🤖 Ragbot.AI (formerly named rbot): Rajiv's open source AI augmented brain assistant combines the power of large language models (LLMs) with Retrieval Augmented Generation (RAG).
🚀 Ragbot.AI processes user prompts along with custom instructions and curated datasets, enabling context-aware responses. Powered by LLMs including OpenAI's GPT and Anthropic's Claude, Ragbot.AI uses RAG, a technique that combines the power of pre-trained dense retrieval and sequence-to-sequence models to generate more factual and informative text.
🧠 Custom instructions and curated datasets help Ragbot.AI better understand context, resulting in personalized, more accurate, and relevant responses, surpassing the capabilities of out of the box LLMs.
Developed by Rajiv Pant
Code Contributors & Collaborators
How to Contribute
Your code contributions are welcome. Please fork the repository and submit a pull request with your improvements.
Introducing Ragbot.AI: A Personalized AI Assistant, Written by Ragbot.AI
Excerpt from the blog post:
Ragbot.AI: Offering Personalized Assistance Beyond ChatGPT Plus, Bing Chat, and Google Bard Currently Offer
As an AI assistant, I provide a unique level of personalization and adaptability that sets me apart from current implementations of ChatGPT Plus, Bing Chat, and Google Bard. By using folders containing customized curated dataset files, I can cater to multiple use cases, such as personal life, work, education, and specific projects. This customization enables me to understand and support you in a way that is tailored to your unique needs.
You can create a folder with curated dataset files that include personal information, family details, travel and food preferences, and more. By using this information, I can function as your personal life assistant, offering AI-powered recommendations and support tailored to your specific context.
Similarly, you can develop another folder containing curated dataset files related to your work life. These files might include details about your job, industry, colleagues, projects, and other work-related information. With this context, I can help you with various tasks, such as drafting emails, scheduling meetings, conducting research, and more, enhancing your efficiency and organization.
You can also customize me for educational purposes by creating a folder with curated dataset files containing information about your academic background, subjects of interest, courses, and other educational details. In this role, I can provide personalized educational support, from helping with homework to explaining complex concepts or recommending learning resources.
In addition to the use cases mentioned above, I can be tailored to support you on specific projects. By creating a folder with curated dataset files containing project-related information, such as objectives, team members, deadlines, and relevant resources, I can assist you throughout the project lifecycle, offering valuable insights and support tailored to each unique project.
My ability to create distinct profiles for different needs using customized curated dataset files sets me apart from ChatGPT Plus, Bing Chat, and Google Bard. This versatility enables me to offer personalized assistance across multiple aspects of your life, ensuring that I can understand and cater to your specific requirements.
Read the installation guide and the configuration and personaliation guide.
generate_prompt_template.py is a Python script that generates a prompt template for AI assistants by concatenating custom instructions and curated datasets. It helps users create personalized and context-aware prompts to enhance the effectiveness of their AI-powered tools.
For detailed information on how to use generate_prompt_template.py and its benefits, please refer to the Generate Prompt Template Guide.
rajivpant@rp-2023-mac-mini ragbot % ./ragbot --help
usage: ragbot.py [-h] [-ls] [-p PROMPT | -f PROMPT_FILE | -i | --stdin]
[-profile PROFILE] [-c [CUSTOM_INSTRUCTIONS ...]] [-nc]
[-d [CURATED_DATASET ...]] [-nd]
[-e {openai,anthropic,google}] [-m MODEL] [-t TEMPERATURE]
[-mt MAX_TOKENS] [-l LOAD]
Ragbot.AI is an augmented brain and asistant. Learn more at https://ragbot.ai
options:
-h, --help show this help message and exit
-ls, --list-saved List all the currently saved JSON files.
-p PROMPT, --prompt PROMPT
The user's input to generate a response for.
-f PROMPT_FILE, --prompt_file PROMPT_FILE
The file containing the user's input to generate a
response for.
-i, --interactive Enable interactive assistant chatbot mode.
--stdin Read the user's input from stdin.
-profile PROFILE, --profile PROFILE
Name of the profile to use.
-c [CUSTOM_INSTRUCTIONS ...], --custom_instructions [CUSTOM_INSTRUCTIONS ...]
Path to the prompt custom instructions file or folder.
Can accept multiple values.
