OllamaEmbeddings 集成

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(
    model="llama3",
)

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(
    model="qwen3-embedding:8b",
    dimensions=1024,
)

# 使用示例文本创建向量存储
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# 将向量存储用作检索器
retriever = vectorstore.as_retriever()

# 检索最相似的文本
retrieved_documents = retriever.invoke("What is LangChain?")

# 显示检索到的文档内容
print(retrieved_documents[0].page_content)

LangChain is the framework for building context-aware reasoning applications

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # 显示向量的前100个字符

[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585,

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # 显示向量的前100个字符

[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585,
[-0.0066985516, 0.009878328, 0.008019467, -0.009384944, -0.029560851, 0.025744654, 0.004872892, -0.0