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jarvis-mr JARVIS Chatbot: a local simple RAG assistant with PDF files
Based on repository activity, growth velocity and community engagement.
30
Step 1: Clone repo
git clone https://github.com/Mr-Jack-Tung/JARVIS-Chatbot-a-simple-RAG-with-PDF-files.git
cd JARVIS-Chatbot-a-simple-RAG-with-PDF-files
Requirements: python=3.12.10
python3.12 -m venv .venv
source .venv/bin/activate
or
conda create -n jarvis python=3.12.10
conda activate jarvis
# Step 2: For Installing...
|> python3 setup.py
# Step 3: For Running...
=======
# For Chroma Database Configuration
# Set ANONYMIZED_TELEMETRY to False to disable telemetry
# 1. Set environment variable:
export ANONYMIZED_TELEMETRY=False
# 2. Or add to .env file in project root:
ANONYMIZED_TELEMETRY=False
# 3. Or add to docker-compose.yml if using Docker:
ANONYMIZED_TELEMETRY=False
# For Running...
|> poetry run python3 main.py
or poetry run python3 -m main
or poetry run python3 -m jarvis_assistant.JARVIS_assistant
or python3 jarvis_assistant/JARVIS_assistant.py
Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.
_
# For dark mode ...
# http://localhost:7860/?__theme=dark
JARVIS Chatbot: a local simple RAG assistant with PDF files
Author: Mr. Jack Tung
Create: 03 July 2024 - 08 PM
Discuss: https://zalo.me/g/mtffzi945
If you like what I do, give me a star ^^ ~> ⭐
Why JARVIS?
All Free ~> 100% Free
Local Run ~> 100% Privacy
Open Source ~> 100% DIY Custom
30 multi-languages support
RAG with PDF, DOCX, TXT files support
Multi-Function calling
Agent calling
Comprehensive error handling
Robust logging system
Features
Support for Qwen3, the latest series of Qwen large language models
Support for 30+ languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more
Long context support of up to 128K tokens with generation up to 8K tokens
Upload PDF files in any language and get responses in your preferred language
Unlimited file uploads to vector database
Support for PDF, DOCX, TXT, MD, MDX files
Multi-file upload capability
Customizable inference settings
Automatic chat history saving to vector database
Custom system prompt support
Ollama model auto-loader
Customizable retrieval settings
Document reference in responses
Support for Groq API, OpenAI API, Gemini API
Local LiteLLM support
Workspace management
Toggle for retrieval augmentation
Toggle for relevance grading
Multi-function calling
ReACT agent integration
ReWOO agent integration
Retrieval grader for better document relevance
OpenAI Strawberry(o1) system prompt for multi-step reasoning
Light and Dark mode support
Web search augmentation when retrieval quality is low
Comprehensive error handling and logging
Cross-platform compatibility (Windows, macOS, Linux)
JARVIS assistant (22 ⭐) .vs
gpt4all (69k ⭐) - https://github.com/nomic-ai/gpt4all
open-webui (40k ⭐) - https://github.com/open-webui/open-webui
anything-llm (23k ⭐) - https://github.com/Mintplex-Labs/anything-llm
LibreChat (17k ⭐) - https://github.com/danny-avila/LibreChat
Perplexica (13k ⭐) - https://github.com/ItzCrazyKns/Perplexica
Kotaemon (12k ⭐) - https://github.com/Cinnamon/kotaemon
Verba (6k ⭐) - https://github.com/weaviate/Verba
gpt-computer-assistant (5.2k ⭐) - https://github.com/onuratakan/gpt-computer-assistant
MindSearch (4.7k ⭐) - https://github.com/InternLM/MindSearch
lagent (1.8k ⭐) - https://github.com/InternLM/lagent
lmstudio-ai (1.4k ⭐) - https://github.com/lmstudio-ai/lms
Update next (v0.x.x)
Update next (v0.1.x) (Multilingual, Multimodal, MultiAgent, MultiDevice, ...)
