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Add operator examples
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‎README.md‎

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<a href="https://synalinks.github.io/synalinks" target="_blank"><strong>Documentation</strong></a> ·
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<a href="https://synalinks.github.io/synalinks/FAQ/" target="_blank"><strong>FAQ</strong></a> ·
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<a href="https://discord.gg/82nt97uXcM" target="_blank"><strong>Discord</strong></a> ·
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<a href="https://synalinks.github.io/synalinks" target="_blank"><strong>Code Examples</strong></a>
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<a href="https://github.com/SynaLinks/synalinks/tree/main/examples" target="_blank"><strong>Code Examples</strong></a>
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</p>
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</div>
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[![Discord](https://img.shields.io/discord/1118241178723291219)](https://discord.gg/82nt97uXcM)
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[![Python package](https://github.com/SynaLinks/Synalinks/actions/workflows/tests.yml/badge.svg)](https://github.com/SynaLinks/SynaLinks/actions/workflows/tests.yml)
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[![License: Apache-2.0](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://opensource.org/license/apache-2-0)
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[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/SynaLinks/synalinks)
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Too busy to read the documentation? Give **[LLM.md](https://raw.githubusercontent.com/SynaLinks/synalinks/refs/heads/main/LLM.md)** to your favorite LM provider.
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‎examples/json_operators/concatenate.py‎

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import synalinks
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# Concatenation with Synalinks
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class Query(synalinks.DataModel):
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query: str
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class Answer(synalinks.DataModel):
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answer: str
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# Synalinks operators works at a metaclass level
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# In that case, the result is a `SymbolicDataModel`
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# A `SymbolicDataModel` can be understand as a data
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# specification/contract. It only contains a JSON schema
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# and cannot be used for computation. It allow Synalinks
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# to build directed acyclic graph (DAG) of computation
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# from inputs and outputs, like the tensor shape
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# in deep learning frameworks.
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qa_pair = Query + Answer
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assert isinstance(qa_pair, synalinks.SymbolicDataModel)
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print(qa_pair.prettify_schema())
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# {
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# "additionalProperties": false,
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# "properties": {
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# "query": {
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# "title": "Query",
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# "type": "string"
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# },
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# "answer": {
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# "title": "Answer",
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# "type": "string"
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# }
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# },
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# "required": [
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# "query",
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# "answer"
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# ],
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# "title": "Query",
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# "type": "object"
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# }
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# Once we concatenate two instanciated data models, the result
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# is a JsonDataModel, a data model containing both a JSON schema and
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# a JSON object containing the actual data.
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qa_pair = \
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Query(query="What is the French city of aeronautics and robotics?") + \
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Answer(answer="Toulouse")
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assert isinstance(qa_pair, synalinks.JsonDataModel)
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print(qa_pair.prettify_json())
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# {
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# "query": "What is the French city of aeronautics and robotics?",
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# "answer": "Toulouse"
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# }

‎examples/json_operators/concatenate_part_2.py‎

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import synalinks
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class Query(synalinks.DataModel):
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query: str
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# Concatenation with Synalinks (Part 2)
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# What happen if you concatenate two data
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# models with the same fields?
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# When property names conflict, numerical suffixes are
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# added to ensure uniqueness.
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two_queries = Query + Query
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print(two_queries.prettify_schema())
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# {
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# "additionalProperties": false,
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# "properties": {
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# "query": {
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# "title": "Query",
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# "type": "string"
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# },
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# "query_1": {
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# "title": "Query 1",
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# "type": "string"
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# }
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# },
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# "required": [
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# "query",
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# "query_1"
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# ],
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# "title": "Query",
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# "type": "object"
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# }
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two_queries = \
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Query(query="Why is neuro-symbolic systems powering the next AI wave?") + \
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Query(query="Can you give a multiple of 5?")
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print(two_queries.prettify_json())
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# {
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# "query": "Why is neuro-symbolic systems powering the next AI wave?",
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# "query_1": "Can you give a multiple of 5?"
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# }
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# Now, what happen when you concatenate with `None`?
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# An exception is raised!
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failing_query = \
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Query(query="Why is neuro-symbolic AI powering the next wave?") + \
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None
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# ValueError: Received x1=query='Why is neuro-symbolic AI powering the next wave?' and x2=None
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# This behavior can be summarized with the following truth table:
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# Truth Table:
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# | `x1` | `x2` | Concat (`+`) |
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# | ------ | ------ | ----------------- |
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# | `x1` | `x2` | `x1 + x2` |
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# | `x1` | `None` | `Exception` |
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# | `None` | `x2` | `Exception` |
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# | `None` | `None` | `Exception` |

