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https://github.com/SciPhi-AI/synthesizer
A multi-purpose LLM framework for RAG and data creation.
https://github.com/SciPhi-AI/synthesizer
agents ai artificial-intelligence machine-learning synthetic-data
Last synced: about 1 month ago
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A multi-purpose LLM framework for RAG and data creation.
- Host: GitHub
- URL: https://github.com/SciPhi-AI/synthesizer
- Owner: SciPhi-AI
- License: apache-2.0
- Archived: true
- Created: 2023-09-15T21:01:46.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-13T05:03:59.000Z (12 months ago)
- Last Synced: 2024-05-19T18:20:02.134Z (8 months ago)
- Topics: agents, ai, artificial-intelligence, machine-learning, synthetic-data
- Language: Python
- Homepage:
- Size: 31.5 MB
- Stars: 590
- Watchers: 11
- Forks: 48
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# 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.
---
## Fast Setup
```bash
pip install sciphi-synthesizer
```
### Using Synthesizer
1. **Generate synthetic question-answer pairs**
```bash
export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
python -m synthesizer.scripts.data_augmenter run --dataset="wiki_qa"
```
```bash
tail augmented_output/config_name_eq_answer_question__dataset_name_eq_wiki_qa.jsonl
{ "formatted_prompt": "... ### Question:\nwhat country did wine originate in\n\n### Input:\n1. URL: https://en.wikipedia.org/wiki/History%20of%20wine (Score: 0.85)\nTitle:History of wine....",
{ "completion": "Wine originated in the South Caucasus, which is now part of modern-day Armenia ..."
```
2. **Evaluate RAG pipeline performance**
```bash
export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
python -m synthesizer.scripts.rag_harness --rag_provider="agent-search" --llm_provider_name="sciphi" --n_samples=25
```
### Documentation
For more detailed information, tutorials, and API references, please visit the official [Synthesizer Documentation](https://sciphi.readthedocs.io/en/latest/).
### Community & Support
- Engage with our vibrant community on [Discord](https://discord.gg/j9GxfbxqAe).
- For tailored inquiries or feedback, please [email us](mailto:[email protected]).
### Developing with Synthesizer
Quickly set up RAG augmented generation with your choice of provider, from OpenAI, Anhtropic, vLLM, and SciPhi:
```python
# Requires SCIPHI_API_KEY in env
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("agent-search"),
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("openai"),
)
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 = raw_prompt.format(rag_context=rag_context)
completion = llm_interface.get_completion(formatted_prompt, generation_config)
```