text
image
MiniCPM-V 2.6: https://github.com/OpenBMB/MiniCPM-V
FLUX: https://github.com/black-forest-labs/flux
Stable Diffusion: https://github.com/runwayml/stable-diffusion
https://github.com/huggingface/diffusers
audio
https://github.com/onuratakan/gpt-computer-assistant
https://github.com/rsxdalv/tts-generation-webui
video
LLaVA-NeXT: https://github.com/LLaVA-VL/LLaVA-NeXT ; https://arxiv.org/abs/2408.03326
NExT-GPT: https://github.com/NExT-GPT/NExT-GPT
Math tool
Internet search agent
Professional Agents(PAgents): https://arxiv.org/abs/2402.03628
CrewAI multi-agents : https://github.com/crewAIInc/crewAICrawl4AI agent : https://github.com/unclecode/crawl4aiAnyTool agent : https://github.com/dyabel/AnyToolOpenDevin agent : https://github.com/OpenDevin/OpenDevinDistillKit : https://github.com/arcee-ai/DistillKitMindSearch agent : https://github.com/InternLM/MindSearch ; https://arxiv.org/abs/2407.20183AgileCoder : https://github.com/FSoft-AI4Code/AgileCoderAgentK : automatic build new tools and agents as needed by itself, in order to complete tasks for a user _ https://github.com/mikekelly/AgentKAI-Scientist : https://github.com/SakanaAI/AI-ScientistOpenResearcher : https://github.com/GAIR-NLP/OpenResearcherADAS : Automated Design of Agentic Systems - https://github.com/ShengranHu/ADASLanguage Agent Tree Search (LAST) : https://github.com/lapisrocks/LanguageAgentTreeSearch ; https://arxiv.org/abs/2310.04406
MobileAgent: https://github.com/X-PLUG/MobileAgent
PERSONA HUB: 200,000 synthetic personas - https://github.com/tencent-ailab/persona-hub
Fine tune embbeding model for RAG pipeline ^^
https://www.philschmid.de/fine-tune-embedding-model-for-rag
https://huggingface.co/blog/matryoshka
https://github.com/thangnch/MiAI_HieuNgo_EmbedingFineTune
Fine tuning Embeddings Model: https://www.youtube.com/watch?v=hdFHYNCmO8U
Dynamically Semantic Router
RouteLLM : https://github.com/lm-sys/RouteLLM ; RouteLLM: Learning to Route LLMs with Preference Data _ https://arxiv.org/abs/2406.18665https://python.langchain.com/v0.1/docs/expression_language/how_to/routing/
https://github.com/aurelio-labs/semantic-router/blob/main/docs/03-basic-langchain-agent.ipynb
https://github.com/aurelio-labs/semantic-router/blob/main/docs/02-dynamic-routes.ipynb
https://mer.vin/2024/07/routellm-code-example/
https://github.com/NirDiamant/RAG_Techniques
WeKnow-RAG: https://arxiv.org/abs/2408.07611
Controllable-RAG-Agent: https://github.com/NirDiamant/Controllable-RAG-Agent
Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks - https://arxiv.org/abs/2407.21059
RAG Flow: https://github.com/infiniflow/ragflow
RAG and RAU Survey: https://github.com/2471023025/RALM_Survey ; https://arxiv.org/abs/2404.19543
RAG Foundry Framework: https://arxiv.org/abs/2408.02545
GraphRAG : https://github.com/microsoft/graphraghttps://www.microsoft.com/en-us/research/blog/graphrag-new-tool-for-complex-data-discovery-now-on-github/
https://github.com/microsoft/graphrag/tree/main/examples_notebooks
https://neo4j.com/developer-blog/global-graphrag-neo4j-langchain/
https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/
https://docs.llamaindex.ai/en/stable/examples/query_engine/knowledge_graph_query_engine/
Continuous Pre-training & Fine-tuning
Continual Pre-training of Language Models: https://arxiv.org/abs/2302.03241
Continual Pre-Training of Large Language Models: How to (re)warm your model? https://arxiv.org/abs/2308.04014
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering: https://arxiv.org/abs/2311.00204
Fine-tune a pretrained model: https://huggingface.co/docs/transformers/en/training
Finetuning: https://docs.mosaicml.com/projects/mcli/en/latest/finetuning/finetuning.html
Selective fine-tuning of Language Models with Spectrum: https://huggingface.co/blog/anakin87/spectrum
LayoutLMv3: https://github.com/microsoft/unilm/tree/master/layoutlmv3
