trivialai 0.4.0

A trivial set of API bindings for AI models, because I'd like them to be easy to use.

TrivialAI

(A set of httpx / boto3-based, trivial bindings for AI models — now with optional streaming)

Install

pip install trivialai
# Optional: HTTP/2 for OpenAI/Anthropic
# pip install "trivialai[http2]"
# Optional: AWS Bedrock support (via boto3)
# pip install "trivialai[bedrock]"

• Requires Python ≥ 3.9.
• Uses httpx for HTTP-based providers and boto3 for Bedrock.

Quick start

>>> from trivialai import claude, gcp, ollama, chatgpt, bedrock

Synchronous usage (unchanged ergonomics)

Ollama

>>> client = ollama.Ollama("gemma2:2b", "http://localhost:11434/")
# or ollama.Ollama("deepseek-coder-v2:latest", "http://localhost:11434/")
# or ollama.Ollama("mannix/llama3.1-8b-abliterated:latest", "http://localhost:11434/")
>>> client.generate("sys msg", "Say hi with 'platypus'.").content
"Hi there—platypus!"
>>> client.generate_json("sys msg", "Return {'name': 'Platypus'} as JSON").content
{'name': 'Platypus'}

Claude (Anthropic API)

>>> client = claude.Claude("claude-3-5-sonnet-20240620", os.environ["ANTHROPIC_API_KEY"])
>>> client.generate("sys msg", "Say hi with 'platypus'.").content
"Hello, platypus!"

GCP (Vertex AI)

>>> client = gcp.GCP("gemini-1.5-flash-001", "/path/to/gcp_creds.json", "us-central1")
>>> client.generate("sys msg", "Say hi with 'platypus'.").content
"Hello, platypus!"

ChatGPT (OpenAI API)

>>> client = chatgpt.ChatGPT("gpt-4o-mini", os.environ["OPENAI_API_KEY"])
>>> client.generate("sys msg", "Say hi with 'platypus'.").content
"Hello, platypus!"

AWS Bedrock (Claude / Llama / Nova / etc)

Bedrock support is provided via the Bedrock client, which implements the same LLMMixin interface as the others.

1) One-time AWS setup

1. Enable Bedrock and model access

   • In the AWS console, pick a Bedrock-supported region (e.g. us-east-1).
   • Go to Amazon Bedrock → Model access and enable access for the models you want (e.g. Claude 3.5 Sonnet, Llama, Nova, etc).

2. IAM permissions

   Grant your user/role permission to call Bedrock runtime APIs, for example:

   {
     "Version": "2012-10-17",
     "Statement": [
       {
         "Effect": "Allow",
         "Action": [
           "bedrock:Converse",
           "bedrock:ConverseStream",
           "bedrock:InvokeModel",
           "bedrock:InvokeModelWithResponseStream"
         ],
         "Resource": "*"
       }
     ]
   }
   

   You can restrict Resource to specific model ARNs later.

3. Credentials

   TrivialAI can use either:

   • the normal AWS credential chain (aws configure, env vars, instance role), or
   • explicit credentials passed into the Bedrock constructor.

2) Choosing the right model_id

Bedrock distinguishes between:

• Foundation model IDs, like: anthropic.claude-3-5-sonnet-20241022-v2:0
• Inference profile IDs, which are region-prefixed, like: us.anthropic.claude-3-5-sonnet-20241022-v2:0

Some newer models (like Claude 3.5 Sonnet v2) must be called via the inference profile ID from certain regions. If you see a ValidationException complaining about “Invocation of model ID ... with on-demand throughput isn’t supported; retry with an inference profile”, swap to the us.-prefixed ID.

