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LlamaIndex LLM Integration: Anthropic

Anthropic is an AI research company focused on developing advanced language models, notably the Claude series. Their flagship model, Claude, is designed to generate human-like text while prioritizing safety and alignment with human intentions. Anthropic aims to create AI systems that are not only powerful but also responsible, addressing potential risks associated with artificial intelligence.

Installation

%pip install llama-index-llms-anthropic
!pip install llama-index

Set Tokenizer

First we want to set the tokenizer, which is slightly different than TikToken.

NOTE: The Claude 3 tokenizer has not been updated yet; using the existing Anthropic tokenizer leads

to context overflow errors for 200k tokens. We've temporarily set the max tokens for Claude 3 to 180k.

Basic Usage

import os

from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings

os.environ["ANTHROPIC_API_KEY"] = "YOUR ANTHROPIC API KEY"
from llama_index.llms.anthropic import Anthropic

To customize your API key, do this

otherwise it will lookup ANTHROPIC_API_KEY from your env variable

llm = Anthropic(api_key="<api_key>")

llm = Anthropic(model="claude-3-opus-20240229")

Settings.tokenizer = llm.tokenizer

resp = llm.complete("Paul Graham is ")
print(resp)

Sample response

Paul Graham is a well-known entrepreneur, programmer, venture capitalist, and essayist.

He is best known for co-founding Viaweb, one of the first web application companies, which was later

sold to Yahoo! in 1998 and became Yahoo! Store. Graham is also the co-founder of Y Combinator, a highly

successful startup accelerator that has helped launch numerous successful companies, such as Dropbox,

Airbnb, and Reddit.

Using Anthropic model through Vertex AI

import os

os.environ["ANTHROPIC_PROJECT_ID"] = "YOUR PROJECT ID HERE"
os.environ["ANTHROPIC_REGION"] = "YOUR PROJECT REGION HERE"

Set region and project_id to make Anthropic use the Vertex AI client

llm = Anthropic(
model="claude-3-5-sonnet@20240620",
region=os.getenv("ANTHROPIC_REGION"),
project_id=os.getenv("ANTHROPIC_PROJECT_ID"),
)

resp = llm.complete("Paul Graham is ")
print(resp)
Chat example with a list of messages

from llama_index.core.llms import ChatMessage
from llama_index.llms.anthropic import Anthropic

messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = Anthropic(model="claude-3-opus-20240229").chat(messages)
print(resp)
Streaming example

from llama_index.llms.anthropic import Anthropic

llm = Anthropic(model="claude-3-opus-20240229", max_tokens=100)
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
Chat streaming with pirate story

llm = Anthropic(model="claude-3-opus-20240229")
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
Configure Model

from llama_index.llms.anthropic import Anthropic

llm = Anthropic(model="claude-3-sonnet-20240229")
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
Async completion

from llama_index.llms.anthropic import Anthropic

llm = Anthropic("claude-3-sonnet-20240229")
resp = await llm.acomplete("Paul Graham is ")
print(resp)
Using Anthropic Tools (Web Search)

from llama_index.llms.anthropic import Anthropic

Initialize with web search tool

llm = Anthropic(
model="claude-3-7-sonnet-latest", # Must be a tool-supported model
max_tokens=1024,
tools=[
{
"type": "web_search_20250305",
"name": "web_search",
"max_uses": 3, # Limit to 3 searches
}
],
)

Get response with citations

response = llm.complete("What are the latest AI research trends?")

Access the main response content

print(response.text)

Access citations if available

for citation in response.citations:
print(f"Source: {citation.get('url')} - {citation.get('cited_text')}")
Structured Prediction Example

from llama_index.llms.anthropic import Anthropic
from llama_index.core.prompts import PromptTemplate
from llama_index.core.bridge.pydantic import BaseModel
from typing import List

class MenuItem(BaseModel):
"""A menu item in a restaurant."""

course_name: str
is_vegetarian: bool

class Restaurant(BaseModel):
"""A restaurant with name, city, and cuisine."""

name: str
city: str
cuisine: str
menu_items: List[MenuItem]

llm = Anthropic("claude-3-5-sonnet-20240620")
prompt_tmpl = PromptTemplate(
"Generate a restaurant in a given city {city_name}"
)

Option 1: Use as_structured_llm

restaurant_obj = (
llm.as_structured_llm(Restaurant)
.complete(prompt_tmpl.format(city_name="Miami"))
.raw
)
print(restaurant_obj)

Option 2: Use structured_predict

restaurant_obj = llm.structured_predict(Restaurant, prompt_tmpl, city_name="Miami")

Streaming Structured Prediction

from llama_index.core.llms import ChatMessage
from IPython.display import clear_output
from pprint import pprint

input_msg = ChatMessage.from_str("Generate a restaurant in San Francisco")

sllm = llm.as_structured_llm(Restaurant)
stream_output = sllm.stream_chat([input_msg])
for partial_output in stream_output:
clear_output(wait=True)
pprint(partial_output.raw.dict())
LLM Implementation example

https://docs.llamaindex.ai/en/stable/examples/llm/anthropic/

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