AI Research Assistant Design

(Due to technical issues, the search service is temporarily unavailable.) ### Synthesized Project Ideas from Your Repositories: 1. **AI-Powered Research Assistant with Knowledge Graph Integration** - *Combines*: Chrome extension (filename generator) + Knowledge Graph + Orchestration (Ollama). - Automatically tags, organizes, and links research content (articles, PDFs, notes) into a knowledge graph as users browse. 2. **Automated Content Pipeline for Multi-Platform Publishing** - *Combines*: Reddit-to-blog conversion (RedToBlog) + Orchestration + Knowledge Graph. - Scrapes content from forums/social media, structures it into blog posts/videos, and maps topics/trends via a knowledge graph. 3. **Contextual File Organizer with Semantic Search** - *Combines*: AI filename generator + Knowledge Graph + Orchestration. - Uses AI to auto-tag files based on content, then builds a queryable knowledge graph for semantic search. --- ### Selected Project: **AI-Powered Research Assistant with Knowledge Graph Integration** **Ideal User**: Researchers, students, or content creators who need to organize and synthesize large volumes of information. --- ### Detailed Project Outline #### **Core Features** 4. **Chrome Extension (Frontend)** - **AI Annotation**: Highlight text → generate summaries/tags via Ollama. - **Auto-Capture**: Save articles/PDFs to a knowledge graph with AI-generated metadata (e.g., "Quantum Computing Basics - MIT, 2024"). - **Quick Search**: Query the knowledge graph directly from the browser. 5. **Knowledge Graph Backend** - **Graph Database**: Neo4j or Amazon Neptune to store entities (articles, authors, concepts) and relationships (citations, topics). - **Semantic Linking**: AI identifies connections between saved content (e.g., links "quantum computing" to "qubit error correction"). 6. **Orchestration Layer (Ollama)** - **Model Pipeline**: - **Summary Model**: Generates abstracts of saved content. - **Tagging Model**: Extracts keywords/topics (e.g., "AI", "Physics"). - **Linker Model**: Maps relationships between entities. - **Batch Processing**: Schedule AI tasks during low-usage periods. 7. **Dashboard** - **Visual Graph Explorer**: Interactive node/link diagram of research topics. - **Semantic Search**: "Find papers criticizing GPT-4’s energy usage." - **Export**: Generate literature reviews or citations in Markdown/LaTeX. --- #### **Tech Stack** - **Frontend**: React-based Chrome extension + Vis.js for graph visualization. - **Backend**: FastAPI + Neo4j + Ollama (for model orchestration). - **Storage**: S3 for PDFs/HTML, Postgres for metadata. - **AI**: Fine-tuned Mistral or Llama-3 for domain-specific tasks. --- #### **Key Challenges & Solutions** - **Data Silos**: Use schema.org-like ontologies to unify diverse content (PDFs, web pages, notes). - **AI Accuracy**: Combine rule-based tagging (e.g., regex for citations) with LLMs. - **Scalability**: Offload embedding generation to batch jobs via Ollama’s queueing. --- #### **Differentiators** - **Passive Knowledge Building**: Users organically grow a graph without manual data entry. - **Multi-Model Workflow**: Ollama routes tasks to specialized models (e.g., summarization vs. linking). --- ### Next Steps 8. Build a Chrome extension MVP that captures URLs and generates AI metadata. 9. Prototype a Neo4j schema for entities/concepts. 10. Test Ollama with a pipeline of Mistral (summarization) and Phi-3 (tagging). https://huggingface.co/docs/transformers.js/en/index --- integrate that link's information into the project ---- same with this repo: https://github.com/kliewerdaniel/KnowledgeGraph01 ----- write a new project from this synthesis

Building upon the initial concept of an **AI-Powered Research Assistant with Knowledge Graph Integration**, we can enhance the project by incorporating **Transformers.js** for in-browser AI capabilities and drawing inspiration from the **KnowledgeGraph01** repository for a robust backend architecture. ### Enhanced Project Outline #### Core Features 1. **In-Browser AI Processing with Transformers.js** - **Client-Side Inference**: Utilize [Transformers.js](https://huggingface.co/docs/transformers.js/en/index) to run transformer models directly in the browser, eliminating the need for server-side processing. This enables features like real-time text summarization, translation, and sentiment analysis as users browse. - **Seamless Integration**: Incorporate models for tasks such as summarization and semantic similarity directly into the Chrome extension, enhancing user experience with immediate AI feedback. 2. **Robust Knowledge Graph Backend Inspired by KnowledgeGraph01** - **Graph Storage with Neo4j**: Implement a Neo4j database to store and manage entities and their relationships, facilitating efficient graph traversal and queries. - **Vector Search with FAISS**: Integrate FAISS for high-speed vector similarity searches, enabling quick retrieval of semantically similar documents. - **LLM-Based Query Parsing**: Employ Large Language Models (LLMs) to parse natural language queries, converting them into structured Cypher queries for the graph database. - **Dynamic Filtering and Optimization**: Incorporate features like temporal pruning and dynamic edge weighting to enhance query performance and relevance, as demonstrated in the [KnowledgeGraph01 repository](https://github.com/kliewerdaniel/KnowledgeGraph01). 3. **Interactive Dashboard** - **Visual Graph Exploration**: Develop an interactive interface using libraries like Vis.js, allowing users to explore and interact with the knowledge graph dynamically. - **Advanced Semantic Search**: Enable users to perform complex searches, such as finding papers that critique specific methodologies, leveraging the integrated AI and knowledge graph. - **Content Export Options**: Provide functionalities to export organized information into various formats like Markdown or LaTeX, aiding in the creation of literature reviews or reports. #### Tech Stack - **Frontend**: React-based Chrome extension enhanced with Transformers.js for in-browser AI processing. - **Backend**: FastAPI server managing interactions between the frontend and the Neo4j graph database. - **Storage**: Utilize S3 for storing raw content such as PDFs and HTML files, and PostgreSQL for managing metadata. - **AI Models**: Deploy models compatible with Transformers.js for client-side tasks, and server-side models for more intensive processing needs. #### Key Challenges & Solutions - **Client-Side Performance**: Optimize in-browser models using quantization techniques and leverage WebGPU capabilities for enhanced performance, as outlined in the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/en/index). - **Data Integration**: Develop automated pipelines for extracting entities and relationships from unstructured text, ensuring seamless integration into the knowledge graph. - **Scalability**: Design the system architecture to handle large-scale data efficiently, utilizing batch processing and dynamic optimization strategies. By integrating in-browser AI processing with a robust backend inspired by existing knowledge graph systems, this project aims to provide researchers and content creators with a powerful tool for organizing and synthesizing information seamlessly.

ok so now plan out from a high level this application and help me build it with a series of prompts which will iteratively build it that I can give to an automated IDE to create

To develop the **AI-Powered Research Assistant with Knowledge Graph Integration**, we'll outline a high-level plan and provide a series of prompts designed for an AI-assisted Integrated Development Environment (IDE) to iteratively build the application. ### High-Level Plan 1. **Frontend Development: Chrome Extension** - **Features**: - In-browser AI processing using [Transformers.js](https://huggingface.co/docs/transformers.js/en/index) for tasks like text summarization and semantic analysis. - User interface components for highlighting text, viewing summaries, and interacting with the knowledge graph. 2. **Backend Development: Knowledge Graph and API** - **Components**: - Graph database setup using Neo4j to store and manage entities and relationships. - RESTful API development with FastAPI to handle data exchange between the Chrome extension and the backend. 3. **AI Model Integration** - **Tasks**: - Implement models for summarization, tagging, and linking using [Transformers.js](https://huggingface.co/docs/transformers.js/en/index) for client-side processing. - Develop server-side AI services for more intensive tasks, ensuring efficient orchestration between client and server. 4. **Dashboard Development** - **Features**: - Interactive visualization of the knowledge graph using libraries like Vis.js. - Semantic search functionality to query the knowledge graph. - Export options for generating reports in formats like Markdown or LaTeX. ### Iterative Development Prompts for AI-Assisted IDE Below is a series of prompts designed to guide an AI-assisted IDE through the development process. Each prompt focuses on a specific component or feature, building upon the previous ones. **Prompt 1: Initialize the Project Structure** *"Create a new project directory named `ai_research_assistant`. Within this directory, set up the following subdirectories: `frontend` for the Chrome extension, `backend` for the FastAPI application, and `models` for AI model scripts. Initialize a Git repository in the root directory."* **Prompt 2: Develop the Chrome Extension Manifest** *"In the `frontend` directory, create a `manifest.json` file for a Chrome extension. Include permissions for activeTab and storage, and specify `popup.html` as the default popup."* **Prompt 3: Build the Popup Interface** *"Within the `frontend` directory, create a `popup.html` file that includes a basic user interface with a text area for displaying summaries and a button labeled 'Summarize'. Style this popup using a `popup.css` file."* **Prompt 4: Implement Content Script for Text Highlighting** *"Develop a `content.js` script in the `frontend` directory that allows users to highlight text on a webpage. When text is highlighted, display a context menu option labeled 'Summarize with AI'."* **Prompt 5: Integrate Transformers.js for In-Browser Summarization** *"In the `frontend` directory, add a `summarizer.js` script that utilizes [Transformers.js](https://huggingface.co/docs/transformers.js/en/index) to perform text summarization directly in the browser. Ensure that the `content.js` script calls this summarizer when the 'Summarize with AI' option is selected."* **Prompt 6: Set Up the FastAPI Backend** *"In the `backend` directory, initialize a FastAPI application with an endpoint `/add_article` that accepts POST requests. This endpoint should receive article data (title, content, metadata) and store it in a Neo4j database."* **Prompt 7: Configure Neo4j Database Connection** *"Within the `backend` directory, create a `database.py` module that establishes a connection to a Neo4j database. Implement functions to add nodes and relationships corresponding to articles, authors, and concepts."* **Prompt 8: Develop AI Models for Tagging and Linking** *"In the `models` directory, create scripts for tagging and linking models. The tagging model should extract keywords and topics from the text, while the linking model identifies relationships between entities. Utilize [Transformers.js](https://huggingface.co/docs/transformers.js/en/index) for client-side models and server-side frameworks for more intensive tasks."* **Prompt 9: Implement Communication Between Frontend and Backend** *"Modify the `summarizer.js` script to send summarized content and metadata to the FastAPI backend's `/add_article` endpoint using a POST request. Ensure proper handling of responses and errors."* **Prompt 10: Build the Interactive Knowledge Graph Dashboard** *"Develop an `index.html` file in the `frontend` directory that serves as a dashboard. Use Vis.js to visualize the knowledge graph, allowing users to explore articles, authors, and concepts. Implement search functionality to query the graph and display relevant nodes and relationships."* **Prompt 11: Add Export Functionality** *"Enhance the dashboard with an 'Export' button that allows users to generate reports of selected nodes and their relationships. Provide options to export in Markdown and LaTeX formats."* **Prompt 12: Implement User Authentication** *"Integrate user authentication into the FastAPI backend using OAuth2. Ensure that only authenticated users can add or modify content in the knowledge graph."* **Prompt 13: Set Up Automated Testing** *"Establish automated testing for both frontend and backend components. Use frameworks like Jest for frontend testing and Pytest for backend testing. Create test cases to validate functionality and ensure code quality."* **Prompt 14: Configure Continuous Integration/Continuous Deployment (CI/CD)** *"Set up a CI/CD pipeline using tools like GitHub Actions. Configure workflows to run tests automatically on code commits and deploy the application to a cloud platform upon successful test completion."* **Prompt 15: Optimize Performance and Security** *"Conduct a performance audit of the application, identifying and addressing bottlenecks in both frontend and backend components. Implement security best practices, including input validation, secure authentication, and protection against common vulnerabilities."* By following these prompts iteratively, an AI-assisted IDE can help construct the AI-Powered Research Assistant systematically, ensuring each component