3) Minimal Bedrock demo

from trivialai import bedrock

# Using an inference profile ID for Claude 3.5 Sonnet v2 from us-east-1:
client = bedrock.Bedrock(
    model_id="us.anthropic.claude-3-5-sonnet-20241022-v2:0",
    region="us-east-1",
    # Either rely on normal AWS creds...
    # aws_profile="my-dev-profile",
    # ...or pass explicit keys (for testing):
    aws_access_key_id="AKIA...",
    aws_secret_access_key="SECRET...",
)

res = client.generate(
    "This is a test message. Make sure your reply contains the word 'margarine'",
    "Hello there! Can you hear me?"
)
print(res.content)
# -> "Yes, I can hear you! ... margarine ..."

# With JSON parsing:
res_json = client.generate_json(
    "You are a JSON-only assistant.",
    "Return {'name':'Platypus'} as JSON."
)
print(res_json.content)
# -> {'name': 'Platypus'}

The Bedrock client fully participates in the same higher-level helpers:

• generate_checked(...)
• generate_json(...)
• generate_tool_call(...)
• generate_many_tool_calls(...)
• stream_checked(...) / stream_json(...)

No special-casing required in downstream code.

---

Streaming (NDJSON-style events)

All providers expose a common streaming shape via stream(...) (sync iterator) and astream(...) (async):

Event schema

• {"type":"start", "provider": "<ollama|openai|anthropic|gcp|bedrock>", "model": "..."}

• {"type":"delta", "text":"...", "scratchpad":"..."}

  • For Ollama, scratchpad contains model “thinking” extracted from <think>…</think>.
  • For ChatGPT, Claude API, GCP, and Bedrock, scratchpad is "" (empty) in deltas.

• {"type":"end", "content":"...", "scratchpad": <str|None>, "tokens": <int>}

• {"type":"error", "message":"..."}

Example: streaming Ollama (sync)

>>> client = ollama.Ollama("gemma2:2b", "http://localhost:11434/")
>>> for ev in client.stream("sys", "Explain, think step-by-step."):
...     if ev["type"] == "delta":
...         # show model output live
...         print(ev["text"], end="")
...     elif ev["type"] == "end":
...         print("\n-- scratchpad --")
...         print(ev["scratchpad"])

Example: streaming Bedrock (sync)

>>> from trivialai import bedrock
>>> client = bedrock.Bedrock(
...     model_id="us.anthropic.claude-3-5-sonnet-20241022-v2:0",
...     region="us-east-1",
... )
>>> events = list(client.stream(
...     "This is a test message. Make sure your reply contains the word 'margarine'",
...     "Hello there! Can you hear me?"
... ))
>>> events[0]
{'type': 'start', 'provider': 'bedrock', 'model': 'us.anthropic.claude-3-5-sonnet-20241022-v2:0'}
>>> events[-1]
{'type': 'end', 'content': 'Yes, I can hear you! ... margarine ...', 'scratchpad': None, 'tokens': 36}

Example: parse-at-end streaming

If you want incremental updates and a structured parse at the end:

from trivialai.util import stream_checked, loadch

for ev in client.stream("sys", "Return a JSON object gradually."):
    # pass-through for UI
    if ev["type"] in {"start","delta"}:
        print(ev)
    elif ev["type"] == "end":
        # now emit the final parsed event
        for final_ev in stream_checked(iter([ev]), loadch):
            print(final_ev)  # {"type":"final","ok":True,"parsed":{...}}

Shortcut: stream_json(system, prompt) yields the same stream and a final parsed event using loadch.

Async flavor

async for ev in client.astream("sys", "Stream something."):
    ...

(For Bedrock, stream(...) is the native streaming interface; astream(...) currently falls back to the default LLMMixin behavior unless you wrap it yourself.)

---

Tool Calls

Use Tools to register Python functions, describe them to the model, and safely execute the model’s chosen call.

