pyaistack 0.2.2

⚡ A lightweight, modular microframework for complete AI applications.

PyAIStack helps you build RAG, agents, agentic tools, AI features, and provider integrations faster. The current release provides a production-oriented foundations with local Ollama and Gemma models by default.

✨ Start simple

from pyaistack import RAG

rag = RAG(
    embedding_model="embeddinggemma",
    llm_model="gemma3:4b",
)

rag.add([
    "AWS Lambda is a serverless compute service.",
    "Amazon S3 is an object storage service.",
])

answer = rag.ask("Which service runs code without managing servers?")
print(answer.text)

📁 Load, chunk, and persist local knowledge

Load a directory of UTF-8 .txt files, split documents before indexing, and retain the index in one SQLite file:

from pyaistack import RAG
from pyaistack.chunking import TextChunker
from pyaistack.loaders import DirectoryLoader
from pyaistack.vectorstores import SQLiteVectorStore

documents = DirectoryLoader("knowledge", file_type="text").load()

rag = RAG(
    vector_store=SQLiteVectorStore("knowledge.db"),
    chunker=TextChunker(chunk_size=1_000, chunk_overlap=150),
)
rag.add_documents(documents)

file_type is required. This release implements only text; PDF, DOCX, Markdown, and web loaders will be added only when implemented. SQLiteVectorStore uses exact cosine search and is intended for local, small-to-medium indexes.

New in 0.2.2: optional hybrid retrieval

Combine SQLite FTS5 keywords with vector ranking while keeping the default local Ollama setup:

from pyaistack import RAG, RAGConfig
from pyaistack.vectorstores import SQLiteVectorStore

with SQLiteVectorStore("knowledge.db") as store:
    rag = RAG(vector_store=store, config=RAGConfig(
        retrieval_mode="hybrid", candidate_k=20, top_k=5, include_sources=True,
    ))
    # Reuse an index built with the same embedding model.
    answer = rag.ask("What does policy FIN-042 require?")
    print(answer.text)

For a complete fresh demo, run python examples/hybrid_index.py, then python examples/hybrid_question.py. See the hybrid guide and benchmark report. Hybrid is opt-in; quality and latency depend on your corpus.

Project links

• GitHub repository
• Documentation
• Changelog
• LinkedIn — Prashant Band

🧠 Requirements

• Python 3.10+
• Ollama running locally
• Ollama version compatible with embeddinggemma (the Ollama model page currently specifies v0.11.10+)

Pull the default models:

ollama pull embeddinggemma
ollama pull gemma3:4b

🚀 Install

python -m venv .venv
source .venv/bin/activate
pip install pyaistack

For development:

pip install -e ".[dev]"

▶️ Run

python examples/basic.py

How it works

documents
   │
   ▼
OllamaEmbeddingProvider
   │
   ▼
InMemoryVectorStore

question
   │
   ▼
embedding
   │
   ▼
cosine retrieval
   │
   ▼
Top-K sources
   │
   ▼
context builder
   │
   ▼
OllamaChatProvider
   │
   ▼
RAGAnswer(text + sources)

🔧 Change the embedding model

Only configuration changes:

rag = RAG(
    embedding_model="qwen3-embedding:0.6b",
    llm_model="gemma3:4b",
)

or:

rag = RAG(
    embedding_model="nomic-embed-text",
    llm_model="gemma3:4b",
)

Note: Do not change embedding models while an index contains vectors. Different models may produce different vector dimensions and, more importantly, incompatible vector spaces. Clear/rebuild the index when changing the embedding model.

✍️ Add application instructions

You can append instructions specific Prompt sufix to your application:

rag = RAG(system_prompt_suffix="Answer with concise bullet points.")

🌐 Configure Ollama host

rag = RAG(
    embedding_model="embeddinggemma",
    llm_model="gemma3:4b",
    ollama_host="http://192.168.1.20:11434",
)

🔎 Access retrieval results without generating

results = rag.search("serverless compute", top_k=3)

for result in results:
    print(result.score)
    print(result.document.text)
    print(result.document.metadata)

🏷️ Add metadata

rag.add(
    ["Leave policy text", "Travel policy text"],
    metadatas=[
        {"file": "leave-policy.pdf", "page": 3},
        {"file": "travel-policy.pdf", "page": 7},
    ],
)

Use normalized metadata filtering with either retrieval or answer generation. When stored metadata is a list, a scalar filter matches one list value:

results = rag.search(
    "What is the leave policy?",
    metadata_filter={"department": "engineering"},
)

answer = rag.ask(
    "What is the leave policy?",
    metadata_filter={"department": "engineering"},
)

LLMMetadataFactory stores generated values as normalized lists, for example {"category": ["sustainability"], "language": ["en"]}.

For large text directories, generate metadata automatically with FolderMetadataFactory from folder names, CSVMetadataFactory from a metadata manifest, or LLMMetadataFactory from a bounded file sample. Use DirectoryLoader.iter_load() with rag.add_documents([document]) to process one file at a time. See loaders and chunking.

⚙️ Configure retrieval

from pyaistack import RAG, RAGConfig

rag = RAG(
    config=RAGConfig(
        top_k=5,
        min_score=0.25,
        max_context_chars=20_000,
        include_sources=True,
    )
)

💬 Friendly errors

Use format_error at your application entry point to show the relevant application line without PyAIStack implementation frames:

from pyaistack import RAG, format_error
from pyaistack.exceptions import ProviderError

rag = RAG()

try:
    rag.add(["A document to index."])
except ProviderError as error:
    print(format_error(error))

For example, a missing embedding model is displayed as:

Traceback (most recent call last):
  File "/path/to/app.py", line 8, in <module>
    rag.add(["A document to index."])
pyaistack.exceptions.ProviderError: Model "embeddinggemma" is not available.
Run: ollama pull embeddinggemma

📄 License

Apache License 2.0. See LICENSE.