# 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:
```bash
pip install flowfoundry
```
With extras:
```bash
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:
```python
from flowfoundry import ping, hello
print(ping()) # -> "flowfoundry: ok"
print(hello("there")) # -> "hello, there!"
```
## Quickstart (Functional API)
```python
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:
```bash
# 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 | – |
```python
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` |
```python
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` |
```python
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 |
```python
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:
```python
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)
```bash
flowfoundry indexing chroma_query \
--kwargs '{"query":"What is people'\''s budget?","path":".ff_chroma","collection":"docs","k":8}' > hits.json
```
Step 2: rerank (BM25)
```bash
flowfoundry rerank bm25_preselect \
--kwargs "{\"query\":\"What is people's budget?\",\"hits\":$(cat hits.json),\"top_k\":5}" > hits_top5.json
```
Step 3: compose answer with OpenAI
``` bash
export OPENAI_API_KEY=...
flowfoundry compose llm \
--kwargs "{\"question\":\"What is people's budget?\",\"hits\":$(cat hits_top5.json),\"provider\":\"openai\",\"model\":\"gpt-4o-mini\",\"max_tokens\":400}"
```
## YAML based run
Planned Schema:
```yaml
version: 1
vars: # optional globals you can reference later
key: value
steps: # ordered list of steps
- id: step_name
use: family.function_name # e.g., chunking.chunk_recursive
with: # kwargs passed to that function
param1: foo
param2: ${{ vars.key }} # reference vars or prior steps
outputs: # optional; what to print at the end
result: ${{ step_name }}
```
Example 1 — Minimal RAG (inline text)
```yaml
version: 1
vars:
data_path: docs/samples/
store_path: .ff_chroma2
collection: docs
question: "Summarize the pdfs"
steps:
# 1) Load PDFs (your existing strategy)
- id: pages
use: ingestion.pdf_loader
with:
path: ${{ vars.data_path }}
# 2) Chunk every page, preserving source/page metadata
- id: chunks
use: chunking.recursive
with:
data: ${{ pages }}
chunk_size: 800
chunk_overlap: 120
# 3) Upsert chunks into Chroma
- id: upsert
use: indexing.chroma_upsert
with:
chunks: ${{ chunks }}
path: ${{ vars.store_path }}
collection: ${{ vars.collection }}
# 4) Retrieve relevant chunks
- id: retrieve
use: indexing.chroma_query
with:
query: ${{ vars.question }}
path: ${{ vars.store_path }}
collection: ${{ vars.collection }}
k: 12
# 5) (Optional) BM25 preselect
- id: preselect
use: rerank.bm25_preselect
with:
query: ${{ vars.question }}
hits: ${{ retrieve }}
top_k: 6
# 6) Compose final answer (pick your provider)
- id: answer
use: compose.llm
with:
question: ${{ vars.question }}
hits: ${{ preselect }}
provider: openai # or "ollama" / "huggingface"
model: gpt-4o-mini
max_tokens: 400
outputs:
final_answer: ${{ answer }}
```
Run:
```bash
pip install "flowfoundry[rag,rerank,openai,llm-openai]"
export OPENAI_API_KEY=...
