ractogateway 0.1.2

A unified, production-ready AI SDK that enforces structured outputs and anti-hallucination prompting via the RACTO principle. One package for OpenAI, Gemini, and Anthropic — with streaming, tool calling, embeddings, and strict Pydantic validation.

RactoGateway

One Python package for all production-grade LLM solutions.

RactoGateway is a unified AI SDK that gives you a single, clean interface to OpenAI, Google Gemini, and Anthropic Claude — with built-in anti-hallucination prompting, strict Pydantic validation, streaming, tool calling, embeddings, fine-tuning, and a full RAG pipeline. No more messy JSON dicts. No more provider lock-in. No more inconsistent response formats.

---

Table of Contents

• Why RactoGateway?
• Installation
• 5-Line Quick Start
• RACTO Prompt Engine
• Developer Kits
• Streaming
• Async Support
• Embeddings
• Tool Calling
• Validated Response Models
• Multi-turn Conversations
• Multimodal Attachments — Images & Files
• Low-Level Gateway
• Switching Providers
• Fine-Tuning
• RAG — Retrieval-Augmented Generation
• Architecture
• Environment Variables

---

Why RactoGateway?

Every LLM provider has a different SDK, different request format, different response structure, and different tool-calling schema. Building production AI applications means writing glue code, parsing deeply nested objects, and manually stripping markdown fences from JSON responses.

RactoGateway solves this by providing:

• RACTO Prompt Engine — a structured prompt framework (Role, Aim, Constraints, Tone, Output) that compiles into optimized, anti-hallucination system prompts
• Three Developer Kits — gpt (OpenAI), gemini (Google), claude (Anthropic) — each with chat(), achat(), stream(), astream(), embed(), and aembed()
• Strict Pydantic models for every input and output — no raw dicts anywhere
• Automatic JSON parsing — responses are cleaned of markdown fences and auto-parsed
• Unified tool calling — define tools once as Python functions, use them with any provider
• Streaming with typed chunks — every StreamChunk has .delta.text, .accumulated_text, .is_final, .usage
• RAG pipeline — ingest files, embed, store, retrieve, and generate answers with one class
• Low-level Gateway — wraps any adapter for direct prompt execution without ChatConfig

---

Installation

# Core package (includes RACTO prompt engine and tool registry)
pip install ractogateway

# With a specific LLM provider
pip install ractogateway[openai]
pip install ractogateway[google]
pip install ractogateway[anthropic]

# All LLM providers
pip install ractogateway[all]

# RAG: base readers + NLP processing
pip install ractogateway[rag]

# RAG: everything (all readers, stores, embedders)
pip install ractogateway[rag-all]

# RAG: individual extras
pip install ractogateway[rag-pdf]       # PDF support
pip install ractogateway[rag-word]      # .docx support
pip install ractogateway[rag-excel]     # .xlsx support
pip install ractogateway[rag-image]     # image OCR support
pip install ractogateway[rag-nlp]       # lemmatizer NLP processing

# RAG: vector stores
pip install ractogateway[rag-chroma]    # ChromaDB
pip install ractogateway[rag-faiss]     # FAISS
pip install ractogateway[rag-pinecone]  # Pinecone
pip install ractogateway[rag-qdrant]    # Qdrant
pip install ractogateway[rag-weaviate]  # Weaviate
pip install ractogateway[rag-milvus]    # Milvus
pip install ractogateway[rag-pgvector]  # PostgreSQL pgvector

# RAG: embedding providers
pip install ractogateway[rag-voyage]    # Voyage AI embeddings

# Development (all providers + testing + linting)
pip install ractogateway[dev]

Requirements: Python 3.10+, Pydantic 2.0+

---

5-Line Quick Start

This is the absolute minimum to get a response from any AI — no configuration needed beyond your API key:

from ractogateway import openai_developer_kit as gpt, RactoPrompt

# 1. Describe what you want the AI to do
prompt = RactoPrompt(
    role="You are a helpful assistant.",
    aim="Answer the user's question clearly.",
    constraints=["Be concise."],
    tone="Friendly",
    output_format="text",
)

# 2. Create your AI chat (reads OPENAI_API_KEY from environment automatically)
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)

# 3. Ask something!
response = kit.chat(gpt.ChatConfig(user_message="What is Python?"))
print(response.content)
# "Python is a beginner-friendly, high-level programming language used for web
#  development, data science, AI, automation, and much more."

That's it. Swap gpt for gemini or claude and the exact same code works with Google or Anthropic.

---

RACTO Prompt Engine

The RACTO principle structures every prompt into five unambiguous sections so the model always knows exactly what to do — and what NOT to do.

Letter	Field	Purpose
R	role	Who the model is
A	aim	What it must accomplish
C	constraints	Hard rules it must never violate
T	tone	Communication style
O	output_format	Exact shape of the response

Defining a Prompt

from ractogateway import RactoPrompt

prompt = RactoPrompt(
    role="You are a senior Python code reviewer at a Fortune 500 company.",
    aim="Review the given code for bugs, security vulnerabilities, and PEP-8 violations.",
    constraints=[
        "Only report issues you are certain about.",
        "Do not suggest stylistic preferences.",
        "If no issues are found, say so explicitly.",
        "Never fabricate code examples that you cannot verify.",
    ],
    tone="Professional and concise",
    output_format="json",
)

All RactoPrompt Fields

Field	Type	Required	Default	Description
role	str	Yes	—	Who the model is
aim	str	Yes	—	Task objective
constraints	list[str]	Yes	—	Hard rules (min 1 item)
tone	str	Yes	—	Communication style
output_format	str | type[BaseModel]	Yes	—	"json", "text", "markdown", free-form description, or a Pydantic class
context	str | None	No	None	Domain background injected between AIM and CONSTRAINTS
examples	list[dict] | None	No	None	Few-shot pairs — each dict requires "input" and "output" keys
anti_hallucination	bool	No	True	Append [GUARDRAILS] block

RactoPrompt Methods

Method	Signature	Returns	Description
compile()	() -> str	str	Generate the full system prompt string
__str__()	() -> str	str	Shortcut for compile()
to_messages()	(user_message, attachments=None, provider="generic") -> list[dict]	list[dict]	Build a provider-ready message list

What prompt.compile() Produces

Calling prompt.compile() (or just str(prompt)) gives you the full system prompt:

[ROLE]
You are a senior Python code reviewer at a Fortune 500 company.

[AIM]
Review the given code for bugs, security vulnerabilities, and PEP-8 violations.

[CONSTRAINTS]
- Only report issues you are certain about.
- Do not suggest stylistic preferences.
- If no issues are found, say so explicitly.
- Never fabricate code examples that you cannot verify.

[TONE]
Professional and concise

[OUTPUT]
Respond ONLY with valid JSON. Do NOT wrap the response in markdown code
fences (```json … ```) or add any commentary before or after the JSON object.

[GUARDRAILS]
- If you are unsure or lack sufficient information, state it explicitly rather than guessing.
- Do NOT fabricate facts, citations, URLs, statistics, or code that you cannot verify.
- Stick strictly to what is asked. Do not add unrequested information.
- If the answer requires assumptions, list each assumption explicitly before proceeding.

Pydantic Model as Output Format

Pass a Pydantic model class as output_format and the full JSON Schema is embedded in the compiled prompt automatically:

from pydantic import BaseModel

class CodeReview(BaseModel):
    issues: list[str]
    severity: str   # "low", "medium", "high"
    suggestion: str

prompt = RactoPrompt(
    role="You are a code reviewer.",
    aim="Review the code.",
    constraints=["Only report real issues."],
    tone="Concise",
    output_format=CodeReview,   # ← JSON Schema auto-embedded in prompt
)

print(prompt.compile())

Compiled output (OUTPUT section):

[OUTPUT]
Respond ONLY with valid JSON that conforms exactly to the following JSON Schema.
Do NOT wrap the JSON in markdown code fences or add any text before or after it.

JSON Schema:
{
  "type": "object",
  "properties": {
    "issues": {"type": "array", "items": {"type": "string"}},
    "severity": {"type": "string"},
    "suggestion": {"type": "string"}
  },
  "required": ["issues", "severity", "suggestion"]
}

Few-Shot Examples

prompt = RactoPrompt(
    role="You are a sentiment classifier.",
    aim="Classify the sentiment of the user's text.",
    constraints=["Only output: positive, negative, or neutral."],
    tone="Concise",
    output_format="json",
    examples=[
        {"input": "I love this product!", "output": '{"sentiment": "positive"}'},
        {"input": "This is broken and useless.", "output": '{"sentiment": "negative"}'},
        {"input": "It arrived yesterday.", "output": '{"sentiment": "neutral"}'},
    ],
)

to_messages() — Ready-to-Send Message List

Input parameters:

Parameter	Type	Default	Description
user_message	str	—	The end-user's query (required)
attachments	list[RactoFile] | None	None	Optional file/image attachments
provider	str	"generic"	"openai", "anthropic", "google", or "generic"

Output: list[dict[str, Any]] — a list of message dicts ready to send to the provider

messages = prompt.to_messages(
    "Review this: def add(a, b): return a + b",
    provider="openai",   # "openai" | "anthropic" | "google" | "generic"
)

# Output:
# [
#   {"role": "system", "content": "<compiled RACTO system prompt>"},
#   {"role": "user",   "content": "Review this: def add(a, b): return a + b"}
# ]

---

Developer Kits

RactoGateway has three kits — one for each AI provider. Import them with names you already know, then call .Chat(...) to create your AI:

from ractogateway import openai_developer_kit as gpt      # ChatGPT / OpenAI
from ractogateway import google_developer_kit as gemini   # Google Gemini
from ractogateway import anthropic_developer_kit as claude # Anthropic Claude

Note: and is a reserved Python keyword in Python, so we use claude instead — cleaner anyway!

