Nova - 与Langchain集成

Nova模型支持使用Langchain来调用

先更新langchain包：

%pip3 install -q langchain langchain_community faiss_cpu pypdf --upgrade

文本理解

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

llm = ChatBedrockConverse(
    model_id="us.amazon.nova-lite-v1:0",
    temperature=0.7
)

messages = [
    ("system", "Provide three alternative song titles for a given user title"),
    ("user", "Teardrops on My Guitar"),
]

response = llm.invoke(messages)
print(f"Request ID: {response.id}")
response.pretty_print()


# Here we can pass the chat history to the model to ask follow up questions
multi_turn_messages = [
    *messages,
    response,
    HumanMessage(content="Select your favorite and tell me why"),
]

response = llm.invoke(multi_turn_messages)
print(f"\n\nRequest ID: {response.id}")
response.pretty_print()

image-20241215213240277

图像理解

可以将各种媒体类型传递给模型：

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

image_path = "sunset.png"
llm = ChatBedrockConverse(
    model="us.amazon.nova-lite-v1:0",
    temperature=0.7
)

with open(image_path, "rb") as image_file:
    binary_data = image_file.read()

message = HumanMessage(
    content=[
        {"image": {"format": "png", "source": {"bytes": binary_data}}},
        {"text": "Provide a summary of this photo"},
    ]
)

response = llm.invoke([message])
print(f"\n\nRequest ID: {response.id}")
response.pretty_print()

image-20241215213429906

视频理解

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

video_path = "the-sea.mp4"
llm = ChatBedrockConverse(
    model="us.amazon.nova-lite-v1:0",
    temperature=0.7
)

with open(video_path, "rb") as video_file:
    binary_data = video_file.read()

message = HumanMessage(
    content=[
        {"video": {"format": "mp4", "source": {"bytes": binary_data}}},
        {"type": "text", "text": "描述以下视频"},
    ]
)

response = llm.invoke([message])
print(f"\n\nRequest ID: {response.id}")
response.pretty_print()

image-20241215213541380

流式传输

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser

llm = ChatBedrockConverse(
    model="us.amazon.nova-lite-v1:0",
    temperature=0.7
)

chain = llm | StrOutputParser()

messages = [
    SystemMessage(content="You are an author with experience writing creative novels"),
    HumanMessage(
        content="Write an outlin for a novel about a wizard named Theodore graduating from college"
    ),
]

for chunk in chain.stream(messages):
    print(chunk, end="")

image-20241215213701380