‎examples/json_operators/logical_and.py‎

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import synalinks
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# Logical AND with Synalinks
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class Query(synalinks.DataModel):
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query: str
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class Answer(synalinks.DataModel):
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answer: str
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qa_pair = Query & Answer
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assert isinstance(qa_pair, synalinks.SymbolicDataModel)
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print(qa_pair.prettify_schema())
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# {
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# "additionalProperties": false,
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# "properties": {
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# "query": {
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# "title": "Query",
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# "type": "string"
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# },
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# "answer": {
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# "title": "Answer",
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# "type": "string"
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# }
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# },
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# "required": [
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# "query",
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# "answer"
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# ],
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# "title": "Query",
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# "type": "object"
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# }
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# When performing an And operation with `None` the output is `None`
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# You can see the logical And as a robust concatenation operation.
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qa_pair = Query(query="Why is neuro-symbolic AI powering the next wave?") & None
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assert isinstance(qa_pair, None)
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# Here is the table summarizing the behavior
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# Truth Table:
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# | `x1` | `x2` | Logical And (`&`) |
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# | ------ | ------ | ----------------- |
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# | `x1` | `x2` | `x1 + x2` |
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# | `x1` | `None` | `None` |
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# | `None` | `x2` | `None` |
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# | `None` | `None` | `None` |

‎examples/json_operators/logical_or.py‎

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import synalinks
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import asyncio
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class Query(synalinks.DataModel):
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query: str
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class Answer(synalinks.DataModel):
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answer: str
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class AnswerWithThinking(synalinks.DataModel):
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thinking: str
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answer: str
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# Logical Or
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# When a two data models are provided, the logical or perform a concatenation
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# of the two data models. However when given a `None`, it ignore it to give
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# you the one that isn't None.
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# This behavior can be summarized in the following truth table:
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# Truth Table:
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# | `x1` | `x2` | Logical Or (`|`) |
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# | ------ | ------ | ---------------- |
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# | `x1` | `x2` | `x1 + x2` |
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# | `x1` | `None` | `x1` |
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# | `None` | `x2` | `x2` |
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# | `None` | `None` | `None` |
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answer = Answer(answer="Toulouse") | None
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print(answer.prettify_json())
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# {
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# "answer": "Toulouse"
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# }
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answer = None | AnswerWithThinking(
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thinking=
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(
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"LAAS CNRS (Laboratoire d'Analyse et d'Architecture des Systèmes) is located in "
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"Toulouse and is renowned for its research in robotics."
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" Toulouse is also widely recognized as a central hub for aeronautics and"
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" space in Europe. It houses the headquarters of Airbus and several "
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"important aerospace research centers. and aeronautics."
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),
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answer="Toulouse")
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print(answer.prettify_json())
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# {
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# "thinking": "LAAS CNRS (Laboratoire d'Analyse et d'Architecture des
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# Syst\u00e8mes) is located in Toulouse and is renowned for its research
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# in robotics. Toulouse is also widely recognized as a central hub for
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# aeronautics and space in Europe. It houses the headquarters of Airbus
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# and several important aerospace research centers. and aeronautics.",
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# "answer": "Toulouse"
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# }
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# Why is that useful ? Let's explain it with an example,
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# imagine you want an adaptative system that is able to
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# answer shortly, or take more time to "think" before answering
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# depending on the question difficulty.
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#
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# Example:
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async def main():
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language_model = synalinks.LanguageModel(model="ollama/mistral")
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inputs = synalinks.Input(data_model=Query)
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answer_without_thinking, answer_with_thinking = await synalinks.Branch(
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question="Evaluate the difficulty of the query",
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labels=["easy", "difficult"],
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branches=[
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synalinks.Generator(
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data_model=Answer,
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language_model=language_model,
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),
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synalinks.Generator(
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data_model=AnswerWithThinking,
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language_model=language_model,
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)
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],
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language_model=language_model,
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# We can optionally return the decision,
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# in Synalinks there is no black-box component!
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# Every LM inference, can be returned
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# for evaluation or explainability
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return_decision=False,
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)(inputs)
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# The outputs is the answer without thinking OR the answer with thinking
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outputs = answer_without_thinking | answer_with_thinking
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program = synalinks.Program(
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inputs=inputs,
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outputs=outputs,
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name="adaptative_qa",
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description="A program that take the time to think if the query is difficult to answer"
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)
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answer = await program(Query(query="What is French city of robotics and aeronautics?"))
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print(answer.prettify_json())
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# {
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# "thinking": "The answer to the given query involves finding a city in
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# France that is known for robotics and aeronautics. While there might be
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# several cities that have significant presence in these fields, Toulouse
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# is one of the most renowned due to the presence of well-established
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# institutions like EADS (European Aeronautic Defence and Space Company),
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# IRIT (Institut de Recherche en Informatique pour le Traitement Automatique des Images)
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# and LAAS CNRS (Laboratoire d'Analyse et d'Architecture des Syst\u00e8mes).",
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# "answer": "Toulouse"
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# }
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if __name__ == "__main__":
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asyncio.run(main())

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