Creating a Chat

Every kit exposes a Chat class — short, readable, and always works the same way:

# Just pick your provider and model — that's it!
kit = gpt.Chat(model="gpt-4o")
kit = gemini.Chat(model="gemini-2.0-flash")
kit = claude.Chat(model="claude-sonnet-4-6")

The API key is read automatically from your environment variable (OPENAI_API_KEY, GEMINI_API_KEY, or ANTHROPIC_API_KEY). No extra setup needed.

Full constructor options (all optional except model):

# OpenAI / ChatGPT
kit = gpt.Chat(
    model="gpt-4o",                            # which model to use
    api_key="sk-...",                          # skip if OPENAI_API_KEY is set
    base_url="https://custom-proxy.com/v1",    # optional: Azure or custom proxy
    embedding_model="text-embedding-3-small",  # for embed() calls
    default_prompt=prompt,                     # auto-used in every chat if set
)

# Google Gemini
kit = gemini.Chat(
    model="gemini-2.0-flash",                  # which model to use
    api_key="AIza...",                         # skip if GEMINI_API_KEY is set
    embedding_model="text-embedding-004",      # for embed() calls
    default_prompt=prompt,                     # auto-used in every chat if set
)

# Anthropic Claude
kit = claude.Chat(
    model="claude-sonnet-4-6",                 # which model to use
    api_key="sk-ant-...",                      # skip if ANTHROPIC_API_KEY is set
    default_prompt=prompt,                     # auto-used in every chat if set
)

OpenAIDeveloperKit / gpt.Chat constructor parameters:

Parameter	Type	Default	Description
model	str	"gpt-4o"	Chat model identifier
api_key	str | None	None	Falls back to OPENAI_API_KEY env var
base_url	str | None	None	Azure OpenAI or proxy base URL
embedding_model	str	"text-embedding-3-small"	Default model for embed() calls
default_prompt	RactoPrompt | None	None	Auto-used when ChatConfig.prompt is None

GoogleDeveloperKit / gemini.Chat constructor parameters:

Parameter	Type	Default	Description
model	str	"gemini-2.0-flash"	Chat model identifier
api_key	str | None	None	Falls back to GEMINI_API_KEY env var
embedding_model	str	"text-embedding-004"	Default model for embed() calls
default_prompt	RactoPrompt | None	None	Auto-used when ChatConfig.prompt is None

AnthropicDeveloperKit / claude.Chat constructor parameters:

Parameter	Type	Default	Description
model	str	—	Chat model identifier (required)
api_key	str | None	None	Falls back to ANTHROPIC_API_KEY env var
default_prompt	RactoPrompt | None	None	Auto-used when ChatConfig.prompt is None

Method Reference

Method	gpt	gemini	claude	Input	Output
chat(config)	Yes	Yes	Yes	ChatConfig	LLMResponse
achat(config)	Yes	Yes	Yes	ChatConfig	LLMResponse
stream(config)	Yes	Yes	Yes	ChatConfig	Iterator[StreamChunk]
astream(config)	Yes	Yes	Yes	ChatConfig	AsyncIterator[StreamChunk]
embed(config)	Yes	Yes	—	EmbeddingConfig	EmbeddingResponse
aembed(config)	Yes	Yes	—	EmbeddingConfig	EmbeddingResponse

Anthropic does not offer a native embedding API. Use the OpenAI or Google kit for embeddings.

---

ChatConfig — Input Model

The single input object for chat(), achat(), stream(), and astream().

config = gpt.ChatConfig(
    user_message="Explain monads in simple terms.",   # required
    prompt=prompt,                                     # optional — overrides kit default
    temperature=0.3,                                   # 0.0–2.0, default 0.0
    max_tokens=2048,                                   # default 4096
    tools=my_tool_registry,                            # optional ToolRegistry
    response_model=MyPydanticModel,                    # optional output validation
    history=[                                          # optional multi-turn context
        gpt.Message(role=gpt.MessageRole.USER, content="What is FP?"),
        gpt.Message(role=gpt.MessageRole.ASSISTANT, content="Functional programming is..."),
    ],
    extra={"top_p": 0.9, "seed": 42},                 # provider-specific pass-through
)

ChatConfig field reference:

Field	Type	Required	Default	Description
user_message	str	Yes	—	End-user's query (min 1 character)
prompt	RactoPrompt | None	No	None	Overrides the kit's default_prompt for this call
temperature	float	No	0.0	Sampling temperature (0.0–2.0)
max_tokens	int	No	4096	Maximum tokens in the completion (>0)
tools	ToolRegistry | None	No	None	Tool registry for function/tool calling
response_model	type[BaseModel] | None	No	None	Validate JSON output against this Pydantic model
history	list[Message]	No	[]	Prior conversation turns for multi-turn chat
extra	dict[str, Any]	No	{}	Provider-specific pass-through kwargs (e.g. top_p, seed, stop)

Note: Either ChatConfig.prompt or the kit's default_prompt must be set — at least one is required.

---

Message and MessageRole

Used to build conversation history for multi-turn chat.

from ractogateway import openai_developer_kit as gpt

msg = gpt.Message(role=gpt.MessageRole.USER, content="What is Python?")

Message field reference:

Field	Type	Description
role	MessageRole	SYSTEM, USER, or ASSISTANT
content	str	The message text

MessageRole enum values:

Value	String	Description
MessageRole.SYSTEM	"system"	System instruction
MessageRole.USER	"user"	Human turn
MessageRole.ASSISTANT	"assistant"	Model turn

---

LLMResponse — Chat Output

Returned by chat() and achat(). Same shape for all three providers.

response = kit.chat(gpt.ChatConfig(user_message="What is 2 + 2?"))

response.content        # "4"  — cleaned text (markdown fences auto-stripped)
response.parsed         # None  (not JSON) or dict/list if JSON
response.tool_calls     # []   — list[ToolCallResult]
response.finish_reason  # FinishReason.STOP
response.usage          # {"prompt_tokens": 42, "completion_tokens": 5, "total_tokens": 47}
response.raw            # the unmodified provider response object (escape hatch)

Full output example — JSON response:

prompt = RactoPrompt(
    role="You are a data extractor.",
    aim="Extract the person's name and age from the text.",
    constraints=["Return only JSON."],
    tone="Concise",
    output_format="json",
)
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)
response = kit.chat(gpt.ChatConfig(user_message="My name is Alice and I am 30 years old."))

print(response.content)
# '{"name": "Alice", "age": 30}'

print(response.parsed)
# {"name": "Alice", "age": 30}   ← auto-parsed Python dict, no json.loads() needed

print(response.finish_reason)
# FinishReason.STOP

print(response.usage)
# {"prompt_tokens": 78, "completion_tokens": 12, "total_tokens": 90}

LLMResponse field reference:

Field	Type	Description
content	str | None	Cleaned text (markdown fences stripped)
parsed	dict | list | None	Auto-parsed JSON — None when response is not JSON
tool_calls	list[ToolCallResult]	Tool calls requested by the model
finish_reason	FinishReason	STOP, TOOL_CALL, LENGTH, CONTENT_FILTER, ERROR
usage	dict[str, int]	prompt_tokens, completion_tokens, total_tokens
raw	Any	The unmodified provider response (escape hatch for advanced use)

FinishReason enum values:

Value	String	When set
FinishReason.STOP	"stop"	Normal completion
FinishReason.TOOL_CALL	"tool_call"	Model requested a function/tool call
FinishReason.LENGTH	"length"	Hit max_tokens limit
FinishReason.CONTENT_FILTER	"content_filter"	Filtered by safety system
FinishReason.ERROR	"error"	Internal error

---

Streaming

stream() and astream() yield StreamChunk objects — one per streaming event.

from ractogateway import openai_developer_kit as gpt, RactoPrompt

prompt = RactoPrompt(
    role="You are a Python teacher.",
    aim="Explain the concept clearly.",
    constraints=["Use simple language.", "Give a short code example."],
    tone="Friendly",
    output_format="text",
)
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)

for chunk in kit.stream(gpt.ChatConfig(user_message="Explain Python generators")):
    print(chunk.delta.text, end="", flush=True)   # incremental text
    if chunk.is_final:
        print()
        print(f"Finish reason : {chunk.finish_reason}")
        print(f"Tokens used   : {chunk.usage}")
        print(f"Full response : {chunk.accumulated_text[:80]}...")

