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About Us
Technical Experience
At Bloberry Consulting, we specialize in pioneering video processing solutions and AI-driven technologies tailored for small to ultra-compact embedded systems. Translating videos (including YouTube videos) with dubbing. Our expertise extends beyond. On the left you can see introduction to https://tothemoon.chat . It is UI for communication with multiple LLM modules designed for document ingest. http://t.me/medicDiagnoseBot bot for Medical diagnostic.
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Why Bloberry Consulting? Our track record of successful implementations speaks to our ability to deliver efficient, resource-friendly, and cost-effective solutions. We are at the forefront of video processing and AI technology on small and ultra-small devices, offering unparalleled expertise that empowers your projects and business innovations.
Empower Your Endeavors: Our services cater to tech enthusiasts, hobbyists, and businesses aiming to elevate their product offerings with the future of video processing and AI technology. With Bloberry Consulting, you're not just adapting to the future; you're leading it. Embrace our state-of-the-art solutions to achieve more with less, and unlock the true potential of your embedded systems.
Join the Revolution in Video Processing and AI Technology: Don't miss this opportunity to transform the way you process video data and interact with AI-driven chatbots. Collaborate with us at Bloberry Consulting and step into the cutting-edge of technology. Contact us now to explore how our innovative solutions can revolutionize your projects.
Technical Experience
Our Offering: Setting Up Local GPT-4All and Ollama Models with Elastic Search, LangChain, and n8n AI Agent
Tailored Solutions: We deliver customized solutions that seamlessly deploy local GPT-4All and Ollama models within existing IT ecosystems. For local LLM capabilities, we leverage Python LangChain to manage models and implement Retrieval-Augmented Generation (RAG), optimizing performance on consumer-grade CPUs and GPUs. Separately, the n8n AI agent enhances these solutions by automating workflows and integrating with external systems, enabling businesses to adopt AI without significant infrastructure costs.
Comprehensive Setup Support: Our team provides end-to-end support tailored to both local LLM and AI agent needs. For GPT-4All and Ollama, we handle installation, configuration, and integration with Elastic Search for document digestion and LangChain for RAG-powered queries. Meanwhile, the n8n AI agent supports automation of setup tasks and connectivity with other tools, ensuring a streamlined process that meets specific corporate requirements.
Security at the Forefront: Security is critical for corporate knowledge databases. For local LLM operations, we implement encrypted storage and secure transmission protocols within GPT-4All and Ollama and LangChain, alongside regular audits to protect sensitive data. The n8n AI agent complements this by securing automated workflows, ensuring end-to-end confidentiality and integrity across the system.
Enhancing Corporate Knowledge Databases: With local LLMs, we integrate GPT-4All and Ollama, Elastic Search, and LangChain’s RAG to transform document repositories into intelligently indexed, searchable knowledge bases, delivering precise, context-aware insights. Separately, the n8n AI agent streamlines access by automating data workflows and can collaborate with other AI agents to enrich analytics, enhancing decision-making processes.
Customizable Security Protocols: We offer tailored security measures for diverse needs. For local LLM usage, LangChain, GPT-4All, and Ollama align with internal policies, while the n8n AI agent ensures automated processes adhere to the same standards, providing a cohesive, secure ecosystem with RAG enhancements.
Conclusion: Our commitment to a secure, efficient, and customizable setup—featuring GPT-4All and Ollama for local LLM power, Elastic Search, Python LangChain, and the n8n AI agent for automation—sets us apart. We empower corporations to leverage their knowledge databases effectively, blending local AI capabilities with agent-driven workflows to stay ahead in the digital landscape.
How GPT-4All and Ollama Work with LangChain and n8n AI Agent
Starting the Server: Local LLM usage begins with initializing the server to load GPT-4All and Ollama large language models (LLMs), managed by LangChain for seamless operation. The n8n AI agent separately automates this startup process, integrating it into broader workflows.
Model Configuration: For local LLMs, we optimize configurations like "Nous-Hermes-2-Mistral-7B-DPO.Q4_0" (for GPT-4All) and similar specs in Ollama, using LangChain to fine-tune embeddings and RAG pipelines for corporate datasets. The n8n AI agent handles external tasks, such as logging or triggering related processes.
Model Selection: GPT-4All and Ollama offer flexibility in choosing models for local use, with LangChain enabling dynamic switching. The n8n AI agent supports this by automating model deployment or coordinating with other AI tools.
Document Digestion and Processing with RAG
Uploading and Indexing: Locally, documents are uploaded, digested, and indexed using Elastic Search and enhanced by LangChain’s RAG framework, which embeds and retrieves relevant chunks for generation with GPT-4All and Ollama. The n8n AI agent automates the ingestion pipeline, ensuring efficient processing across systems.
Natural Language Requests: For local LLM interaction, users query digested content via GPT-4All and Ollama and LangChain’s RAG, blending retrieval from Elastic Search with contextual generation. The n8n AI agent extends this by automating responses or triggering actions based on queries, working alongside other AI agents as needed.
GPT models with Elastic search solutions
1. Perform Elasticsearch Query
First, always perform the Elasticsearch query regardless of the question's length or apparent complexity. Elasticsearch is indeed fast and capable of handling complex queries efficiently, especially if the indices are well-designed and the cluster is properly tuned.
2. Analyze Query Results for Relevance
After obtaining the results from Elasticsearch, the next step is to determine the relevance of these results to the question. This can be achieved through several techniques:
- Keyword Matching: Simple but can be effective. Check if keywords from the question appear in the documents returned by Elasticsearch.
- Semantic Similarity: Use NLP models to compute the semantic similarity between the question and the context documents. This approach can identify relevant documents even if they don't share exact keywords with the question.
- Custom Scoring Algorithm: Implement a scoring algorithm that combines multiple factors (e.g., keyword match, semantic similarity, document freshness) to rank the documents by relevance.
3. Use Context Conditionally
Based on the relevance analysis, decide whether to include the Elasticsearch results as context in your subsequent operations (e.g., generating an answer with a language model).
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Satisfaction Guaranteed
The world of technology can be fast-paced and scary. That's why our goal is to provide an experience that is tailored to your company's needs. No matter the budget, we pride ourselves on providing professional customer service. We guarantee you will be satisfied with our work.
High ROI
Do you spend most of your IT budget on maintaining your current system? Many companies find that constant maintenance eats into their budget for new technology. By outsourcing your IT management to us, you can focus on what you do best--running your business.