OllamaLLM
本页面介绍了如何使用LangChain与Ollama模型进行交互。
安装
# install package
%pip install -U langchain-ollama
设置
首先，按照 这些说明 设置并运行本地 Ollama 实例：
- 下载 并安装 Ollama 到可用的支持平台（包括 Windows 子系统 Linux）
- 通过
ollama pull <name-of-model>获取可用的 LLM 模型- 通过 模型库 查看可用模型的列表
- 例如，
ollama pull llama3
- 这将下载模型的默认标记版本。通常，默认指向最新的、参数最小的模型。
在 Mac 上，模型将下载到
~/.ollama/models在 Linux（或 WSL）上，模型将存储在
/usr/share/ollama/.ollama/models
- 指定感兴趣模型的确切版本，如
ollama pull vicuna:13b-v1.5-16k-q4_0（在此实例中查看Vicuna模型的 各种标签） - 要查看所有已拉取的模型，请使用
ollama list - 要直接从命令行与模型聊天，请使用
ollama run <name-of-model> - 查看 Ollama 文档 以获取更多命令。在终端中运行
ollama help也可以查看可用命令。
用法
from langchain_core.prompts import ChatPromptTemplate
from langchain_ollama.llms import OllamaLLM
template = """Question: {question}
Answer: Let's think step by step."""
prompt = ChatPromptTemplate.from_template(template)
model = OllamaLLM(model="llama3")
chain = prompt | model
chain.invoke({"question": "What is LangChain?"})
'A great start!\n\nLangChain is a type of AI model that uses language processing techniques to generate human-like text based on input prompts or chains of reasoning. In other words, it can have a conversation with humans, understanding the context and responding accordingly.\n\nHere\'s a possible breakdown:\n\n* "Lang" likely refers to its focus on natural language processing (NLP) and linguistic analysis.\n* "Chain" suggests that LangChain is designed to generate text in response to a series of connected ideas or prompts, rather than simply generating random text.\n\nSo, what do you think LangChain\'s capabilities might be?'
多模态
Ollama 支持多模态 LLM，例如 bakllava 和 llava。
ollama pull bakllava
确保更新 Ollama，以便您拥有最新版本以支持多模态。
import base64
from io import BytesIO
from IPython.display import HTML, display
from PIL import Image
def convert_to_base64(pil_image):
"""
Convert PIL images to Base64 encoded strings
:param pil_image: PIL image
:return: Re-sized Base64 string
"""
buffered = BytesIO()
pil_image.save(buffered, format="JPEG") # You can change the format if needed
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
return img_str
def plt_img_base64(img_base64):
"""
Display base64 encoded string as image
:param img_base64: Base64 string
"""
# Create an HTML img tag with the base64 string as the source
image_html = f'<img src="data:image/jpeg;base64,{img_base64}" />'
# Display the image by rendering the HTML
display(HTML(image_html))
file_path = "../../../static/img/ollama_example_img.jpg"
pil_image = Image.open(file_path)
image_b64 = convert_to_base64(pil_image)
plt_img_base64(image_b64)
from langchain_ollama import OllamaLLM
llm = OllamaLLM(model="bakllava")
llm_with_image_context = llm.bind(images=[image_b64])
llm_with_image_context.invoke("What is the dollar based gross retention rate:")
'90%'