To address complex questions in dense knowledge graphs, especially when seeking to uncover the nuanced contextual connections between events, you need a multifaceted strategy that blends structural analysis, semantic understanding, and dynamic filtering. The process starts by enhancing path-based exploration through weighted shortest paths, where edge weights—based on factors like co-occurrence frequency, semantic strength, and temporal proximity—guide algorithms such as Dijkstra’s or A* to prioritize the most meaningful connections. Retrieving multiple paths using techniques that offer diversified results, and ranking these paths by semantic relevance, node centrality, or even path length, can reveal a richer tapestry of insights, while meta-path learning allows you to define semantic templates—like an event leading to a person, then an organization, and back to another event—that steer the traversal in a purposeful direction. Graph analytics further refines this process by employing centrality measures such as PageRank, betweenness, and eigenvector centrality to highlight influential nodes and by using community detection algorithms to cluster related subgraphs, thereby exposing deeper intra-community relationships. By integrating semantic embeddings from models like Node2Vec, TransE, or GraphSAGE, and combining them with vector similarity measures, you create a hybrid retrieval system where initial candidate nodes are identified and then refined through targeted graph traversal techniques like personalized PageRank or breadth-first search. At the same time, contextual NLP integration plays a crucial role: language models can evaluate the relevance of edge descriptions—for instance, scoring phrases like “caused by” or “occurred after”—and generate natural language summaries that encapsulate the key relationships within the graph. More advanced methods involve graph neural networks, where attention mechanisms dynamically weigh neighbor nodes, and subgraph reasoning techniques, such as those used in relational graph convolutional networks, allow for sophisticated inference over heterogeneous graphs. Dynamic contextual filtering further sharpens the search by pruning nodes and edges that fall outside a relevant time window or adjusting edge weights based on the specific context of the query. Underpinning all these techniques is a wealth of research—from the Path Ranking Algorithm to studies on embedding-based retrieval and GNNs for question answering—and a robust ecosystem of tools and libraries, including graph databases like Neo4j, AWS Neptune, or TigerGraph, analytics frameworks like NetworkX and PyTorch Geometric, and embedding resources available through HuggingFace Transformers. Continuous evaluation, using metrics such as path precision, answer relevance, and human feedback, along with ensemble methods that blend shortest path scores with embedding similarity, ensures that the approach remains both efficient and context-aware, ultimately transforming a dense graph into an insightful, dynamic map of knowledge. ----- incorporate this into the next iterative prompt for the project

To enhance the **AI-Powered Research Assistant with Knowledge Graph Integration**, we'll incorporate advanced techniques for exploring complex queries within dense knowledge graphs. This involves integrating sophisticated graph traversal algorithms, semantic understanding, and dynamic filtering mechanisms. Below is an iterative prompt designed for an AI-assisted Integrated Development Environment (IDE) to implement these enhancements. **Prompt 16: Implement Advanced Graph Traversal and Semantic Analysis** *"Enhance the existing knowledge graph backend to support complex query handling by implementing the following features: 1. **Weighted Graph Traversal**: - **Edge Weighting**: Assign weights to edges based on factors such as co-occurrence frequency, semantic strength, and temporal proximity. - **Shortest Path Algorithms**: Utilize algorithms like Dijkstra's or A* to find paths that prioritize meaningful connections between nodes. 2. **Meta-Path Learning**: - **Semantic Templates**: Define patterns (e.g., event → person → organization → event) to guide traversal and uncover contextual relationships. 3. **Graph Analytics Integration**: - **Centrality Measures**: Implement metrics like PageRank, betweenness, and eigenvector centrality to identify influential nodes. - **Community Detection**: Apply algorithms to cluster related subgraphs, revealing deeper intra-community relationships. 4. **Semantic Embedding and Hybrid Retrieval**: - **Node Embeddings**: Generate embeddings using models such as Node2Vec or GraphSAGE. - **Hybrid Search**: Combine vector similarity measures with graph traversal techniques like personalized PageRank to refine search results. 5. **Contextual Natural Language Processing (NLP) Integration**: - **Edge Description Analysis**: Use language models to evaluate the relevance of edge descriptions (e.g., "caused by," "occurred after"). - **Natural Language Summarization**: Generate summaries that encapsulate key relationships within the graph. 6. **Graph Neural Networks (GNNs) for Enhanced Reasoning**: - **Attention Mechanisms**: Incorporate attention-based GNNs to dynamically weigh neighboring nodes during information propagation. - **Subgraph Reasoning**: Utilize relational graph convolutional networks for sophisticated inference over heterogeneous graphs. 7. **Dynamic Contextual Filtering**: - **Temporal Pruning**: Filter nodes and edges based on relevant time windows. - **Context-Based Edge Weighting**: Adjust edge weights dynamically according to the specific context of the query. Ensure that these features are seamlessly integrated into the existing system, leveraging tools and libraries such as Neo4j, NetworkX, and PyTorch Geometric. Maintain continuous evaluation using metrics like path precision and answer relevance to optimize performance."* By implementing these advanced functionalities, the research assistant will be better equipped to handle complex queries, uncover nuanced contextual connections, and provide insightful, dynamic mappings of knowledge.

