Create rag_pipeline.py

rag_pipeline.py

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+import os
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader, TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from llama_cpp import Llama
+
+# Embedder
+embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# LLM
+llm = Llama(
+    model_path="model/qwen2_5-7b-instruct-q4_K_M.gguf",
+    n_ctx=4096,
+    n_threads=4,
+    chat_format="chatml"
+)
+
+def load_documents():
+    docs = []
+    for file in os.listdir("docs"):
+        path = f"docs/{file}"
+        if file.endswith(".pdf"):
+            loader = PyPDFLoader(path)
+        else:
+            loader = TextLoader(path)
+        docs.extend(loader.load())
+    return docs
+
+def prepare_vector_store():
+    if os.path.exists("vectorstore.faiss"):
+        return FAISS.load_local("vectorstore", embedder)
+
+    docs = load_documents()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=150)
+    chunks = splitter.split_documents(docs)
+
+    embeddings = embedder.encode([c.page_content for c in chunks])
+
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings).astype("float32"))
+
+    vectorstore = FAISS(embedding_function=embedder, index=index, docs=chunks)
+    vectorstore.save_local("vectorstore")
+
+    return vectorstore
+
+vectorstore = prepare_vector_store()
+
+def ask_rag(question):
+    results = vectorstore.similarity_search(question, k=5)
+    context = "\n".join([r.page_content for r in results])
+
+    template = f"""
+Aşağıdaki bağlama göre soruyu cevapla:
+
+BAĞLAM:
+{context}
+
+SORU:
+{question}
+
+Cevap:
+"""
+
+    out = llm(
+        template,
+        max_tokens=500,
+        temperature=0.4,
+        stop=["</s>"]
+    )
+
+    return out["choices"][0]["text"].strip()