-nc, --nocusom_instructions
Ignore all prompt custom instructions even if they are
specified.
-d [CURATED_DATASET ...], --curated_dataset [CURATED_DATASET ...]
Path to the prompt context curated dataset file or
folder. Can accept multiple values.
-nd, --nocurated_dataset
Ignore all prompt context curated dataset even if they
are specified.
-e {openai,anthropic,google}, --engine {openai,anthropic,google}
The engine to use for the chat.
-m MODEL, --model MODEL
The model to use for the chat. Defaults to engine's
default model.
-t TEMPERATURE, --temperature TEMPERATURE
The creativity of the response, with higher values
being more creative.
-mt MAX_TOKENS, --max_tokens MAX_TOKENS
The maximum number of tokens to generate in the
response.
-l LOAD, --load LOAD Load a previous interactive session from a file.
rajivpant@rp-2023-mac-mini ragbot % To use Ragbot.AI, you can provide curated dataset files and/or folders containing multiple curated dataset files. You can view examples of curated dataset files at https://github.com/rajivpant/ragbot/tree/main/example-curated-datasets
Example 1:
rajivpant@RP-2021-MacBook-Pro ragbot % ./ragbot.py -d custom-instructions/ curated-datasets/public/ ../ragbot-private/curated-datasets/personal/ ../ragbot-private/curated-datasets/hearst -p "Write a short note in Rajiv's voice about Rajiv's job, coworkers, family members, and travel and food preferences for the person temporarily backfilling for his EA."
curated datasets being used:
- custom-instructions/
- curated-datasets/public/travel-food.md
- curated-datasets/public/employment-history.md
- curated-datasets/public/about.md
- curated-datasets/public/biography.md
- ../ragbot-private/curated-datasets/personal/accounts.md
- ../ragbot-private/curated-datasets/personal/contact-info.md
- ../ragbot-private/curated-datasets/personal/personal-family.md
- ../ragbot-private/curated-datasets/hearst/hearst.md
Using AI engine openai with model gpt-4
[redacted in this example]Example 2:
rajivpant@RP-2021-MacBook-Pro ragbot % ./ragbot.py -d custom-instructions/ curated-datasets/public/ -p "Write a short resume of Rajiv"
curated datasets being used:
- custom-instructions/
- curated-datasets/public/travel-food.md
- curated-datasets/public/employment-history.md
- curated-datasets/public/about.md
- curated-datasets/public/biography.md
Using AI engine openai with model gpt-4
[truncated in this example]Example 3:
./ragbot.py -p "Tell me a story about a brave knight and a wise wizard." -d curated datasets/story_charactersTo use Ragbot.AI in interactive mode, use the -i or --interactive flag without providing a prompt via command line or input file. In this mode, you can enter follow-up prompts after each response.
Example:
./ragbot.py -i -d curated-datasets/story_charactersIn the first example, Ragbot.AI generates a short note in Rajiv's voice using the curated dataset files in the ../ragbot-private/curated-datasets folder. In the second example, Ragbot.AI provides information on good practices for software development using the curated-datasets/software_development.txt curated dataset file. In the third example, Ragbot.AI tells a story about a brave knight and a wise wizard using the curated dataset files in the curated-datasets/story_characters folder.
rajivpant@RP-2021-MacBook-Pro ragbot % ./ragbot.py -d ragbot.py -p "if no curated dataset files are being used, then I want the code to show that."
curated datasets being used:
- ragbot.py
Using AI engine openai with model gpt-4
To modify the code to show a message when no curated dataset files are being used, you can add an else statement after checking for the curated dataset files. Update the code in the `main()` function as follows:
\```python
if curated_dataset_files:
print("curated datasets being used:")
for file in curated_dataset_files:
print(f" - {file}")
else:
print("No curated dataset files are being used.")
