Custom RAG pipeline (PDF/Docs)
Ready-to-use Python script for building a chatbot that talks to your documents.
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Expert Agent Implementation
This LANGCHAIN configuration is a specialized AI Agent prompt optimized for high-performance automation tasks within the Engineering & DevOps sector. It leverages expert design patterns to minimize hallucination and maximize output reliability.
At AIAgentsReady.com, we test every blueprint for robustness. This specific configuration for Custom RAG pipeline (PDF/Docs) has been verified to meet our community standards for efficiency and effectiveness.
🚀 Best Used With
- ChatGPT 5.4 (Advanced Reasoning)
- Gemini 3.1 Ultra (Long Context)
- Claude 4.0 Sonnet (Technical Tasks)
🎯 Common Use Cases
- Building autonomous Chat-With-Your-Docs bots for sensitive internal files.
- Developing vector databases for lightning-fast retrieval of local documentation.
- Creating specialized AI knowledge bases for legal or technical teams.
Disclaimer: This prompt is for educational and utility purposes only. It does NOT constitute professional medical, legal, or financial advice. AIAgentsReady.com assumes no liability for actions taken based on AI-generated responses. Always consult a qualified professional before proceeding.
Expert Agent Prompt
Copy and paste this into your AI agent or chatbot:
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
# 1. Load and Split
loader = PyPDFLoader({{FILE_PATH}})
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# 2. Embed and Store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)
# 3. Query Engine
qa = RetrievalQA.from_chain_type(
llm=ChatOpenAI(model_name="gpt-4"),
chain_type="stuff",
retriever=vectorstore.as_retriever()
)
query = "What are the safety requirements in this document?"
print(qa.run(query))Similar Engineering & DevOps Prompts
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