Knowledge Editing: https://github.com/zjunlp/KnowledgeEditingPapers
Update September 12, 2025 (v0.1.7)
update: dockerfile and docker-compose.yaml updated
for build: docker compose build --no-cache
for run: docker compose up -d
update: streaming response
add: thinking tonggle
Update April 28, 2025 (v0.1.7)
update: Improved error handling throughout the codebase
update: Enhanced logging system with file-based logging
update: Better code organization with proper documentation
update: Fixed date inconsistencies across files
update: Improved file handling with proper resource management
update: Enhanced vectorstore initialization with directory creation if needed
update: Added fallback mechanisms when operations fail
update: Improved cross-platform compatibility
update: Enhanced grader module with support for all LLM providers (Ollama, OpenAI, Groq, Gemini)
add: New get_model_info() method for easy status reporting
add: Comprehensive docstrings for all functions
add: New hallucination_grader to detect unsupported claims
add: New answer_grader to evaluate response quality
add: New question_rewriter to improve query clarity
add: New sub_query_generator to break down complex questions
Update April 27, 2025 (v0.1.6)
update: Qwen3:4b model support
update: mask API keys textbox support
update: manual retrieval grader support
update: multi-API-providers Grader, ReAct and ReWOO agents support
add: web augmented search support
Update September 24, 2024 (v0.1.5)
update: Auto control installing dependencies with Poetry ^^
Update September 21, 2024 (v0.1.5)
update: system prompt choosing or editing
udpate: Light Mode | Dark Mode
Update September 18, 2024 (v0.1.5)
update: OpenAI Strawberry(o1) likely system prompt for Multi-Step Reasoning chains
update: support Qwen2.5 is the latest series of Qwen large language models.
Update September 02, 2024 (v0.1.5)
update: Nomic Embed v1.5
update: separate source code files
add: Retrieval grader
update: using LangGraph
add: ReWOO agent - https://blog.langchain.dev/planning-agents ; https://github.com/langchain-ai/langgraph/blob/main/examples/rewoo/rewoo.ipynb
+ -------------------- workflow ---------------------------------
| v0.1.7
| main.py / JARVIS_assistant.py
| |
| ~> gui.py ~> custom_ui_style.py
| |
| ~> gui_action.py ~> model_settings.py , tools.py , prompts.py , utils.py , get_model_list.py
| |
| ~> db_helper.py ~> file_readers.py
| |
| ~> datasource_router.py , grader.py: retrieval_grader() ,
| hallucination_grader() , answer_grader() ,
| question_rewriter() , sub_query_generator()
+ -----------------------------------------------------------------
Update Jul 31, 2024 (v0.1.4)
add: Multi-Function calling
add: ReACT agent
add: API Keys management
add: 3 rounds chat-history memory
update: support both MacOS and Windows
Update Jul 28, 2024 (v0.1.3)
add: Workspaces management
add: On/Off Retrieval
add: support DOCX files
Update Jul 15, 2024 (v0.1.2)
add: Groq Cloud API (https://docs.litellm.ai/docs/providers/groq ; https://console.groq.com/docs/api-reference)
add: OpenAi API (https://docs.litellm.ai/docs/providers/openai ; https://platform.openai.com/docs/models)
add: Gemini API (https://docs.litellm.ai/docs/providers/gemini ; https://ai.google.dev/gemini-api)
add: LiteLLM local (https://docs.litellm.ai/docs/)
Installation
Step 1:
Create and activate new conda enviroment with python==3.11.9 : https://docs.anaconda.com/navigator/tutorials/manage-environments/
Ollama installation ~> https://ollama.com
Step 2:
git clone https://github.com/Mr-Jack-Tung/JARVIS-Chatbot-a-simple-RAG-with-PDF-files.git
Step 3:
python JARVIS_assistant.py
open web browser on local URL: http://127.0.0.1:7860
Notes: https://github.com/chroma-core/chroma/issues/189#issuecomment-1454418844
if you have an issue on Windows OS, while doing pip install chromadb. To Resolve this issue,
You need to download https://visualstudio.microsoft.com/visual-cpp-build-tools/ first.
