https://github.com/endevsols/long-trainer
Introducing LongTrainer, a sophisticated extension of the LangChain framework designed specifically for managing multiple bots and providing isolated, context-aware chat sessions. Ideal for developers and businesses looking to integrate complex conversational AI into their systems, LongTrainer simplifies the deployment and customization of LLMs.
https://github.com/endevsols/long-trainer
gpt langchain langchain-python llm-training longtrainer openai rag
Last synced: 3 months ago
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Introducing LongTrainer, a sophisticated extension of the LangChain framework designed specifically for managing multiple bots and providing isolated, context-aware chat sessions. Ideal for developers and businesses looking to integrate complex conversational AI into their systems, LongTrainer simplifies the deployment and customization of LLMs.
- Host: GitHub
- URL: https://github.com/endevsols/long-trainer
- Owner: ENDEVSOLS
- License: mit
- Created: 2023-12-07T16:37:26.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-12-17T13:17:34.000Z (10 months ago)
- Last Synced: 2025-06-24T04:04:54.920Z (4 months ago)
- Topics: gpt, langchain, langchain-python, llm-training, longtrainer, openai, rag
- Language: Python
- Homepage: https://endevsols.github.io/Long-Trainer/
- Size: 842 KB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
LongTrainer - Production-Ready LangChain
# Official Documentation
Explore the comprehensive [LongTrainer Documentation](https://endevsols.github.io/Long-Trainer/) for detailed
instructions on installation, features, and API usage.
# Installation
Introducing LongTrainer, a sophisticated extension of the LangChain framework designed specifically for managing
multiple bots and providing isolated, context-aware chat sessions. Ideal for developers and businesses looking to
integrate complex conversational AI into their systems, LongTrainer simplifies the deployment and customization of LLMs.
```markdown
pip install longtrainer
```
## Installation Instructions for Required Libraries and Tools
### 1. Linux (Ubuntu/Debian)
To install the required packages on a Linux system (specifically Ubuntu or Debian), you can use the apt package manager.
The following command installs several essential libraries and tools:
```markdown
sudo apt install libmagic-dev poppler-utils tesseract-ocr qpdf libreoffice pandoc
```
### 2. macOS
On macOS, you can install these packages using brew, the Homebrew package manager. If you don't have Homebrew installed,
you can install it from brew.sh.
```markdown
brew install libmagic poppler tesseract qpdf libreoffice pandoc
```
# Features 🌟
- ✅ **Long Memory:** Retains context effectively for extended interactions.
- ✅ **Multi-Bot Management:** Easily configure and manage multiple bots within a single framework, perfect for scaling
across various use cases
- ✅ **Isolated Chat Sessions:** Each bot operates within its own session, ensuring interactions remain distinct and
contextually relevant without overlap.
- ✅ **Context-Aware Interactions:** Utilize enhanced memory capabilities to maintain context over extended dialogues,
significantly improving user experience
- ✅ **Scalable Architecture:** Designed to scale effortlessly with your needs, whether you're handling hundreds of users
or just a few.
- ✅ **Enhanced Customization:** Tailor the behavior to fit specific needs.
- ✅ **Memory Management:** Efficient handling of chat histories and contexts.
- ✅ **GPT Vision Support:** Integration Context Aware GPT-powered visual models.
- ✅ **Different Data Formats:** Supports various data input formats.
- ✅ **VectorStore Management:** Advanced management of vector storage for efficient retrieval.
## Diverse Use Cases:
- ✅ **Enterprise Solutions:** Streamline customer interactions, automate responses, and manage multiple departmental
bots from a single platform.
- ✅ **Educational Platforms:** Enhance learning experiences with AI tutors capable of maintaining context throughout
sessions.
- ✅ **Healthcare Applications:** Support patient management with bots that provide consistent, context-aware
interactions.
## Works for All Langchain Supported LLM and Embeddings
- ✅ OpenAI (default)
- ✅ VertexAI
- ✅ HuggingFace
- ✅ AWS Bedrock
- ✅ Groq
- ✅ TogetherAI
# Example
VertexAI LLMs
```python
from longtrainer.trainer import LongTrainer
from langchain_community.llms import VertexAI
llm = VertexAI()
trainer = LongTrainer(mongo_endpoint='mongodb://localhost:27017/', llm=llm)
```
TogetherAI LLMs
```python
from longtrainer.trainer import LongTrainer
from langchain_community.llms import Together
llm = Together(
model="togethercomputer/RedPajama-INCITE-7B-Base",
temperature=0.7,
max_tokens=128,
top_k=1,
# together_api_key="..."
)
trainer = LongTrainer(mongo_endpoint='mongodb://localhost:27017/', llm=llm)
```
## Usage Example 🚀
Here's a quick start guide on how to use LongTrainer:
```python
from longtrainer.trainer import LongTrainer
import os
# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "sk-"
# Initialize LongTrainer
trainer = LongTrainer(mongo_endpoint='mongodb://localhost:27017/', ensemble=True, encrypt_chats=True)
bot_id = trainer.initialize_bot_id()
print('Bot ID: ', bot_id)
# Add Data
path = 'path/to/your/data'
trainer.add_document_from_path(path, bot_id)
# Initialize Bot
trainer.create_bot(bot_id)
# Start a New Chat
chat_id = trainer.new_chat(bot_id)
# Send a Query and Get a Response
query = 'Your query here'
response = trainer.get_response(query, bot_id, chat_id)
print('Response: ', response)
```
Here's a guide on how to use Vision Chat:
```python
chat_id = trainer.new_vision_chat(bot_id)
query = 'Your query here'
image_paths = ['nvidia.jpg']
response = trainer.get_vision_response(query, image_paths, str(bot_id), str(vision_id))
print('Response: ', response)
```
List Chats and Display Chat History:
```python
trainer.list_chats(bot_id)
trainer.get_chat_by_id(chat_id=chat_id)
```
This project is still under active development. Community feedback and contributions are highly appreciated.
## Citation
If you utilize this repository, please consider citing it with:
```
@misc{longtrainer,
author = {Endevsols},
title = {LongTrainer: Production-Ready LangChain},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ENDEVSOLS/Long-Trainer}},
}
```