search

vectors = np.random.rand(num_vectors, 128).astype('float32') self.index.add(vectors) query_vector = np.random.rand(1, 128).astype('float32') start_time = time.time() _, _ = self.in…

asyncio.run(my_async_function()) ``` ### Step 6: Load Testing 1. **Simulate Load**: - Use load testing tools like `JMeter`, `Locust`, or `wrk` to simulate high load scenarios. ```sh locust -f my_locust_file.py ``` 2. **…

# Generate ground truth for evaluation ground_truth_indices = np.random.choice(len(db.vectors), size=10, replace=False) # Evaluate the accuracy accuracy = calculate_accuracy(db, query_vector, ground_truth_indices) print(f"Search accuracy: …

# Perform search results = search(COLLECTION_NAME, query_vector, TOP_K) print(results) ``` ### Explanation 1. **Collection Creation**: - `create_collection`: Creates a collection with specified parameters, including dimensi…

"dimension": dimension, "index_file_size": 1024, # Size of each segment file in MB "metric_type": METRIC_TYPE } milvus.create_collection(param) # Create an index def create_index(name, index_type, nlist): …

faiss.normalize_L2(query_vector) # Search for similar vectors distances, indices = index.search(query_vector.reshape(1, -1), k) return distances, indices # Test the function query_vector = np.random.rand(128).asty…

Ensure that your Milvus server is running on optimized hardware and that the configuration settings are tuned for your workload. #### Example: - **Use SSDs:** Solid-state drives can significantly improve read/write speeds. - **Increase RAM…

[2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…

[2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…

ground_truth = generate_ground_truth(num_queries, num_relevant) with Timer() as timer: results = engine.search(test_data) total_duration += timer.duration total_throughput += num_queries…

[2025-12-05 23:37] omega [bot]: 🔧 4/7: listTools ✅ Success ```json { "success": true, "totalTools": 150, "coreTools": 7, "filteredCount": 0, "categories": [], "response": "📊 **Tool Summary**\nTotal tools: 150\nCore tools: 7\nFil…

[2025-12-13 14:46] omega [bot]: 🔧 1/1: githubCreateIssue ✅ Success **Args:** ```json { "title": "Close issue #884 after logging 'first rule of blah' decision", "body": "User request: Close GitHub issue #884 now that the decision about t…

[2025-12-13 14:53] omega [bot]: Sounds like uncloseai is really wrestling with that 502 error! They’re definitely in a bit of a bind, but I love the idea of shifting focus to dinner brainstorming. A quinoa and black bean salad sounds refr…

This script provides a comprehensive way to benchmark both Weaviate and FAISS for indexing and search performance. By running this script, you can compare the indexing and search times for both systems and make an informed decision based on…

- If the role has no permissions, it returns an empty list. 3. **Granular Permissions**: - Roles are defined with more specific permissions like `view`, `edit`, and `delete`. - This allows for finer control over who can view, ed…

- Ensure that the 90th percentile search speed meets the target of 180ms. ### Example Optimization Suppose the profiling data shows that the `simulate_search` function is taking too long due to I/O operations. You can optimize it by us…

ss.analyze_performance() ``` ### Explanation 1. **Detailed Timing**: - The `search` method records the start and end times for each query and stores the duration in `self.queries`. 2. **Profiling**: - The `search` method also profi…

FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …

- **Query Handler**: Processes incoming queries. - **Result Aggregator**: Combines results from multiple sources. - **Cache Manager**: Manages query caching. #### Example Code Structure ```python class Indexer: def __init__(self, es_cl…

- Adjust the search parameters like `efSearch` for `IndexHNSW` to balance between speed and accuracy. ### Example Implementation Here's an optimized version of your code using `IndexIVFPQ` and enabling multi-threading: ```python impor…

M = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Search for n…

vectors = np.random.rand(100000, 128).astype('float32') # Set the number of threads for parallel processing faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create a…

M = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) try: # Train the index index.train(vectors) except Exception as e: logging.error(f"Error …

app.run(debug=True) ``` ### Explanation: 1. **Keycloak Configuration**: - Configure Keycloak with the necessary realm, client, and roles. - Use the `KeycloakOpenID` client to interact with Keycloak. 2. **Authentication**: - …

index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran…

Ensure each microservice is isolated and can operate independently. This includes having its own database, configuration, and deployment process. ### Step 3: Communication Between Services Use a lightweight communication protocol like gRP…

except requests.exceptions.RequestException as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/health") def health_check(): return {"status": "OK"} ``` ### Step 5: Handle Errors and Exceptions Handle pot…

response = requests.post(f"http://dense-retrieval:80/search", json=query.dict()) response.raise_for_status() return response.json() @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): t…

return response.json() except requests.exceptions.HTTPError as e: raise HTTPException(status_code=response.status_code, detail=str(e)) except requests.exceptions.ConnectionError as e: raise HTT…

tokens = self.tokenizer.convert_ids_to_tokens(inputs['input_ids'][0]) return tokens def search(self, query): tokens = self.tokenize(query) # Perform search using the tokens return tokens # I…

# Create the index es.indices.create(index=index_name, body={ 'settings': { 'index': { 'number_of_shards': 1, 'number_of_replicas': 0 } }, 'mappings': { 'properties': { …

self.correction = None class Trie: def __init__(self): self.root = TrieNode() def insert(self, word, correction): node = self.root for char in word: if char not in node.children: …

correction = self.trie.search(words[i]) if correction: # replace the word with its correction words[i] = correction # join the corrected words back into a query string…