deepSeek e o futuro dos LLMs: por que a abordagem independente de LLM do MongoDB é importante

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    !pip install --quiet -U pymongo sentence-transformers datasets accelerate

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    from sentence_transformers import SentenceTransformer
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    # Load the embedding model
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    embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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    # Function to generate embeddings from text
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    def generate_embedding(text):
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        return embedding_model.encode([text])[0].tolist()
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    # Generate and attach embeddings to the dataset
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    dataset_df["embedding"] = dataset_df["fullplot"].apply(generate_embedding)

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    import pymongo
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    def get_mongo_client(mongo_uri):
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        """Establish and validate connection to MongoDB."""
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        client = pymongo.MongoClient(mongo_uri, appname="devrel.showcase.rag.deepseek_rag_movies.python")
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        ping_result = client.admin.command('ping')
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        if ping_result.get('ok') == 1.0:
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            print("Connection to MongoDB successful")
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            return client
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        else:
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            print("Connection to MongoDB failed")
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        return None
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    MONGO_URI = os.environ['MONGO_URI']
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    mongo_client = get_mongo_client(MONGO_URI)
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    DB_NAME = "movies_database"
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    COLLECTION_NAME = "movies_collection"
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    # Create (or get) the database and collection
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    db = mongo_client[DB_NAME]
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    collection = db[COLLECTION_NAME]
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    # Clear any existing data in the collection
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    collection.delete_many({})

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    import time
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    from pymongo.operations import SearchIndexModel
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    embedding_field_name = "embedding"  # Field name containing embeddings
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    vector_search_index_name = "vector_index"
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    def create_vector_index_definition(dimensions):
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        return {
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            "fields": [
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                {
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                    "type": "vector",
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                    "path": embedding_field_name,
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                    "numDimensions": dimensions,
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                    "similarity": "cosine"
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                }
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            ]
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        }
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    def setup_vector_search_index(collection, index_definition, index_name="vector_index"):
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        new_vector_search_index_model = SearchIndexModel(
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            definition=index_definition,
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            name=index_name,
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            type="vectorSearch"
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        )
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        try:
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            result = collection.create_search_index(model=new_vector_search_index_model)
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            print(f"Creating index '{index_name}'...")
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            print(f"Waiting for 30 seconds to allow index '{index_name}' to be created...")
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            time.sleep(30)
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            print(f"Index '{index_name}' is ready.")
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            return result
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        except Exception as e:
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            print(f"Error creating new vector search index '{index_name}': {str(e)}")
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            return None
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    DIMENSIONS = 384
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    vector_index_definition = create_vector_index_definition(dimensions=DIMENSIONS)
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    setup_vector_search_index(collection, vector_index_definition, vector_search_index_name)

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    def vector_search(user_query, top_k=150):
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        """
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        Perform a vector search in the MongoDB collection based on the user query.
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        """
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        query_embedding = generate_embedding(user_query)
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        if query_embedding is None:
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            return "Invalid query or embedding generation failed."
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        vector_search_stage = {
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            "$vectorSearch": {
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                "index": vector_search_index_name,
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                "queryVector": query_embedding,
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                "path": embedding_field_name,
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                "numCandidates": top_k,
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                "limit": 5,  # Return top 5 matches
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            }
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        }
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        project_stage = {
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            "$project": {
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                "_id": 0,
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                "fullplot": 1,
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                "title": 1,
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                "genres": 1,
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                "score": {"$meta": "vectorSearchScore"},
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            }
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        }
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        pipeline = [vector_search_stage, project_stage]
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        results = collection.aggregate(pipeline)
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        return list(results)

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    import pprint
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    query = "What are the some interesting action movies to watch that include business?"
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    results = vector_search(query)
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    print(f"\nTop 5 results for query '{query}':")
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    for result in results:
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        print(f"Title: {result['title']}, Score: {result['score']:.4f}")
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    # Optionally, preview results as a DataFrame
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    pd.DataFrame(results).head()

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    from transformers import AutoTokenizer, AutoModelForCausalLM
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    # Load DeepSeek model and tokenizer
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    tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", device_map="cuda")
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    model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
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    model.to("cuda")
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    def rag_query(user_query):
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        # Retrieve matching documents using vector search
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        retrieved_docs = vector_search(user_query)
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        # Combine the query and search results into a context string
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        combined_information = f"Query: {user_query}\nContinue to answer the query using the Search Results:\n{retrieved_docs}."
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        # Tokenize input and move to GPU
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        input_ids = tokenizer(combined_information, return_tensors="pt").to("cuda")
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        response = model.generate(**input_ids, max_new_tokens=1000)
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        return tokenizer.decode(response[0], skip_special_tokens=False)
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    # Example query to generate a concise answer
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    print(rag_query("What's a romantic movie that I can watch with my wife? Make your response concise"))