Example output:

A generator in Python is a special function that yields values one at a time,
allowing you to iterate over a sequence without loading everything into memory.

def count_up(n):
    for i in range(n):
        yield i

for num in count_up(5):
    print(num)  # 0, 1, 2, 3, 4

Finish reason : FinishReason.STOP
Tokens used   : {"prompt_tokens": 55, "completion_tokens": 120, "total_tokens": 175}
Full response : A generator in Python is a special function that yields values one at a time...

StreamChunk Field Reference

Field	Type	Description
delta	StreamDelta	Incremental content in this chunk
accumulated_text	str	Full text accumulated from all chunks so far
is_final	bool	True only on the very last chunk
finish_reason	FinishReason | None	Set only on the final chunk
tool_calls	list[ToolCallResult]	Populated on the final chunk only (if tool calls occurred)
usage	dict[str, int]	Token usage — populated on the final chunk only
raw	Any	Raw provider streaming event

StreamDelta Field Reference

Field	Type	Description
text	str	Incremental text added in this chunk (empty string when no text)
tool_call_id	str | None	Call ID of the tool call being streamed
tool_call_name	str | None	Name of the tool being called
tool_call_args_fragment	str | None	Partial JSON argument fragment

---

Async Support

Every method has a matching async variant.

import asyncio
from ractogateway import openai_developer_kit as gpt, RactoPrompt

prompt = RactoPrompt(
    role="You are a helpful assistant.",
    aim="Answer the user's question.",
    constraints=["Be concise."],
    tone="Friendly",
    output_format="text",
)
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)

async def main():
    # Async chat — returns LLMResponse
    response = await kit.achat(gpt.ChatConfig(user_message="What is SOLID?"))
    print(response.content)
    # "SOLID is a set of five object-oriented design principles: Single Responsibility,
    #  Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion."

    # Async streaming — yields StreamChunk
    async for chunk in kit.astream(gpt.ChatConfig(user_message="Explain SOLID briefly")):
        print(chunk.delta.text, end="", flush=True)
        if chunk.is_final:
            print(f"\nDone. Tokens: {chunk.usage}")

asyncio.run(main())

---

Embeddings

EmbeddingConfig — Input

config = gpt.EmbeddingConfig(
    texts=["Hello world", "Goodbye world"],   # required — list of strings (min 1)
    model="text-embedding-3-large",            # optional (overrides kit default)
    dimensions=512,                            # optional — for models that support truncation
)

EmbeddingConfig field reference:

Field	Type	Required	Default	Description
texts	list[str]	Yes	—	List of strings to embed (minimum 1)
model	str | None	No	None	Override kit default embedding model
dimensions	int | None	No	None	Output dimensionality (for supported models)
extra	dict[str, Any]	No	{}	Provider-specific pass-through kwargs

EmbeddingResponse — Output

from ractogateway import openai_developer_kit as gpt

kit = gpt.Chat(model="gpt-4o", embedding_model="text-embedding-3-small")

response = kit.embed(gpt.EmbeddingConfig(texts=["cat", "dog", "automobile"]))

print(response.model)
# "text-embedding-3-small"

print(response.usage)
# {"prompt_tokens": 3, "total_tokens": 3}

print(len(response.vectors))
# 3

for v in response.vectors:
    print(f"[{v.index}] '{v.text}' → vector dim={len(v.embedding)}, first5={v.embedding[:5]}")
# [0] 'cat'        → vector dim=1536, first5=[0.023, -0.015, 0.041, ...]
# [1] 'dog'        → vector dim=1536, first5=[0.019, -0.012, 0.038, ...]
# [2] 'automobile' → vector dim=1536, first5=[-0.003, 0.027, -0.011, ...]

EmbeddingResponse field reference:

Field	Type	Description
vectors	list[EmbeddingVector]	One embedding per input text, in order
model	str	The model used for embedding
usage	dict[str, int]	prompt_tokens, total_tokens
raw	Any	Unmodified provider response

EmbeddingVector field reference:

Field	Type	Description
index	int	0-based position in the input texts list
text	str	The original input text
embedding	list[float]	The dense float vector

---

Tool Calling

Define tools as plain Python functions — never write nested JSON dicts by hand. RactoGateway translates them into the correct format for each provider.

Register Tools with @registry.register

from ractogateway import ToolRegistry

registry = ToolRegistry()

@registry.register
def get_weather(city: str, unit: str = "celsius") -> str:
    """Get the current weather for a city.

    :param city: The city name
    :param unit: Temperature unit — celsius or fahrenheit
    """
    # Your real implementation here
    return f"Weather in {city}: 22°{unit[0].upper()}, partly cloudy"

@registry.register
def search_web(query: str, max_results: int = 3) -> list[str]:
    """Search the web for information.

    :param query: The search query
    :param max_results: Maximum number of results to return
    """
    return [f"Result {i}: ..." for i in range(1, max_results + 1)]

Register Tools with the Standalone @tool Decorator

from ractogateway import tool, ToolRegistry

@tool
def calculate_mortgage(
    principal: float,
    annual_rate: float,
    years: int,
) -> float:
    """Calculate monthly mortgage payment.

    :param principal: Loan amount in dollars
    :param annual_rate: Annual interest rate as a decimal (e.g., 0.05 for 5%)
    :param years: Loan term in years
    """
    monthly_rate = annual_rate / 12
    n = years * 12
    return principal * monthly_rate * (1 + monthly_rate) ** n / ((1 + monthly_rate) ** n - 1)

# Then add the decorated function to a registry
registry = ToolRegistry()
registry.register(calculate_mortgage)

Register Pydantic Models as Tools

from pydantic import BaseModel, Field

class SearchQuery(BaseModel):
    """Search the knowledge base for relevant documents."""
    query: str = Field(description="The search query string")
    max_results: int = Field(default=5, description="Maximum results to return")
    category: str = Field(default="all", description="Filter by category")

registry.register(SearchQuery)

Use Tools with Any Kit

config = gpt.ChatConfig(
    user_message="What's the weather in Tokyo and in Paris?",
    tools=registry,
)
response = kit.chat(config)

print(response.finish_reason)
# FinishReason.TOOL_CALL

for tc in response.tool_calls:
    print(f"Tool   : {tc.name}")
    print(f"Args   : {tc.arguments}")
    print(f"Call ID: {tc.id}")
    print()

# Tool   : get_weather
# Args   : {"city": "Tokyo", "unit": "celsius"}
# Call ID: call_abc123
#
# Tool   : get_weather
# Args   : {"city": "Paris", "unit": "celsius"}
# Call ID: call_def456

# Execute the tool and get the result
fn = registry.get_callable("get_weather")
result = fn(**response.tool_calls[0].arguments)
print(result)
# "Weather in Tokyo: 22°C, partly cloudy"

ToolRegistry Method Reference

Method / Property	Signature	Returns	Description
register	(fn_or_model, name=None, description=None)	None	Register a callable or Pydantic model as a tool
schemas	(property)	list[ToolSchema]	All registered tool schemas
get_schema	(name: str)	ToolSchema | None	Look up a tool schema by name
get_callable	(name: str)	Callable | None	Retrieve the original registered function
__len__	len(registry)	int	Total number of registered tools
__contains__	name in registry	bool	Check whether a tool name is registered

ToolCallResult Field Reference

Field	Type	Description
id	str	Provider-assigned call ID
name	str	Function name
arguments	dict[str, Any]	Parsed argument dict (ready to **unpack)

ToolSchema — Internal Schema Representation

Field	Type	Description
name	str	Tool name
description	str	Tool description
parameters	list[ParamSchema]	List of parameter descriptors

ToolSchema methods:

Method	Returns	Description
to_json_schema()	dict[str, Any]	Produce OpenAI-compatible JSON Schema for the parameters

---

Validated Response Models

Force the LLM output into a specific Pydantic shape. If the model doesn't produce valid JSON matching your model, you get a clear validation error — not silent garbage.

from pydantic import BaseModel
from ractogateway import openai_developer_kit as gpt, RactoPrompt

class SentimentResult(BaseModel):
    sentiment: str    # "positive", "negative", "neutral"
    confidence: float # 0.0 to 1.0
    reasoning: str    # short explanation

prompt = RactoPrompt(
    role="You are a sentiment analysis model.",
    aim="Classify the sentiment of the given text.",
    constraints=["Only classify as positive, negative, or neutral.", "Confidence must be between 0.0 and 1.0."],
    tone="Precise",
    output_format=SentimentResult,
)

kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)

config = gpt.ChatConfig(
    user_message="Analyze sentiment: 'This product is absolutely amazing!'",
    response_model=SentimentResult,
)
response = kit.chat(config)

print(response.content)
# '{"sentiment": "positive", "confidence": 0.97, "reasoning": "Strong positive adjective 'amazing' with intensifier 'absolutely'."}'

print(response.parsed)
# {"sentiment": "positive", "confidence": 0.97, "reasoning": "Strong positive..."}

# Access as validated Pydantic object
result = SentimentResult(**response.parsed)
print(result.sentiment)    # "positive"
print(result.confidence)   # 0.97
print(result.reasoning)    # "Strong positive adjective 'amazing' with intensifier 'absolutely'."

