Needle makes it easy to create your RAG pipelines with minimal effort.
For more details, refer to our API documentation
OverviewThe Needle Document Loader is a utility for integrating Needle collections with LangChain. It enables seamless storage, retrieval, and utilization of documents for Retrieval-Augmented Generation (RAG) workflows.
This example demonstrates:
Before starting, ensure you have the following environment variables set:
os.environ["NEEDLE_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
To initialize the NeedleLoader, you need the following parameters:
from langchain_community.document_loaders.needle import NeedleLoader
collection_id = "clt_01J87M9T6B71DHZTHNXYZQRG5H"
document_loader = NeedleLoader(
needle_api_key=os.getenv("NEEDLE_API_KEY"),
collection_id=collection_id,
)
To add files to the Needle collection:
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)
The lazy_load method allows you to iteratively load documents from the Needle collection, yielding each document as it is fetched:
Usage Use within a chainBelow is a complete example of setting up a RAG pipeline with Needle within a chain:
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)
retriever = NeedleRetriever(
needle_api_key=os.getenv("NEEDLE_API_KEY"),
collection_id="clt_01J87M9T6B71DHZTHNXYZQRG5H",
)
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}")]
)
question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
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.'}
For detailed documentation of all Needle features and configurations head to the API reference: https://docs.needle-ai.com
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