跳到主要内容
Fleet AI Context 是一个高质量的嵌入数据集，包含 1200 个最受欢迎和最宽松的 Python 库及其文档。 Fleet AI 团队致力于嵌入世界上最重要的数据。他们已开始嵌入前 1200 个 Python 库，以实现具有最新知识的代码生成。
让我们看看如何使用这些嵌入来支持文档检索系统，并最终支持一个简单的代码生成链！ 
pip install -qU  langchain fleet-context langchain-openai pandas faiss-cpu # faiss-gpu for CUDA supported GPU

from operator import itemgetter
from typing import Any, Optional, Type

import pandas as pd
from langchain_classic.retrievers import MultiVectorRetriever
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_core.stores import BaseStore
from langchain_core.vectorstores import VectorStore
from langchain_openai import OpenAIEmbeddings


def load_fleet_retriever(
    df: pd.DataFrame,
    *,
    vectorstore_cls: Type[VectorStore] = FAISS,
    docstore: Optional[BaseStore] = None,
    **kwargs: Any,
):
    vectorstore = _populate_vectorstore(df, vectorstore_cls)
    if docstore is None:
        return vectorstore.as_retriever(**kwargs)
    else:
        _populate_docstore(df, docstore)
        return MultiVectorRetriever(
            vectorstore=vectorstore, docstore=docstore, id_key="parent", **kwargs
        )


def _populate_vectorstore(
    df: pd.DataFrame,
    vectorstore_cls: Type[VectorStore],
) -> VectorStore:
    if not hasattr(vectorstore_cls, "from_embeddings"):
        raise ValueError(
            f"Incompatible vector store class {vectorstore_cls}."
            "Must implement `from_embeddings` class method."
        )
    texts_embeddings = []
    metadatas = []
    for _, row in df.iterrows():
        texts_embeddings.append((row.metadata["text"], row["dense_embeddings"]))
        metadatas.append(row.metadata)
    return vectorstore_cls.from_embeddings(
        texts_embeddings,
        OpenAIEmbeddings(model="text-embedding-ada-002"),
        metadatas=metadatas,
    )


def _populate_docstore(df: pd.DataFrame, docstore: BaseStore) -> None:
    parent_docs = []
    df = df.copy()
    df["parent"] = df.metadata.apply(itemgetter("parent"))
    for parent_id, group in df.groupby("parent"):
        sorted_group = group.iloc[
            group.metadata.apply(itemgetter("section_index")).argsort()
        ]
        text = "".join(sorted_group.metadata.apply(itemgetter("text")))
        metadata = {
            k: sorted_group.iloc[0].metadata[k] for k in ("title", "type", "url")
        }
        text = metadata["title"] + "\n" + text
        metadata["id"] = parent_id
        parent_docs.append(Document(page_content=text, metadata=metadata))
    docstore.mset(((d.metadata["id"], d) for d in parent_docs))

​
检索器分块

作为嵌入过程的一部分，Fleet AI 团队首先将长文档分块，然后再进行嵌入。这意味着向量对应于 LangChain 文档页面中的各个部分，而不是整个页面。默认情况下，当我们从这些嵌入中启动检索器时，我们将检索这些嵌入的分块。 我们将使用 Fleet Context 的 download_embeddings() 来获取 LangChain 的文档嵌入。您可以在 fleet.so/context 查看所有支持库的文档。
from context import download_embeddings

df = download_embeddings("langchain")
vecstore_retriever = load_fleet_retriever(df)

vecstore_retriever.invoke("How does the multi vector retriever work")

​
其他包

您可以从此 Dropbox 链接下载并使用其他嵌入。

​
检索父文档

Fleet AI 提供的嵌入包含元数据，指示哪些嵌入块对应于相同的原始文档页面。如果需要，我们可以使用此信息检索整个父文档，而不仅仅是嵌入的块。在底层，我们将使用 MultiVectorRetriever 和 BaseStore 对象来搜索相关块，然后将它们映射到其父文档。 
from langchain.storage import InMemoryStore

parent_retriever = load_fleet_retriever(
    "https://www.dropbox.com/scl/fi/4rescpkrg9970s3huz47l/libraries_langchain_release.parquet?rlkey=283knw4wamezfwiidgpgptkep&dl=1",
    docstore=InMemoryStore(),
)

parent_retriever.invoke("How does the multi vector retriever work")

​
放入链中

让我们尝试在简单链中使用我们的检索系统！ 
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            """You are a great software engineer who is very familiar \
with Python. Given a user question or request about a new Python library called LangChain and \
parts of the LangChain documentation, answer the question or generate the requested code. \
Your answers must be accurate, should include code whenever possible, and should assume anything \
about LangChain which is note explicitly stated in the LangChain documentation. If the required \
information is not available, just say so.

LangChain Documentation
------------------

{context}""",
        ),
        ("human", "{question}"),
    ]
)

model = ChatOpenAI(model="gpt-3.5-turbo-16k")

chain = (
    {
        "question": RunnablePassthrough(),
        "context": parent_retriever
        | (lambda docs: "\n\n".join(d.page_content for d in docs)),
    }
    | prompt
    | model
    | StrOutputParser()
)

for chunk in chain.invoke(
    "How do I create a FAISS vector store retriever that returns 10 documents per search query"
):
    print(chunk, end="", flush=True)
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
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