---

Multi-turn Conversations

Pass history to maintain context across turns.

from ractogateway import openai_developer_kit as gpt, RactoPrompt

prompt = RactoPrompt(
    role="You are a helpful coding assistant.",
    aim="Help the user write and debug Python code.",
    constraints=["Always provide runnable code examples.", "Explain errors clearly."],
    tone="Friendly and educational",
    output_format="text",
)
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)

# Turn 1
r1 = kit.chat(gpt.ChatConfig(user_message="Write a function to reverse a string in Python."))
print(r1.content)
# "def reverse_string(s: str) -> str:\n    return s[::-1]"

# Turn 2 — pass history so the model remembers turn 1
r2 = kit.chat(gpt.ChatConfig(
    user_message="Now make it handle None input gracefully.",
    history=[
        gpt.Message(role=gpt.MessageRole.USER, content="Write a function to reverse a string in Python."),
        gpt.Message(role=gpt.MessageRole.ASSISTANT, content=r1.content),
    ],
))
print(r2.content)
# "def reverse_string(s: str | None) -> str | None:\n    if s is None:\n        return None\n    return s[::-1]"

---

Multimodal Attachments

RactoFile lets you attach images, PDFs, plain-text files, and any binary file to a prompt. Use prompt.to_messages() to build provider-ready message lists that include the attachments in the correct format for each provider.

Creating a RactoFile

from ractogateway.prompts.engine import RactoFile

# From a file path — MIME type is auto-detected
img  = RactoFile.from_path("/path/to/photo.jpg")      # image/jpeg
doc  = RactoFile.from_path("/path/to/report.pdf")     # application/pdf
txt  = RactoFile.from_path("/path/to/notes.txt")      # text/plain

# From raw bytes — supply MIME type explicitly
with open("chart.png", "rb") as fh:
    chart = RactoFile.from_bytes(fh.read(), "image/png", name="chart.png")

# From a URL response
import requests
resp = requests.get("https://example.com/diagram.png")
diagram = RactoFile.from_bytes(resp.content, "image/png", name="diagram.png")

RactoFile constructor methods:

Method	Signature	Returns	Description
from_path	(path: str | Path) -> RactoFile	RactoFile	Load from file path; MIME auto-detected
from_bytes	(data: bytes, mime_type: str, name: str) -> RactoFile	RactoFile	Create from raw bytes

RactoFile property reference:

Member	Type	Description
data	bytes	Raw file content
mime_type	str	MIME type, e.g. "image/png"
name	str	Filename hint
base64_data	str	Base-64 encoded file content
is_image	bool	True for JPEG, PNG, GIF, WebP
is_pdf	bool	True for application/pdf
is_text	bool	True for any text/* MIME

Building Multimodal Message Lists

Use prompt.to_messages() with the attachments parameter to build a multimodal message list, then pass it directly to the provider or low-level adapter:

from ractogateway import RactoPrompt, Gateway
from ractogateway.adapters.openai_kit import OpenAILLMKit
from ractogateway.prompts.engine import RactoFile

prompt = RactoPrompt(
    role="You are a data analyst specialising in chart interpretation.",
    aim="Describe what the attached chart shows and extract the key insights.",
    constraints=[
        "Only describe what is visible in the image.",
        "Never invent data points not shown in the chart.",
    ],
    tone="Clear and concise",
    output_format="text",
)

# Build multimodal messages using to_messages()
attachment = RactoFile.from_path("sales_q4.png")
messages = prompt.to_messages(
    "What does this chart show?",
    attachments=[attachment],
    provider="openai",
)

# messages is now a list ready to send directly to the OpenAI API
# [
#   {"role": "system", "content": "<compiled RACTO prompt>"},
#   {"role": "user", "content": [
#       {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}},
#       {"type": "text", "text": "What does this chart show?"}
#   ]}
# ]

Provider Content-Block Translation

Each provider receives a different content-block format — to_messages() handles it transparently.

OpenAI (provider="openai") — images become image_url blocks with inline data URIs:

[
    {"role": "system", "content": "<compiled RACTO system prompt>"},
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgAB..."}
            },
            {"type": "text", "text": "Describe the image."}
        ]
    }
]

Anthropic (provider="anthropic") — images become image blocks, PDFs become document blocks:

[
    {"role": "system", "content": "<compiled RACTO system prompt>"},
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {"type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQSkZJRgAB..."}
            },
            {"type": "text", "text": "Describe the image."}
        ]
    }
]

Google Gemini (provider="google") — files become inline_data parts:

[
    {"role": "system", "content": "<compiled RACTO system prompt>"},
    {
        "role": "user",
        "content": [
            {"inline_data": {"mime_type": "image/jpeg", "data": "/9j/4AAQSkZJRgAB..."}},
            {"text": "Describe the image."}
        ]
    }
]

Supported File Types

File type	MIME type	OpenAI	Anthropic	Google
JPEG	image/jpeg	image_url	image block	inline_data
PNG	image/png	image_url	image block	inline_data
GIF	image/gif	image_url	image block	inline_data
WebP	image/webp	image_url	image block	inline_data
PDF	application/pdf	image_url (data URI)	document block	inline_data
Plain text	text/plain	text block	text block	text part
Any other	*/*	image_url (data URI)	labelled text block	inline_data

---

Low-Level Gateway

Gateway is a thin wrapper around any BaseLLMAdapter. Use it when you need direct access to prompt + adapter without the ChatConfig convenience layer — for example, when you want fine-grained control over individual calls.

Creating and Using a Gateway

from ractogateway import RactoPrompt, Gateway, ToolRegistry
from ractogateway.adapters.openai_kit import OpenAILLMKit

adapter = OpenAILLMKit(model="gpt-4o", api_key="sk-...")
prompt = RactoPrompt(
    role="You are a code reviewer.",
    aim="Identify bugs in the given code.",
    constraints=["Report only real bugs.", "If no bugs, say so."],
    tone="Concise",
    output_format="json",
)

gw = Gateway(adapter=adapter, default_prompt=prompt)

# Sync execution
response = gw.run(user_message="Review: def div(a, b): return a / b")
print(response.parsed)
# {"bugs": ["ZeroDivisionError if b is 0"], "severity": "high"}

# Async execution
import asyncio
async def main():
    response = await gw.arun(user_message="Review: x = 1; del x; print(x)")
    print(response.parsed)

asyncio.run(main())

Gateway constructor parameters:

Parameter	Type	Required	Default	Description
adapter	BaseLLMAdapter	Yes	—	A concrete adapter (OpenAILLMKit, GoogleLLMKit, AnthropicLLMKit)
tools	ToolRegistry | None	No	None	Default tool registry for all calls
default_prompt	RactoPrompt | None	No	None	Fallback prompt when run() is called without one

Gateway.run() and Gateway.arun() parameters:

Parameter	Type	Default	Description
prompt	RactoPrompt | None	None	Override default_prompt for this call
user_message	str	""	The end-user's query
tools	ToolRegistry | None	None	Override gateway-level tool registry
temperature	float	0.0	Sampling temperature
max_tokens	int	4096	Maximum response tokens
response_model	type[BaseModel] | None	None	Validate JSON output against this Pydantic model
**kwargs	Any	—	Passed through to the adapter

Returns: LLMResponse

---

Switching Providers

Same ChatConfig, different kit. Zero code changes to your prompt or config.

from ractogateway import openai_developer_kit as gpt
from ractogateway import google_developer_kit as gemini
from ractogateway import anthropic_developer_kit as claude
from ractogateway import RactoPrompt

prompt = RactoPrompt(
    role="You are a helpful assistant.",
    aim="Answer the user's question accurately.",
    constraints=["Be concise.", "Cite sources when possible."],
    tone="Friendly and professional",
    output_format="text",
)

config = gpt.ChatConfig(user_message="What is quantum computing?")

# OpenAI — use "gpt" alias
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)
print(kit.chat(config).content)
# "Quantum computing uses quantum bits (qubits) that can exist in superposition,
#  enabling calculations that classical computers cannot do efficiently..."

# Google Gemini — swap to "gemini" alias, everything else stays the same!
kit = gemini.Chat(model="gemini-2.0-flash", default_prompt=prompt)
print(kit.chat(config).content)
# "Quantum computing harnesses the principles of quantum mechanics..."