pip install -r requirements.txt Collecting fastapi==0.109.1 (from -r requirements.txt (line 1)) Using cached fastapi-0.109.1-py3-none-any.whl.metadata (25 kB) Collecting uvicorn==0.27.1 (from -r requirements.txt (line 2)) Using cached uvicorn-0.27.1-py3-none-any.whl.metadata (6.3 kB) Collecting neo4j==5.17.0 (from -r requirements.txt (line 3)) Using cached neo4j-5.17.0.tar.gz (197 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting python-dotenv==1.0.1 (from -r requirements.txt (line 4)) Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB) Collecting pydantic==2.6.1 (from -r requirements.txt (line 5)) Using cached pydantic-2.6.1-py3-none-any.whl.metadata (83 kB) Collecting networkx==2.8.8 (from -r requirements.txt (line 6)) Using cached networkx-2.8.8-py3-none-any.whl.metadata (5.1 kB) Requirement already satisfied: torch in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from -r requirements.txt (line 7)) (2.6.0) Collecting torch-geometric==2.5.0 (from -r requirements.txt (line 8)) Using cached torch_geometric-2.5.0-py3-none-any.whl.metadata (64 kB) Collecting transformers==4.37.2 (from -r requirements.txt (line 9)) Using cached transformers-4.37.2-py3-none-any.whl.metadata (129 kB) Collecting scikit-learn==1.4.0 (from -r requirements.txt (line 10)) Using cached scikit-learn-1.4.0.tar.gz (7.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting numpy==1.26.3 (from -r requirements.txt (line 11)) Using cached numpy-1.26.3.tar.gz (15.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting pandas==2.2.0 (from -r requirements.txt (line 12)) Using cached pandas-2.2.0.tar.gz (4.4 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting community==1.0.0b1 (from -r requirements.txt (line 13)) Using cached community-1.0.0b1.tar.gz (2.2 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting node2vec==0.4.6 (from -r requirements.txt (line 14)) Using cached node2vec-0.4.6-py3-none-any.whl.metadata (743 bytes) Collecting starlette<0.36.0,>=0.35.0 (from fastapi==0.109.1->-r requirements.txt (line 1)) Using cached starlette-0.35.1-py3-none-any.whl.metadata (5.8 kB) Requirement already satisfied: typing-extensions>=4.8.0 in /Users/danielkliewer/ai_research_assistan t/venv/lib/python3.13/site-packages (from fastapi==0.109.1->-r requirements.txt (line 1)) (4.12.2) Collecting click>=7.0 (from uvicorn==0.27.1->-r requirements.txt (line 2)) Using cached click-8.1.8-py3-none-any.whl.metadata (2.3 kB) Collecting h11>=0.8 (from uvicorn==0.27.1->-r requirements.txt (line 2)) Using cached h11-0.14.0-py3-none-any.whl.metadata (8.2 kB) Collecting pytz (from neo4j==5.17.0->-r requirements.txt (line 3)) Using cached pytz-2025.1-py2.py3-none-any.whl.metadata (22 kB) Collecting annotated-types>=0.4.0 (from pydantic==2.6.1->-r requirements.txt (line 5)) Using cached annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB) Collecting pydantic-core==2.16.2 (from pydantic==2.6.1->-r requirements.txt (line 5)) Using cached pydantic_core-2.16.2.tar.gz (368 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting tqdm (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) Requirement already satisfied: scipy in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (1.15.2) Requirement already satisfied: fsspec in /Users/danielkliewer/ai_research_assistant/venv/lib/python3 .13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (2025.2.0) Requirement already satisfied: jinja2 in /Users/danielkliewer/ai_research_assistant/venv/lib/python3 .13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (3.1.5) Collecting aiohttp (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached aiohttp-3.11.12-cp313-cp313-macosx_11_0_arm64.whl.metadata (7.7 kB) Collecting requests (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached requests-2.32.3-py3-none-any.whl.metadata (4.6 kB) Collecting pyparsing (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached pyparsing-3.2.1-py3-none-any.whl.metadata (5.0 kB) Collecting psutil>=5.8.0 (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl.metadata (22 kB) Requirement already satisfied: filelock in /Users/danielkliewer/ai_research_assistant/venv/lib/pytho n3.13/site-packages (from transformers==4.37.2->-r requirements.txt (line 9)) (3.17.0) Collecting huggingface-hub<1.0,>=0.19.3 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached huggingface_hub-0.29.1-py3-none-any.whl.metadata (13 kB) Collecting packaging>=20.0 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached packaging-24.2-py3-none-any.whl.metadata (3.2 kB) Collecting pyyaml>=5.1 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached PyYAML-6.0.2-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.1 kB) Collecting regex!=2019.12.17 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached regex-2024.11.6-cp313-cp313-macosx_11_0_arm64.whl.metadata (40 kB) Collecting tokenizers<0.19,>=0.14 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached tokenizers-0.15.2-cp313-cp313-macosx_11_0_arm64.whl.metadata (6.7 kB) Collecting safetensors>=0.4.1 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached safetensors-0.5.2-cp38-abi3-macosx_11_0_arm64.whl.metadata (3.8 kB) Collecting joblib>=1.2.0 (from scikit-learn==1.4.0->-r requirements.txt (line 10)) Using cached joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB) Collecting threadpoolctl>=2.0.0 (from scikit-learn==1.4.0->-r requirements.txt (line 10)) Using cached threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB) Collecting python-dateutil>=2.8.2 (from pandas==2.2.0->-r requirements.txt (line 12)) Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB) Collecting tzdata>=2022.7 (from pandas==2.2.0->-r requirements.txt (line 12)) Using cached tzdata-2025.1-py2.py3-none-any.whl.metadata (1.4 kB) Collecting Flask (from community==1.0.0b1->-r requirements.txt (line 13)) Using cached flask-3.1.0-py3-none-any.whl.metadata (2.7 kB) Collecting gensim<5.0.0,>=4.1.2 (from node2vec==0.4.6->-r requirements.txt (line 14)) Using cached gensim-4.3.3.tar.gz (23.3 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Requirement already satisfied: setuptools in /Users/danielkliewer/ai_research_assistant/venv/lib/pyt hon3.13/site-packages (from torch->-r requirements.txt (line 7)) (75.8.0) Requirement already satisfied: sympy==1.13.1 in /Users/danielkliewer/ai_research_assistant/venv/lib/ python3.13/site-packages (from torch->-r requirements.txt (line 7)) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/danielkliewer/ai_research_assistant/venv /lib/python3.13/site-packages (from sympy==1.13.1->torch->-r requirements.txt (line 7)) (1.3.0) Collecting scipy (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached scipy-1.13.1.tar.gz (57.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... error error: subprocess-exited-with-error × Preparing metadata (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [44 lines of output] + meson setup /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-d5myeadh/sc ipy_175dddb02cac468e8d8d5921ad3c263b /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-d5myeadh/scipy_175dddb02cac468e8d8d5921ad3c263b/.mesonpy-ysyya5gg -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-d5myeadh/scipy_175dddb02cac468e8d8d5921ad3c263b/.mesonpy-ysyya5gg/meson-python-native-file.ini The Meson build system Version: 1.7.0 Source dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-d5myeadh/scip y_175dddb02cac468e8d8d5921ad3c263b Build dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-d5myeadh/scipy _175dddb02cac468e8d8d5921ad3c263b/.mesonpy-ysyya5gg Build type: native build Project name: scipy Project version: 1.13.1 C compiler for the host machine: cc (clang 16.0.0 "Apple clang version 16.0.0 (clang-1600.0.26 .6)") C linker for the host machine: cc ld64 1115.7.3 C++ compiler for the host machine: c++ (clang 16.0.0 "Apple clang version 16.0.0 (clang-1600.0 .26.6)") C++ linker for the host machine: c++ ld64 1115.7.3 Cython compiler for the host machine: cython (cython 3.0.12) Host machine cpu family: aarch64 Host machine cpu: aarch64 Program python found: YES (/Users/danielkliewer/ai_research_assistant/venv/bin/python3.13) Found pkg-config: YES (/opt/homebrew/bin/pkg-config) 2.3.0 Run-time dependency python found: YES 3.13 Program cython found: YES (/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build- env-s4xzn3sg/overlay/bin/cython) Compiler for C supports arguments -Wno-unused-but-set-variable: YES Compiler for C supports arguments -Wno-unused-function: YES Compiler for C supports arguments -Wno-conversion: YES Compiler for C supports arguments -Wno-misleading-indentation: YES Library m found: YES Fortran compiler for the host machine: gfortran (gcc 14.2.0 "GNU Fortran (Homebrew GCC 14.2.0_ 1) 14.2.0") Fortran linker for the host machine: gfortran ld64 1115.7.3 Compiler for Fortran supports arguments -Wno-conversion: YES Compiler for C supports link arguments -Wl,-ld_classic: YES Checking if "-Wl,--version-script" links: NO Program pythran found: YES 0.15.0 0.15.0 (/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000 gn/T/pip-build-env-s4xzn3sg/overlay/bin/pythran) Found CMake: /opt/homebrew/bin/cmake (3.31.3) WARNING: CMake Toolchain: Failed to determine CMake compilers state Run-time dependency xsimd found: NO (tried pkgconfig, framework and cmake) Run-time dependency threads found: YES Library npymath found: YES Library npyrandom found: YES Run-time dependency pybind11 found: YES 2.13.6 Run-time dependency scipy-openblas found: NO (tried pkgconfig) Run-time dependency openblas found: NO (tried pkgconfig, framework and cmake) Run-time dependency openblas found: NO (tried pkgconfig, framework and cmake) ../scipy/meson.build:163:9: ERROR: Dependency "OpenBLAS" not found, tried pkgconfig, framework and cmake A full log can be found at /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-instal l-d5myeadh/scipy_175dddb02cac468e8d8d5921ad3c263b/.mesonpy-ysyya5gg/meson-logs/meson-log.txt [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details.

Installing `scipy` version 1.13.1 on macOS can sometimes lead to errors related to the OpenBLAS dependency. This issue often arises because `scipy` requires OpenBLAS during its build process, and if OpenBLAS isn't properly installed or recognized, the installation will fail. **Common Causes:** 1. **Missing OpenBLAS Installation:** If OpenBLAS isn't installed on your system, `scipy` won't find the necessary libraries during installation. 2. **Incorrect Environment Configuration:** Even with OpenBLAS installed, if environment variables like `PKG_CONFIG_PATH` aren't set correctly, the build process may not locate OpenBLAS. **Steps to Resolve:** 1. **Install OpenBLAS:** - Use Homebrew to install OpenBLAS: ```bash brew install openblas ``` - This command installs OpenBLAS and makes it available system-wide. 2. **Set Environment Variables:** - After installing OpenBLAS, set the `PKG_CONFIG_PATH` to help `scipy` locate OpenBLAS during installation: ```bash export PKG_CONFIG_PATH=$(brew --prefix openblas)/lib/pkgconfig ``` - This command sets the `PKG_CONFIG_PATH` to the directory where OpenBLAS's package configuration files are located. 3. **Install or Upgrade `numpy`:** - Ensure that `numpy` is installed and up-to-date, as it's a prerequisite for `scipy`: ```bash pip install --upgrade numpy ``` - `scipy` 1.13.1 requires `numpy` version 1.22.4 or greater. citeturn0search3 4. **Install `scipy`:** - With OpenBLAS installed and environment variables set, proceed to install `scipy`: ```bash pip install scipy ``` - This command should now successfully install `scipy` with the correct OpenBLAS dependency. If you continue to experience issues, consider consulting the [SciPy Building from Source Guide](https://docs.scipy.org/doc/scipy/building/index.html) for more detailed instructions. By following these steps, you should be able to resolve the OpenBLAS dependency error and successfully install `scipy` on your macOS system.

