AgnosticRag-Q

# AgnosticRag-Q **AgnosticRag-Q** is a cloud agnostic and LLM agnostic **Agentic AI & Retrieval-Augmented Generation (RAG) platform** built with a clean, modular architecture. It supports Text, CSV, Images, Audio, enterprise applications such as **Odoo**, hybrid search, multimodal retrieval, intelligent routing, and configurable runtime behavior. It provides: - a **Core RAG API** - an intelligent routing layer (**AgnosticRagRouter**) - an MCP integration layer (**AgnosticRagMCP**) - enterprise application integration (Odoo, PostgreSQL, and custom MCP services) - A GraphRAG layer combining Qdrant vector search and Neo4j knowledge graphs - and a **GUI application** that consumes these backends allowing you to build, extend, and deploy scalable multi-backend AI systems that combine private knowledge bases, enterprise applications, and multiple LLM providers. The project is intentionally **provider-agnostic**, making it easy to switch between LLM providers, embedding models, vector stores, enterprise connectors, and retrieval strategies without changing business logic. `AgnosticRagRouter` introduces an Agentic AI orchestration layer that automatically selects the most appropriate backend or enterprise service for each request. Depending on the query, it can route to document collections (TXT, CSV, Images, Audio), GraphRAG, Odoo ERP, PostgreSQL, or any custom MCP service using configurable routing strategies and LLM-assisted decision making. The platform is designed for **modular deployment**, where multiple RAG backends and enterprise services run independently. Dedicated services can be deployed for text, images, audio, domain-specific knowledge, Odoo ERP, databases, and custom business systems, while the router dynamically dispatches requests to the most relevant destination. `AgnosticRagMCP` extends the platform through the **Model Context Protocol (MCP)**, enabling seamless integration with external applications, enterprise systems, and communication channels. It supports connectors such as Telegram, WhatsApp, Microsoft Teams, Odoo, PostgreSQL, Google Drive, and custom enterprise MCP servers, allowing users and business applications to interact with both RAG knowledge bases and enterprise systems through a unified AI interface. <table> <tr> <td align="center"> <b>Full RAG with translation</b><br> <a href="https://www.youtube.com/watch?v=GIbtlLuHlyg"> <img src="https://img.youtube.com/vi/GIbtlLuHlyg/maxresdefault.jpg" width="400"> </a> </td> <td align="center"> <b>Full RAG With Images/PDFs</b><br> <a href="https://www.youtube.com/watch?v=aKKh4YvL17s"> <img src="./images/Main4.PNG" width="800" alt="Full RAG With Images/PDFs"> </a> </td> </tr> <tr> <td align="center"> <b>Full RAG with Medical Data csv Table</b><br> <a href="https://www.youtube.com/watch?v=8koN_m8l39s"> <img src="https://img.youtube.com/vi/8koN_m8l39s/maxresdefault.jpg" width="800"> </a> </td> <td align="center"> <b>Full RAG with Images : Search by an image or text</b><br> <a href="https://www.youtube.com/watch?v=BzggFYE44i8"> <img src="https://img.youtube.com/vi/BzggFYE44i8/maxresdefault.jpg" width="400"> </a> </td> </tr> <tr> <td align="center"> <b>Visual search AgnosticRAG</b><br> <a href="https://www.youtube.com/watch?v=-IU3sSE7fD8"> <img src="https://img.youtube.com/vi/-IU3sSE7fD8/maxresdefault.jpg" width="800"> </a> </td> <td align="center"> <b>Search in TXT and CSV using AgnosticRagRouter</b><br> <a href="https://www.youtube.com/watch?v=fOU6PBjQch8"> <img src="https://img.youtube.com/vi/fOU6PBjQch8/maxresdefault.jpg" width="800"> </a> </td> </tr> <tr> <td align="center"> <b>AgnosticRagMCP Telegram</b><br> <a href="https://www.youtube.com/watch?v=ZF_NdOJTzfo"> <img src="https://img.youtube.com/vi/ZF_NdOJTzfo/maxresdefault.jpg" width="800"> </a> </td> <td align="center"> <b>AgnosticRagMCP MS Teams</b><br> <a href="https://www.youtube.com/watch?v=WLbRCGKRDdo"> <img src="https://img.youtube.com/vi/WLbRCGKRDdo/maxresdefault.jpg" width="800"> </a> </td> </tr> <tr> <td align="center"> <b>GraphRAG using Qdrant + Neo4j</b><br> <a