flowfoundry run rag_sample.yaml -V question="Summarize the PDFs"
```
## Custom Logic
Create a file anywhere (e.g., examples/external_plugins/pdf_loader_openai.py):
```python
# examples/external_plugins/pdf_loader_openai.py
from __future__ import annotations
from pathlib import Path
from typing import Dict, List, Union
from flowfoundry.utils import register_strategy, FFIngestionError
@register_strategy("ingestion", "pdf_loader_openai")
def pdf_loader_openai(path: Union[str, Path]) -> List[Dict]:
"""
Return page dicts compatible with FlowFoundry indexing:
{"source": str, "page": int, "text": str}
"""
p = Path(path)
if not p.exists():
raise FFIngestionError(f"Path not found: {p}")
pdfs = [p] if (p.is_file() and p.suffix.lower()==".pdf") else list(p.rglob("*.pdf"))
if not pdfs:
raise FFIngestionError(f"No PDFs under {p}")
# Replace with your own logic. This stub just makes empty pages:
return [{"source": str(pdf.resolve()), "page": 1, "text": f"stub text for {pdf.name}"} for pdf in pdfs]
# Optional: bind this function into `flowfoundry.functional` for ergonomic imports
FF_EXPORTS = [
("ingestion", "pdf_loader_openai", "pdf_loader_openai"),
# You can also add a convenience alias:
# ("ingestion", "pdf_loader_openai", "pdf_loader"),
]
```
Use it from Python
```python
from flowfoundry.utils.plugin_loader import load_plugins
from flowfoundry.utils.functional_registry import strategies
# 1) Load your file(s) so decorators run (and optional FF_EXPORTS bind)
load_plugins(["examples/external_plugins/pdf_loader_openai.py"], export_to_functional=True)
# 2) Grab it by registry name (robust)
pdf_loader = strategies.get("ingestion", "pdf_loader_openai")
pages = pdf_loader("docs/samples")
# 3) Continue with the Functional API
from flowfoundry.functional import chunk_recursive, index_chroma_upsert
chunks = []
for pg in pages:
for ch in chunk_recursive(pg["text"], chunk_size=500, chunk_overlap=50, doc_id="demo"):
ch["meta"] = {"source": pg["source"], "page": pg["page"]}
chunks.append(ch)
index_chroma_upsert(chunks, path=".ff_chroma", collection="docs")
```
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"description": "# FlowFoundry\n\n*A strategy-first, cloud-agnostic framework for LLM workflows.* \nCompose chunking, indexing, retrieval, reranking, and agentic flows \u2014 with **Keras-like ergonomics** over LangChain / LangGraph.\n\n---\n\n## \u2728 Features\n\n- **Strategies**: chunking, indexing, retrieval, reranking \n- **Functional API**: call strategies directly as Python functions \n- **Blocks API**: compose strategies like layers \n- **Nodes & Graphs**: LangGraph-backed workflows (YAML or Python) \n- **Extensible**: register custom strategies or nodes \n\n---\n\n## Installation\n\nCore only:\n\n```bash\npip install flowfoundry\n```\n\nWith extras:\n```bash\npip install \"flowfoundry[rag,search,rerank,qdrant,openai,llm-openai]\"\n```\n\nExtras include: chromadb, qdrant-client, sentence-transformers, rank-bm25, openai, etc.\nAll examples run offline by default (echo LLM). Missing deps no-op gracefully.\n\nSanity check:\n```python\nfrom flowfoundry import ping, hello\nprint(ping()) # -> \"flowfoundry: ok\"\nprint(hello(\"there\")) # -> \"hello, there!\"\n```\n\n## Quickstart (Functional API)\n\n```python\nfrom flowfoundry.functional import (\n chunk_recursive, index_chroma_upsert, index_chroma_query, preselect_bm25\n)\n\ntext = \"FlowFoundry lets you mix strategies to build RAG.\"\nchunks = chunk_recursive(text, chunk_size=120, chunk_overlap=20, doc_id=\"demo\")\n\n# Index & query (requires chromadb extra)\nindex_chroma_upsert(chunks, path=\".ff_chroma\", collection=\"docs\")\nhits = index_chroma_query(\"What is FlowFoundry?\", path=\".ff_chroma\", collection=\"docs\", k=8)\n\n# Optional rerank (requires rank-bm25)\nhits = preselect_bm25(\"What is FlowFoundry?\", hits, top_k=5)\n\nprint(hits[0][\"text\"])\n```\n\n### CLI\n\nAll registered strategies are available via the flowfoundry CLI.