Prompt: Need to update the reasonchain's input taking, as we are currently taking PDF files only, what if we want the data to take in also here /home/sunny-bedi/practise/ReasonChain/reasonchain/rag/vector/add_to_vector_db.py. Also need to make sure the model /home/sunny-bedi/practise/ReasonChain/reasonchain/rag/embeddings/embedding_config.py if we need to add our own model too, even the fine-tuned model saved in our path or in HF, we need that liberty to add too, this config just make it less open.
Changes Made:
- Updated documentation to show support for multiple file formats beyond PDFs
- Added comprehensive file format support:
- Documents: PDF, DOC/DOCX, TXT, RTF, LaTeX (.tex)
- Spreadsheets: XLS/XLSX, CSV
- Web & Markup: HTML/HTM, Markdown (.md), JSON
- Presentations: PPT/PPTX
- eBooks: EPUB, MOBI
- Media: Images (PNG, JPG, JPEG, TIFF), Videos (MP4, AVI, MOV), Audio (MP3, WAV)
- Added file validation with supported extensions checking
- Enhanced error handling with descriptive warnings for unsupported files
- Updated examples to demonstrate multi-format processing
- Improved function parameters documentation
- Made configuration more flexible and open for custom models
- Added custom model registration functions:
register_custom_model()
- General custom model registrationregister_huggingface_model()
- For HuggingFace models including fine-tuned onesregister_local_model()
- For local model filesregister_fine_tuned_model()
- Specifically for fine-tuned models
- Added utility functions:
list_available_models()
- List all registered modelsget_model_dimension()
- Get embedding dimensionsremove_custom_model()
- Remove custom models
- Extended default configurations with latest OpenAI models
- Added support for:
- Local model paths
- HuggingFace repository models
- Fine-tuned model tracking with base model references
- Custom provider categories
- Maintained backward compatibility with existing code
- Created
enhanced_multi_format_rag_example.py
demonstrating:
- Multi-format document processing
- Custom embedding model registration and usage
- Complete RAG pipeline with mixed content types
- Error handling and validation
- Interactive cleanup functionality
Key Benefits:
- Flexibility: Users can now process any supported file format, not just PDFs
- Extensibility: Easy registration of custom, fine-tuned, and local embedding models
- Robustness: Enhanced error handling and file validation
- Documentation: Comprehensive examples and documentation
- Backward Compatibility: All existing code continues to work unchanged
- User Experience: Clear feedback on processing status and supported formats
Usage Examples:
# Multi-format processing
file_paths = [
'documents/report.pdf',
'data/spreadsheet.xlsx',
'content/article.md',
'presentations/slides.pptx'
]
add_data_to_vector_db(file_paths, db_path='mixed_db.index')
# Custom model registration
register_huggingface_model(
"your-username/your-fine-tuned-model",
768,
"Your custom fine-tuned model"
)
# Use custom model
add_data_to_vector_db(
file_paths,
embedding_provider="hugging_face",
embedding_model="your-username/your-fine-tuned-model"
)
Prompt: Now what if I do not want to use files and files are extracted elsewhere and need to add the data to the vector db that option I don't see in here.
Changes Made:
add_raw_data_to_vector_db()
- Add raw text data or pre-computed embeddings directlyadd_structured_data_to_vector_db()
- Add structured data (JSON-like) from APIs/databasesadd_external_source_to_vector_db()
- Add data from external sources (databases, APIs, etc.)
- Raw Text Input: Add text data directly with automatic embedding generation
- Pre-computed Embeddings: Use embeddings computed elsewhere
- Structured Data Processing: Handle API responses, database results, JSON data
- External Source Integration: Built-in connectors for databases, APIs, Elasticsearch
- Batch Processing: Handle large datasets efficiently
- Metadata Support: Preserve and query metadata alongside text content
- Raw Text Lists: Simple text arrays with automatic embedding generation
- Pre-computed Embeddings: NumPy arrays or lists of embeddings with associated texts
- Structured Data: JSON-like objects with text and metadata fields
- Database Queries: PostgreSQL, SQLite with SQL queries
- REST APIs: HTTP endpoints returning JSON data
- Elasticsearch: Search indices with complex queries
- Mixed Data Types: Handle various text lengths, special characters, unicode
direct_data_input_example.py
demonstrating all direct input methods- 6 different examples covering common use cases
- Query testing to verify data was added correctly
- Batch processing for scalability
- Error handling and validation
Key Benefits:
- No File Dependency: Add data directly without file processing
- Flexible Input: Support text, embeddings, structured data, external sources
- Metadata Preservation: Keep important metadata for filtering and context
- Scalable Processing: Batch processing for large datasets
- External Integration: Built-in connectors for common data sources
- Production Ready: Comprehensive error handling and validation
Usage Examples:
# Add raw text data directly
texts = ["Document 1 content", "Document 2 content"]
add_raw_data_to_vector_db(texts=texts, db_path='raw_db.index')
# Add pre-computed embeddings
import numpy as np
embeddings = np.random.rand(2, 768) # 2 embeddings, 768 dimensions