# Anthropic Claude — swap to "claude" alias, that's it!
kit = claude.Chat(model="claude-sonnet-4-6", default_prompt=prompt)
print(kit.chat(config).content)
# "Quantum computing is a type of computation that leverages quantum phenomena..."

---

Fine-Tuning

RactoGateway includes a production-grade fine-tuning pipeline that works with OpenAI, Google Gemini, and Anthropic using a single, unified dataset API.

from ractogateway import (
    RactoDataset,
    RactoTrainingExample,
    RactoTrainingMessage,
    OpenAIFineTuner,
    GeminiFineTuner,
    AnthropicFineTuner,
)

Core Classes

Class	Role
RactoTrainingMessage	One conversation turn — role + text + optional RactoFile attachments
RactoTrainingExample	One full training record (a conversation) — list of RactoTrainingMessage
RactoDataset	Collection of examples with validation, split, shuffle, and JSONL export
OpenAIFineTuner	Upload → create job → poll on OpenAI
GeminiFineTuner	Create tuning job → poll on Google AI
AnthropicFineTuner	Upload → create job → poll on Anthropic

RactoTrainingMessage Field Reference

Field	Type	Required	Description
role	str	Yes	"system", "user", or "assistant"
content	str	Yes	Text content of the message
attachments	list[RactoFile]	No	Optional multimodal file attachments

RactoTrainingMessage serialization methods:

Method	Returns	Description
to_openai()	dict	Serialize to OpenAI message format
to_anthropic()	dict	Serialize to Anthropic message format
to_gemini_parts()	list	Serialize to Gemini content parts

RactoTrainingExample Factory Methods

Factory Method	Signature	Description
from_pair	(user, assistant, system="", user_attachments=None)	Single-turn from strings
from_conversation	([(role, content), ...])	Multi-turn from list of tuples

RactoTrainingExample serialization methods:

Method	Returns	Description
to_openai_dict()	dict	OpenAI fine-tuning format
to_anthropic_dict()	dict	Anthropic fine-tuning format
to_gemini_dict()	dict	Gemini fine-tuning format

Step 1 — Build a Dataset

Quickest path — text pairs

from ractogateway import RactoDataset

ds = RactoDataset.from_pairs(
    [
        ("What is a Python list?",  "An ordered, mutable sequence of items."),
        ("What is a Python dict?",  "An unordered key-value mapping."),
        ("What is a Python tuple?", "An ordered, immutable sequence."),
    ],
    system="You are a concise Python tutor. Answer in one sentence.",
)

print(ds.summary())
# {"examples": 3, "total_messages": 9, "avg_turns_per_example": 3.0, "multimodal_examples": 0}

Multi-turn conversation

from ractogateway import RactoTrainingExample, RactoDataset

example = RactoTrainingExample.from_conversation([
    ("system",    "You are a helpful travel assistant."),
    ("user",      "I want to visit Japan. What season is best?"),
    ("assistant", "Spring (March–May) for cherry blossoms, or Autumn (Sept–Nov) for foliage."),
    ("user",      "Which cities should I visit?"),
    ("assistant", "Tokyo, Kyoto, Osaka, and Hiroshima are the most popular."),
])
ds = RactoDataset([example])

Multimodal example — image + text

from ractogateway import RactoTrainingExample, RactoDataset
from ractogateway.prompts.engine import RactoFile

example = RactoTrainingExample.from_pair(
    user="Describe the trend shown in this chart.",
    assistant="Revenue grew by 23% quarter-over-quarter, peaking in December.",
    system="You are a data analyst. Be concise and factual.",
    user_attachments=[RactoFile.from_path("sales_chart.png")],
)
ds = RactoDataset([example])

print(ds.summary())
# {"examples": 1, "total_messages": 3, "avg_turns_per_example": 3.0, "multimodal_examples": 1}

Add examples incrementally

ds = RactoDataset()
ds.add(RactoTrainingExample.from_pair("Q1", "A1", system="You are helpful."))
ds.add(RactoTrainingExample.from_pair("Q2", "A2", system="You are helpful."))
ds.extend([
    RactoTrainingExample.from_pair(u, a)
    for u, a in [("Q3", "A3"), ("Q4", "A4")]
])

Step 2 — Validate and Split

errors = ds.validate(provider="openai")   # or "anthropic" / "gemini"
if errors:
    for e in errors:
        print(e)
else:
    print("Dataset is valid.")

# Reproducible 80/20 train-validation split
train_ds, val_ds = ds.split(train_ratio=0.8, seed=42)
print(f"Train: {len(train_ds)}  |  Val: {len(val_ds)}")
# Train: 80  |  Val: 20

Step 3 — Export to JSONL (optional inspection)

train_ds.export_jsonl("train.jsonl",     provider="openai",    overwrite=True)
val_ds.export_jsonl("val.jsonl",         provider="openai",    overwrite=True)
train_ds.export_jsonl("train_ant.jsonl", provider="anthropic", overwrite=True)
train_ds.export_jsonl("train_gem.jsonl", provider="gemini",    overwrite=True)

OpenAI JSONL format (train.jsonl):

{"messages": [{"role": "system", "content": "You are a Python tutor."}, {"role": "user", "content": "What is a list?"}, {"role": "assistant", "content": "An ordered, mutable sequence."}]}
{"messages": [{"role": "system", "content": "You are a Python tutor."}, {"role": "user", "content": "What is a dict?"}, {"role": "assistant", "content": "A key-value mapping."}]}

Anthropic JSONL format (train_ant.jsonl):

{"system": "You are a Python tutor.", "messages": [{"role": "user", "content": "What is a list?"}, {"role": "assistant", "content": "An ordered, mutable sequence."}]}

Gemini JSONL format (train_gem.jsonl):

{"text_input": "What is a list?", "output": "An ordered, mutable sequence."}

OpenAI multimodal format (image in user turn):

{
  "messages": [
    {"role": "system", "content": "You are a data analyst."},
    {
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBOR..."}},
        {"type": "text", "text": "Describe the trend."}
      ]
    },
    {"role": "assistant", "content": "Revenue grew 23% quarter-over-quarter."}
  ]
}

Step 4 — Fine-Tune

OpenAI — one call

from ractogateway import OpenAIFineTuner

tuner = OpenAIFineTuner(api_key="sk-...")   # or set OPENAI_API_KEY

fine_tuned_model = tuner.run_pipeline(
    train_ds,
    model="gpt-4o-mini-2024-07-18",
    validation_dataset=val_ds,
    n_epochs=3,
    suffix="python-tutor",
    verbose=True,
)
# [OpenAIFineTuner] Uploading 80 training examples…
# [OpenAIFineTuner] Training file: file-abc123
# [OpenAIFineTuner] Job created: ftjob-xyz789
# [OpenAIFineTuner] Job ftjob-xyz789 → running
# [OpenAIFineTuner] Done!  Fine-tuned model: ft:gpt-4o-mini-2024-07-18:org::python-tutor-abc

# Use immediately
from ractogateway import openai_developer_kit as gpt
kit = gpt.Chat(model=fine_tuned_model)
response = kit.chat(gpt.ChatConfig(user_message="What is a generator?"))
print(response.content)
# "A generator is a function that uses yield to produce values lazily, one at a time."

OpenAI — step by step

tuner = OpenAIFineTuner()

train_file_id = tuner.upload_dataset(train_ds)
val_file_id   = tuner.upload_dataset(val_ds)

job_id = tuner.create_job(
    train_file_id,
    model="gpt-4o-mini-2024-07-18",
    validation_file=val_file_id,
    n_epochs=3,
    suffix="python-tutor",
)

print(tuner.get_status(job_id))
# {"id": "ftjob-…", "status": "running", "model": "gpt-4o-mini-2024-07-18", ...}

for event in tuner.list_events(job_id, limit=10):
    print(event["message"])

fine_tuned_model = tuner.wait_for_completion(job_id, poll_interval=30)

OpenAIFineTuner method reference:

Method	Signature	Returns	Description
run_pipeline	(train_ds, model, validation_dataset=None, n_epochs=3, suffix="", verbose=False)	str	Full pipeline — upload, create job, wait, return model name
upload_dataset	(ds: RactoDataset)	str	Upload dataset, return file ID
create_job	(train_file_id, model, validation_file=None, n_epochs=3, suffix="")	str	Create fine-tune job, return job ID
get_status	(job_id: str)	dict	Get current job status
list_events	(job_id: str, limit=10)	list[dict]	Get recent job events
wait_for_completion	(job_id: str, poll_interval=30)	str	Poll until done, return fine-tuned model name

Google Gemini — one call

from ractogateway import GeminiFineTuner

tuner = GeminiFineTuner(api_key="AIza...")