\n\nRun:\n```bash\n# list families and functions\nflowfoundry list\n\n# call a strategy directly\nflowfoundry chunking fixed --kwargs '{\"data\":\"hello world\",\"chunk_size\":5}'\n\n# equivalent generic call\nflowfoundry call chunking fixed --kwargs '{\"data\":\"hello world\",\"chunk_size\":5}'\n```\n\n## Functional API Reference\n\nAvailable in `flowfoundry.functional`:\n\n---\n\n### Chunking\n\n| Function | Purpose | Extra deps |\n|-------------------|----------------------|------------|\n| `chunk_fixed` | Fixed-size splitter | \u2013 |\n| `chunk_recursive` | Recursive splitter | `langchain-text-splitters` |\n| `chunk_hybrid` | Hybrid splitter | \u2013 |\n\n```python\nchunk_fixed(text, *, chunk_size=800, chunk_overlap=80, doc_id=\"doc\") -> list[Chunk]\nchunk_recursive(text, *, chunk_size=800, chunk_overlap=80, doc_id=\"doc\") -> list[Chunk]\nchunk_hybrid(text, **kwargs) -> list[Chunk]\n```\n\n---\n\n### Indexing (Chroma)\n\n| Function | Purpose | Extra deps |\n|-----------------------|----------------------|------------|\n| `index_chroma_upsert` | Upsert chunks into Chroma |`chromadb` |\n| `index_chroma_query` | Query Chroma | `chromadb` |\n\n```python\nindex_chroma_upsert(chunks, *, path=\".ff_chroma\", collection=\"docs\") -> str\nindex_chroma_query(query, *, path, collection, k=5) -> list[Hit]\n```\n---\n\n### Reranking\n\n| Function | Purpose | Extra deps |\n|-------------------|----------------------|------------|\n| `rerank_identity` | No-op reranker | \u2013 |\n| `preselect_bm25` | BM25 preselect | `rank-bm25` |\n| `rerank_cross_encoder` | Cross-encoder reranker |`sentence-transformers` |\n\n```python\nrerank_identity(query, hits, top_k=None) -> list[Hit]\npreselect_bm25(query, hits, top_k=20) -> list[Hit]\nrerank_cross_encoder(query, hits, *, model, top_k=None) -> list[Hit]\n```\n\n### Composition (LLM Answering)\n\n| Function | Purpose | Providers supported | Extra deps |\n|-------------------|----------------------|------------|----------------------|\n| `compose_llm` | Generate an answer from hits via an LLM | openai, ollama, huggingface, langchain | provider-specific |\n\n```python\ncompose_llm(\n question: str,\n hits: list[Hit],\n *,\n provider: str, # \"openai\", \"ollama\", \"huggingface\", \"langchain\"\n model: str, # e.g. \"gpt-4o-mini\", \"llama3:8b\", \"distilgpt2\"\n max_context_chars=6000,\n max_tokens=512,\n reuse_provider=True,\n **provider_kwargs # api_key, host, backend, device, etc.\n) -> str\n```\n\n## Example Code:\n```python\nfrom flowfoundry import index_chroma_query, preselect_bm25, compose_llm\n\nquestion = \"What is people's budget?\"\nhits = index_chroma_query(question, path=\".ff_chroma\", collection=\"docs\", k=8)\nhits = preselect_bm25(question, hits, top_k=5)\n\n# OpenAI provider\nanswer = compose_llm(\n question, hits,\n provider=\"openai\",\n model=\"gpt-4o-mini\",\n max_tokens=400,\n)\nprint(answer)\n\n# Ollama provider\nanswer = compose_llm(\n question, hits,\n provider=\"ollama\",\n model=\"llama3:8b\",\n host=\"http://localhost:11434\",\n max_tokens=400,\n)\nprint(answer)\n\n# HuggingFace local transformers\nanswer = compose_llm(\n question, hits,\n provider=\"huggingface\",\n model=\"distilgpt2\",\n max_tokens=200,\n)\nprint(answer)\n```\n\nExample (CLI)\n\nSave retrieval hits into JSON first, then pass them to compose_llm:\n\nStep 1: query (Chroma)\n```bash\nflowfoundry indexing chroma_query \\\n --kwargs '{\"query\":\"What is people'\\''s budget?\",\"path\":\".ff_chroma\",\"collection\":\"docs\",\"k\":8}' > hits.json\n```\n\nStep 2: rerank (BM25)\n```bash\n flowfoundry rerank bm25_preselect \\\n --kwargs \"{\\\"query\\\":\\\"What is people's budget?\\\",\\\"hits\\\":$(cat hits.json),\\\"top_k\\\":5}\" > hits_top5.json\n```\n\nStep 3: compose answer with OpenAI\n``` bash\nexport OPENAI_API_KEY=...\nflowfoundry compose llm \\\n --kwargs \"{\\\"question\\\":\\\"What is people's budget?