hayagriva 0.2.5.1

Modular RAG framework with support for FAISS, Weaviate, and Hybrid Search.

Hayagriva – Modular Retrieval-Augmented Generation Framework

सर्वविद्याप्रसूत्यर्थं हयग्रीवोऽवतारतः वेदान् संरक्ष्य जगतां हितकारी सदा भवेत्

"To restore and protect all knowledge, Hayagriva has manifested. He safeguards the Vedas for the good of the world."

Hayagriva is a lightweight, modular Retrieval-Augmented Generation (RAG) framework that connects LLMs with efficient document retrieval. It focuses on grounded answers, fast iteration, and easy integration for developers and research teams.

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Key Features

Retrieval-Augmented Generation

Combine contextual retrieval with LLMs to produce grounded, source-aware answers.

Supported LLM backend today: Groq.

Retrieval Strategies

Hayagriva supports multiple retrieval strategies (depending on vector store):

• Vector Search: Dense semantic retrieval.
• BM25: Sparse keyword retrieval (Weaviate).
• Hybrid Search: Vector + keyword with configurable weighting (Weaviate).

Chunking Strategies

• Word (Default): Sliding window on word count.
• Recursive: Split by separators to preserve structure.
• Semantic: Embedding-aware topic shifts.
• Hierarchical: Parent/child chunking for broad + precise context.

Modular Vector Stores

• FAISS: Lightweight, in-memory vector store.
• Weaviate: Production-grade vector DB with hybrid/BM25.

Flexible Document Handling

• Programmatic document ingestion.
• File and directory ingestion helpers.
  • Directory ingestion reads .txt files by default.

Automatic chunking and metadata assignment are built in.

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Installation

Default (Lightweight)

Core libraries only:

pip install hayagriva

CPU Support (Recommended for Local Testing)

Installs sentence-transformers and faiss-cpu:

pip install "hayagriva[cpu]"

GPU Support

Installs sentence-transformers and faiss-gpu:

pip install "hayagriva[cuda]"

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Python Usage

Example setup scripts are available in setup/, and capability-focused examples are grouped under examples/.

Example Categories

The examples are split by what part of the RAG pipeline you want to try:

• examples/ingestion/: load text or .txt files without embeddings or LLM calls.
• examples/chunking/: run word, recursive, semantic, or hierarchical chunking.
• examples/indexing/: embed chunks and write them to FAISS or Weaviate.
• examples/retrieval/: retrieve chunks without calling Groq.
• examples/end_to_end/: complete Groq-powered RAG flows.
• examples/vectorstores/weaviate/: Weaviate vector, BM25, and hybrid examples.

For local FAISS and embedding examples:

bash setup/install_local_deps.sh
export GROQ_API_KEY="YOUR_GROQ_KEY"

For Weaviate examples:

bash setup/install_local_deps.sh
bash setup/start_weaviate.sh
export GROQ_API_KEY="YOUR_GROQ_KEY"

Basic Example (FAISS + Vector Search)

from hayagriva import Hayagriva, HayagrivaConfig

config = HayagrivaConfig(
    backend="groq",
    api_key="YOUR_GROQ_KEY",
    model="llama-3.1-8b-instant",
)

rag = Hayagriva(config)
rag.add_documents(["Hayagriva restores forgotten knowledge."])

response = "".join(rag.ask("Who retrieved the lost Vedas?"))
print(response)

Building Block Example (Chunking Only)

from hayagriva.config import ChunkingConfig
from hayagriva.core.chunker import WordChunker

chunker = WordChunker(ChunkingConfig(chunk_size=8, overlap=2))
chunks, metadata = chunker.chunk([
    "Hayagriva restores knowledge and RAG retrieves context before generation."
])

print(chunks)
print(metadata)

Structured Response (Answer + Metadata)

resp = rag.ask("Who retrieved the lost Vedas?", return_metadata=True)
print(resp["answer"])
print(resp["chunks"][0])
print(resp["retrieval"]["strategy"])

Returned metadata includes: retrieved chunk ranks/scores, chunking strategy, retrieval strategy, model backend, and vector store.

Customizing Embeddings

config = HayagrivaConfig(
    backend="groq",
    api_key="YOUR_KEY",
    embedding_model="intfloat/multilingual-e5-large",
)

Advanced Example (Weaviate + Hybrid Search)

from hayagriva import Hayagriva, HayagrivaConfig
from hayagriva.config import WeaviateConfig, RetrievalConfig, ChunkingConfig

weaviate_config = WeaviateConfig(
    url="http://localhost:8080",
    index_name="HayagrivaDocs",
)

config = HayagrivaConfig(
    backend="groq",
    api_key="YOUR_GROQ_KEY",
    vector_store="weaviate",
    weaviate=weaviate_config,
    retrieval=RetrievalConfig(strategy="hybrid", alpha=0.5, top_k=4),
    chunking=ChunkingConfig(strategy="recursive", chunk_size=500, overlap=50),
)

rag = Hayagriva(config)
rag.add_documents(["Hayagriva is an avatar of Vishnu."])

for token in rag.ask("Who is Hayagriva?"):
    print(token, end="", flush=True)

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Requirements

• Python 3.10+
• API key for Groq

Optional (only if you use local embeddings or FAISS):

• sentence-transformers
• faiss-cpu or faiss-gpu

If using external vector databases:

• weaviate-client

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Roadmap

Expanded LLM Provider Support

• Anthropic Claude
• Google Gemini
• DeepSeek
• Local GGUF models and llama.cpp

Additional Vector Database Integration

• ChromaDB
• Additional pluggable backends

Memory-Augmented Chat

• Multi-turn memory
• Embedding-based long-term memory
• Summarization-based memory compression

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Use Cases

• Retrieval-augmented assistants
• Knowledge-base and enterprise search
• Research and benchmarking of RAG pipelines
• Lightweight production deployments
• Internal document Q&A systems

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Contributing

Contributions to model integrations, retrieval modules, and documentation are welcome. Submit issues or pull requests.

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Support

For questions, bugs, or feature requests, open an issue on the project repository.