pip install -r requirements.txt Collecting fastapi==0.109.1 (from -r requirements.txt (line 1)) Using cached fastapi-0.109.1-py3-none-any.whl.metadata (25 kB) Collecting uvicorn==0.27.1 (from -r requirements.txt (line 2)) Using cached uvicorn-0.27.1-py3-none-any.whl.metadata (6.3 kB) Collecting neo4j==5.17.0 (from -r requirements.txt (line 3)) Using cached neo4j-5.17.0.tar.gz (197 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting python-dotenv==1.0.1 (from -r requirements.txt (line 4)) Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB) Collecting pydantic==2.6.1 (from -r requirements.txt (line 5)) Using cached pydantic-2.6.1-py3-none-any.whl.metadata (83 kB) Collecting networkx==2.8.8 (from -r requirements.txt (line 6)) Using cached networkx-2.8.8-py3-none-any.whl.metadata (5.1 kB) Requirement already satisfied: torch in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from -r requirements.txt (line 7)) (2.6.0) Collecting torch-geometric==2.5.0 (from -r requirements.txt (line 8)) Using cached torch_geometric-2.5.0-py3-none-any.whl.metadata (64 kB) Collecting transformers==4.37.2 (from -r requirements.txt (line 9)) Using cached transformers-4.37.2-py3-none-any.whl.metadata (129 kB) Collecting scikit-learn==1.4.0 (from -r requirements.txt (line 10)) Using cached scikit-learn-1.4.0.tar.gz (7.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting numpy==1.26.3 (from -r requirements.txt (line 11)) Using cached numpy-1.26.3.tar.gz (15.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting pandas==2.2.0 (from -r requirements.txt (line 12)) Using cached pandas-2.2.0.tar.gz (4.4 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting community==1.0.0b1 (from -r requirements.txt (line 13)) Using cached community-1.0.0b1.tar.gz (2.2 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting node2vec==0.4.6 (from -r requirements.txt (line 14)) Using cached node2vec-0.4.6-py3-none-any.whl.metadata (743 bytes) Collecting starlette<0.36.0,>=0.35.0 (from fastapi==0.109.1->-r requirements.txt (line 1)) Using cached starlette-0.35.1-py3-none-any.whl.metadata (5.8 kB) Requirement already satisfied: typing-extensions>=4.8.0 in /Users/danielkliewer/ai_research_assistan t/venv/lib/python3.13/site-packages (from fastapi==0.109.1->-r requirements.txt (line 1)) (4.12.2) Collecting click>=7.0 (from uvicorn==0.27.1->-r requirements.txt (line 2)) Using cached click-8.1.8-py3-none-any.whl.metadata (2.3 kB) Collecting h11>=0.8 (from uvicorn==0.27.1->-r requirements.txt (line 2)) Using cached h11-0.14.0-py3-none-any.whl.metadata (8.2 kB) Collecting pytz (from neo4j==5.17.0->-r requirements.txt (line 3)) Using cached pytz-2025.1-py2.py3-none-any.whl.metadata (22 kB) Collecting annotated-types>=0.4.0 (from pydantic==2.6.1->-r requirements.txt (line 5)) Using cached annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB) Collecting pydantic-core==2.16.2 (from pydantic==2.6.1->-r requirements.txt (line 5)) Using cached pydantic_core-2.16.2.tar.gz (368 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting tqdm (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) Requirement already satisfied: scipy in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (1.15.2) Requirement already satisfied: fsspec in /Users/danielkliewer/ai_research_assistant/venv/lib/python3 .13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (2025.2.0) Requirement already satisfied: jinja2 in /Users/danielkliewer/ai_research_assistant/venv/lib/python3 .13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (3.1.5) Collecting aiohttp (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached aiohttp-3.11.12-cp313-cp313-macosx_11_0_arm64.whl.metadata (7.7 kB) Collecting requests (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached requests-2.32.3-py3-none-any.whl.metadata (4.6 kB) Collecting pyparsing (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached pyparsing-3.2.1-py3-none-any.whl.metadata (5.0 kB) Collecting psutil>=5.8.0 (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl.metadata (22 kB) Requirement already satisfied: filelock in /Users/danielkliewer/ai_research_assistant/venv/lib/pytho n3.13/site-packages (from transformers==4.37.2->-r requirements.txt (line 9)) (3.17.0) Collecting huggingface-hub<1.0,>=0.19.3 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached huggingface_hub-0.29.1-py3-none-any.whl.metadata (13 kB) Collecting packaging>=20.0 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached packaging-24.2-py3-none-any.whl.metadata (3.2 kB) Collecting pyyaml>=5.1 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached PyYAML-6.0.2-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.1 kB) Collecting regex!=2019.12.17 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached regex-2024.11.6-cp313-cp313-macosx_11_0_arm64.whl.metadata (40 kB) Collecting tokenizers<0.19,>=0.14 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached tokenizers-0.15.2-cp313-cp313-macosx_11_0_arm64.whl.metadata (6.7 kB) Collecting safetensors>=0.4.1 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached safetensors-0.5.2-cp38-abi3-macosx_11_0_arm64.whl.metadata (3.8 kB) Requirement already satisfied: joblib>=1.2.0 in /Users/danielkliewer/ai_research_assistant/venv/lib/ python3.13/site-packages (from scikit-learn==1.4.0->-r requirements.txt (line 10)) (1.4.2) Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/danielkliewer/ai_research_assistant/ve nv/lib/python3.13/site-packages (from scikit-learn==1.4.0->-r requirements.txt (line 10)) (3.5.0) Collecting python-dateutil>=2.8.2 (from pandas==2.2.0->-r requirements.txt (line 12)) Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB) Collecting tzdata>=2022.7 (from pandas==2.2.0->-r requirements.txt (line 12)) Using cached tzdata-2025.1-py2.py3-none-any.whl.metadata (1.4 kB) Collecting Flask (from community==1.0.0b1->-r requirements.txt (line 13)) Using cached flask-3.1.0-py3-none-any.whl.metadata (2.7 kB) Collecting gensim<5.0.0,>=4.1.2 (from node2vec==0.4.6->-r requirements.txt (line 14)) Using cached gensim-4.3.3.tar.gz (23.3 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Requirement already satisfied: setuptools in /Users/danielkliewer/ai_research_assistant/venv/lib/pyt hon3.13/site-packages (from torch->-r requirements.txt (line 7)) (75.8.0) Requirement already satisfied: sympy==1.13.1 in /Users/danielkliewer/ai_research_assistant/venv/lib/ python3.13/site-packages (from torch->-r requirements.txt (line 7)) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/danielkliewer/ai_research_assistant/venv /lib/python3.13/site-packages (from sympy==1.13.1->torch->-r requirements.txt (line 7)) (1.3.0) Collecting scipy (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached scipy-1.13.1.tar.gz (57.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... error error: subprocess-exited-with-error × Preparing metadata (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [119 lines of output] + meson setup /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-80399fd_/sc ipy_39e4a6e942034d598fc31c24eb57ddcb /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-80399fd_/scipy_39e4a6e942034d598fc31c24eb57ddcb/.mesonpy-bhb_hsns -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-80399fd_/scipy_39e4a6e942034d598fc31c24eb57ddcb/.mesonpy-bhb_hsns/meson-python-native-file.ini The Meson build system Version: 1.7.0 Source dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-80399fd_/scip y_39e4a6e942034d598fc31c24eb57ddcb Build dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-80399fd_/scipy _39e4a6e942034d598fc31c24eb57ddcb/.mesonpy-bhb_hsns Build type: native build Project name: scipy Project version: 1.13.1 C compiler for the host machine: cc (clang 16.0.0 "Apple clang version 16.0.0 (clang-1600.0.26 .6)") C linker for the host machine: cc ld64 1115.7.3 C++ compiler for the host machine: c++ (clang 16.0.0 "Apple clang version 16.0.0 (clang-1600.0 .26.6)") C++ linker for the host machine: c++ ld64 1115.7.3 Cython compiler for the host machine: cython (cython 3.0.12) Host machine cpu family: aarch64 Host machine cpu: aarch64 Program python found: YES (/Users/danielkliewer/ai_research_assistant/venv/bin/python3.13) Found pkg-config: YES (/opt/homebrew/bin/pkg-config) 2.3.0 Run-time dependency python found: YES 3.13 Program cython found: YES (/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build- env-jjhwtifn/overlay/bin/cython) Compiler for C supports arguments -Wno-unused-but-set-variable: YES Compiler for C supports arguments -Wno-unused-function: YES Compiler for C supports arguments -Wno-conversion: YES Compiler for C supports arguments -Wno-misleading-indentation: YES Library m found: YES Fortran compiler for the host machine: gfortran (gcc 14.2.0 "GNU Fortran (Homebrew GCC 14.2.0_ 1) 14.2.0") Fortran linker for the host machine: gfortran ld64 1115.7.3 Compiler for Fortran supports arguments -Wno-conversion: YES Compiler for C supports link arguments -Wl,-ld_classic: YES Checking if "-Wl,--version-script" links: NO Program pythran found: YES 0.15.0 0.15.0 (/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000 gn/T/pip-build-env-jjhwtifn/overlay/bin/pythran) Found CMake: /opt/homebrew/bin/cmake (3.31.3) WARNING: CMake Toolchain: Failed to determine CMake compilers state Run-time dependency xsimd found: NO (tried pkgconfig, framework and cmake) Run-time dependency threads found: YES Library npymath found: YES Library npyrandom found: YES Run-time dependency pybind11 found: YES 2.13.6 Run-time dependency scipy-openblas found: NO (tried pkgconfig) Run-time dependency openblas found: YES 0.3.29 Dependency openblas found: YES 0.3.29 (cached) Compiler for C supports arguments -Wno-maybe-uninitialized: NO Compiler for C supports arguments -Wno-discarded-qualifiers: NO Compiler for C supports arguments -Wno-empty-body: YES Compiler for C supports arguments -Wno-implicit-function-declaration: YES Compiler for C supports arguments -Wno-parentheses: YES Compiler for C supports arguments -Wno-switch: YES Compiler for C supports arguments -Wno-unused-label: YES Compiler for C supports arguments -Wno-unused-result: YES Compiler for C supports arguments -Wno-unused-variable: YES Compiler for C++ supports arguments -Wno-cpp: YES Compiler for C++ supports arguments -Wno-deprecated-declarations: YES Compiler for C++ supports arguments -Wno-class-memaccess: NO Compiler for C++ supports arguments -Wno-format-truncation: NO Compiler for C++ supports arguments -Wno-non-virtual-dtor: YES Compiler for C++ supports arguments -Wno-sign-compare: YES Compiler for C++ supports arguments -Wno-switch: YES Compiler for C++ supports arguments -Wno-terminate: NO Compiler for C++ supports arguments -Wno-unused-but-set-variable: YES Compiler for C++ supports arguments -Wno-unused-function: YES Compiler for C++ supports arguments -Wno-unused-local-typedefs: YES Compiler for C++ supports arguments -Wno-unused-variable: YES Compiler for C++ supports arguments -Wno-int-in-bool-context: YES Compiler for Fortran supports arguments -Wno-argument-mismatch: YES Compiler for Fortran supports arguments -Wno-conversion: YES (cached) Compiler for Fortran supports arguments -Wno-intrinsic-shadow: YES Compiler for Fortran supports arguments -Wno-maybe-uninitialized: YES Compiler for Fortran supports arguments -Wno-surprising: YES Compiler for Fortran supports arguments -Wno-uninitialized: YES Compiler for Fortran supports arguments -Wno-unused-dummy-argument: YES Compiler for Fortran supports arguments -Wno-unused-label: YES Compiler for Fortran supports arguments -Wno-unused-variable: YES Compiler for Fortran supports arguments -Wno-tabs: YES Compiler for Fortran supports arguments -Wno-argument-mismatch: YES (cached) Compiler for Fortran supports arguments -Wno-conversion: YES (cached) Compiler for Fortran supports arguments -Wno-maybe-uninitialized: YES (cached) Compiler for Fortran supports arguments -Wno-unused-dummy-argument: YES (cached) Compiler for Fortran supports arguments -Wno-unused-label: YES (cached) Compiler for Fortran supports arguments -Wno-unused-variable: YES (cached) Compiler for Fortran supports arguments -Wno-tabs: YES (cached) Checking if "Check atomic builtins without -latomic" links: YES Configuring __config__.py using configuration Checking for function "open_memstream" : YES Configuring messagestream_config.h using configuration Compiler for Fortran supports arguments -w: YES Checking for size of "void*" : 8 Compiler for Fortran supports arguments -w: YES (cached) Build targets in project: 237 scipy 1.13.1 User defined options Native files: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-80399fd _/scipy_39e4a6e942034d598fc31c24eb57ddcb/.mesonpy-bhb_hsns/meson-python-native-file.ini b_ndebug : if-release b_vscrt : md buildtype : release Found ninja-1.11.1.git.kitware.jobserver-1 at /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw 0000gn/T/pip-build-env-jjhwtifn/normal/bin/ninja + /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-jjhwtifn/normal/bin/n inja [1/1475] Generating scipy/generate-version with a custom command [2/1475] Compiling C object scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g7 7_abi.c.o FAILED: scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o cc -Iscipy/libdummy_g77_abi_wrappers.a.p -Iscipy -I../scipy -I../../../pip-build-env-jjhwtifn/ overlay/lib/python3.13/site-packages/numpy/_core/include -I/opt/homebrew/opt/python@3.13/Frameworks/Python.framework/Versions/3.13/include/python3.13 -I/opt/homebrew/Cellar/openblas/0.3.29/include -fdiagnostics-color=always -DNDEBUG -Wall -Winvalid-pch -std=c99 -O3 -Wno-unused-but-set-variable -Wno-unused-function -Wno-conversion -Wno-misleading-indentation -DNPY_NO_DEPRECATED_API=NPY_1_9_API_VERSION -fopenmp -MD -MQ scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o -MF scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o.d -o scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o -c ../scipy/_build_utils/src/wrap_dummy_g77_abi.c clang: error: unsupported option '-fopenmp' [3/1475] Copying file scipy/optimize.pxd [4/1475] Copying file scipy/_lib/__init__.py [5/1475] Copying file scipy/_lib/_ccallback_c.pxd [6/1475] Copying file scipy/linalg.pxd [7/1475] Copying file scipy/special.pxd [8/1475] Copying file scipy/__init__.py [9/1475] Copying file scipy/_lib/ccallback.pxd [10/1475] Copying file scipy/_lib/messagestream.pxd [11/1475] Copying file scipy/special/__init__.py [12/1475] Compiling C object scipy/_lib/_fpumode.cpython-313-darwin.so.p/_fpumode.c.o [13/1475] Copying file scipy/special/_agm.pxd [14/1475] Compiling C object scipy/_lib/_test_ccallback.cpython-313-darwin.so.p/src__test_ccal lback.c.o [15/1475] Compiling C++ object scipy/_lib/_uarray/_uarray.cpython-313-darwin.so.p/vectorcall.c xx.o [16/1475] Compiling C object scipy/lib_fortranobject.a.p/b4d294669387e6d2163c1d7b2f77b6b214916 da6_site-packages_numpy_f2py_src_fortranobject.c.o [17/1475] Compiling C++ object scipy/_lib/_uarray/_uarray.cpython-313-darwin.so.p/_uarray_disp atch.cxx.o ninja: build stopped: subcommand failed. [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details.

