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b29e221 f136964 b29e221 f136964 b29e221 f136964 b29e221 f136964 b29e221 f136964 b29e221 f136964 b29e221 f136964 b29e221 f136964 b29e221 f136964 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import os
import json
from PyPDF2 import PdfReader # FIXED: Correct case (was "PyPDF2")
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_groq import ChatGroq
from langchain.chains.retrieval_qa.base import RetrievalQA # FIXED: Updated import path
# Setup working directory
working_dir = os.path.dirname(os.path.abspath(__file__))
os.environ["GROQ_API_KEY"] = os.getenv('GROQ_API_KEY')
# Load embedding model
embedding = HuggingFaceEmbeddings()
# Initialize LLM from Groq
llm = ChatGroq(
model="deepseek-r1-distill-llama-70b",
temperature=0
)
def extract_text_from_pdf(file_path):
"""
Extract text content from a PDF file using PyPDF2.
"""
try:
reader = PdfReader(file_path)
text = ""
for page in reader.pages:
extracted = page.extract_text()
if extracted:
text += extracted + "\n"
if not text.strip():
raise ValueError(f"⚠️ No text extracted from '{os.path.basename(file_path)}'. The file might be empty or image-based.")
return text
except Exception as e:
raise RuntimeError(f"⚠️ Error extracting text from PDF: {e}")
def process_document_to_chroma_db(directory_path):
"""
Process all PDF documents in the given directory, split their text,
and store embeddings in a persistent ChromaDB.
"""
try:
all_texts = []
# Iterate through all PDF files in the directory
for file_name in os.listdir(directory_path):
if file_name.endswith(".pdf"):
file_path = os.path.join(directory_path, file_name)
print(f"📂 Processing document: {file_name}")
# Extract text from the PDF
text = extract_text_from_pdf(file_path)
# Split text into chunks
print("🔄 Splitting document into smaller chunks...")
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=2000,
chunk_overlap=200
)
texts = text_splitter.split_text(text)
all_texts.extend(texts)
if not all_texts:
raise ValueError("⚠️ No PDF files with extractable text found in the directory.")
# Create a persistent ChromaDB instance with all texts
print("💾 Storing embeddings in ChromaDB...")
vectordb = Chroma.from_texts(
texts=all_texts,
embedding=embedding,
persist_directory=os.path.join(working_dir, "doc_vectorstore")
)
# No need to call persist() explicitly with Chroma 0.4+
print("✅ All documents successfully processed and stored in ChromaDB.")
return "✅ Documents successfully processed and stored in ChromaDB."
except Exception as e:
raise RuntimeError(f"⚠️ Error processing documents: {e}")
def answer_question(user_question):
"""
Retrieve and generate an answer for the given user question
based on the stored document embeddings.
"""
try:
# Load the persistent vector database
vectordb_path = os.path.join(working_dir, "doc_vectorstore")
if not os.path.exists(vectordb_path):
raise FileNotFoundError("⚠️ ChromaDB vector store not found. Please process a document first.")
print("📂 Loading vector database...")
vectordb = Chroma(
persist_directory=vectordb_path,
embedding_function=embedding
)
# Create a retriever from the vector database
retriever = vectordb.as_retriever(search_kwargs={"k": 3})
# Create a QA chain with DeepSeek-R1
print("🤖 Initializing Retrieval QA chain...")
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True # Optional: helps debugging
)
# Invoke the QA chain with the user question
print("💬 Generating answer...")
response = qa_chain.invoke({"query": user_question})
answer = response.get("result", "⚠️ No response generated.")
return answer
except Exception as e:
raise RuntimeError(f"⚠️ Error generating response: {e}") |