documents

#### Step 7: Search and Retrieve ```python query = "Query in a rare language" query_language = detect_language(query) if query_language == 'rare_language': query_embedding = language_specific_model.encode(query, convert_to_tensor=True…

1. **Generate Documents and Relevant Labels**: Create synthetic documents and labels indicating which documents are relevant. 2. **Implement Retrieval Tools**: Define how each retrieval tool works. For simplicity, let's assume each tool ret…

pipeline.add_documents(documents) # Run query query = "What is the meaning of life?" results = pipeline.run_pipeline(query) # Print retrieved documents for doc in results["documents"]: print(f"Document: {doc.content}") ``` ### Explan…

format='%(asctime)s - %(levelname)s - %(message)s') def ingest_document(document): try: # ingestion logic here logging.info(f"Ingesting document: {document}") # Simulate ingestion logic …

documents = np.random.rand(10000, 128).astype("float32") # Vectorize documents vectors = vectorize_documents(documents) ``` Run the script with `mprof`: ```bash mprof run --include-children your_script.py mprof plot ``` This will genera…

# Vectorization function with batch processing def vectorize_documents(documents, batch_size=1000): vectors = [] for i in range(0, len(documents), batch_size): batch = documents[i:i+batch_size] batch_vectors = [np.ra…

# Configure logging logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s') def index_document(es, index_name, document): try: # Index the document es.index(index=index_name, body=do…

# Indexing code pass except Exception as e: logging.error(f"Error indexing document: {e}", exc_info=True) # Example usage documents = ["doc1", "doc2", "doc3"] catch_bm25_indexing_failures(documents) ``` …

# Example usage es = Elasticsearch(["http://localhost:9200"]) indexer = Indexer(es) query_handler = QueryHandler(es) result_aggregator = ResultAggregator() cache_manager = CacheManager() documents = ["Document 1", "Document 2", "Document 3…

url = 'https://api-free.deepl.com/v2/translate' data = { 'auth_key': api_key, 'text': text, 'target_lang': target_lang } response = requests.post(url, data=data) return response.js…

["term1", "term2", "term3"], ["term2", "term3", "term4"], ["term1", "term2", "term3", "term4"] ] # Calculate the term frequencies term_frequencies = calculate_term_frequencies(documents) print(term_frequencies) ``` ### Conclus…

def calculate_term_frequencies(documents): # Flatten the list of documents into a single list of terms all_terms = [term for document in documents for term in document] # Use Counter to count the frequency of each term …

} }) # Bulk index some data documents = [ {'_index': index_name, '_source': {'text': 'This is some example text'}}, {'_index': index_name, '_source': {'text': 'Another example text'}}, {'_index': index_name, '_source': {'te…