1) Define tools

You can register functions directly or with a decorator. Docstring = description. Type hints become the argument schema.

from typing import Optional, List
from trivialai.tools import Tools

tools = Tools()  # or Tools(extras={"api_key": "..."}), see below

@tools.define()
def screenshot(url: str, selectors: Optional[List[str]] = None) -> None:
    """Take a screenshot of a page; optionally highlight CSS selectors."""
    print("shot", url, selectors)

# Or:
def search(query: str, top_k: int = 5) -> List[str]:
    """Search and return top results."""
    return [f"res{i}" for i in range(top_k)]
tools.define(search)

2) Show tools to the model

tools.list() returns LLM-friendly metadata:

>>> tools.list()
[{
  "name": "screenshot",
  "description": "Take a screenshot of a page; optionally highlight CSS selectors.",
  "type": {"url": <class 'str'>, "selectors": typing.Optional[typing.List[str]]},
  "args": {
    "url": {"type": "string"},
    "selectors": {"type": "array", "items": {"type": "string"}, "nullable": True}
  }
},
{
  "name": "search",
  "description": "Search and return top results.",
  "type": {"query": <class 'str'>, "top_k": <class 'int'>},
  "args": {
    "query": {"type": "string"},
    "top_k": {"type": "int"}
  }
}]

3) Ask the model to choose a tool

All LLM clients (Ollama, Claude API, ChatGPT, GCP, Bedrock) support a helper that prompts for a tool call and validates it:

from trivialai import ollama
client = ollama.Ollama("gemma2:2b", "http://localhost:11434/")

res = client.generate_tool_call(
    tools,
    system="You are a tool-use router.",
    prompt="Take a screenshot of https://example.com and highlight the search box."
)

# Validated, parsed dict:
>>> res.content
{'functionName': 'screenshot', 'args': {'url': 'https://example.com', 'selectors': ['#search']}}

Multiple calls? Use generate_many_tool_calls(...):

multi = client.generate_many_tool_calls(
    tools,
    prompt="Search for 'platypus', then screenshot the first result."
)
# -> [{'functionName': 'search', ...}, {'functionName': 'screenshot', ...}]

4) Validate/execute (with robust errors)

• Validation rules: all required params present; optional params may be omitted; unknown params are rejected.
• On invalid input, methods raise TransformError (no None returns).

from trivialai.util import TransformError

tool_call = res.content  # {'functionName': 'screenshot', 'args': {...}}

# Validate explicitly (optional; call() validates too)
assert tools.validate(tool_call)

# Execute
try:
    tools.call(tool_call)
except TransformError as e:
    print("Tool call failed:", e.message, e.raw)

If you already have a raw JSON string from a model and want to validate+parse:

parsed = tools.transform('{"functionName":"search","args":{"query":"platypus"}}')
# or for a list of calls:
calls = tools.transform_multi('[{"functionName":"search","args":{"query":"platypus"}}]')

5) Extras / environment defaults

Attach fixed kwargs (e.g., tokens, org IDs) that merge into every call:

tools = Tools(extras={"api_key": "SECRET"})  # extras override user args by default
tools.call(tool_call)

# Per-call control:
tools.call_with_extras({"api_key": "OTHER"}, tool_call, override=True)   # extras win
tools.call_with_extras({"api_key": "OTHER"}, tool_call, override=False)  # user args win

Notes

• Return values are whatever your function returns—side effects are on you. Keep tools small and deterministic when possible.
• tools.list() keeps the original type hints for backward compatibility and adds a normalized args schema that’s friendlier for prompts.
• Safety: only register functions you actually want the model to invoke.

Embeddings

The embeddings module uses httpx and supports Ollama embeddings:

from trivialai.embedding import OllamaEmbedder
embed = OllamaEmbedder(model="nomic-embed-text", server="http://localhost:11434")
vec = embed("hello world")

---

Notes & compatibility

• Dependencies: httpx replaces requests. Use httpx[http2] if you want HTTP/2 for OpenAI/Anthropic. Use boto3 for AWS Bedrock.
• Python: ≥ 3.9 (we use asyncio.to_thread).
• Scratchpad: only Ollama surfaces <think> content; others emit scratchpad as "" in deltas and None in the final event.
• GCP/Vertex AI: primarily for setup/auth. No native provider streaming; astream falls back to a single final chunk unless you override.
• Bedrock: stream(...) uses converse_stream(); token counts (when available) are surfaced as tokens in the final end event.