It appears that the installation of your Python dependencies failed due to an error encountered while building the `scipy` package. The specific error message is: ``` clang: error: unsupported option '-fopenmp' ``` This indicates that the compiler `clang` does not support the `-fopenmp` flag, which is used to enable OpenMP for parallel processing. This issue is common on macOS systems, as Apple's version of `clang` does not include OpenMP support by default. **To resolve this issue, you can try the following steps:** 1. **Install a Compiler with OpenMP Support:** - **Using Homebrew:** - Install `gcc`, which includes support for OpenMP: ``` brew install gcc ``` - Note that this will install the latest version of `gcc`. You can check the installed version by running `gcc --version`. 2. **Set Environment Variables to Use `gcc`:** - Before installing your packages, set the `CC` and `CXX` environment variables to point to the `gcc` and `g++` compilers, respectively. Replace `gcc-<version>` and `g++-<version>` with the version installed by Homebrew (e.g., `gcc-14` for GCC version 14): ``` export CC=gcc-<version> export CXX=g++-<version> ``` - For example, if GCC version 14 is installed: ``` export CC=gcc-14 export CXX=g++-14 ``` 3. **Reattempt Installation:** - With the environment variables set, try installing your requirements again: ``` pip install -r requirements.txt ``` **Alternative Solution:** If the above steps do not resolve the issue, consider installing `scipy` using a pre-built binary to avoid compilation errors: - **Using `pip`:** ``` pip install scipy ``` This will install the latest version of `scipy` from the Python Package Index (PyPI). - **Using `conda` (if you're using Anaconda or Miniconda):** ``` conda install scipy ``` This will install a pre-built binary compatible with your system. **Note:** After installing `scipy` separately, ensure that your `requirements.txt` file specifies a compatible version or remove the specific version requirement for `scipy` to prevent conflicts. By following these steps, you should be able to resolve the installation issue related to `scipy` on your macOS system.

