Skip to main content
本 notebook 展示如何使用 PineconeRerank 通过 Pinecone 托管的重排序 API 实现两阶段向量检索重排序，如 langchain_pinecone/libs/pinecone/rerank.py 所示。

​
设置

安装 langchain-pinecone 包。 
pip install -qU "langchain-pinecone"

​
凭证

设置您的 Pinecone API 密钥以使用重排序 API。 
import os
from getpass import getpass

os.environ["PINECONE_API_KEY"] = os.getenv("PINECONE_API_KEY") or getpass(
    "Enter your Pinecone API key: "
)

​
实例化

使用 PineconeRerank 按照与查询的相关性对文档列表进行重排序。 
from langchain_core.documents import Document
from langchain_pinecone import PineconeRerank

# Initialize reranker
reranker = PineconeRerank(model="bge-reranker-v2-m3")

# Sample documents
documents = [
    Document(page_content="Paris is the capital of France."),
    Document(page_content="Berlin is the capital of Germany."),
    Document(page_content="The Eiffel Tower is in Paris."),
]

# Rerank documents
query = "What is the capital of France?"
reranked_docs = reranker.compress_documents(documents, query)

# Print results
for doc in reranked_docs:
    score = doc.metadata.get("relevance_score")
    print(f"Score: {score:.4f} | Content: {doc.page_content}")

/Users/jakit/customers/aurelio/langchain-pinecone/libs/pinecone/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Score: 0.9998 | Content: Paris is the capital of France.
Score: 0.1950 | Content: The Eiffel Tower is in Paris.
Score: 0.0042 | Content: Berlin is the capital of Germany.

​
用法

​
使用 Top-N 重排序

指定 top_n 以限制返回的文档数量。 
# Return only top-1 result
reranker_top1 = PineconeRerank(model="bge-reranker-v2-m3", top_n=1)
top1_docs = reranker_top1.compress_documents(documents, query)
print("Top-1 Result:")
for doc in top1_docs:
    print(f"Score: {doc.metadata['relevance_score']:.4f} | Content: {doc.page_content}")

Top-1 Result:
Score: 0.9998 | Content: Paris is the capital of France.

​
使用自定义排序字段重排序

如果文档是字典格式或具有自定义字段，请使用 rank_fields 指定排序字段。 
# Sample dictionary documents with 'text' field
docs_dict = [
    {
        "id": "doc1",
        "text": "Article about renewable energy.",
        "title": "Renewable Energy",
    },
    {"id": "doc2", "text": "Report on economic growth.", "title": "Economic Growth"},
    {
        "id": "doc3",
        "text": "News on climate policy changes.",
        "title": "Climate Policy",
    },
]

# Initialize reranker with rank_fields
reranker_text = PineconeRerank(model="bge-reranker-v2-m3", rank_fields=["text"])
climate_docs = reranker_text.rerank(docs_dict, "Latest news on climate change.")

# Show IDs and scores
for res in climate_docs:
    print(f"ID: {res['id']} | Score: {res['score']:.4f}")

ID: doc3 | Score: 0.9892
ID: doc1 | Score: 0.0006
ID: doc2 | Score: 0.0000
也可以基于 title 字段进行重排序 
economic_docs = reranker_text.rerank(docs_dict, "Economic forecast.")

# Show IDs and scores
for res in economic_docs:
    print(
        f"ID: {res['id']} | Score: {res['score']:.4f} | Title: {res['document']['title']}"
    )

ID: doc2 | Score: 0.8918 | Title: Economic Growth
ID: doc3 | Score: 0.0002 | Title: Climate Policy
ID: doc1 | Score: 0.0000 | Title: Renewable Energy

​
使用附加参数重排序

可以将模型特定的参数（例如 truncate）直接传递给 .rerank()。 用于处理超过模型支持长度的输入。可接受的值为：END 或 NONE。 END 在输入 token 限制处截断输入序列；NONE 在输入超过 token 限制时返回错误。 
# Rerank with custom truncate parameter
docs_simple = [
    {"id": "docA", "text": "Quantum entanglement is a physical phenomenon..."},
    {"id": "docB", "text": "Classical mechanics describes motion..."},
]

reranked = reranker.rerank(
        documents=docs_simple,
        query="Explain the concept of quantum entanglement.",
        truncate="END",
)
# Print reranked IDs and scores
for res in reranked:
    print(f"ID: {res['id']} | Score: {res['score']:.4f}")

ID: docA | Score: 0.6950
ID: docB | Score: 0.0001

​
API 参考

  • PineconeRerank(model, top_n, rank_fields, return_documents)
  • .rerank(documents, query, rank_fields=None, model=None, top_n=None, truncate="END")
  • .compress_documents(documents, query)（返回元数据中包含 relevance_score 的 Document 对象）
在 GitHub 上编辑此页 或 提交 issue。
将这些文档连接到 Claude、VSCode 等，通过 MCP 获取实时解答。