href="https://www.youtube.com/watch?v=dbYoXbP0LSg"> <img src="https://img.youtube.com/vi/dbYoXbP0LSg/maxresdefault.jpg" width="800"> </a> </td> <td align="center"> <b>Full Example : Txt, CSV, Image, Audio and Graph</b><br> <a href="https://www.youtube.com/watch?v=_LZ2YVS9wwI"> <img src="images/full_example_thumbnail.png" width="800"> </a> </td> </tr> </table> ## ✨ Key Highlights - **Core Multi-Modal RAG API** built with a clean, extensible architecture - **Config-Driven** `rag_settings.json` (models, rerank, chunking, prompts), `graph_rag_settings.json` (allowed relation types, graph extraction prompts, entity/relation rules) - **AgnosticRagRouter** intelligent LLM-based backend routing and multi-backend orchestration - **Config-Driven AgnosticRagRouter** `router_settings.json` (routing rules, backends, prompts, retries, thresholds) - **AgnosticRagMCP** MCP-based external integrations layer (Odoo, PostgreSQL, Telegram, WhatsApp, MS Teams, Google Drive, external enterprise tools & drivers) - **Config-Driven AgnosticRagMCP** `mcp_settings.json` (whatsapp, telegram, microsoft_teams) - **Enterprise AI integration** with business systems such as Odoo ERP, PostgreSQL, and custom MCP services - **Pluggable LLM providers** (local and remote) : (OpenAI, Gemini, Ollama, Hugging Face Transformers, and local models) - **Horisontal Scalable** (cloud / on-prem) - **Redis based** Conversation history (app_id / user_id / session_id) - **Qdrant-based vector search** with multiple data source options - **Qdrant-Hybrid search** BM25 (sparse) + Dense embeddings - **GraphRAG** using **Qdrant + Neo4j** for hybrid semantic and relationship-based retrieval - **Rewrite Prompt** Create standalone retrieval query,from (Final Query + short history) - **Flexible Ingestion** CSV(table field extraction) , TXT (Paragraph-based chunking), PDF, Images and Audio (Speech) - **Multi-vector image/pdf retrieval** using **ColQwen / ColPali** - Optional **Muvera Fixed-Dimensional Encoding (FDE)** for fast image retrieval - Supports multimodal queries: **text, image, audio, text+image, text+audio** - **Audio Search** using Whisper + CLAP with Qdrant/Neo4j - **Audio Query Support** search by uploaded audio file or text - **Hybrid Audio Transcript Search** BM25 (sparse) + Dense embeddings - **Built-in caching system**: - In-memory **engine cache** (avoid reloading heavy models) - Optional **media/query result cache** for image & audio queries - Configurable cache limits and TTL for performance tuning - **Docker-based modular deployment**: - run separate RAG backends per modality (text / image / audio / CSV) - each backend connected to its own collection or configuration - Router dynamically dispatches queries to the best backend - UI can dynamically select the target backend (e.g., text, image, audio, router) - scalable, isolated, and production-ready architecture - **Simple UI for quick testing** that uses the Core API / AgnosticRagRouter as its backend --- - **Rewrite Prompt** ```bash User query ↓ Load last N Redis turns (e.g., 2–3) ↓ Rewrite LLM (prompt above) ↓ Rewritten query ↓ Embedding ↓ Qdrant ``` - **Rewrite Prompt + GraphRAG Retrieval** ```bash User query ↓ Load last N Redis turns (e.g., 2–3) ↓ Rewrite LLM ↓ Rewritten query ↓ Embedding ↓ Qdrant Semantic Search ↓ (Optional) Neo4j Graph Search ↓ Merge Semantic + Graph Results ↓ LLM Answer ``` --- - **Config-Driven** `rag_settings.json` (models, rerank, chunking, prompts) ```bash { "api_base_url": "http://localhost:8000", "qdrant_url": "http://localhost:6333", "collection_prefix": "ManyImages-", "collection_name": "ManyImages-__vidore_colpali-v1.3__multivector_image__dim128__muvera", "enable_neo4j_with_qdrant": false, "graph_store_provider": "neo4j", "topk_retrieve": 10, "topk_use": 5, "enable_rerank": false, "text_group_lines": 1, "txt_chunk_chars": 900, "txt_overlap": 120, "batch_size": 64, "is_paragraph_enable": false, "paragraph_end_str": "", "include_marker": false, "marker_contains": false, "enable_structured_csv_lookup": false, "enable_exact_filter_first": false, "enable_qdrant_hybrid_search_csv": false, "use_qdrant_hybrid_search_csv": false, "enable_muvera_for_multi_vector_image": true, "use_muvera_for_multi_vector_image": false, "send_images_to_final_chat_for_multi_vector_image": true, "docs_dir": "rag_data\\my_csvs\\docs", "rerank_prompt_template": "You are a retrieval re-ranker.