19 @@
-from typing import Any, Callable, Dict, Optional, Sequence
-from anthropic.types import ContentBlockDeltaEvent
+import anthropic
+import json
+from anthropic.types import ContentBlockDeltaEvent, TextBlock
+from anthropic.types.beta.tools import ToolUseBlock
+from typing import (
+ Any,
+ Callable,
+ Dict,
+ List,
+ Optional,
+ Sequence,
+ Tuple,
+ Union,
+ TYPE_CHECKING,
+)
+
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
@@ -24,21 +38,26 @@ from llama_index.core.base.llms.generic_utils import (
chat_to_completion_decorator,
stream_chat_to_completion_decorator,
)
-from llama_index.core.llms.llm import LLM
+from llama_index.core.llms.function_calling import FunctionCallingLLM, ToolSelection
from llama_index.core.types import BaseOutputParser, PydanticProgramMode
from llama_index.llms.anthropic.utils import (
anthropic_modelname_to_contextsize,
+ force_single_tool_call,
+ is_function_calling_model,
messages_to_anthropic_messages,
)
from llama_index.core.utils import Tokenizer
-import anthropic
+if TYPE_CHECKING:
+ from llama_index.core.chat_engine.types import AgentChatResponse
+ from llama_index.core.tools.types import BaseTool
+
DEFAULT_ANTHROPIC_MODEL = "claude-2.1"
DEFAULT_ANTHROPIC_MAX_TOKENS = 512
-class Anthropic(LLM):
+class Anthropic(FunctionCallingLLM):
"""Anthropic LLM.
Examples:
@@ -146,6 +165,7 @@ class Anthropic(LLM):
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
+ is_function_calling_model=is_function_calling_model(self.model),
)
@property
@@ -170,20 +190,38 @@ class Anthropic(LLM):
**kwargs,
}
+ def _get_content_and_tool_calls(
+ self, response: Any
+ ) -> Tuple[str, List[ToolUseBlock]]:
+ tool_calls = []
+ content = ""
+ for content_block in response.content:
+ if isinstance(content_block, TextBlock):
+ content += content_block.text
+ elif isinstance(content_block, ToolUseBlock):
+ tool_calls.append(content_block.dict())
+
+ return content, tool_calls
+
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
anthropic_messages, system_prompt = messages_to_anthropic_messages(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
- response = self._client.messages.create(
+ response = self._client.beta.tools.messages.create(
messages=anthropic_messages,
stream=False,
system=system_prompt,
**all_kwargs,
)
+
+ content, tool_calls = self._get_content_and_tool_calls(response)
+
return ChatResponse(
message=ChatMessage(
- role=MessageRole.ASSISTANT, content=response.content[0].text
+ role=MessageRole.ASSISTANT,
+ content=content,
+ additional_kwargs={"tool_calls": tool_calls},
),
raw=dict(response),
)
@@ -235,15 +273,20 @@ class Anthropic(LLM):
anthropic_messages, system_prompt = messages_to_anthropic_messages(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
- response = await self._aclient.messages.create(
+ response = await self._aclient.beta.tools.messages.create(
messages=anthropic_messages,
system=system_prompt,
stream=False,
**all_kwargs,
)
+
+ content, tool_calls = self._get_content_and_tool_calls(response)
+
return ChatResponse(
message=ChatMessage(
- role=MessageRole.ASSISTANT, content=response.content[0].text
+ role=MessageRole.ASSISTANT,
+ content=content,
+ additional_kwargs={"tool_calls": tool_calls},
),
raw=dict(response),
)
@@ -287,3 +330,112 @@ class Anthropic(LLM):
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, **kwargs)
+
+ def chat_with_tools(
+ self,
+ tools: List["BaseTool"],
+ user_msg: Optional[Union[str, ChatMessage]] = None,
+ chat_history: Optional[List[ChatMessage]] = None,
+ verbose: bool = False,
+ allow_parallel_tool_calls: bool = False,
+ **kwargs: Any,
+ ) -> ChatResponse:
+ """Predict and call the tool."""
+ chat_history = chat_history or []
+
+ if isinstance(user_msg, str):
+ user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
+ chat_history.append(user_msg)
+
+ tool_dicts = []
+ for tool in tools:
+ tool_dicts.append(
+ {
+ "name": tool.metadata.name,
+ "description": tool.metadata.description,
+ "input_schema": tool.metadata.get_parameters_dict(),
+ }
+ )
+
+ response = self.chat(chat_history, tools=tool_dicts, **kwargs)
+
+ if not allow_parallel_tool_calls:
+ force_single_tool_call(response)
+
+ return response
+
+ async def achat_with_tools(
+ self,
+ tools: List["BaseTool"],
+ user_msg: Optional[Union[str, ChatMessage]] = None,
+ chat_history: Optional[List[ChatMessage]] = None,
+ verbose: bool = False,
+ allow_parallel_tool_calls: bool = False,
+ **kwargs: Any,
+ ) -> ChatResponse:
+ """Predict and call the tool."""