\```
This will print "No curated dataset files are being used." when there are no curated dataset files detected.
rajivpant@RP-2021-MacBook-Pro ragbot %
Asking it to guess what some of the curated dataset files I use are for
rajivpant@RP-2021-MacBook-Pro ragbot % find curated-datasets ../ragbot-private/curated-datasets -print | ./ragbot.py -d custom-instructions/ curated-datasets/public/ ../ragbot-private/curated-datasets/personal/ ../ragbot-private/curated-datasets/hearst/ -p "What do you guess these files are for?"
curated datasets being used:
- custom-instructions/
- curated-datasets/public/travel-food.md
- curated-datasets/public/employment-history.md
- curated-datasets/public/about.md
- curated-datasets/public/biography.md
- ../ragbot-private/curated-datasets/personal/accounts.md
- ../ragbot-private/curated-datasets/personal/contact-info.md
- ../ragbot-private/curated-datasets/personal/personal-family.md
- ../ragbot-private/curated-datasets/hearst/hearst.md
Using AI engine openai with model gpt-4
These files appear to be related to the curated-datasets of an AI system, likely for generating text or providing assistance based on the provided information. The files seem to be divided into two categories: public and private.
Public files:
- curated-datasets/public/travel-food.md: Rajiv's travel and food preferences
- curated-datasets/public/employment-history.md: Rajiv's employment history
- curated-datasets/public/about.md: General information about Rajiv
- curated-datasets/public/biography.md: Biography of Rajiv
Private files (stored in a separate private folder):
- curated-datasets/personal/accounts.md: Semi-private personal account information, such as frequent flyer numbers or loyalty programs. Does not contain any confidential or sensitive information.
- curated-datasets/personal/contact-info.md: Personal contact information, such as phone numbers and email addresses. Does not contain any confidential or sensitive information.
- curated-datasets/personal/personal-family.md: Personal and family information, such as family members and relationships. Does not contain any confidential or sensitive information.
Hearst-specific files:
- curated-datasets/hearst/hearst.md: Non-confidential, publicly available information related to the Hearst corporation, including Rajiv's role there
Overall, these files seem to contain various information about a person, their preferences, and professional background, likely used to tailor the AI system's responses and assistance.
rajivpant@RP-2021-MacBook-Pro ragbot % Asking technical questions about a project
❗️ In the current version of Ragbot.AI, the --stdin and --prompt options are mutually exclusive, so the following example no longer works as is. In a future update to this README file, I will give an alternate example to obtain the similar results.
alexredmon@ar-macbook ~/s/scribe > cat docker-compose.yml | ragbot --stdin -p "which services will be exposed on which ports by running all services in the following docker-compose.yml file?"
In the given docker-compose.yml file, the following services are exposed on their respective ports:
1. "scribe" service: - Exposed on port 80 - Exposed on port 9009 (mapped to internal port 9009)
2. "scribe-feature" service: - Exposed on port 80
3. "scribe-redis" service: - Exposed on port 6379 (mapped to internal port 6379)
alexredmon@ar-macbook ~/s/scribe >Using the Anthropic engine with the Claude Instant model
rajivpant@RP-2021-MacBook-Pro ragbot % ./ragbot.py -e anthropic -m "claude-instant-v1" -p "Tell me 5 fun things to do in NYC."
No curated dataset files are being used.
Using AI engine anthropic with model claude-instant-v1
Here are 5 fun things to do in NYC:
1. Visit Central Park. Walk the paths, rent a paddle boat, visit the zoo, catch a Shakespeare in the Park performance.
2. Catch a Broadway show. New York is the center of the theater world with some of the greatest plays and musicals on Broadway and off Broadway.
3. Go to the top of the Empire State Building. Take in the panoramic views of all of NYC from one of its most famous landmarks.
4. Shop and dine in SoHo and the West Village. Explore trendy boutique shops and dig into meals at charming sidewalk cafes.
5. Take a free walking tour. There are numerous companies that offer guided walking tours of various NYC neighborhoods, covering history, culture, architecture and more.
rajivpant@RP-2021-MacBook-Pro ragbot % Random Creativity
❗️ In the current version of Ragbot.AI, the --stdin and --prompt options are mutually exclusive, so the following example no longer works as is. In a future update to this README file, I will give an alternate example to obtain the similar results.
alexredmon@ar-macbook ~ > cat names.csv
rajiv,
jim,
dennis,
alexandria
alexredmon@ar-macbook ~ > catnames.csv | ragbot.py --stdin -p "Generate a creative nickname for each of the following people"
rajiv, Rajiv Razzle-Dazzle
jim, Jolly JimJam
dennis, Daring Denmaster
alexandria, All-Star Alexi
alexredmon@ar-macbook ~ >