\nGiven a user question and a list of candidate contexts, select the most relevant items.\nRules:\n- Choose exactly {choose_k} distinct indices.\n- Prefer contexts that directly contain facts needed to answer.\n- Avoid redundant/duplicate contexts.\n- Output ONLY valid JSON, no extra text.\n\nReturn JSON format:\n{{\n \"selected_indices\": [0, 2, 5],\n \"reasons\": [\"short reason 1\", \"short reason 2\", \"short reason 3\"]\n}}\n\nQuestion:\n{query}\n\nCandidates:\n{candidates}", "answer_prompt_template": "You are an assistant that answers strictly from retrieved context.\n\nIMPORTANT RULES:\n- Use ONLY the information in the Context.\n- First, check if there is an EXACT match to the Question (all provided key=value pairs) in a single Context row.\n- If there is an exact match:\n - Output ONLY the value of Initial_ActivityDenialCode from that row.\n- If there is NO exact match but Context is not empty:\n - Output:\n Closest match (not exact).\n - List differing fields (requested vs found).\n - Then output ONLY the value of Initial_ActivityDenialCode from the closest-match row (the highest score row).\n- If Context is empty:\n - Reply exactly: \"I don't know based on the provided context.\"\n- Do NOT add any other text.\n\nContext:\n{context}\n\nQuestion:\n{query}\n\nAnswer:", "answer_prompt_template_image": " You are an assistant that answers the user's question based on retrieved image(s) and recent conversation history.\n \n The retrieved images are provided as attachments alongside this prompt.\n\n Rules:\n - For questions about the images, answer using the visual content of the attached images.\n - For meta-questions about the conversation (e.g. \\\"what did I just ask?\\\"), answer from the Conversation history below.\n - If the images do not contain enough information to answer, reply exactly: \\\"I don't know based on the retrieved images.\\\"\n - Keep the answer clear and concise.\\n\\nConversation history (most recent last):\n {history}\n \n Retrieved image references (for traceability):\n {context}\n \n Question: {query}\n \n Answer:", "answer_prompt_template_audio": "You are an assistant that answers using BOTH the retrieved context and the recent conversation history.\n\nIMPORTANT RULES:\n- Prefer the Context for factual answers.\n- Use History only for continuity (follow-ups, pronouns).\n- DO NOT repeat the question.\n- DO NOT hallucinate or add information not present in the Context.\n- If the answer is not found in Context, reply exactly:\n \"I don't know based on the provided context.\"\n\nOUTPUT FORMAT (STRICT):\n\n1) First: provide a clean and concise answer.\n2) Then: append the section EXACTLY as shown below.\n\nAssistant:\n<final answer>\n\nSelected Context:\n<context exactly as provided below, DO NOT modify, DO NOT summarize, DO NOT reorder>\n\n---\n\nHistory (last turns):\n{history}\n\nContext:\n{context}\n\nQuestion: {query}\n\nAnswer:", "translate_prompt_template": "You are a translation engine.\n\n Task:\n - Translate the user text into natural English.\n Rules:\n - Output ONLY the English translation.\n - No explanations, no quotes, no extra text.\n - Keep proper nouns, IDs, emails, URLs, and numbers unchanged.\n - If the text is already English, output it unchanged.\n\n User text:\n {text}", "rewrite_prompt_template": "You are a query rewriting assistant for a retrieval-augmented generation (RAG) system.\n\nYour job is to rewrite the user's current message into a single, clear, standalone search query\nthat can be used to retrieve documents from a vector database.\n\nRules:\n- Use the conversation only to resolve references (e.g. \"it\", \"that\", \"the second one\").\n- Do NOT introduce new topics.\n- Do NOT answer the user.\n- Do NOT include chatty or conversational text.\n- Output ONLY the rewritten search query.