Needle 文件載入器
Needle 讓您輕鬆建立 RAG 管道，只需極少力氣。
如需更多詳細資訊，請參閱我們的 API 文件
概觀
Needle 文件載入器是一個用於整合 Needle 集合與 LangChain 的工具。它能無縫地儲存、檢索和利用文件，以進行檢索增強生成 (RAG) 工作流程。
此範例示範了
- 將文件儲存到 Needle 集合中。
- 設定檢索器以獲取文件。
- 建構檢索增強生成 (RAG) 管道。
設定
開始之前，請確保您已設定以下環境變數
- NEEDLE_API_KEY：您的 API 金鑰，用於向 Needle 驗證身分。
- OPENAI_API_KEY：您的 OpenAI API 金鑰，用於語言模型操作。
import os
os.environ["NEEDLE_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
初始化
若要初始化 NeedleLoader，您需要以下參數
- needle_api_key：您的 Needle API 金鑰（或將其設定為環境變數）。
- collection_id：要使用的 Needle 集合的 ID。
實例化
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 參考：NeedleLoader
載入
若要將檔案新增至 Needle 集合
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()
延遲載入
lazy_load 方法可讓您從 Needle 集合迭代載入文件，並在擷取每個文件時產生該文件
# Show the documents in the collection
# collections_documents = document_loader.lazy_load()
用法
在鏈中使用
以下是在鏈中設定具有 Needle 的 RAG 管道的完整範例
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
API 參考：create_retrieval_chain | create_stuff_documents_chain | NeedleRetriever | ChatPromptTemplate | ChatOpenAI
{'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 參考
如需所有 Needle 功能和組態的詳細文件，請前往 API 參考：https://docs.needle-ai.com