LongTrainer - Production-Ready LangChain

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Features 🌟

• ✅ Long Memory: Retains context effectively for extended interactions.
• ✅ Unique Bots/Chat Management: Sophisticated management of multiple chatbots.
• ✅ 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.

Works for All Langchain Supported LLM and Embeddings

• ✅ OpenAI (default)
• ✅ VertexAI
• ✅ HuggingFace

Example

VertexAI LLMs

from longtrainer.trainer import LongTrainer
from langchain_community.llms import VertexAI

llm = VertexAI()

trainer = LongTrainer(mongo_endpoint='mongodb://localhost:27017/', llm=llm)

TogetherAI LLMs

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 🚀

pip install longtrainer

Here's a quick start guide on how to use LongTrainer:

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/', 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:

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:

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}},
}