sciphi-synthesizer 1.0.0

Synthesizer: A Framework for LLM Powered Data.

Synthesizer[ΨΦ]: A multi-purpose LLM framework 💡

With Synthesizer, users can:

• Custom Data Creation: Generate datasets via LLMs that are tailored to your needs.
  • Anthropic, OpenAI, vLLM, and HuggingFace.
• Retrieval-Augmented Generation (RAG) on Demand: Built-in RAG Provider Interface to anchor generated data to real-world sources.
  • Turnkey integration with Agent Search API.
• Custom Data Creation: Generate datasets via LLMs that are tailored to your needs, for LLM training, RAG, and more.

---

Documentation

For more detailed information, tutorials, and API references, please visit the official Synthesizer Documentation.

Fast Setup

pip install sciphi-synthesizer

Features

Community & Support

• Engage with our vibrant community on Discord.
• For tailored inquiries or feedback, please email us.

Example

The following example demonstrates how to construct a connection to the AgentSearch API with the synthesizer RAG interface. Then, the example goes on to use the RAG interface to generate a response with an OpenAI hosted LLM.

   from synthesizer.core import LLMProviderName, RAGProviderName
   from synthesizer.interface import (
      LLMInterfaceManager,
      RAGInterfaceManager,
   )
   from synthesizer.llm import GenerationConfig

   # RAG Provider Settings
   rag_interface = RAGInterfaceManager.get_interface_from_args(
      RAGProviderName(rag_provider_name),
      api_base=rag_api_base,
      limit_hierarchical_url_results=rag_limit_hierarchical_url_results,
      limit_final_pagerank_results=rag_limit_final_pagerank_results,
   )
   rag_context = rag_interface.get_rag_context(query)

   # LLM Provider Settings
   llm_interface = LLMInterfaceManager.get_interface_from_args(
      LLMProviderName(llm_provider_name),
   )

   generation_config = GenerationConfig(
      model_name=llm_model_name,
      max_tokens_to_sample=llm_max_tokens_to_sample,
      temperature=llm_temperature,
      top_p=llm_top_p,
      # other generation params here ...
   )

   formatted_prompt = rag_prompt.format(rag_context=rag_context)
   completion = llm_interface.get_completion(
      formatted_prompt, generation_config
   )
   print(completion)