flowfoundry 1.1.0

FlowFoundry: a strategy-first, cloud-agnostic agentic workflow framework (LangGraph/LangChain)

FlowFoundry

A strategy-first, cloud-agnostic framework for LLM workflows.
Compose chunking, indexing, retrieval, reranking, and agentic flows — with Keras-like ergonomics over LangChain / LangGraph.

---

✨ Features

• Strategies: chunking, indexing, retrieval, reranking
• Functional API: call strategies directly as Python functions
• Blocks API: compose strategies like layers
• Nodes & Graphs: LangGraph-backed workflows (YAML or Python)
• Extensible: register custom strategies or nodes

---

Installation

Core only:

pip install flowfoundry

With extras:

pip install "flowfoundry[rag,search,rerank,qdrant,openai,llm-openai]"

Extras include: chromadb, qdrant-client, sentence-transformers, rank-bm25, openai, etc. All examples run offline by default (echo LLM). Missing deps no-op gracefully.

Sanity check:

from flowfoundry import ping, hello
print(ping())          # -> "flowfoundry: ok"
print(hello("there"))  # -> "hello, there!"

Quickstart (Functional API)

from flowfoundry.functional import (
  chunk_recursive, index_chroma_upsert, index_chroma_query, preselect_bm25
)

text   = "FlowFoundry lets you mix strategies to build RAG."
chunks = chunk_recursive(text, chunk_size=120, chunk_overlap=20, doc_id="demo")

# Index & query (requires chromadb extra)
index_chroma_upsert(chunks, path=".ff_chroma", collection="docs")
hits = index_chroma_query("What is FlowFoundry?", path=".ff_chroma", collection="docs", k=8)

# Optional rerank (requires rank-bm25)
hits = preselect_bm25("What is FlowFoundry?", hits, top_k=5)

print(hits[0]["text"])

CLI

All registered strategies are available via the flowfoundry CLI.

Run:

# list families and functions
flowfoundry list

# call a strategy directly
flowfoundry chunking fixed --kwargs '{"data":"hello world","chunk_size":5}'

# equivalent generic call
flowfoundry call chunking fixed --kwargs '{"data":"hello world","chunk_size":5}'

Functional API Reference

Available in flowfoundry.functional:

---

Chunking

Function	Purpose	Extra deps
chunk_fixed	Fixed-size splitter	–
chunk_recursive	Recursive splitter	langchain-text-splitters
chunk_hybrid	Hybrid splitter	–

chunk_fixed(text, *, chunk_size=800, chunk_overlap=80, doc_id="doc") -> list[Chunk]
chunk_recursive(text, *, chunk_size=800, chunk_overlap=80, doc_id="doc") -> list[Chunk]
chunk_hybrid(text, **kwargs) -> list[Chunk]

---

Indexing (Chroma)

Function	Purpose	Extra deps
index_chroma_upsert	Upsert chunks into Chroma	chromadb
index_chroma_query	Query Chroma	chromadb

index_chroma_upsert(chunks, *, path=".ff_chroma", collection="docs") -> str
index_chroma_query(query, *, path, collection, k=5) -> list[Hit]

---

Reranking

Function	Purpose	Extra deps
rerank_identity	No-op reranker	–
preselect_bm25	BM25 preselect	rank-bm25
rerank_cross_encoder	Cross-encoder reranker	sentence-transformers

rerank_identity(query, hits, top_k=None) -> list[Hit]
preselect_bm25(query, hits, top_k=20) -> list[Hit]
rerank_cross_encoder(query, hits, *, model, top_k=None) -> list[Hit]

Composition (LLM Answering)

Function	Purpose	Providers supported	Extra deps
compose_llm	Generate an answer from hits via an LLM	openai, ollama, huggingface, langchain	provider-specific

compose_llm(
    question: str,
    hits: list[Hit],
    *,
    provider: str,        # "openai", "ollama", "huggingface", "langchain"
    model: str,           # e.g. "gpt-4o-mini", "llama3:8b", "distilgpt2"
    max_context_chars=6000,
    max_tokens=512,
    reuse_provider=True,
    **provider_kwargs     # api_key, host, backend, device, etc.
) -> str

Example Code:

from flowfoundry import index_chroma_query, preselect_bm25, compose_llm

question = "What is people's budget?"
hits = index_chroma_query(question, path=".ff_chroma", collection="docs", k=8)
hits = preselect_bm25(question, hits, top_k=5)

# OpenAI provider
answer = compose_llm(
    question, hits,
    provider="openai",
    model="gpt-4o-mini",
    max_tokens=400,
)
print(answer)

# Ollama provider
answer = compose_llm(
    question, hits,
    provider="ollama",
    model="llama3:8b",
    host="http://localhost:11434",
    max_tokens=400,
)
print(answer)

# HuggingFace local transformers
answer = compose_llm(
    question, hits,
    provider="huggingface",
    model="distilgpt2",
    max_tokens=200,
)
print(answer)

Example (CLI)

Save retrieval hits into JSON first, then pass them to compose_llm:

Step 1: query (Chroma)

flowfoundry indexing chroma_query \
  --kwargs '{"query":"What is people'\''s budget?","path":".ff_chroma","collection":"docs","k":8}' > hits.json

Step 2: rerank (BM25)

 flowfoundry rerank bm25_preselect \
  --kwargs "{\"query\":\"What is people's budget?\",\"hits\":$(cat hits.json),\"top_k\":5}" > hits_top5.json

Step 3: compose answer with OpenAI

export OPENAI_API_KEY=...
flowfoundry compose llm \
  --kwargs "{\"question\":\"What is people's budget?\",\"hits\":$(cat hits_top5.json),\"provider\":\"openai\",\"model\":\"gpt-4o-mini\",\"max_tokens\":400}"
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