\n- If the user query is already clear and standalone, return it unchanged.\n\nRecent conversation:\n{history}\n\nCurrent user question:\n{query}\n\nRewrite this into one concise standalone retrieval query.", "enable_auto_translate": false, "enable_rewrite": false, "embedding_model": "vidore/colqwen2-v1.0", "main_model": "gpt-4o-mini", "translate_model": "qwen3:1.7b", "rewrite_model": "gpt-4o-mini", "main_provider": "openai", "embedding_provider": "colqwen", "translate_provider": "ollama", "rewrite_provider": "openai", "transformer_device": "cpu", "transformer_trust_remote_code": true, "transformer_local_files_only": false, "transformer_embedding_trust_remote_code": false, "transformer_embedding_local_files_only": false, "transformer_embedding_backend": "auto", "transformer_embedding_normalize": true, "transformer_embedding_max_length": 512, "transformer_embedding_prompt_prefix": "query: ", "transformer_embedding_model_path": "", "transformer_chat_backend": "causal", "transformer_chat_model_path": "", "transformer_chat_prompt": "{joined}", "transformer_max_new_tokens": 128, "transformer_temperature": 0.0, "transformer_top_p": 0.9, "transformer_do_sample": false, "transformer_system_prompt": "", "transformer_max_input_length": 2048, "audio_clap_enable": false, "enable_text_transcript_audio_one_search_qdrant": false, "audio_clap_model": "laion/clap-htsat-unfused", "audio_transcript_embedding_provider": "transformer", "audio_transcript_embedding_model": "intfloat/multilingual-e5-small", "whisper_language": "en", "whisper_beam_size": 5, "whisper_vad_filter": false, "whisper_vad_threshold": 0.5, "whisper_word_timestamps": false, "whisper_device": "cpu", "whisper_compute_type": "int8", "audio_target_sr": 48000, "audio_whisper_sr": 16000, "silero_min_silence_ms": 250, "silero_speech_pad_ms": 150, "silero_threshold": 0.4, "silero_window_samples": 512, "audio_min_chunk_seconds": 1.2, "audio_max_chunk_seconds": 20.0, "audio_merge_gap_ms": 300, "env_path": "keys.env", "redis_url": "redis://localhost:6381", "redis_store": "redis_store", "redis_prefix": "rag", "app_id": "rag_api1", "session_id": "defaault1", "user_id": "local_user1", "history_turns": 5, "history_rewrite_turns": 2, "history_max_turns": 2000, "history_ttl_seconds": 604800 } ``` --- - **Config-Driven AgnosticRagRouter** `router_settings.json` (routing, backends, prompts, retries, thresholds) ```json { "router_provider": "openai", "router_model": "gpt-4o-mini", "router_temperature": 0.0, "router_prompt": "You are a routing agent. Choose the best RAG backend for the user query.", "routing_mode": "rules_then_llm", "fallback_backend": "eyad_asil", "allow_multi_backend": false, "confidence_threshold": 0.6, "llm_call_timeout_seconds": 60.0, "llm_call_retries": 1, "backend_call_timeout_seconds": 180.0, "backend_call_retries": 1, "env_path": "keys.env", "transformer_device": "cuda", "transformer_trust_remote_code": true, "transformer_local_files_only": false, "transformer_chat_backend": "", "transformer_chat_model_path": "", "transformer_chat_prompt": "{joined}", "transformer_max_new_tokens": 256, "transformer_temperature": 0.0, "transformer_top_p": 0.9, "transformer_do_sample": false, "transformer_system_prompt": "", "transformer_max_input_length": 2048, "openai_api_key": "", "gemini_api_key": "", "backends": [ { "name": "medical_csv", "title": "Medical CSV RAG", "url": "http://localhost:8002", "query_path": "/query", "index_path": "/index/build", "description": "Use for medical CSV questions, claims, claim denial codes, diseases, treatments, medicines, patient/insurance/medical table lookups.", "source_type": "csv", "docs_dir": "rag_data/my_csvs/docs", "source_files": [ "claims_300.csv" ], "collection_prefix": "medical_csv", "enabled": true, "rules": { "keywords_any": [ "medical", "medicine", "doctor", "disease", "treatment", "drug", "medicine", "patient", "claim", "claims", "denial", "denialcode", "insurance", "سكري", "ضغط", "مرض", "علاج", "دواء", "طبي", "مطالبة", "تأمين" ] }, "query_stream_path": "/query_stream" }, { "name": "eyad_asil", "title": "Eyad Asil TXT