wehub-resource-sync
185 lines
8.1 KiB
Python
185 lines
8.1 KiB
Python
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from tqdm import tqdm
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from qdrant_client import models
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from qdrant_client import QdrantClient
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faq_text = """Question 1: What is the first step before building a machine learning model?
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Answer 1: Understand the problem, define the objective, and identify the right metrics for evaluation.
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Question 2: How important is data cleaning in ML?
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Answer 2: Extremely important. Clean data improves model performance and reduces the chance of misleading results.
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Question 3: Should I normalize or standardize my data?
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Answer 3: Yes, especially for models sensitive to feature scales like SVMs, KNN, and neural networks.
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Question 4: When should I use feature engineering?
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Answer 4: Always consider it. Well-crafted features often yield better results than complex models.
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Question 5: How to handle missing values?
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Answer 5: Use imputation techniques like mean/median imputation, or model-based imputation depending on the context.
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Question 6: Should I balance my dataset for classification tasks?
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Answer 6: Yes, especially if the classes are imbalanced. Techniques include resampling, SMOTE, and class-weighting.
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Question 7: How do I select features for my model?
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Answer 7: Use domain knowledge, correlation analysis, or techniques like Recursive Feature Elimination or SHAP values.
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Question 8: Is it good to use all features available?
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Answer 8: Not always. Irrelevant or redundant features can reduce performance and increase overfitting.
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Question 9: How do I avoid overfitting?
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Answer 9: Use techniques like cross-validation, regularization, pruning (for trees), and dropout (for neural nets).
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Question 10: Why is cross-validation important?
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Answer 10: It provides a more reliable estimate of model performance by reducing bias from a single train-test split.
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Question 11: What’s a good train-test split ratio?
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Answer 11: Common ratios are 80/20 or 70/30, but use cross-validation for more robust evaluation.
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Question 12: Should I tune hyperparameters?
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Answer 12: Yes. Use grid search, random search, or Bayesian optimization to improve model performance.
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Question 13: What’s the difference between training and validation sets?
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Answer 13: Training set trains the model, validation set tunes hyperparameters, and test set evaluates final performance.
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Question 14: How do I know if my model is underfitting?
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Answer 14: It performs poorly on both training and test sets, indicating it hasn’t learned patterns well.
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Question 15: What are signs of overfitting?
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Answer 15: High accuracy on training data but poor generalization to test or validation data.
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Question 16: Is ensemble modeling useful?
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Answer 16: Yes. Ensembles like Random Forests or Gradient Boosting often outperform individual models.
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Question 17: When should I use deep learning?
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Answer 17: Use it when you have large datasets, complex patterns, or tasks like image and text processing.
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Question 18: What is data leakage and how to avoid it?
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Answer 18: Data leakage is using future or target-related information during training. Avoid by carefully splitting and preprocessing.
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Question 19: How do I measure model performance?
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Answer 19: Choose appropriate metrics: accuracy, precision, recall, F1, ROC-AUC for classification; RMSE, MAE for regression.
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Question 20: Why is model interpretability important?
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Answer 20: It builds trust, helps debug, and ensures compliance—especially important in high-stakes domains like healthcare.
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"""
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new_faq_text = [i.replace("\n", " ") for i in faq_text.split("\n\n")]
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def batch_iterate(lst, batch_size):
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for i in range(0, len(lst), batch_size):
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yield lst[i : i + batch_size]
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class EmbedData:
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def __init__(self,
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embed_model_name="nomic-ai/nomic-embed-text-v1.5",
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batch_size=32):
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self.embed_model_name = embed_model_name
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self.embed_model = self._load_embed_model()
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self.batch_size = batch_size
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self.embeddings = []
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def _load_embed_model(self):
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embed_model = HuggingFaceEmbedding(model_name=self.embed_model_name,
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trust_remote_code=True,
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# cache_folder='./hf_cache'
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)
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return embed_model
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def generate_embedding(self, context):
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return self.embed_model.get_text_embedding_batch(context)
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def embed(self, contexts):
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self.contexts = contexts
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for batch_context in tqdm(batch_iterate(contexts, self.batch_size),
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total=len(contexts)//self.batch_size,
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desc="Embedding data in batches"):
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batch_embeddings = self.generate_embedding(batch_context)
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self.embeddings.extend(batch_embeddings)
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class QdrantVDB:
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def __init__(self, collection_name, vector_dim=768, batch_size=512):
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self.collection_name = collection_name
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self.batch_size = batch_size
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self.vector_dim = vector_dim
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self.define_client()
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def define_client(self):
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self.client = QdrantClient(url="http://localhost:6333",
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prefer_grpc=True)
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def create_collection(self):
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if not self.client.collection_exists(collection_name=self.collection_name):
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self.client.create_collection(collection_name=self.collection_name,
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vectors_config=models.VectorParams(
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size=self.vector_dim,
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distance=models.Distance.DOT,
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on_disk=True),
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optimizers_config=models.OptimizersConfigDiff(
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default_segment_number=5,
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indexing_threshold=0)
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)
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def ingest_data(self, embeddata):
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for batch_context, batch_embeddings in tqdm(zip(batch_iterate(embeddata.contexts, self.batch_size),
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batch_iterate(embeddata.embeddings, self.batch_size)),
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total=len(embeddata.contexts)//self.batch_size,
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desc="Ingesting in batches"):
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self.client.upload_collection(collection_name=self.collection_name,
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vectors=batch_embeddings,
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payload=[{"context": context} for context in batch_context])
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self.client.update_collection(collection_name=self.collection_name,
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optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000)
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)
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class Retriever:
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def __init__(self, vector_db, embeddata):
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self.vector_db = vector_db
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self.embeddata = embeddata
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def search(self, query):
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query_embedding = self.embeddata.embed_model.get_query_embedding(query)
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# select the top 3 results
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result = self.vector_db.client.search(
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collection_name=self.vector_db.collection_name,
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query_vector=query_embedding,
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search_params=models.SearchParams(
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quantization=models.QuantizationSearchParams(
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ignore=True,
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rescore=True,
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oversampling=2.0,
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)
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),
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limit=3,
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timeout=1000,
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)
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context = [dict(data) for data in result]
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combined_prompt = []
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for entry in context[:3]:
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context = entry["payload"]["context"]
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combined_prompt.append(context)
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final_output = "\n\n---\n\n".join(combined_prompt)
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return final_output |