Skip to main content
Open In Colab在 GitHub 上打开

针

针头检索器

Needle 使以最少的工作量轻松创建 RAG 管道。

有关更多详细信息，请参阅我们的 API 文档

概述

Needle Document Loader 是用于将 Needle 集合与 LangChain 集成的实用程序。它支持无缝存储、检索和利用文档，用于检索增强生成 （RAG） 工作流程。

此示例演示：

  • 将文档存储到 Needle 集合中。
  • 设置检索器以获取文档。
  • 构建检索增强生成 （RAG） 管道。

设置

在开始之前，请确保您设置了以下环境变量：

  • NEEDLE_API_KEY：用于使用 Needle 进行身份验证的 API 密钥。
  • OPENAI_API_KEY：用于语言模型作的 OpenAI API 密钥。

初始化

要初始化 NeedleLoader，您需要以下参数：

  • needle_api_key：您的 Needle API 密钥（或将其设置为环境变量）。
  • collection_id：要使用的 Needle 集合的 ID。
import os
os.environ["NEEDLE_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""

实例

from langchain_community.document_loaders.needle import NeedleLoader

collection_id = "clt_01J87M9T6B71DHZTHNXYZQRG5H"

# Initialize NeedleLoader to store documents to the collection
document_loader = NeedleLoader(
    needle_api_key=os.getenv("NEEDLE_API_KEY"),
    collection_id=collection_id,
)
API 参考：NeedleLoader

负荷

要将文件添加到 Needle 收藏夹：

files = {
    "tech-radar-30.pdf": "https://www.thoughtworks.com/content/dam/thoughtworks/documents/radar/2024/04/tr_technology_radar_vol_30_en.pdf"
}

document_loader.add_files(files=files)
# Show the documents in the collection
# collections_documents = document_loader.load()

用法

在链内使用

以下是在链中使用 Needle 设置 RAG 管道的完整示例：

import os

from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.retrievers.needle import NeedleRetriever
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0)

# Initialize the Needle retriever (make sure your Needle API key is set as an environment variable)
retriever = NeedleRetriever(
    needle_api_key=os.getenv("NEEDLE_API_KEY"),
    collection_id="clt_01J87M9T6B71DHZTHNXYZQRG5H",
)

# Define system prompt for the assistant
system_prompt = """
    You are an assistant for question-answering tasks. 
    Use the following pieces of retrieved context to answer the question.
    If you don't know, say so concisely.\n\n{context}
    """

prompt = ChatPromptTemplate.from_messages(
    [("system", system_prompt), ("human", "{input}")]
)

# Define the question-answering chain using a document chain (stuff chain) and the retriever
question_answer_chain = create_stuff_documents_chain(llm, prompt)

# Create the RAG (Retrieval-Augmented Generation) chain by combining the retriever and the question-answering chain
rag_chain = create_retrieval_chain(retriever, question_answer_chain)

# Define the input query
query = {"input": "Did RAG move to accepted?"}

response = rag_chain.invoke(query)

