Merge branch 'NevaMind-AI:main' into feat/json-mode-reasoning-effort

Author: kolmogorov-quyet

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -17,6 +17,28 @@ body:
     validations:
       required: true
 
+  - type: textarea
+    id: motivation
+    attributes:
+      label: Motivation
+      description: Why do we need this feature? What problem does it solve?
+      placeholder: "Explain the use case and why this feature would be valuable."
+    validations:
+      required: true
+
+  - type: dropdown
+    id: platform
+    attributes:
+      label: Platform
+      description: Which platform(s) does this feature apply to?
+      options:
+        - Platform Independent
+        - Linux
+        - macOS
+        - Windows
+    validations:
+      required: true
+
   - type: dropdown
     id: priority
     attributes:

README.md

@@ -356,7 +356,7 @@ View detailed experimental data: [memU-experiment](https://github.com/NevaMind-A
 <div align="center">
 
 <a href="https://github.com/TEN-framework/ten-framework"><img src="https://avatars.githubusercontent.com/u/113095513?s=200&v=4" alt="Ten" height="40" style="margin: 10px;"></a>
-<a href="GitHub - openagents-org/openagents: OpenAgents - AI Agent Networks for Open Collaboration"><img src="assets/partners/openagents.png" alt="OpenAgents" height="40" style="margin: 10px;"></a>
+<a href="https://openagents.org"><img src="assets/partners/openagents.png" alt="OpenAgents" height="40" style="margin: 10px;"></a>
 <a href="https://github.com/milvus-io/milvus"><img src="https://miro.medium.com/v2/resize:fit:2400/1*-VEGyAgcIBD62XtZWavy8w.png" alt="Milvus" height="40" style="margin: 10px;"></a>
 <a href="https://xroute.ai/"><img src="assets/partners/xroute.png" alt="xRoute" height="40" style="margin: 10px;"></a>
 <a href="https://jaaz.app/"><img src="assets/partners/jazz.png" alt="Jazz" height="40" style="margin: 10px;"></a>
@@ -368,6 +368,62 @@ View detailed experimental data: [memU-experiment](https://github.com/NevaMind-A
 
 ---
 
+## 🤝 How to Contribute
+
+We welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your help is appreciated.
+
+### Getting Started
+
+To start contributing to MemU, you'll need to set up your development environment:
+
+#### Prerequisites
+- Python 3.13+
+- [uv](https://github.com/astral-sh/uv) (Python package manager)
+- Git
+
+#### Setup Development Environment
+
+```bash
+# 1. Fork and clone the repository
+git clone https://github.com/YOUR_USERNAME/memU.git
+cd memU
+
+# 2. Install development dependencies
+make install
+```
+
+The `make install` command will:
+- Create a virtual environment using `uv`
+- Install all project dependencies
+- Set up pre-commit hooks for code quality checks
+
+#### Running Quality Checks
+
+Before submitting your contribution, ensure your code passes all quality checks:
+
+```bash
+make check
+```
+
+The `make check` command runs:
+- **Lock file verification**: Ensures `pyproject.toml` consistency
+- **Pre-commit hooks**: Lints code with Ruff, formats with Black
+- **Type checking**: Runs `mypy` for static type analysis
+- **Dependency analysis**: Uses `deptry` to find obsolete dependencies
+
+### Contributing Guidelines
+
+For detailed contribution guidelines, code standards, and development practices, please see [CONTRIBUTING.md](CONTRIBUTING.md).
+
+**Quick tips:**
+- Create a new branch for each feature or bug fix
+- Write clear commit messages
+- Add tests for new functionality
+- Update documentation as needed
+- Run `make check` before pushing
+
+---
+
 ## 📄 License
 
 [Apache License 2.0](LICENSE.txt)
@@ -376,7 +432,7 @@ View detailed experimental data: [memU-experiment](https://github.com/NevaMind-A
 
 ## 🌍 Community
 
-- **GitHub Issues**: [Report bugs & request features](NevaMind-AI/memU)
+- **GitHub Issues**: [Report bugs & request features](https://github.com/NevaMind-AI/memU/issues)
 - **Discord**: [Join the community](https://discord.com/invite/hQZntfGsbJ)
 - **X (Twitter)**: [Follow @memU_ai](https://x.com/memU_ai)
 - **Contact**: info@nevamind.ai

src/memu/database/inmemory/vector.py

@@ -16,16 +16,37 @@ def cosine_topk(
     corpus: Iterable[tuple[str, list[float] | None]],
     k: int = 5,
 ) -> list[tuple[str, float]]:
-    q = np.array(query_vec, dtype=np.float32)
-    scored: list[tuple[str, float]] = []
+    # Filter out None vectors and collect valid entries
+    ids: list[str] = []
+    vecs: list[list[float]] = []
     for _id, vec in corpus:
-        if vec is None:
-            continue
-        vec_list = cast(list[float], vec)
-        v = np.array(vec_list, dtype=np.float32)
-        scored.append((_id, _cosine(q, v)))
-    scored.sort(key=lambda x: x[1], reverse=True)
-    return scored[:k]
+        if vec is not None:
+            ids.append(_id)
+            vecs.append(cast(list[float], vec))
+
+    if not vecs:
+        return []
+
+    # Vectorized computation: stack all vectors into a matrix
+    q = np.array(query_vec, dtype=np.float32)
+    matrix = np.array(vecs, dtype=np.float32)  # shape: (n, dim)
+
+    # Compute all cosine similarities at once
+    q_norm = np.linalg.norm(q)
+    vec_norms = np.linalg.norm(matrix, axis=1)
+    scores = matrix @ q / (vec_norms * q_norm + 1e-9)
+
+    # Use argpartition for O(n) topk selection instead of O(n log n) sort
+    n = len(scores)
+    actual_k = min(k, n)
+    if actual_k == n:
+        topk_indices = np.argsort(scores)[::-1]
+    else:
+        # Get indices of top k elements (unordered), then sort only those
+        topk_indices = np.argpartition(scores, -actual_k)[-actual_k:]
+        topk_indices = topk_indices[np.argsort(scores[topk_indices])[::-1]]
+
+    return [(ids[i], float(scores[i])) for i in topk_indices]
 
 
 def query_cosine(query_vec: list[float], vecs: list[list[float]]) -> list[tuple[int, float]]: