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from datasets import load_dataset
from transformers import AutoTokenizer, AutoModel
import faiss
import torch
import numpy as np
def log(message):
print(f"✅ {message}")
# ✅ Load datasets
datasets = {
"sales": load_dataset("goendalf666/sales-conversations"),
"blended": load_dataset("blended_skill_talk"),
"dialog": load_dataset("daily_dialog"),
"multiwoz": load_dataset("multi_woz_v22"),
}
# ✅ Load MiniLM model and tokenizer
model_name = "sentence-transformers/all-MiniLM-L6-v2" # Model for embeddings
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
def embed_text(texts):
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt", max_length=512)
with torch.no_grad():
embeddings = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
return embeddings
# ✅ Extract and embed the datasets
def create_embeddings(dataset_name, dataset):
print(f"📥 Creating embeddings for {dataset_name}...")
if dataset_name == "goendalf666/sales-conversations":
texts = [" ".join(row.values()) for row in dataset["train"]]
elif dataset_name == "AlekseyKorshuk/persona-chat":
texts = [" ".join(utterance["candidates"]) for utterance in dataset["train"]["utterances"]]
elif dataset_name == "blended_skill_talk":
texts = [" ".join(row["free_messages"] + row["guided_messages"]) for row in dataset["train"]]
elif dataset_name == "daily_dialog":
texts = [" ".join(row["dialog"]) for row in dataset["train"]]
elif dataset_name == "multi_woz_v22":
texts = [" ".join(row["turns"]["utterance"]) for row in dataset["train"]]
else:
print(f"⚠️ Warning: Dataset {dataset_name} not handled properly!")
texts = []
# ✅ Verify dataset extraction
if len(texts) == 0:
print(f"❌ ERROR: No text extracted from {dataset_name}! Check dataset structure.")
else:
print(f"✅ Extracted {len(texts)} texts from {dataset_name}. Sample:\n{texts[:3]}")
return texts
# ✅ Embed and store in FAISS
for name, dataset in datasets.items():
texts = create_embeddings(name, dataset)
if len(texts) > 0: # ✅ Only embed if texts exist
embeddings = embed_text(texts)
print(f"✅ Generated embeddings shape: {embeddings.shape}")
index = save_embeddings_to_faiss(embeddings)
print(f"✅ Embeddings for {name} saved to FAISS.")
else:
print(f"⚠️ Skipping embedding for {name} (No valid texts).")
# ✅ Save embeddings to a database
def save_embeddings_to_faiss(embeddings, index_name="my_embeddings"):
print("Saving embeddings to FAISS...")
index = faiss.IndexFlatL2(embeddings.shape[1]) # Assuming 512-dimensional embeddings
index.add(np.array(embeddings).astype(np.float32))
faiss.write_index(index, index_name) # Save FAISS index to file
return index
# ✅ Create embeddings for all datasets
for name, dataset in datasets.items():
embeddings = create_embeddings(name, dataset)
index = save_embeddings_to_faiss(embeddings)
print(f"Embeddings for {name} saved to FAISS.")
# ✅ Check FAISS index after saving
index = faiss.read_index("my_embeddings") # Load the index
print(f"📊 FAISS index contains {index.ntotal} vectors.") # Check how many embeddings were stored
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