pip install -r requirements.txt Collecting fastapi==0.109.1 (from -r requirements.txt (line 1)) Using cached fastapi-0.109.1-py3-none-any.whl.metadata (25 kB) Collecting uvicorn==0.27.1 (from -r requirements.txt (line 2)) Using cached uvicorn-0.27.1-py3-none-any.whl.metadata (6.3 kB) Collecting neo4j==5.17.0 (from -r requirements.txt (line 3)) Using cached neo4j-5.17.0.tar.gz (197 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting python-dotenv==1.0.1 (from -r requirements.txt (line 4)) Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB) Collecting pydantic==2.6.1 (from -r requirements.txt (line 5)) Using cached pydantic-2.6.1-py3-none-any.whl.metadata (83 kB) Collecting networkx==2.8.8 (from -r requirements.txt (line 6)) Using cached networkx-2.8.8-py3-none-any.whl.metadata (5.1 kB) Requirement already satisfied: torch in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from -r requirements.txt (line 7)) (2.6.0) Collecting torch-geometric==2.5.0 (from -r requirements.txt (line 8)) Using cached torch_geometric-2.5.0-py3-none-any.whl.metadata (64 kB) Collecting transformers==4.37.2 (from -r requirements.txt (line 9)) Using cached transformers-4.37.2-py3-none-any.whl.metadata (129 kB) Collecting scikit-learn==1.4.0 (from -r requirements.txt (line 10)) Using cached scikit-learn-1.4.0.tar.gz (7.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... error error: subprocess-exited-with-error × Preparing metadata (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [81 lines of output] Partial import of sklearn during the build process. Traceback (most recent call last): File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/_distutils/spawn.py", line 70, in spawn subprocess.check_call(cmd, env=_inject_macos_ver(env)) ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/ python3.13/subprocess.py", line 416, in check_call retcode = call(*popenargs, **kwargs) File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/ python3.13/subprocess.py", line 397, in call with Popen(*popenargs, **kwargs) as p: ~~~~~^^^^^^^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/ python3.13/subprocess.py", line 1038, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ pass_fds, cwd, env, ^^^^^^^^^^^^^^^^^^^ ...<5 lines>... gid, gids, uid, umask, ^^^^^^^^^^^^^^^^^^^^^^ start_new_session, process_group) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/ python3.13/subprocess.py", line 1974, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: 'gcc-14.2.0_1' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/_distutils/unixccompiler.py", line 200, in _compile self.spawn(compiler_so + cc_args + [src, '-o', obj] + extra_postargs) ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/_distutils/ccompiler.py", line 1052, in spawn spawn(cmd, dry_run=self.dry_run, **kwargs) ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/_distutils/spawn.py", line 72, in spawn raise DistutilsExecError( f"command {_debug(cmd)!r} failed: {exc.args[-1]}" ) from exc distutils.errors.DistutilsExecError: command 'gcc-14.2.0_1' failed: No such file or directory During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/danielkliewer/ai_research_assistant/venv/lib/python3.13/site-packages/pip/_vend or/pyproject_hooks/_in_process/_in_process.py", line 389, in <module> main() ~~~~^^ File "/Users/danielkliewer/ai_research_assistant/venv/lib/python3.13/site-packages/pip/_vend or/pyproject_hooks/_in_process/_in_process.py", line 373, in main json_out["return_val"] = hook(**hook_input["kwargs"]) ~~~~^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/ai_research_assistant/venv/lib/python3.13/site-packages/pip/_vend or/pyproject_hooks/_in_process/_in_process.py", line 175, in prepare_metadata_for_build_wheel return hook(metadata_directory, config_settings) File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/build_meta.py", line 377, in prepare_metadata_for_build_wheel self.run_setup() ~~~~~~~~~~~~~~^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/build_meta.py", line 522, in run_setup super().run_setup(setup_script=setup_script) ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/build_meta.py", line 320, in run_setup exec(code, locals()) ~~~~^^^^^^^^^^^^^^^^ File "<string>", line 639, in <module> File "<string>", line 633, in setup_package File "<string>", line 565, in configure_extension_modules File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-aehsx20k/scikit-l earn_b625bc465fb54482a6bbbcd1230acb1c/sklearn/_build_utils/__init__.py", line 46, in cythonize_extensions basic_check_build() ~~~~~~~~~~~~~~~~~^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-aehsx20k/scikit-l earn_b625bc465fb54482a6bbbcd1230acb1c/sklearn/_build_utils/pre_build_helpers.py", line 73, in basic_check_build compile_test_program(code) ~~~~~~~~~~~~~~~~~~~~^^^^^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-aehsx20k/scikit-l earn_b625bc465fb54482a6bbbcd1230acb1c/sklearn/_build_utils/pre_build_helpers.py", line 31, in compile_test_program ccompiler.compile( ~~~~~~~~~~~~~~~~~^ ["test_program.c"], output_dir="objects", extra_postargs=extra_postargs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/_distutils/ccompiler.py", line 607, in compile self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-eqoxcw1o/overla y/lib/python3.13/site-packages/setuptools/_distutils/unixccompiler.py", line 202, in _compile raise CompileError(msg) distutils.errors.CompileError: command 'gcc-14.2.0_1' failed: No such file or directory [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details.

It appears you're encountering an error while installing the `scikit-learn==1.4.0` package due to a missing C compiler, specifically `gcc-14.2.0_1`. This compiler is necessary to build certain Python packages from source. **Steps to Resolve:** 1. **Install the Required Compiler:** - **macOS with Homebrew:** - Install the latest GCC version: ```bash brew install gcc ``` - Note that Homebrew may install a different version, such as `gcc-13`. You can verify the installed version using: ```bash gcc --version ``` - If the installed version differs from `gcc-14.2.0_1`, create a symbolic link to match the expected compiler name: ```bash ln -s /usr/local/bin/gcc-13 /usr/local/bin/gcc-14.2.0_1 ``` Adjust the paths based on your actual installation. 2. **Set the Compiler Environment Variable:** - Before installing your packages, set the `CC` environment variable to point to the installed GCC compiler: ```bash export CC=/usr/local/bin/gcc-14.2.0_1 ``` Replace the path with the correct path to your installed GCC compiler. 3. **Retry Installing the Packages:** - With the compiler installed and the environment variable set, attempt to install your packages again: ```bash pip install -r requirements.txt ``` **Alternative Solution:** If installing the compiler is not feasible, consider installing a pre-compiled binary of `scikit-learn`: - **Using `pip` Wheels:** - Many Python packages offer pre-compiled binaries (wheels) for common platforms. Install `scikit-learn` using: ```bash pip install scikit-learn ``` - This command will fetch the appropriate binary for your system, avoiding the need for a compiler. **Additional Considerations:** - **Python Version Compatibility:** - Ensure that the version of `scikit-learn` specified in your `requirements.txt` is compatible with your Python version. Compatibility issues can lead to installation problems. - **Virtual Environment:** - It's advisable to use a virtual environment to manage your Python packages. This practice isolates project dependencies and prevents conflicts: ```bash python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate` ``` By following these steps, you should be able to resolve the installation error and successfully install your required packages.

pip install -r requirements.txt Collecting fastapi==0.109.1 (from -r requirements.txt (line 1)) Using cached fastapi-0.109.1-py3-none-any.whl.metadata (25 kB) Collecting uvicorn==0.27.1 (from -r requirements.txt (line 2)) Using cached uvicorn-0.27.1-py3-none-any.whl.metadata (6.3 kB) Collecting neo4j==5.17.0 (from -r requirements.txt (line 3)) Using cached neo4j-5.17.0.tar.gz (197 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting python-dotenv==1.0.1 (from -r requirements.txt (line 4)) Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB) Collecting pydantic==2.6.1 (from -r requirements.txt (line 5)) Using cached pydantic-2.6.1-py3-none-any.whl.metadata (83 kB) Collecting networkx==2.8.8 (from -r requirements.txt (line 6)) Using cached networkx-2.8.8-py3-none-any.whl.metadata (5.1 kB) Requirement already satisfied: torch in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from -r requirements.txt (line 7)) (2.6.0) Collecting torch-geometric==2.5.0 (from -r requirements.txt (line 8)) Using cached torch_geometric-2.5.0-py3-none-any.whl.metadata (64 kB) Collecting transformers==4.37.2 (from -r requirements.txt (line 9)) Using cached transformers-4.37.2-py3-none-any.whl.metadata (129 kB) Requirement already satisfied: scikit-learn in /Users/danielkliewer/ai_research_assistant/venv/lib/p ython3.13/site-packages (from -r requirements.txt (line 10)) (1.6.1) Collecting numpy==1.26.3 (from -r requirements.txt (line 11)) Using cached numpy-1.26.3.tar.gz (15.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... error error: subprocess-exited-with-error × Preparing metadata (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [14 lines of output] + /Users/danielkliewer/ai_research_assistant/venv/bin/python3.13 /private/var/folders/58/t6jcw g652pjgp9ksqzjfkqtw0000gn/T/pip-install-nrbkd3_8/numpy_773000fcaf2240cabbb84d7fff6f73e8/vendored-meson/meson/meson.py setup /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-nrbkd3_8/numpy_773000fcaf2240cabbb84d7fff6f73e8 /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-nrbkd3_8/numpy_773000fcaf2240cabbb84d7fff6f73e8/.mesonpy-xknqhww6/build -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-nrbkd3_8/numpy_773000fcaf2240cabbb84d7fff6f73e8/.mesonpy-xknqhww6/build/meson-python-native-file.ini The Meson build system Version: 1.2.99 Source dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-nrbkd3_8/nump y_773000fcaf2240cabbb84d7fff6f73e8 Build dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-nrbkd3_8/numpy _773000fcaf2240cabbb84d7fff6f73e8/.mesonpy-xknqhww6/build Build type: native build Project name: NumPy Project version: 1.26.3 ../../meson.build:1:0: ERROR: Unknown compiler(s): [['/usr/local/bin/gcc-14.2.0_1']] The following exception(s) were encountered: Running `/usr/local/bin/gcc-14.2.0_1 --version` gave "[Errno 2] No such file or directory: '/u sr/local/bin/gcc-14.2.0_1'" A full log can be found at /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-instal l-nrbkd3_8/numpy_773000fcaf2240cabbb84d7fff6f73e8/.mesonpy-xknqhww6/build/meson-logs/meson-log.txt [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details.