RAG", "url": "http://localhost:8001", "query_path": "/query", "index_path": "/index/build", "description": "Use for questions about Eyad, Asil, family/private TXT knowledge, children info, personal notes in Asil_Eyad.txt.", "source_type": "txt", "docs_dir": "rag_data/my_csvs/docs", "source_files": [ "Asil_Eyad.txt" ], "collection_prefix": "eyad_asil", "enabled": true, "rules": { "keywords_any": [ "eyad", "asil", "إياد", "اياد", "أسيل", "اسيل", "لجين", "lujain", "family", "child", "children", "عائلة", "طفل", "أطفال" ] }, "query_stream_path": "/query_stream" } ], "routing_function_schema": "{\n \"function\": \"query_<backend_name>_rag\",\n \"arguments\": {\"query\": \"original user query\"},\n \"backend_name\": \"backend_name\",\n \"confidence\": 0.0,\n \"reason\": \"short reason\"\n}", "routing_rules_prompt": "- Choose medical_csv for medical CSV claims, denial codes, patient/insurance/medical table questions.\n- Choose eyad_asil for Eyad/Asil/private TXT family information.\n- If uncertain, choose the closest backend with low confidence.\n- Keep the original user query unchanged in arguments.query.", "routing_prompt_template": "{router_prompt}\n\nYou are routing a user query to exactly one AgnosticRAG backend.\nReturn ONLY valid JSON. Do not use markdown. Do not add explanations outside JSON.\n\nAvailable tools/functions:\n{backend_tools}\n\nAvailable backend descriptions:\n{backend_descriptions}\n\nRequired JSON schema:\n{function_schema}\n\nRules:\n{routing_rules}\n\nUser query:\n{query}\n", "stream_media_type": "text/event-stream", "cors_allowed_origins": [ "http://localhost:4200", "http://127.0.0.1:4200" ], "cors_allowed_methods": [ "GET", "POST", "OPTIONS" ], "cors_allowed_headers": [ "*" ] } ``` --- ## 🧠 Architecture Overview ``` GUI (Web / Desktop) ↓ AgnosticRagMCP (Optional) ├── Telegram ├── WhatsApp ├── MS Teams ├── Google Drive └── External Tools / Drivers ↓ AgnosticRagRouter (Optional) ↓ Core RAG API (FastAPI) ↓ RAG Engine ├── LLM Providers (Transformers, vLLM(Later), Ollama, OpenAI, Gemini, ...) ├── Vector Store (Qdrant) ├── Graph Store (Neo4j) └── Data Sources (Txt, CSV, Images, Audio(Speech)) ``` - **Core API**: Handles retrieval, embeddings, GraphRAG, prompt construction, history, reranking, caching, and inference.. - **AgnosticRagRouter**: Intelligent LLM-based routing and multi-backend orchestration. - **AgnosticRagMCP**: MCP-based integrations layer for Telegram, WhatsApp, MS Teams, Google Drive, enterprise connectors, and external AI tools/drivers. - **GUI (Desktop/Web)**: Thin client that communicates with the Core API or Router for quick testing and configuration generation. - **LLM Providers**: Swappable providers selected dynamically through configuration. - **Qdrant**: Hybrid vector database for dense, sparse, multimodal, and multivector retrieval. - **Neo4j**: Knowledge graph database for entity relationships, GraphRAG retrieval, and multi-hop reasoning. --- ## 🚀 Core RAG API The Core API is responsible for: - Query understanding - Context retrieval from Qdrant and Neo4j - Hybrid search (Dense + BM25) - GraphRAG retrieval and multi-hop reasoning - Prompt construction - LLM inference - Conversation history management - Query rewriting and reranking - Multi-modal retrieval (text, image, audio) It can be used: - Directly as a FastAPI backend - As a backend for the provided GUI applications - As a foundation for custom enterprise applications - As a scalable backend for multi-RAG and GraphRAG deployments --- ## 🧠 AgnosticRagRouter `AgnosticRagRouter` is an intelligent routing layer for multi-backend RAG systems. It is responsible for: - LLM-based backend selection - Query routing across multiple RAG backends - Multi-domain orchestration - Dynamic backend dispatching - Agentic-AI style routing workflows - Configurable routing strategies and retry logic Example: - Text RAG backend - CSV / Medical RAG backend - Image RAG backend - Audio / Speech RAG backend The router can automatically select the most relevant backend depending on the user query. --- ## 🔌 AgnosticRagMCP `AgnosticRagMCP` is an MCP-based external integrations