Next, navigate to "Individual components", find these two
MSVC v143 - VS2002 C++ x64/86 build tools (lates)
and Windows 10 SDK
then:
pip install -U chromadb
Update Jul 09, 2024 (v0.1.1)
Add: custom Retrieval settings
Add: Source reference response
Add: LLaMAX3-8B-Alpaca (supports over 100 languages)_ https://ollama.com/mrjacktung/mradermacher-llamax3-8b-alpaca-gguf _ bonus: "How To Create Custom Ollama Models From HuggingFace ( GGUF ) file"
ollama pull mrjacktung/mradermacher-llamax3-8b-alpaca-gguf
Screenshot Jul 03, 2024 (v0.1.0)
Qwen2-7B model support 30 languages (https://huggingface.co/Qwen/Qwen2-7B-Instruct-GGUF)
support custom System Prompt
custom Inferece settings
support multi-files upload: .PDF, .TXT format
unlimit & auto save chat history to database
(v0.1.0) only 360 lines of python code in only 1 file ^^
BONUS: How To Create Custom Ollama Models From HuggingFace ( GGUF ) file URL: https://ollama.com/mrjacktung/mradermacher-llamax3-8b-alpaca-gguf
https://huggingface.co/mradermacher/LLaMAX3-8B-Alpaca-GGUF
step 2: Download GGUF file
Choice: Q4_K_M | 5.0GB | fast | recommended
step 3: Create Modelfile (from Terminal)
$ echo ‘FROM “./LLaMAX3-8B-Alpaca.Q4_K_M.gguf”\nTEMPLATE “{{ .System }}\n### Input:\n{{ .Prompt }}\n### Response:”’ >> Modelfile
step 4: Login your Ollama account (eg. mrjacktung)
My models ~> New ~> create new space with name: mradermacher-llamax3-8b-alpaca-gguf
step 5: Create repository
ollama create -f Modelfile mrjacktung/mradermacher-llamax3-8b-alpaca-gguf
ollama push mrjacktung/mradermacher-llamax3-8b-alpaca-gguf
ollama run mrjacktung/mradermacher-llamax3-8b-alpaca-gguf
Michael Radermacher: https://huggingface.co/mradermacher/LLaMAX3-8B-Alpaca-GGUF
Data Science Basics: Ollama, How To Create Custom Models From HuggingFace (GGUF) _ https://www.youtube.com/watch?v=TFwYvHZV6j0
Docker Compose Usage To run the application using Docker Compose, follow these steps:
Clone the repository to your local machine:
git clone https://github.com/Mr-Jack-Tung/JARVIS-Chatbot-a-simple-RAG-with-PDF-files.git
cd JARVIS-Chatbot-a-simple-RAG-with-PDF-files
Make sure you have Docker and Docker Compose installed on your system.
Navigate to the project directory in your terminal.
Run the command docker-compose up -d to start the application in detached mode.
This will build the Docker image and start the container. You can then access the application in your web browser.
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