+ chat_history = chat_history or []
+
+ if isinstance(user_msg, str):
+ user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
+ chat_history.append(user_msg)
+
+ tool_dicts = []
+ for tool in tools:
+ tool_dicts.append(
+ {
+ "name": tool.metadata.name,
+ "description": tool.metadata.description,
+ "input_schema": tool.metadata.get_parameters_dict(),
+ }
+ )
+
+ response = await self.achat(chat_history, tools=tool_dicts, **kwargs)
+
+ if not allow_parallel_tool_calls:
+ force_single_tool_call(response)
+
+ return response
+
+ def get_tool_calls_from_response(
+ self,
+ response: "AgentChatResponse",
+ error_on_no_tool_call: bool = True,
+ **kwargs: Any,
+ ) -> List[ToolSelection]:
+ """Predict and call the tool."""
+ tool_calls = response.message.additional_kwargs.get("tool_calls", [])
+
+ if len(tool_calls) < 1:
+ if error_on_no_tool_call:
+ raise ValueError(
+ f"Expected at least one tool call, but got {len(tool_calls)} tool calls."
+ )
+ else:
+ return []
+
+ tool_selections = []
+ for tool_call in tool_calls:
+ if (
+ "input" not in tool_call
+ or "id" not in tool_call
+ or "name" not in tool_call
+ ):
+ raise ValueError("Invalid tool call.")
+ if tool_call["type"] != "tool_use":
+ raise ValueError("Invalid tool type. Unsupported by Anthropic")
+ argument_dict = (
+ json.loads(tool_call["input"])
+ if isinstance(tool_call["input"], str)
+ else tool_call["input"]
+ )
+
+ tool_selections.append(
+ ToolSelection(
+ tool_id=tool_call["id"],
+ tool_name=tool_call["name"],
+ tool_kwargs=argument_dict,
+ )
+ )
+
+ return tool_selections
diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py
index 28f5aba9e..961eabf04 100644
--- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py
+++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py
@@ -1,8 +1,9 @@
from typing import Dict, Sequence, Tuple
-from llama_index.core.base.llms.types import ChatMessage, MessageRole
+from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole
from anthropic.types import MessageParam, TextBlockParam
+from anthropic.types.beta.tools import ToolResultBlockParam, ToolUseBlockParam
HUMAN_PREFIX = "\n\nHuman:"
ASSISTANT_PREFIX = "\n\nAssistant:"
@@ -19,6 +20,10 @@ CLAUDE_MODELS: Dict[str, int] = {
}
+def is_function_calling_model(modelname: str) -> bool:
+ return "claude-3" in modelname
+
+
def anthropic_modelname_to_contextsize(modelname: str) -> int:
if modelname not in CLAUDE_MODELS:
raise ValueError(
@@ -63,14 +68,43 @@ def messages_to_anthropic_messages(
for message in messages:
if message.role == MessageRole.SYSTEM:
system_prompt = message.content
+ elif message.role == MessageRole.FUNCTION or message.role == MessageRole.TOOL:
+ content = ToolResultBlockParam(
+ tool_use_id=message.additional_kwargs["tool_call_id"],
+ type="tool_result",
+ content=[TextBlockParam(text=message.content, type="text")],
+ )
+ anth_message = MessageParam(
+ role=MessageRole.USER.value,
+ content=[content],
+ )
+ anthropic_messages.append(anth_message)
else:
- message = MessageParam(
+ content = []
+ if message.content:
+ content.append(TextBlockParam(text=message.content, type="text"))
+
+ tool_calls = message.additional_kwargs.get("tool_calls", [])
+ for tool_call in tool_calls:
+ assert "id" in tool_call
+ assert "input" in tool_call
+ assert "name" in tool_call
+
+ content.append(
+ ToolUseBlockParam(
+ id=tool_call["id"],
+ input=tool_call["input"],
+ name=tool_call["name"],
+ type="tool_use",
+ )
+ )
+
+ anth_message = MessageParam(
role=message.role.value,
- content=[
- TextBlockParam(text=message.content, type="text")
- ], # TODO: type detect for multimodal
+ content=content, # TODO: type detect for multimodal
)
- anthropic_messages.append(message)
+ anthropic_messages.append(anth_message)
+
return __merge_common_role_msgs(anthropic_messages), system_prompt
@@ -103,3 +137,9 @@ def messages_to_anthropic_prompt(messages: Sequence[ChatMessage]) -> str:
str_list = [_message_to_anthropic_prompt(message) for message in messages]
return "".join(str_list)
+
+
+def force_single_tool_call(response: ChatResponse) -> None:
+ tool_calls = response.message.additional_kwargs.get("tool_calls", [])
+ if len(tool_calls) > 1:
+ response.message.additional_kwargs["tool_calls"] = [tool_calls[0]]
diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml
index af7ab4cd6..393bc782d 100644
--- a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml
+++ b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml
@@ -27,12 +27,12 @@ exclude = ["**/BUILD"]
license = "MIT"
name = "llama-index-llms-anthropic"
readme = "README.md"
-version = "0.1.8"
+version = "0.1.9"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
llama-index-core = "^0.10.1"
-anthropic = "^0.20.0"
+anthropic = "^0.23.1"
[tool.poetry.group.dev.dependencies]
ipython = "8.10.0"
--
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