response
{'input': 'Did RAG move to accepted?',
 'context': [Document(metadata={}, page_content='New Moved in/out No change\n\n© Thoughtworks, Inc. All Rights Reserved. 12\n\nTechniques\n\n1. Retrieval-augmented generation (RAG)\nAdopt\n\nRetrieval-augmented generation (RAG) is the preferred pattern for our teams to improve the quality of \nresponses generated by a large language model (LLM). We’ve successfully used it in several projects, \nincluding the popular Jugalbandi AI Platform. With RAG, information about relevant and trustworthy \ndocuments — in formats like HTML and PDF — are stored in databases that supports a vector data \ntype or efficient document search, such as pgvector, Qdrant or Elasticsearch Relevance Engine. For \na given prompt, the database is queried to retrieve relevant documents, which are then combined \nwith the prompt to provide richer context to the LLM. This results in higher quality output and greatly \nreduced hallucinations. The context window — which determines the maximum size of the LLM input \n— is limited, which means that selecting the most relevant documents is crucial. We improve the \nrelevancy of the content that is added to the prompt by reranking. Similarly, the documents are usually \ntoo large to calculate an embedding, which means they must be split into smaller chunks. This is often \na difficult problem, and one approach is to have the chunks overlap to a certain extent.'),
  Document(metadata={}, page_content='New Moved in/out No change\n\n© Thoughtworks, Inc. All Rights Reserved. 12\n\nTechniques\n\n1. Retrieval-augmented generation (RAG)\nAdopt\n\nRetrieval-augmented generation (RAG) is the preferred pattern for our teams to improve the quality of \nresponses generated by a large language model (LLM). We’ve successfully used it in several projects, \nincluding the popular Jugalbandi AI Platform. With RAG, information about relevant and trustworthy \ndocuments — in formats like HTML and PDF — are stored in databases that supports a vector data \ntype or efficient document search, such as pgvector, Qdrant or Elasticsearch Relevance Engine. For \na given prompt, the database is queried to retrieve relevant documents, which are then combined \nwith the prompt to provide richer context to the LLM. This results in higher quality output and greatly \nreduced hallucinations. The context window — which determines the maximum size of the LLM input \n— is limited, which means that selecting the most relevant documents is crucial. We improve the \nrelevancy of the content that is added to the prompt by reranking. Similarly, the documents are usually \ntoo large to calculate an embedding, which means they must be split into smaller chunks. This is often \na difficult problem, and one approach is to have the chunks overlap to a certain extent.'),
  Document(metadata={}, page_content='New Moved in/out No change\n\n© Thoughtworks, Inc. All Rights Reserved. 12\n\nTechniques\n\n1. Retrieval-augmented generation (RAG)\nAdopt\n\nRetrieval-augmented generation (RAG) is the preferred pattern for our teams to improve the quality of \nresponses generated by a large language model (LLM). We’ve successfully used it in several projects, \nincluding the popular Jugalbandi AI Platform. With RAG, information about relevant and trustworthy \ndocuments — in formats like HTML and PDF — are stored in databases that supports a vector data \ntype or efficient document search, such as pgvector, Qdrant or Elasticsearch Relevance Engine. For \na given prompt, the database is queried to retrieve relevant documents, which are then combined \nwith the prompt to provide richer context to the LLM. This results in higher quality output and greatly \nreduced hallucinations. The context window — which determines the maximum size of the LLM input \n— is limited, which means that selecting the most relevant documents is crucial. We improve the \nrelevancy of the content that is added to the prompt by reranking. Similarly, the documents are usually \ntoo large to calculate an embedding, which means they must be split into smaller chunks. This is often \na difficult problem, and one approach is to have the chunks overlap to a certain extent.'),
  Document(metadata={}, page_content='New Moved in/out No change\n\n© Thoughtworks, Inc. All Rights Reserved. 12\n\nTechniques\n\n1. Retrieval-augmented generation (RAG)\nAdopt\n\nRetrieval-augmented generation (RAG) is the preferred pattern for our teams to improve the quality of \nresponses generated by a large language model (LLM). We’ve successfully used it in several projects, \nincluding the popular Jugalbandi AI Platform. With RAG, information about relevant and trustworthy \ndocuments — in formats like HTML and PDF — are stored in databases that supports a vector data \ntype or efficient document search, such as pgvector, Qdrant or Elasticsearch Relevance Engine. For \na given prompt, the database is queried to retrieve relevant documents, which are then combined \nwith the prompt to provide richer context to the LLM. This results in higher quality output and greatly \nreduced hallucinations. The context window — which determines the maximum size of the LLM input \n— is limited, which means that selecting the most relevant documents is crucial. We improve the \nrelevancy of the content that is added to the prompt by reranking. Similarly, the documents are usually \ntoo large to calculate an embedding, which means they must be split into smaller chunks. This is often \na difficult problem, and one approach is to have the chunks overlap to a certain extent.'),
  Document(metadata={}, page_content='https://www.thoughtworks.com/radar/tools/nemo-guardrails\nhttps://www.thoughtworks.com/radar/platforms/langfuse\nhttps://www.thoughtworks.com/radar/techniques/retrieval-augmented-generation-rag\nhttps://cruisecontrol.sourceforge.net/\nhttps://martinfowler.com/articles/continuousIntegration.html\nhttps://www.thoughtworks.com/radar/techniques/peer-review-equals-pull-request\nhttps://martinfowler.com/bliki/ContinuousIntegrationCertification.html\nhttps://linearb.io/platform/gitstream\nhttps://www.thoughtworks.com/radar/tools/github-merge-queue\nhttps://stacking.dev/\n\n© Thoughtworks, Inc. All Rights Reserved. 8\n\nHold HoldAssess AssessTrial TrialAdopt Adopt\n\n18\n\n8\n\n24\n\n29\n\n30\n31\n\n32\n33\n\n34 35\n\n36\n37\n\n38 39\n\n40\n41\n\n42\n43\n\n26\n\n2\n\n3\n\n4\n\n5\n\n6 7\n\n9\n\n15\n\n16\n\n17\n\n10\n\n11\n\n12\n\n13 14\n\n44\n\n47\n49\n\n50\n\n65\n66\n\n67 68\n69\n\n70\n71\n\n72\n\n73 74\n\n75\n\n76 77\n\n78\n79\n\n80\n81\n\n82\n\n83\n\n51\n\n52 54\n\n59\n\n53\n56\n\n58\n\n61\n\n62\n63\n\n64\n\n85\n\n88 89\n\n90 91\n\n92\n93\n\n94\n95 96\n\n97\n\n98 99\n\n100\n\n101\n102\n\n103\n\n104\n\n86\n\n87\n1921\n\n22\n\n20\n28\n\n25\n\n27\n\n23\n\n84\n\n105\n\n1\n45\n\n46\n\n48\n\n55\n57')],
 'answer': 'Yes, RAG has moved to the "Adopt" status.'}

API 参考

有关所有Needle功能和配置前往 API 参考：https://docs.needle-ai.com