Needle 文档加载器
Needle 使得创建您的RAG管道变得轻而易举，无需过多努力。
要获取更多详细信息，请参阅我们的API文档
概览
The Needle 文档加载器是一个用于将 Needle 集合与 LangChain 结合的实用工具。它使得文档的无缝存储、检索和利用成为可能，适用于 Retrieval-Augmented Generation (RAG) 工作流。
这个例子展示了：<br>
- 将文档存储到Needle集合中。
- 设置检索器以获取文档。
- 构建一种检索增强生成（RAG）管道。
设置
开始之前，请确保已设置以下环境变量：
- NEEDLE_API_KEY: 您的针 NEEDLE API密钥，用于与 Needle 进行身份验证。
- OPENAI_API_KEY: 你的OpenAI API密钥，用于语言模型操作。
import os
os.environ["NEEDLE_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
初始化
要初始化NeedleLoader，您需要以下参数：
- n needle_api_key: 你的Needle API密钥（或将其设置为环境变量）。
- collection_id: 要工作的Needle集合的ID。
Instantiation
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
加载
要将文件添加到 Needle 集合中：
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()
懒加载
lazy_load 方法允许您逐步加载 Needle 集合中的文档，并在获取每个文档时依次生成它们：
# Show the documents in the collection
# collections_documents = document_loader.lazy_load()
用法
使用在链中
以下是一个使用Needle在链中设置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='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.')],
'answer': 'Yes, RAG has been adopted as the preferred pattern for improving the quality of responses generated by a large language model.'}
API 参考
详细文档请参阅所有Needle功能和配置：
https://docs.needle-ai.com