texts = ["Text 1", "Text 2"]
add_raw_data_to_vector_db(texts=texts, embeddings=embeddings)
# Add structured data from API
api_data = [
{"text": "Article content", "title": "Article 1", "category": "tech"},
{"text": "Blog content", "title": "Blog 1", "category": "science"}
]
add_structured_data_to_vector_db(
api_data,
metadata_fields=['title', 'category']
)
# Add from database
db_config = {
'connection_string': 'postgresql://user:pass@host/db',
'query': 'SELECT content as text, title, category FROM articles',
'metadata_fields': ['title', 'category']
}
add_external_source_to_vector_db('database', db_config)
Prompt: "lets update the readme too as what we have added"
Changes Made:
-
Updated RAG Integration section to include new capabilities:
- Direct Data Input functionality
- External Source Integration
- Multi-Format Processing enhancements
-
Added Custom Embedding Models section with examples for:
- Registering HuggingFace models
- Using fine-tuned models
- Listing available models
-
Added comprehensive Direct Data Input & External Sources section covering:
- Raw text data input with automatic embedding generation
- Pre-computed embeddings integration
- Structured data processing from APIs/databases
- External source integration (databases, APIs, Elasticsearch)
-
Updated Examples section to include new example files:
direct_data_input_example.py
enhanced_multi_format_rag_example.py
fine_tuned_model_example.py
- Domain templates examples directory
-
Updated code examples to use new function names:
- Changed from
add_pdf_to_vector_db
to add_data_to_vector_db
- Updated to show multi-format file processing
-
Enhanced Features list with:
- Custom embedding models support
- Batch processing capabilities
- Updated numbering for better organization
-
Updated Future Enhancements to reflect implemented features and add new goals:
- Cloud storage integration
- Advanced multi-agent orchestration
- Real-time data streaming
- Analytics dashboard
Files Modified:
Readme.md
- Comprehensive updates to document new functionality
New Functionality Documented:
- Direct data input capabilities (raw text, pre-computed embeddings, structured data)
- External source integration (databases, APIs, Elasticsearch)
- Custom embedding model registration and management
- Enhanced multi-format file processing
- Batch processing optimizations
Prompt: "Currnelty in embeddings and llm we have made it very tight ther eis no way to use anything other than that like anothe rllm or embedding model to use n reason chain we shodul see hwo can we make it a little open"
Changes Made:
base_provider.py
- Abstract base classes for LLM and embedding providers
BaseLLMProvider
- Interface for all LLM providersBaseEmbeddingProvider
- Interface for all embedding providersProviderConfig
- Configuration management class
provider_registry.py
- Dynamic provider registration and management
LLMProviderRegistry
- Register, retrieve, and manage LLM providersEmbeddingProviderRegistry
- Register, retrieve, and manage embedding providers- Caching system for provider instances
- List and unregister capabilities
providers/
- Package containing concrete provider implementations
OpenAIProvider
- GPT-4, GPT-3.5, etc.GroqProvider
- Fast inference with Llama, MixtralOllamaProvider
- Local models (Llama, Mistral, etc.)CustomModelProvider
- Fine-tuned and local HuggingFace modelsAnthropicProvider
- Claude models (example of easy extensibility)
register_providers.py
- Automatic registration of built-in providers- Providers are registered when the module is imported
- No manual registration needed for built-in providers
- Refactored
model_manager.py
to use provider system
- Maintains full backward compatibility with existing code
- Falls back to legacy mode if provider system fails
- Supports both provider objects and string-based API selection
custom_provider_example.py
demonstrating:
- Using built-in providers
- Creating custom LLM providers (Cohere example)
- Creating custom embedding providers (Voyage AI example)
- Local model providers
- Listing all available providers
- Enhanced README.md with:
- New "Extensible Provider Architecture" section
- Code examples for creating custom providers
- List of built-in providers
- Benefits and use cases
Key Benefits:
- Open Architecture: Add ANY LLM or embedding service without modifying core code
- Simple Interface: Create a provider by implementing 2-3 methods
- Backward Compatible: All existing code works unchanged
- Plugin System: Share custom providers as packages
- Future-Proof: Support for new services added instantly
- No Vendor Lock-in: Easily switch between providers
Supporting ANY Service Now:
- ✅ OpenAI (GPT-4, GPT-3.5)
- ✅ Anthropic (Claude models)
- ✅ Groq (Fast Llama, Mixtral)
- ✅ Ollama (Local models)
- ✅ Cohere (via custom provider)
- ✅ AI21 (via custom provider)
- ✅ Voyage AI (embeddings)
- ✅ Any custom/local model
- ✅ Any future LLM service
Usage Example:
# Built-in provider (no changes needed)
agent = Agent(name="GPT_Agent", model_name="gpt-4", api="openai")
# New provider added in 3 steps:
# 1. Implement BaseLLMProvider
class CohereProvider(BaseLLMProvider):
def generate_response(self, prompt, **kwargs):
return self.client.generate(prompt=prompt)
def generate_chat_response(self, messages, **kwargs):
# ... implement chat
# 2. Register provider
LLMProviderRegistry.register('cohere', CohereProvider)
