PDF Extraction 🗂️📄
Step: Use PyPDF to extract text from PDF documents.
Process:
def extract_text_from_pdf(pdf_path):
reader = PdfReader(pdf_path)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
Explanation: PyPDF 📄🔍 goes through each page and extracts the text 📝 from the PDF 📂.
Document Indexing 🗂️📚
Step: Index the extracted text for efficient retrieval.
def index_text(text):
index = faiss.IndexFlatL2(512) # Creating an index
embeddings = embed_text(text) # Convert text to embeddings
index.add(embeddings) # Add embeddings to the index
return index
Explanation: The text 📝 is converted to embeddings (vector representations) 🔢 and indexed 📚 using FAISS for quick retrieval 🔍.
Query Processing 🤖🔍
Step: Use LangChain to handle the sequence of operations: query processing, document retrieval, and response generation.
Process:
def create_response_chain():
llm = OpenAI(model_name="gpt-3.5-turbo") # Choose the LLM
chain = LLMChain(llm=llm) # Create the chain
return chain
Explanation: LangChain 🤖 manages the sequence of operations to process the query ❓, retrieve relevant documents 📚, and generate a response 💬.
Response Generation 📝✨
Step: Generate a response based on the retrieved text.
Process:
pdf_path = "example.pdf"
text = extract_text_from_pdf(pdf_path)
index = index_text(text)
chain = create_response_chain()
query = "What is the main topic of the document?"
response = chain.run(input={"query": query, "index": index})
print(response)
Explanation: The user's query ❓ is processed by LangChain 🤖, which retrieves relevant text passages 📚 and uses the LLM 📝 to generate a coherent response ✨.
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