It appears that during the installation of your Python project's dependencies, the process is encountering errors related to missing or misconfigured compilers, specifically `gcc-14.2.0_1`. This issue arises when certain packages, like `scikit-learn` and `numpy`, require compilation during installation but cannot locate the necessary compiler. **Understanding the Issue:** - **Missing Compiler:** The error message `FileNotFoundError: [Errno 2] No such file or directory: 'gcc-14.2.0_1'` indicates that the installation process is attempting to use a specific version of GCC (`gcc-14.2.0_1`) that isn't installed or isn't in the system's PATH. **Steps to Resolve:** 1. **Install the Required Compiler:** - **macOS with Homebrew:** - Install GCC: ```bash brew install gcc ``` - Homebrew typically installs GCC with versioned names like `gcc-13`. To determine the exact version installed: ```bash ls /usr/local/bin | grep gcc- ``` - If the installed version differs from `gcc-14.2.0_1`, you have two options: - **Symlink the Installed Version:** - Create a symbolic link to match the expected compiler name: ```bash ln -s /usr/local/bin/gcc-<installed_version> /usr/local/bin/gcc-14.2.0_1 ``` - Replace `<installed_version>` with the actual version number, e.g., `gcc-13`. - **Modify the Compiler Configuration:** - Set environment variables to point to the available compiler: ```bash export CC=/usr/local/bin/gcc-<installed_version> export CXX=/usr/local/bin/g++-<installed_version> ``` - Replace `<installed_version>` with your installed GCC version. 2. **Ensure Xcode Command Line Tools are Installed (macOS):** - Some packages require Xcode's command line tools. Install them with: ```bash xcode-select --install ``` 3. **Use Pre-Compiled Binaries (Wheels):** - To avoid compilation issues, configure `pip` to prefer binary packages: ```bash pip install --prefer-binary -r requirements.txt ``` - This instructs `pip` to install pre-compiled versions of packages when available. 4. **Upgrade `pip`, `setuptools`, and `wheel`:** - Outdated tools can lead to installation problems. Ensure they're up-to-date: ```bash pip install --upgrade pip setuptools wheel ``` **Additional Considerations:** - **Virtual Environments:** Ensure you're working within a virtual environment to manage dependencies effectively. - **Alternative Compilers:** If installing GCC is challenging, consider using Clang, which comes pre-installed on macOS: - Set environment variables to use Clang: ```bash export CC=clang export CXX=clang++ ``` By following these steps, you should be able to resolve the compiler-related issues during your package installation process.

pip install --prefer-binary -r requirements.txt Collecting fastapi==0.109.1 (from -r requirements.txt (line 1)) Using cached fastapi-0.109.1-py3-none-any.whl.metadata (25 kB) Collecting uvicorn==0.27.1 (from -r requirements.txt (line 2)) Using cached uvicorn-0.27.1-py3-none-any.whl.metadata (6.3 kB) Collecting neo4j==5.17.0 (from -r requirements.txt (line 3)) Using cached neo4j-5.17.0.tar.gz (197 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting python-dotenv==1.0.1 (from -r requirements.txt (line 4)) Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB) Collecting pydantic==2.6.1 (from -r requirements.txt (line 5)) Using cached pydantic-2.6.1-py3-none-any.whl.metadata (83 kB) Collecting networkx==2.8.8 (from -r requirements.txt (line 6)) Using cached networkx-2.8.8-py3-none-any.whl.metadata (5.1 kB) Requirement already satisfied: torch in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from -r requirements.txt (line 7)) (2.6.0) Collecting torch-geometric==2.5.0 (from -r requirements.txt (line 8)) Using cached torch_geometric-2.5.0-py3-none-any.whl.metadata (64 kB) Collecting transformers==4.37.2 (from -r requirements.txt (line 9)) Using cached transformers-4.37.2-py3-none-any.whl.metadata (129 kB) Requirement already satisfied: scikit-learn in /Users/danielkliewer/ai_research_assistant/venv/lib/p ython3.13/site-packages (from -r requirements.txt (line 10)) (1.6.1) Collecting numpy==1.26.3 (from -r requirements.txt (line 11)) Using cached numpy-1.26.3.tar.gz (15.7 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting pandas==2.2.0 (from -r requirements.txt (line 12)) Using cached pandas-2.2.0.tar.gz (4.4 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting community==1.0.0b1 (from -r requirements.txt (line 13)) Using cached community-1.0.0b1.tar.gz (2.2 kB) Preparing metadata (setup.py) ... done Collecting node2vec==0.4.6 (from -r requirements.txt (line 14)) Using cached node2vec-0.4.6-py3-none-any.whl.metadata (743 bytes) Collecting starlette<0.36.0,>=0.35.0 (from fastapi==0.109.1->-r requirements.txt (line 1)) Using cached starlette-0.35.1-py3-none-any.whl.metadata (5.8 kB) Requirement already satisfied: typing-extensions>=4.8.0 in /Users/danielkliewer/ai_research_assistan t/venv/lib/python3.13/site-packages (from fastapi==0.109.1->-r requirements.txt (line 1)) (4.12.2) Collecting click>=7.0 (from uvicorn==0.27.1->-r requirements.txt (line 2)) Using cached click-8.1.8-py3-none-any.whl.metadata (2.3 kB) Collecting h11>=0.8 (from uvicorn==0.27.1->-r requirements.txt (line 2)) Using cached h11-0.14.0-py3-none-any.whl.metadata (8.2 kB) Collecting pytz (from neo4j==5.17.0->-r requirements.txt (line 3)) Using cached pytz-2025.1-py2.py3-none-any.whl.metadata (22 kB) Collecting annotated-types>=0.4.0 (from pydantic==2.6.1->-r requirements.txt (line 5)) Using cached annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB) Collecting pydantic-core==2.16.2 (from pydantic==2.6.1->-r requirements.txt (line 5)) Using cached pydantic_core-2.16.2.tar.gz (368 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting tqdm (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) Requirement already satisfied: scipy in /Users/danielkliewer/ai_research_assistant/venv/lib/python3. 13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (1.15.2) Requirement already satisfied: fsspec in /Users/danielkliewer/ai_research_assistant/venv/lib/python3 .13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (2025.2.0) Requirement already satisfied: jinja2 in /Users/danielkliewer/ai_research_assistant/venv/lib/python3 .13/site-packages (from torch-geometric==2.5.0->-r requirements.txt (line 8)) (3.1.5) Collecting aiohttp (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached aiohttp-3.11.12-cp313-cp313-macosx_11_0_arm64.whl.metadata (7.7 kB) Collecting requests (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached requests-2.32.3-py3-none-any.whl.metadata (4.6 kB) Collecting pyparsing (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached pyparsing-3.2.1-py3-none-any.whl.metadata (5.0 kB) Collecting psutil>=5.8.0 (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl.metadata (22 kB) Requirement already satisfied: filelock in /Users/danielkliewer/ai_research_assistant/venv/lib/pytho n3.13/site-packages (from transformers==4.37.2->-r requirements.txt (line 9)) (3.17.0) Collecting huggingface-hub<1.0,>=0.19.3 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached huggingface_hub-0.29.1-py3-none-any.whl.metadata (13 kB) Collecting packaging>=20.0 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached packaging-24.2-py3-none-any.whl.metadata (3.2 kB) Collecting pyyaml>=5.1 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached PyYAML-6.0.2-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.1 kB) Collecting regex!=2019.12.17 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached regex-2024.11.6-cp313-cp313-macosx_11_0_arm64.whl.metadata (40 kB) Collecting tokenizers<0.19,>=0.14 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached tokenizers-0.15.2-cp313-cp313-macosx_11_0_arm64.whl.metadata (6.7 kB) Collecting safetensors>=0.4.1 (from transformers==4.37.2->-r requirements.txt (line 9)) Using cached safetensors-0.5.2-cp38-abi3-macosx_11_0_arm64.whl.metadata (3.8 kB) Collecting python-dateutil>=2.8.2 (from pandas==2.2.0->-r requirements.txt (line 12)) Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB) Collecting tzdata>=2022.7 (from pandas==2.2.0->-r requirements.txt (line 12)) Using cached tzdata-2025.1-py2.py3-none-any.whl.metadata (1.4 kB) Collecting Flask (from community==1.0.0b1->-r requirements.txt (line 13)) Using cached flask-3.1.0-py3-none-any.whl.metadata (2.7 kB) Collecting gensim<5.0.0,>=4.1.2 (from node2vec==0.4.6->-r requirements.txt (line 14)) Using cached gensim-4.3.3.tar.gz (23.3 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Requirement already satisfied: joblib<2.0.0,>=1.1.0 in /Users/danielkliewer/ai_research_assistant/ve nv/lib/python3.13/site-packages (from node2vec==0.4.6->-r requirements.txt (line 14)) (1.4.2) Requirement already satisfied: setuptools in /Users/danielkliewer/ai_research_assistant/venv/lib/pyt hon3.13/site-packages (from torch->-r requirements.txt (line 7)) (75.8.0) Requirement already satisfied: sympy==1.13.1 in /Users/danielkliewer/ai_research_assistant/venv/lib/ python3.13/site-packages (from torch->-r requirements.txt (line 7)) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/danielkliewer/ai_research_assistant/venv /lib/python3.13/site-packages (from sympy==1.13.1->torch->-r requirements.txt (line 7)) (1.3.0) Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/danielkliewer/ai_research_assistant/ve nv/lib/python3.13/site-packages (from scikit-learn->-r requirements.txt (line 10)) (3.5.0) Collecting scipy (from torch-geometric==2.5.0->-r requirements.txt (line 8)) Using cached scipy-1.13.1.tar.gz (57.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... error error: subprocess-exited-with-error × Preparing metadata (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [119 lines of output] + meson setup /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-r4sb1qjp/sc ipy_07a8a10b31854d738a8d9a0c0981d31d /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-r4sb1qjp/scipy_07a8a10b31854d738a8d9a0c0981d31d/.mesonpy-y3fi8v3k -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-r4sb1qjp/scipy_07a8a10b31854d738a8d9a0c0981d31d/.mesonpy-y3fi8v3k/meson-python-native-file.ini The Meson build system Version: 1.7.0 Source dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-r4sb1qjp/scip y_07a8a10b31854d738a8d9a0c0981d31d Build dir: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-r4sb1qjp/scipy _07a8a10b31854d738a8d9a0c0981d31d/.mesonpy-y3fi8v3k Build type: native build Project name: scipy Project version: 1.13.1 C compiler for the host machine: clang (clang 16.0.0 "Apple clang version 16.0.0 (clang-1600.0 .26.6)") C linker for the host machine: clang ld64 1115.7.3 C++ compiler for the host machine: clang++ (clang 16.0.0 "Apple clang version 16.0.0 (clang-16 00.0.26.6)") C++ linker for the host machine: clang++ ld64 1115.7.3 Cython compiler for the host machine: cython (cython 3.0.12) Host machine cpu family: aarch64 Host machine cpu: aarch64 Program python found: YES (/Users/danielkliewer/ai_research_assistant/venv/bin/python3.13) Found pkg-config: YES (/opt/homebrew/bin/pkg-config) 2.3.0 Run-time dependency python found: YES 3.13 Program cython found: YES (/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build- env-4g5skgqk/overlay/bin/cython) Compiler for C supports arguments -Wno-unused-but-set-variable: YES Compiler for C supports arguments -Wno-unused-function: YES Compiler for C supports arguments -Wno-conversion: YES Compiler for C supports arguments -Wno-misleading-indentation: YES Library m found: YES Fortran compiler for the host machine: gfortran (gcc 14.2.0 "GNU Fortran (Homebrew GCC 14.2.0_ 1) 14.2.0") Fortran linker for the host machine: gfortran ld64 1115.7.3 Compiler for Fortran supports arguments -Wno-conversion: YES Compiler for C supports link arguments -Wl,-ld_classic: YES Checking if "-Wl,--version-script" links: NO Program pythran found: YES 0.15.0 0.15.0 (/private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000 gn/T/pip-build-env-4g5skgqk/overlay/bin/pythran) Found CMake: /opt/homebrew/bin/cmake (3.31.3) WARNING: CMake Toolchain: Failed to determine CMake compilers state Run-time dependency xsimd found: NO (tried pkgconfig, framework and cmake) Run-time dependency threads found: YES Library npymath found: YES Library npyrandom found: YES Run-time dependency pybind11 found: YES 2.13.6 Run-time dependency scipy-openblas found: NO (tried pkgconfig) Run-time dependency openblas found: YES 0.3.29 Dependency openblas found: YES 0.3.29 (cached) Compiler for C supports arguments -Wno-maybe-uninitialized: NO Compiler for C supports arguments -Wno-discarded-qualifiers: NO Compiler for C supports arguments -Wno-empty-body: YES Compiler for C supports arguments -Wno-implicit-function-declaration: YES Compiler for C supports arguments -Wno-parentheses: YES Compiler for C supports arguments -Wno-switch: YES Compiler for C supports arguments -Wno-unused-label: YES Compiler for C supports arguments -Wno-unused-result: YES Compiler for C supports arguments -Wno-unused-variable: YES Compiler for C++ supports arguments -Wno-cpp: YES Compiler for C++ supports arguments -Wno-deprecated-declarations: YES Compiler for C++ supports arguments -Wno-class-memaccess: NO Compiler for C++ supports arguments -Wno-format-truncation: NO Compiler for C++ supports arguments -Wno-non-virtual-dtor: YES Compiler for C++ supports arguments -Wno-sign-compare: YES Compiler for C++ supports arguments -Wno-switch: YES Compiler for C++ supports arguments -Wno-terminate: NO Compiler for C++ supports arguments -Wno-unused-but-set-variable: YES Compiler for C++ supports arguments -Wno-unused-function: YES Compiler for C++ supports arguments -Wno-unused-local-typedefs: YES Compiler for C++ supports arguments -Wno-unused-variable: YES Compiler for C++ supports arguments -Wno-int-in-bool-context: YES Compiler for Fortran supports arguments -Wno-argument-mismatch: YES Compiler for Fortran supports arguments -Wno-conversion: YES (cached) Compiler for Fortran supports arguments -Wno-intrinsic-shadow: YES Compiler for Fortran supports arguments -Wno-maybe-uninitialized: YES Compiler for Fortran supports arguments -Wno-surprising: YES Compiler for Fortran supports arguments -Wno-uninitialized: YES Compiler for Fortran supports arguments -Wno-unused-dummy-argument: YES Compiler for Fortran supports arguments -Wno-unused-label: YES Compiler for Fortran supports arguments -Wno-unused-variable: YES Compiler for Fortran supports arguments -Wno-tabs: YES Compiler for Fortran supports arguments -Wno-argument-mismatch: YES (cached) Compiler for Fortran supports arguments -Wno-conversion: YES (cached) Compiler for Fortran supports arguments -Wno-maybe-uninitialized: YES (cached) Compiler for Fortran supports arguments -Wno-unused-dummy-argument: YES (cached) Compiler for Fortran supports arguments -Wno-unused-label: YES (cached) Compiler for Fortran supports arguments -Wno-unused-variable: YES (cached) Compiler for Fortran supports arguments -Wno-tabs: YES (cached) Checking if "Check atomic builtins without -latomic" links: YES Configuring __config__.py using configuration Checking for function "open_memstream" : YES Configuring messagestream_config.h using configuration Compiler for Fortran supports arguments -w: YES Checking for size of "void*" : 8 Compiler for Fortran supports arguments -w: YES (cached) Build targets in project: 237 scipy 1.13.1 User defined options Native files: /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-install-r4sb1qj p/scipy_07a8a10b31854d738a8d9a0c0981d31d/.mesonpy-y3fi8v3k/meson-python-native-file.ini b_ndebug : if-release b_vscrt : md buildtype : release Found ninja-1.11.1.git.kitware.jobserver-1 at /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw 0000gn/T/pip-build-env-4g5skgqk/normal/bin/ninja + /private/var/folders/58/t6jcwg652pjgp9ksqzjfkqtw0000gn/T/pip-build-env-4g5skgqk/normal/bin/n inja [1/1475] Generating scipy/generate-version with a custom command [2/1475] Compiling C object scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g7 7_abi.c.o FAILED: scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o clang -Iscipy/libdummy_g77_abi_wrappers.a.p -Iscipy -I../scipy -I../../../pip-build-env-4g5skg qk/overlay/lib/python3.13/site-packages/numpy/_core/include -I/opt/homebrew/opt/python@3.13/Frameworks/Python.framework/Versions/3.13/include/python3.13 -I/opt/homebrew/Cellar/openblas/0.3.29/include -fdiagnostics-color=always -DNDEBUG -Wall -Winvalid-pch -std=c99 -O3 -Wno-unused-but-set-variable -Wno-unused-function -Wno-conversion -Wno-misleading-indentation -DNPY_NO_DEPRECATED_API=NPY_1_9_API_VERSION -fopenmp -MD -MQ scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o -MF scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o.d -o scipy/libdummy_g77_abi_wrappers.a.p/_build_utils_src_wrap_dummy_g77_abi.c.o -c ../scipy/_build_utils/src/wrap_dummy_g77_abi.c clang: error: unsupported option '-fopenmp' [3/1475] Copying file scipy/_lib/_ccallback_c.pxd [4/1475] Copying file scipy/_lib/messagestream.pxd [5/1475] Copying file scipy/special.pxd [6/1475] Copying file scipy/_lib/__init__.py [7/1475] Copying file scipy/__init__.py [8/1475] Copying file scipy/linalg.pxd [9/1475] Copying file scipy/optimize.pxd [10/1475] Copying file scipy/_lib/ccallback.pxd [11/1475] Copying file scipy/special/__init__.py [12/1475] Compiling C object scipy/_lib/_fpumode.cpython-313-darwin.so.p/_fpumode.c.o [13/1475] Copying file scipy/special/_agm.pxd [14/1475] Compiling C++ object scipy/_lib/_uarray/_uarray.cpython-313-darwin.so.p/vectorcall.c xx.o [15/1475] Compiling C object scipy/_lib/_test_ccallback.cpython-313-darwin.so.p/src__test_ccal lback.c.o [16/1475] Compiling C object scipy/lib_fortranobject.a.p/c52640e8cd7ef9549bdf29937220fb02ffffe 688_site-packages_numpy_f2py_src_fortranobject.c.o [17/1475] Compiling C++ object scipy/_lib/_uarray/_uarray.cpython-313-darwin.so.p/_uarray_disp atch.cxx.o ninja: build stopped: subcommand failed. [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details.