# 3. Use it!
agent = Agent(name="Cohere_Agent", model_name="command", api="cohere")
Files Created:
reasonchain/llm_models/base_provider.py
reasonchain/llm_models/provider_registry.py
reasonchain/llm_models/register_providers.py
reasonchain/llm_models/providers/__init__.py
reasonchain/llm_models/providers/openai_provider.py
reasonchain/llm_models/providers/groq_provider.py
reasonchain/llm_models/providers/ollama_provider.py
reasonchain/llm_models/providers/custom_provider.py
reasonchain/llm_models/providers/anthropic_provider.py
examples/custom_provider_example.py
Files Modified:
reasonchain/llm_models/model_manager.py
- Enhanced with provider systemReadme.md
- Added extensibility documentationprompts.md
- This file
Impact:
This change transforms ReasonChain from a closed system supporting 3-4 specific LLM services into an open, extensible platform that can support ANY LLM or embedding service, present or future. Users can now add support for new services in minutes without touching core code.
Prompt: "now what about embedding models"
Changes Made:
-
providers/sentence_transformers_provider.py
- Sentence Transformers support
- Supports all sentence-transformers models
- Configurable normalization and device selection
- Batch processing with progress bars
- Auto-detection of embedding dimensions
-
providers/openai_embeddings_provider.py
- OpenAI Embeddings
- text-embedding-ada-002 (1536d)
- text-embedding-3-small (1536d)
- text-embedding-3-large (3072d)
- Automatic batch processing
-
providers/huggingface_provider.py
- HuggingFace Transformers
- Support for any HuggingFace model (BERT, RoBERTa, DistilBERT)
- Multiple pooling strategies (mean, cls, max)
- GPU/CPU support
- Custom and fine-tuned models
register_embedding_providers.py
- Automatic registration- Registers sentence_transformers, openai, and hugging_face providers
- Works on import without manual setup
-
Enhanced embedding_initializer.py
- Uses provider system by default
- Falls back to legacy mode if needed
- 100% backward compatible
use_provider_system
parameter for control
-
Enhanced VectorDB.py
- Detects BaseEmbeddingProvider instances
- Uses efficient batch processing
- Maintains legacy support
- Seamless integration
custom_embedding_provider_example.py
demonstrating:
- Using built-in providers (Sentence Transformers, OpenAI, HuggingFace)
- Creating custom Cohere embedding provider
- Creating custom Voyage AI provider
- Local embedding service provider
- Direct provider usage
- Listing all providers
- Enhanced README.md with:
- Built-in embedding providers section
- Example code for custom embedding providers
- Usage with RAG pipelines
- Integration examples
Key Benefits:
- Unified Interface: All embedding providers follow the same pattern
- Built-in Providers: Sentence Transformers, OpenAI, HuggingFace ready to use
- Easy Extension: Add any embedding service (Cohere, Voyage AI, local models)
- Efficient Processing: Automatic batch processing for performance
- Backward Compatible: Existing code works unchanged
- Type Safe: Abstract base classes ensure consistency
Supporting ANY Embedding Service Now:
- ✅ Sentence Transformers (all models)
- ✅ OpenAI Embeddings (ada-002, 3-small, 3-large)
- ✅ HuggingFace (BERT, RoBERTa, DistilBERT, custom)
- ✅ Cohere (via custom provider)
- ✅ Voyage AI (via custom provider)
- ✅ Any local embedding service
- ✅ Any future embedding service
Usage Example:
# Built-in provider (no changes needed)
add_data_to_vector_db(
file_paths=["documents.pdf"],
embedding_provider="sentence_transformers",
embedding_model="all-mpnet-base-v2"
)
# Custom provider in 3 steps:
# 1. Implement BaseEmbeddingProvider
class CohereEmbeddingProvider(BaseEmbeddingProvider):
def embed_text(self, text):
return self.client.embed([text]).embeddings[0]
def embed_batch(self, texts, batch_size=96):
return self.client.embed(texts).embeddings
def get_dimension(self):
return 1024
# 2. Register provider
EmbeddingProviderRegistry.register('cohere', CohereEmbeddingProvider)
# 3. Use it!
add_data_to_vector_db(
file_paths=["documents.pdf"],
embedding_provider="cohere",
embedding_model="embed-english-v3.0"
)
Files Created:
reasonchain/rag/embeddings/providers/__init__.py
reasonchain/rag/embeddings/providers/sentence_transformers_provider.py
reasonchain/rag/embeddings/providers/openai_embeddings_provider.py
reasonchain/rag/embeddings/providers/huggingface_provider.py
reasonchain/rag/embeddings/register_embedding_providers.py
examples/custom_embedding_provider_example.py
Files Modified:
reasonchain/rag/embeddings/embedding_initializer.py
- Integrated provider systemreasonchain/rag/vector/VectorDB.py
- Added provider supportReadme.md
- Added embedding provider documentationprompts.md
- This file
Impact:
The embedding system is now as extensible as the LLM system. Users can add support for ANY embedding service (Cohere, Voyage AI, custom APIs, local models) in minutes by implementing 3 simple methods. The system automatically handles batch processing, dimension detection, and integration with all vector databases.