tuned_model = tuner.run_pipeline(
    train_ds,
    base_model="models/gemini-1.5-flash-001-tuning",
    display_name="python-tutor",
    epoch_count=5,
    batch_size=4,
    verbose=True,
)
# [GeminiFineTuner] Starting tuning with 80 examples…
# [GeminiFineTuner] State: CREATING (12%)
# [GeminiFineTuner] Done!  Tuned model: tunedModels/python-tutor-abc123

from ractogateway import google_developer_kit as gemini
kit = gemini.Chat(model=tuned_model)

Anthropic Claude — one call

from ractogateway import AnthropicFineTuner

tuner = AnthropicFineTuner(api_key="sk-ant-...")

fine_tuned_model = tuner.run_pipeline(
    train_ds,
    model="claude-3-haiku-20240307",
    validation_dataset=val_ds,
    suffix="python-tutor",
    hyperparameters={"n_epochs": 3},
    verbose=True,
)
# [AnthropicFineTuner] Uploading 80 training examples…
# [AnthropicFineTuner] Training file: file-…
# [AnthropicFineTuner] Job created: ftjob-…
# [AnthropicFineTuner] Done!  Fine-tuned model: claude-3-haiku-20240307:ft:…

RactoDataset API Reference

Member	Signature	Returns	Description
RactoDataset.from_pairs	(pairs, system="")	RactoDataset	Build from [(user, assistant)] text tuples
RactoDataset.from_jsonl	(path, provider="openai")	RactoDataset	Load a previously exported JSONL file
.add	(example: RactoTrainingExample)	None	Append one example
.extend	(examples: list)	None	Append a list of examples
.validate	(provider: str)	list[str]	Returns list of errors (empty = valid)
.split	(train_ratio=0.8, seed=42)	(RactoDataset, RactoDataset)	Reproducible train/val split
.shuffle	(seed: int)	RactoDataset	Returns a new shuffled dataset
.export_jsonl	(path, provider, overwrite=True)	None	Write to .jsonl file on disk
.to_jsonl_string	(provider: str)	str	Return JSONL as a string (no I/O)
.summary	()	dict	Stats: examples, total_messages, multimodal_examples, …

Provider Fine-Tuning Support Matrix

Feature	OpenAI	Gemini	Anthropic
Text-only fine-tuning	Yes	Yes	Yes
Multimodal (image) fine-tuning	Yes (gpt-4o-2024-08-06)	Vertex AI only	Yes
Multi-turn conversations	Yes	Vertex AI only	Yes
Validation dataset	Yes	No	Yes
Hyperparameter control	epochs, batch, LR	epochs, batch, LR	epochs
run_pipeline() one-liner	Yes	Yes	Yes

---

RAG

RactoGateway ships a full Retrieval-Augmented Generation (RAG) pipeline. In plain English: you feed it documents, it breaks them into chunks, converts them to number vectors, stores them, and later retrieves the most relevant chunks to answer a question — all in one class.

Document → Read → Chunk → Process → Embed → Store
                                              ↓
                              Query → Embed → Retrieve → Generate → Answer

RAG Installation

pip install ractogateway[rag-all]    # everything
# or pick what you need:
pip install ractogateway[rag]        # base readers + NLP
pip install ractogateway[rag-pdf]    # PDF
pip install ractogateway[rag-chroma] # ChromaDB

Quickstart — 4 Lines

from ractogateway import openai_developer_kit as gpt
from ractogateway.rag.pipeline import RactoRAG
from ractogateway.rag.embedders import OpenAIEmbedder
from ractogateway.rag.stores import InMemoryVectorStore

kit = gpt.Chat(model="gpt-4o")
rag = RactoRAG(
    vector_store=InMemoryVectorStore(),
    embedder=OpenAIEmbedder(),
    llm_kit=kit,
)
rag.ingest("report.pdf")
response = rag.query("What were the key findings?")
print(response.answer.content)
# "The key findings were: (1) revenue increased 22% YoY, (2) customer churn
#  dropped by 4 percentage points, (3) the APAC region became the fastest-growing market."

RactoRAG Constructor Parameters

Parameter	Type	Required	Default	Description
vector_store	BaseVectorStore	Yes	—	Where chunks are indexed and searched
embedder	BaseEmbedder	Yes	—	Converts text to float vectors
chunker	BaseChunker | None	No	RecursiveChunker(512, 50)	How documents are split
processors	list[BaseProcessor] | None	No	[TextCleaner()]	Text cleaning pipeline
llm_kit	Any | None	No*	None	Required for .query() / .aquery()
context_template	str | None	No	Built-in	Template for injecting context into the LLM
reader_registry	FileReaderRegistry | None	No	Built-in	Dispatches files to the correct reader
default_prompt	RactoPrompt | None	No	Built-in RAG prompt	System prompt used during generation

* llm_kit is optional at construction time but required when calling .query() or .aquery().

Ingesting Documents

# Single file (auto-detected reader based on extension)
chunks = rag.ingest("report.pdf")
chunks = rag.ingest("notes.txt")
chunks = rag.ingest("data.xlsx")
chunks = rag.ingest("page.html")

print(len(chunks))
# 47   ← number of chunks created from the document

print(chunks[0])
# Chunk(
#   chunk_id="3f8a2c1d-...",
#   doc_id="a1b2c3d4-...",
#   content="The annual report shows revenue growth of 22%...",
#   embedding=[0.023, -0.015, 0.041, ...],  # 1536-dim vector
#   metadata=ChunkMetadata(
#       source="/path/to/report.pdf",
#       page=1,
#       chunk_index=0,
#       total_chunks=47,
#       start_char=0,
#       end_char=512,
#       doc_id="a1b2c3d4-...",
#       extra={}
#   )
# )

# Entire directory (recursively, all supported file types)
chunks = rag.ingest_dir("./docs/", pattern="**/*.pdf")

# Raw text string — no file needed
chunks = rag.ingest_text(
    "The quick brown fox jumps over the lazy dog.",
    source="manual-input",
    category="test",    # extra metadata
)

# Async variants
chunks = await rag.aingest("big_report.pdf")
chunks = await rag.aingest_dir("./docs/")
chunks = await rag.aingest_text("some text", source="api")

ingest() / aingest() parameters:

Parameter	Type	Description
path	str | Path	File path to ingest
**metadata	Any	Extra key-value pairs stored in ChunkMetadata.extra

Returns: list[Chunk]

ingest_dir() / aingest_dir() parameters:

Parameter	Type	Default	Description
directory	str | Path	—	Directory to walk
pattern	str	"**/*"	Glob pattern to filter files
**metadata	Any	—	Extra metadata attached to all chunks

Returns: list[Chunk]

ingest_text() / aingest_text() parameters:

Parameter	Type	Default	Description
text	str	—	Raw text content to ingest
source	str	"manual"	Label for this text source
**metadata	Any	—	Extra metadata attached to all chunks

Returns: list[Chunk]

RAG Data Models

Document field reference:

Field	Type	Description
doc_id	str	Auto-generated UUID for this document
content	str	Full extracted text content
source	str	File path, URL, or caller-supplied label
metadata	dict[str, Any]	Arbitrary metadata dict

Chunk field reference:

Field	Type	Description
chunk_id	str	Auto-generated UUID for this chunk
doc_id	str	UUID of the parent Document
content	str	Text content of this chunk
embedding	list[float] | None	Dense float vector (None until embedded)
metadata	ChunkMetadata	Provenance info for this chunk

ChunkMetadata field reference:

Field	Type	Description
source	str	File path or URL
page	int | None	Page number for PDFs (1-based), else None
chunk_index	int	0-based position within the parent document
total_chunks	int	Total chunks created from the parent document
start_char	int	Character offset where this chunk starts
end_char	int	Character offset where this chunk ends
doc_id	str	UUID of the parent document
extra	dict[str, Any]	Caller-supplied metadata (from ingest(**metadata))

Retrieving Without Generating

results = rag.retrieve("What is the revenue growth?", top_k=3)

for r in results:
    print(f"Rank {r.rank} | Score {r.score:.4f} | Source: {r.chunk.metadata.source}")
    print(f"  {r.chunk.content[:100]}...")
    print()

# Rank 1 | Score 0.9231 | Source: /path/to/report.pdf
#   The company achieved revenue growth of 22% year-over-year, driven by...
#
# Rank 2 | Score 0.8847 | Source: /path/to/report.pdf
#   In FY2024, total revenue reached $12.4 million, compared to $10.2 million...
#
# Rank 3 | Score 0.8102 | Source: /path/to/report.pdf
#   The APAC region contributed most significantly to revenue growth, with...

retrieve() / aretrieve() parameters:

Parameter	Type	Default	Description
query	str	—	The search query text
top_k	int	5	Maximum number of results to return
filters	dict | None	None	Metadata filters (store-specific)

Returns: list[RetrievalResult]

RetrievalResult field reference:

Field	Type	Description
chunk	Chunk	The retrieved text chunk
score	float	Similarity score (higher = more relevant)
rank	int	1-based rank (1 = most relevant)

Full RAG Query — Retrieve + Generate

rag_response = rag.query(
    "What is the revenue growth and which region performed best?",
    top_k=5,           # retrieve 5 most relevant chunks
    temperature=0.0,   # factual answers — keep temperature low
    max_tokens=2048,
)

print(rag_response.answer.content)
# "Based on the provided context:
#  1. Revenue grew 22% year-over-year, reaching $12.4M in FY2024.
#  2. The APAC region was the top performer, contributing significantly to growth.
#  Source: report.pdf (page 3)"

print(f"Query  : {rag_response.query}")
# Query  : What is the revenue growth and which region performed best?

print(f"Sources: {len(rag_response.sources)}")
# Sources: 5

for r in rag_response.sources:
    print(f"  [{r.rank}] score={r.score:.3f} → {r.chunk.content[:60]}...")
# [1] score=0.923 → The company achieved revenue growth of 22% year-over-year...
# [2] score=0.885 → In FY2024, total revenue reached $12.4 million...
# [3] score=0.810 → The APAC region contributed most significantly...
# [4] score=0.776 → North America remained the largest single market...
# [5] score=0.741 → EMEA recorded moderate growth of 9% year-over-year...

query() / aquery() parameters:

Parameter	Type	Default	Description
question	str	—	The user's question (required)
top_k	int	5	Chunks to retrieve and inject as context
filters	dict | None	None	Metadata filters (store-specific)
prompt	RactoPrompt | None	None	Override default RAG prompt
temperature	float	0.0	Sampling temperature for generation
max_tokens	int	2048	Maximum tokens in the generated answer

Returns: RAGResponse

RAGResponse field reference:

Field	Type	Description
answer	LLMResponse	The generated answer (same as a normal chat() response)
sources	list[RetrievalResult]	Chunks used as context for generation
query	str	The original question
context_used	str	Verbatim context string injected into the LLM

Async RAG

chunks = await rag.aingest("big_report.pdf")
results = await rag.aretrieve("key findings", top_k=3)
response = await rag.aquery("What were the key findings?")
print(response.answer.content)

RAG — Chunking Strategies

from ractogateway.rag.chunkers import (
    FixedChunker,      # Split at exactly N characters
    RecursiveChunker,  # Smart split on paragraphs → sentences → words (default)
    SentenceChunker,   # Split on sentence boundaries
    SemanticChunker,   # Split where meaning changes (requires embedder)
)

# Fixed — simple, predictable
rag = RactoRAG(
    vector_store=InMemoryVectorStore(),
    embedder=OpenAIEmbedder(),
    chunker=FixedChunker(chunk_size=256, overlap=32),
    llm_kit=kit,
)

# Recursive — good default, respects paragraph/sentence structure
rag = RactoRAG(
    vector_store=InMemoryVectorStore(),
    embedder=OpenAIEmbedder(),
    chunker=RecursiveChunker(chunk_size=512, overlap=50),
    llm_kit=kit,
)

# Sentence — split on natural sentence boundaries
rag = RactoRAG(
    vector_store=InMemoryVectorStore(),
    embedder=OpenAIEmbedder(),
    chunker=SentenceChunker(max_sentences=5),
    llm_kit=kit,
)

# Semantic — split where meaning changes (requires an embedder reference)
from ractogateway.rag.chunkers import SemanticChunker
embedder = OpenAIEmbedder()
rag = RactoRAG(
    vector_store=InMemoryVectorStore(),
    embedder=embedder,
    chunker=SemanticChunker(embedder=embedder, threshold=0.8),
    llm_kit=kit,
)

Chunker parameter reference:

Chunker	Key Parameters	Description
FixedChunker	chunk_size=256, overlap=32	Split at exactly chunk_size characters with overlap overlap
RecursiveChunker	chunk_size=512, overlap=50	Hierarchical: paragraphs → sentences → words
SentenceChunker	max_sentences=5	Split every max_sentences sentence boundaries
SemanticChunker	embedder, threshold=0.8	Split where cosine similarity drops below threshold

BaseChunker interface:

Method	Signature	Returns	Description
chunk	(document: Document) -> list[Chunk]	list[Chunk]	Split a document into chunks

RAG — Embedders

from ractogateway.rag.embedders import OpenAIEmbedder, GoogleEmbedder, VoyageEmbedder

# OpenAI
embedder = OpenAIEmbedder(
    model="text-embedding-3-small",   # default
    api_key="sk-...",                 # or OPENAI_API_KEY
)

# Google
embedder = GoogleEmbedder(
    model="text-embedding-004",        # default
    api_key="AIza...",                 # or GEMINI_API_KEY
)

# Voyage AI (great for RAG)
embedder = VoyageEmbedder(
    model="voyage-3",
    api_key="pa-...",
)

BaseEmbedder interface:

Method / Property	Signature	Returns	Description
dimension	(property)	int	Embedding dimension size (-1 if unknown before first call)
embed	(texts: list[str]) -> list[list[float]]	list[list[float]]	Synchronous batch embedding
aembed	(texts: list[str]) -> list[list[float]]	list[list[float]]	Async batch embedding

RAG — Vector Stores

from ractogateway.rag.stores import (
    InMemoryVectorStore,   # no setup, great for prototyping
    ChromaStore,           # pip install ractogateway[rag-chroma]
    FAISSStore,            # pip install ractogateway[rag-faiss]
    PineconeStore,         # pip install ractogateway[rag-pinecone]
    QdrantStore,           # pip install ractogateway[rag-qdrant]
    WeaviateStore,         # pip install ractogateway[rag-weaviate]
    MilvusStore,           # pip install ractogateway[rag-milvus]
    PGVectorStore,         # pip install ractogateway[rag-pgvector]
)

# In-memory (no setup)
store = InMemoryVectorStore()

# ChromaDB (local persistence)
store = ChromaStore(collection="my_docs", persist_directory="./chroma_db")

# FAISS (fast local search)
store = FAISSStore(index_path="./faiss.index", dimension=1536)

# Pinecone (cloud)
store = PineconeStore(index_name="my-index", api_key="...")

# Qdrant (self-hosted or cloud)
store = QdrantStore(collection="my_docs", url="http://localhost:6333")

# PostgreSQL pgvector
store = PGVectorStore(connection_string="postgresql://user:pass@localhost/db", table="embeddings")

BaseVectorStore interface:

Method	Signature	Returns	Description
add	(chunks: list[Chunk]) -> None	None	Index chunks (must have embeddings set)
search	(embedding: list[float], top_k=5, filters=None) -> list[RetrievalResult]	list[RetrievalResult]	Find most similar chunks
delete	(chunk_ids: list[str]) -> None	None	Remove chunks by ID
clear	() -> None	None	Remove all indexed chunks
count	() -> int	int	Total indexed chunk count

RAG — Readers

Documents are loaded automatically based on file extension:

Reader	Extensions	Install
TextReader	.txt, .md, .rst, .csv	Built-in
HtmlReader	.html, .htm	Built-in
PdfReader	.pdf	ractogateway[rag-pdf]
WordReader	.docx	ractogateway[rag-word]
SpreadsheetReader	.xlsx, .xls	ractogateway[rag-excel]
ImageReader	.jpg, .jpeg, .png, .gif	ractogateway[rag-image]

BaseReader interface:

Method / Property	Signature	Returns	Description
supported_extensions	(property)	frozenset[str]	File extensions this reader handles
read	(path: Path) -> Document	Document	Load a file and return a Document

RAG — File Reader Registry

FileReaderRegistry auto-dispatches file reads to the correct reader based on extension.

from ractogateway import FileReaderRegistry
from ractogateway.rag.readers import TextReader, PdfReader

# The registry used by RactoRAG is built-in (auto-registers all available readers)
# You can also create a custom one:
registry = FileReaderRegistry()
registry.register(TextReader())     # manually register a reader
registry.register(PdfReader())

# Read a file — dispatches automatically
doc = registry.read("report.pdf")  # → Document
print(doc.content)                  # extracted text
print(doc.source)                   # "report.pdf"

FileReaderRegistry method reference:

Method	Signature	Returns	Description
register	(reader: BaseReader) -> None	None	Add a reader for its supported_extensions
read	(path: str | Path) -> Document	Document	Auto-dispatch to the matching reader
can_read	(path: str | Path) -> bool	bool	Check if any reader handles this extension

RAG — Processing Pipeline

Text processors clean and normalise chunks before embedding:

from ractogateway.rag.processors import TextCleaner, Lemmatizer, ProcessingPipeline

rag = RactoRAG(
    vector_store=InMemoryVectorStore(),
    embedder=OpenAIEmbedder(),
    processors=[
        TextCleaner(),    # strip extra whitespace, fix encoding
        Lemmatizer(),     # reduce words to root form (pip install ractogateway[rag-nlp])
    ],
    llm_kit=kit,
)