It appears that the installation of your Python dependencies is failing due to an issue with the `scipy` package. The error message indicates that the compiler `clang` does not support the `-fopenmp` option, which is used for enabling OpenMP (a parallel programming model). This is a common issue on macOS systems, as Apple's version of `clang` doesn't include OpenMP support by default. To resolve this issue, you can install a version of `clang` that includes OpenMP support via Homebrew, and then set up your environment to use this compiler during the installation. Here's how you can do it: 1. **Install `llvm` via Homebrew**: This will provide you with a version of `clang` that supports OpenMP. ```bash brew install llvm ``` 2. **Set Environment Variables**: Before installing your Python packages, set the following environment variables to use the Homebrew-installed `clang` and its associated tools: ```bash export CC=/opt/homebrew/opt/llvm/bin/clang export CXX=/opt/homebrew/opt/llvm/bin/clang++ export LDFLAGS="-L/opt/homebrew/opt/llvm/lib" export CPPFLAGS="-I/opt/homebrew/opt/llvm/include" export PATH="/opt/homebrew/opt/llvm/bin:$PATH" ``` You can add these lines to your shell profile (e.g., `.bash_profile`, `.zshrc`) to make them permanent. 3. **Install `scipy` with OpenMP Support**: With the environment variables set, try installing your requirements again: ```bash pip install --prefer-binary -r requirements.txt ``` By configuring your environment to use a version of `clang` that supports OpenMP, the `-fopenmp` option should be recognized, allowing `scipy` and other packages that rely on OpenMP to compile successfully. **Note**: Ensure that your `pip`, `setuptools`, and `wheel` are up to date, as outdated tools can sometimes cause installation issues: ```bash pip install --upgrade pip setuptools wheel ``` If you continue to experience issues, consider installing pre-built binary versions of the packages when available, as they don't require local compilation. For example: ```bash pip install scipy ``` This will download and install a pre-compiled binary wheel, avoiding the need to compile from source. If problems persist, please provide additional details about your environment (e.g., macOS version, Python version) so that further assistance can be provided.