\\\",\\\"hits\\\":$(cat hits_top5.json),\\\"provider\\\":\\\"openai\\\",\\\"model\\\":\\\"gpt-4o-mini\\\",\\\"max_tokens\\\":400}\"\n```\n\n## YAML based run\n\nPlanned Schema:\n```yaml\nversion: 1\nvars: # optional globals you can reference later\n key: value\nsteps: # ordered list of steps\n - id: step_name\n use: family.function_name # e.g., chunking.chunk_recursive\n with: # kwargs passed to that function\n param1: foo\n param2: ${{ vars.key }} # reference vars or prior steps\noutputs: # optional; what to print at the end\n result: ${{ step_name }}\n```\n\nExample 1 \u2014 Minimal RAG (inline text)\n```yaml\nversion: 1\n\nvars:\n data_path: docs/samples/ \n store_path: .ff_chroma2\n collection: docs\n question: \"Summarize the pdfs\"\n\nsteps:\n # 1) Load PDFs (your existing strategy)\n - id: pages\n use: ingestion.pdf_loader\n with:\n path: ${{ vars.data_path }}\n\n # 2) Chunk every page, preserving source/page metadata\n - id: chunks\n use: chunking.recursive\n with:\n data: ${{ pages }}\n chunk_size: 800\n chunk_overlap: 120\n\n # 3) Upsert chunks into Chroma\n - id: upsert\n use: indexing.chroma_upsert\n with:\n chunks: ${{ chunks }}\n path: ${{ vars.store_path }}\n collection: ${{ vars.collection }}\n\n # 4) Retrieve relevant chunks\n - id: retrieve\n use: indexing.chroma_query\n with:\n query: ${{ vars.question }}\n path: ${{ vars.store_path }}\n collection: ${{ vars.collection }}\n k: 12\n\n # 5) (Optional) BM25 preselect\n - id: preselect\n use: rerank.bm25_preselect\n with:\n query: ${{ vars.question }}\n hits: ${{ retrieve }}\n top_k: 6\n\n # 6) Compose final answer (pick your provider)\n - id: answer\n use: compose.llm\n with:\n question: ${{ vars.question }}\n hits: ${{ preselect }}\n provider: openai # or \"ollama\" / \"huggingface\"\n model: gpt-4o-mini\n max_tokens: 400\n\noutputs:\n final_answer: ${{ answer }}\n```\n\nRun:\n```bash\npip install \"flowfoundry[rag,rerank,openai,llm-openai]\"\nexport OPENAI_API_KEY=...\nflowfoundry run rag_sample.yaml -V question=\"Summarize the PDFs\"\n```\n\n## Custom Logic\n\nCreate a file anywhere (e.g., examples/external_plugins/pdf_loader_openai.py):\n\n```python\n# examples/external_plugins/pdf_loader_openai.py\nfrom __future__ import annotations\nfrom pathlib import Path\nfrom typing import Dict, List, Union\nfrom flowfoundry.utils import register_strategy, FFIngestionError\n\n@register_strategy(\"ingestion\", \"pdf_loader_openai\")\ndef pdf_loader_openai(path: Union[str, Path]) -> List[Dict]:\n \"\"\"\n Return page dicts compatible with FlowFoundry indexing:\n {\"source\": str, \"page\": int, \"text\": str}\n \"\"\"\n p = Path(path)\n if not p.exists():\n raise FFIngestionError(f\"Path not found: {p}\")\n pdfs = [p] if (p.is_file() and p.suffix.lower()==\".pdf\") else list(p.rglob(\"*.pdf\"))\n if not pdfs:\n raise FFIngestionError(f\"No PDFs under {p}\")\n\n # Replace with your own logic. This stub just makes empty pages:\n return [{\"source\": str(pdf.resolve()), \"page\": 1, \"text\": f\"stub text for {pdf.name}\"} for pdf in pdfs]\n\n# Optional: bind this function into `flowfoundry.functional` for ergonomic imports\nFF_EXPORTS = [\n (\"ingestion\", \"pdf_loader_openai\", \"pdf_loader_openai\"),\n # You can also add a convenience alias:\n # (\"ingestion\", \"pdf_loader_openai\", \"pdf_loader\"),\n]\n```\n\nUse it from Python\n\n```python\nfrom flowfoundry.utils.plugin_loader import load_plugins\nfrom flowfoundry.utils.functional_registry import strategies\n\n# 1) Load your file(s) so decorators run (and optional FF_EXPORTS bind)\nload_plugins([\"examples/external_plugins/pdf_loader_openai.py\"], export_to_functional=True)\n\n# 2) Grab it by registry name (robust)\npdf_loader = strategies.get(\"ingestion\", \"pdf_loader_openai\")\npages = pdf_loader(\"docs/samples\")\n\n# 3) Continue with the Functional API\nfrom flowfoundry.functional import chunk_recursive, index_chroma_upsert\nchunks = []\nfor pg in pages:\n for ch in chunk_recursive(pg[\"text\"], chunk_size=500, chunk_overlap=50, doc_id=\"demo\"):\n ch[\"meta\"] = {\"source\": pg[\"source\"], \"page\": pg[\"page\"]}\n chunks.append(ch)\nindex_chroma_upsert(chunks, path=\".ff_chroma\", collection=\"docs\")\n```\n",
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