# ProcessingPipeline chains multiple processors manually
pipeline = ProcessingPipeline([TextCleaner(), Lemmatizer()])
cleaned_text = pipeline.process("  Running   quickly through the fields...  ")
# "run quickly through the field"

BaseProcessor interface:

Method	Signature	Returns	Description
process	(text: str) -> str	str	Transform text and return cleaned result

ProcessingPipeline — chains processors:

Method	Signature	Returns	Description
__init__	(processors: list[BaseProcessor])	—	Build the pipeline
process	(text: str) -> str	str	Run text through all processors in order

Full RAG Pipeline Example — Production Setup

from ractogateway import openai_developer_kit as gpt, RactoPrompt
from ractogateway.rag.pipeline import RactoRAG
from ractogateway.rag.embedders import OpenAIEmbedder
from ractogateway.rag.stores import ChromaStore
from ractogateway.rag.chunkers import RecursiveChunker
from ractogateway.rag.processors import TextCleaner

# 1. Build the kit
kit = gpt.Chat(model="gpt-4o")

# 2. Custom RAG prompt
rag_prompt = RactoPrompt(
    role="You are a precise document Q&A assistant.",
    aim="Answer the user's question using only the provided context.",
    constraints=[
        "Never fabricate information not in the context.",
        "If the context doesn't contain the answer, say so clearly.",
        "Cite the source document and page number when available.",
    ],
    tone="Professional and concise",
    output_format="text",
)

# 3. Assemble the pipeline
rag = RactoRAG(
    vector_store=ChromaStore(collection="company_docs", persist_directory="./db"),
    embedder=OpenAIEmbedder(model="text-embedding-3-large"),
    chunker=RecursiveChunker(chunk_size=512, overlap=64),
    processors=[TextCleaner()],
    llm_kit=kit,
    default_prompt=rag_prompt,
)

# 4. Ingest your document library
total_chunks = rag.ingest_dir("./company_docs/", pattern="**/*.pdf")
print(f"Indexed {rag.count()} chunks from {len(total_chunks)} files")
# Indexed 1247 chunks from 23 files

# 5. Answer questions
response = rag.query("What is our refund policy for digital products?", top_k=5)

print(response.answer.content)
# "According to the company policy document (page 4):
#  Digital products are eligible for a full refund within 14 days of purchase,
#  provided the product has not been downloaded more than 3 times.
#  After 14 days, refunds are issued as store credit only."

print(f"\nContext came from {len(response.sources)} sources:")
for r in response.sources:
    src = r.chunk.metadata.source.split("/")[-1]
    pg  = f", page {r.chunk.metadata.page}" if r.chunk.metadata.page else ""
    print(f"  [{r.rank}] {src}{pg} (score={r.score:.3f})")
# [1] refund_policy.pdf, page 4 (score=0.941)
# [2] refund_policy.pdf, page 5 (score=0.882)
# [3] customer_handbook.pdf, page 12 (score=0.791)
# [4] faq.pdf, page 2 (score=0.743)
# [5] terms_of_service.pdf, page 7 (score=0.701)

RactoRAG Method Reference

Method	Signature	Returns	Description
ingest	(path, **metadata)	list[Chunk]	Read, chunk, embed, and store a file
ingest_dir	(directory, pattern="**/*", **metadata)	list[Chunk]	Recursively ingest all supported files
ingest_text	(text, source="manual", **metadata)	list[Chunk]	Ingest raw text directly
aingest	(path, **metadata)	list[Chunk]	Async variant of ingest
aingest_dir	(directory, pattern, **metadata)	list[Chunk]	Async variant of ingest_dir
aingest_text	(text, source, **metadata)	list[Chunk]	Async variant of ingest_text
retrieve	(query, top_k=5, filters=None)	list[RetrievalResult]	Embed query and return top-k chunks
aretrieve	(query, top_k=5, filters=None)	list[RetrievalResult]	Async variant of retrieve
query	(question, top_k=5, filters=None, prompt=None, temperature=0.0, max_tokens=2048)	RAGResponse	Retrieve + generate → full RAG answer
aquery	(...)	RAGResponse	Async variant of query
count	()	int	Total indexed chunks
clear	()	None	Remove all indexed chunks
store	(property)	BaseVectorStore	Access the underlying vector store
embedder	(property)	BaseEmbedder	Access the underlying embedder

---

Architecture

src/ractogateway/
├── __init__.py                          # Top-level: RactoPrompt, ToolRegistry, kits, RAG, fine-tuning
├── py.typed                             # PEP 561 typed package marker
│
├── _models/                             # Shared Pydantic input/output models
│   ├── chat.py                          #   ChatConfig, Message, MessageRole
│   ├── stream.py                        #   StreamChunk, StreamDelta
│   └── embedding.py                     #   EmbeddingConfig, EmbeddingResponse, EmbeddingVector
│
├── prompts/                             # RACTO Prompt Engine
│   └── engine.py                        #   RactoPrompt, RactoFile, compile(), to_messages()
│
├── finetune/                            # Multimodal Fine-Tuning Pipeline
│   ├── dataset.py                       #   RactoTrainingMessage, RactoTrainingExample, RactoDataset
│   ├── openai_tuner.py                  #   OpenAIFineTuner
│   ├── gemini_tuner.py                  #   GeminiFineTuner
│   └── anthropic_tuner.py               #   AnthropicFineTuner
│
├── tools/                               # Tool Registry
│   └── registry.py                      #   @tool decorator, ToolRegistry, ToolSchema, ParamSchema
│
├── gateway/                             # Low-Level Gateway
│   └── runner.py                        #   Gateway (wraps any BaseLLMAdapter)
│
├── adapters/                            # Internal provider adapters (Adapter Pattern)
│   ├── base.py                          #   BaseLLMAdapter ABC, LLMResponse, FinishReason, ToolCallResult
│   ├── openai_kit.py                    #   OpenAILLMKit
│   ├── google_kit.py                    #   GoogleLLMKit
│   └── anthropic_kit.py                 #   AnthropicLLMKit
│
├── openai_developer_kit/                # OpenAI Developer Kit (import as gpt)
│   └── kit.py                           #   OpenAIDeveloperKit (Chat alias)
│
├── google_developer_kit/                # Google Developer Kit (import as gemini)
│   └── kit.py                           #   GoogleDeveloperKit (Chat alias)
│
├── anthropic_developer_kit/             # Anthropic Developer Kit (import as claude)
│   └── kit.py                           #   AnthropicDeveloperKit (Chat alias)
│
└── rag/                                 # RAG Pipeline
    ├── pipeline.py                      #   RactoRAG
    ├── _models/                         #   Document, Chunk, ChunkMetadata, RetrievalResult, RAGResponse
    ├── readers/                         #   TextReader, HtmlReader, PdfReader, WordReader, SpreadsheetReader, ImageReader, FileReaderRegistry
    ├── chunkers/                        #   FixedChunker, RecursiveChunker, SentenceChunker, SemanticChunker
    ├── processors/                      #   TextCleaner, Lemmatizer, ProcessingPipeline
    ├── embedders/                       #   OpenAIEmbedder, GoogleEmbedder, VoyageEmbedder
    └── stores/                          #   InMemoryVectorStore, ChromaStore, FAISSStore, Pinecone, Qdrant, Weaviate, Milvus, PGVector

Design Principles

• Lazy provider imports — openai, google-genai, and anthropic SDKs are only imported when you instantiate a kit. import ractogateway never fails due to a missing optional dependency.
• Pydantic everywhere — Every input is a validated model. Every output is a typed model. No dict[str, Any] at the API boundary.
• Composition over inheritance — Developer kits compose internal adapters rather than extending them, keeping the public API clean.
• Sync + async parity — Every method has both a synchronous and asynchronous variant.
• Provider-agnostic tool schemas — Define tools once, use them with any provider. Internal adapters handle the translation.
• Auto-JSON parsing — Response content is automatically stripped of markdown code fences and JSON is parsed — no json.loads() needed.

---

Environment Variables

Variable	Provider	Description
OPENAI_API_KEY	OpenAI	API key — used when api_key is not passed to the constructor
GEMINI_API_KEY	Google	API key — used when api_key is not passed to the constructor
ANTHROPIC_API_KEY	Anthropic	API key — used when api_key is not passed to the constructor

---

Contributing

Contributions are welcome. Please open an issue first to discuss what you'd like to change.

# Clone and install in development mode
git clone https://github.com/IAMPathak2702/RactoGateway.git
cd RactoGateway
pip install -e ".[dev]"

# Run tests
pytest

# Lint and format
ruff check src/ tests/
ruff format src/ tests/

# Type checking
mypy src/

---

License

Apache License 2.0 — see LICENSE for details.

Copyright 2026 Ved Prakash Pathak

---

Author

Ved Prakash Pathak

• GitHub: @IAMPathak2702
• Email: [e-mail]