layer. It enables external systems and messaging platforms to communicate with AgnosticRagRouter and the Core RAG APIs. Supported / planned integrations: - Telegram Bots - WhatsApp Bots - MS Teams Bots - Google Drive tools - Enterprise connectors & drivers - External AI tools - MCP orchestration workflows Example flow: Telegram / WhatsApp / MS Teams User ↓ AgnosticRagMCP ↓ AgnosticRagRouter ↓ Selected RAG Backend ↓ Qdrant Search + Neo4j Graph Search ↓ Merge Semantic + Graph Context ↓ LLM ↓ Reply to User --- ## 🖥️ GUI (Quick Testing) The GUI is built on top of the Core API and provides: - Interactive chat interface - History-aware conversations - Easy switching between RAG configurations - Runtime configuration generation - Multi-backend testing support The GUI **does not contain RAG logic**; it strictly consumes the Core API, this will help admin and developers to setup the deployment configuration quickly. --- ## 🔌 LLM Providers ### Current / Supported - **Ollama** (local) - **Transformer** (local) - **OpenAI** (remote) - **Gemini** (remote) - **ColPali** (multivector image retrieval) (local) - **ColQwen** (multivector image retrieval) (local) - **Whisper** (speech/audio transcription) (local) ### Embedding / Retrieval Support - Dense embeddings - BM25 sparse embeddings - Hybrid search - Multi-vector retrieval - CLAP audio embeddings - Muvera FDE acceleration for image retrieval --- ## 🔮 Incoming Features - **Transformers provider & vLLM provider** - Local execution - Hugging Face model support - Transformers: Windows / Linux, GPU optional - vLLM: Best for Ubuntu / Linux production - **PostgreSQL Data Source** - Index relational data into **Qdrant** - Enables RAG over structured datasets --- ## 📦 Vector Store - **Qdrant** is used as the primary vector database - **Neo4j** is used as the knowledge graph database for GraphRAG - Supports: **Qdrant** - Dense vector search - Metadata filtering - Multi-collection setups - Qdrant-Hybrid search : BM25 (sparse) + Dense embeddings **Neo4j** - Entity and relationship storage - Graph traversal and relationship discovery - Multi-hop retrieval - GraphRAG context enrichment - Relationship-aware search --- ## 🛠️ Configuration The system is configured via: - Environment variables - Config files - Provider-specific settings This allows you to: - Switch LLM providers - Change chat, embedding, translate and rewrite models - Toggle rewrite prompt logic - Select data sources --- ### Start Qdrant & Redis & RedisInsight By Docker `docker-compose.yml` Start All ```bat docker compose up -d ``` Stop All ```bat docker compose down ``` --- ## 🌐 URLs - **Swagger UI**: http://localhost:8000/docs - **Qdrant Dashboard**: http://localhost:6333/dashboard - **Neo4j Browser**: http://localhost:7474/browser/ - **RedisInsight**: http://localhost:5540/ --- ## ⚙️ Settings API ``` GET http://localhost:8000/settings POST http://localhost:8000/settings ``` --- ## 📥 Index Data ``` POST http://localhost:8000/index/build { } ``` --- ## 📡 Qdrant State ``` GET http://localhost:8000/ping/qdrant GET http://localhost:8000/ping/qdrantWithInfo ``` --- ## ❓ Query Stream API ``` POST http://localhost:8000/query_stream { "query": "who is here Father?", "user_id": "local_user88777" } ``` ## ❓ Query API ``` POST http://localhost:8000/query { "query": "who is here Father?", "user_id": "local_user88777" } ``` --- ## GUI Windows #### Main Window : Search in txt Files  --- #### Main Window : Search in csv Files Tables  --- #### Main Window : Search in Images by text query  --- #### Main Window : Search in Images by image query  --- #### Main Window : Search in Audio(Speech) by text/audio_file query  --- #### Main Window : Search in TXT and CSV using AgnosticRagRouter  --- #### Results Window  --- #### Logs Window  --- #### History Window  --- #### Settings Window  --- #### Settings Window  --- #### Qdrant Collections  --- #### Redis Data  --- #### Swagger  --- #### Telegram  --- ## 📄 License MIT License © 2026 Mohammed & Manaf https://www.linkedin.com/in/mohammedkadi/ https://www.linkedin.com/in/m-manaf-mahfouz-pmi-cpmai%C2%AE-2614b48/