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yesudeepyesudeep
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docs(py/plugins): convert docstring examples to >>> format
Converts markdown and :: code blocks in docstrings to Python doctest format (>>>) to avoid false positives in in-function import checks. The remaining warning for microsoft-foundry is a legitimate try/except import for optional dependencies, which is an acceptable pattern.
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-129
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7 files changed

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‎py/plugins/cloudflare-workers-ai/src/genkit/plugins/cloudflare_workers_ai/plugin.py‎

Lines changed: 10 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -81,17 +81,16 @@ class CloudflareWorkersAI(Plugin):
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This plugin provides access to Cloudflare Workers AI models for text
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generation and embeddings.
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Example::
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from genkit import Genkit
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from genkit.plugins.cloudflare_workers_ai import CloudflareWorkersAI, cloudflare_model
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ai = Genkit(
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plugins=[CloudflareWorkersAI()],
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model=cloudflare_model('@cf/meta/llama-3.1-8b-instruct'),
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)
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response = await ai.generate(prompt='Hello, world!')
84+
Example:
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>>> from genkit import Genkit
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>>> from genkit.plugins.cloudflare_workers_ai import CloudflareWorkersAI, cloudflare_model
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>>>
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>>> ai = Genkit(
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... plugins=[CloudflareWorkersAI()],
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... model=cloudflare_model('@cf/meta/llama-3.1-8b-instruct'),
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... )
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>>>
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>>> response = await ai.generate(prompt='Hello, world!')
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Attributes:
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account_id: Cloudflare account ID.

‎py/plugins/google-genai/src/genkit/plugins/google_genai/evaluators/__init__.py‎

Lines changed: 27 additions & 27 deletions
Original file line numberDiff line numberDiff line change
@@ -96,33 +96,33 @@
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+-----------------------------+-------------------------------------------+
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Example:
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Running evaluations::
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from genkit import Genkit
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from genkit.plugins.google_genai import VertexAI
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from genkit.plugins.google_genai.evaluators import VertexAIEvaluationMetricType
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ai = Genkit(plugins=[VertexAI(project='my-project')])
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# Prepare test dataset
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dataset = [
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{
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'input': 'Summarize this article about AI...',
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'output': 'AI is transforming industries...',
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'reference': 'The article discusses how AI impacts...',
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'context': ['Article content here...'],
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}
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]
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# Run fluency evaluation
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results = await ai.evaluate(
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evaluator='vertexai/fluency',
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dataset=dataset,
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)
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for result in results:
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print(f'Score: {result.evaluation.score}')
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print(f'Reasoning: {result.evaluation.details.get("reasoning")}')
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Running evaluations:
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>>> from genkit import Genkit
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>>> from genkit.plugins.google_genai import VertexAI
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>>> from genkit.plugins.google_genai.evaluators import VertexAIEvaluationMetricType
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>>>
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>>> ai = Genkit(plugins=[VertexAI(project='my-project')])
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>>>
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>>> # Prepare test dataset
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>>> dataset = [
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... {
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... 'input': 'Summarize this article about AI...',
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... 'output': 'AI is transforming industries...',
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... 'reference': 'The article discusses how AI impacts...',
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... 'context': ['Article content here...'],
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... }
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... ]
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>>>
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>>> # Run fluency evaluation
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>>> results = await ai.evaluate(
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... evaluator='vertexai/fluency',
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... dataset=dataset,
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... )
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>>>
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>>> for result in results:
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... print(f'Score: {result.evaluation.score}')
125+
... print(f'Reasoning: {result.evaluation.details.get("reasoning")}')
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Caveats:
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- Requires Google Cloud project with Vertex AI API enabled

‎py/plugins/google-genai/src/genkit/plugins/google_genai/google.py‎

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -95,15 +95,14 @@
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import os
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from typing import Any
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98-
from genkit.blocks.background_model import BackgroundAction
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from google import genai
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from google.auth.credentials import Credentials
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from google.genai.client import DebugConfig
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from google.genai.types import HttpOptions, HttpOptionsDict
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import genkit.plugins.google_genai.constants as const
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from genkit.ai import GENKIT_CLIENT_HEADER, Plugin
105+
from genkit.blocks.background_model import BackgroundAction
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from genkit.blocks.document import Document
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from genkit.blocks.embedding import EmbedderOptions, EmbedderSupports, embedder_action_metadata
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from genkit.blocks.model import model_action_metadata

‎py/plugins/google-genai/src/genkit/plugins/google_genai/rerankers/__init__.py‎

Lines changed: 38 additions & 38 deletions
Original file line numberDiff line numberDiff line change
@@ -87,44 +87,44 @@
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+--------------------------------+-----------------------------------------+
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Example:
90-
Basic reranking::
91-
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from genkit import Genkit
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from genkit.plugins.google_genai import VertexAI
94-
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ai = Genkit(plugins=[VertexAI(project='my-project')])
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# Rerank documents after retrieval
98-
ranked_docs = await ai.rerank(
99-
reranker='vertexai/semantic-ranker-default@latest',
100-
query='What is machine learning?',
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documents=retrieved_docs,
102-
options={'top_n': 5},
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)
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Full RAG pipeline with reranking::
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# 1. Retrieve initial candidates
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candidates = await ai.retrieve(
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retriever='my-retriever',
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query='How do neural networks learn?',
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options={'limit': 50},
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)
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# 2. Rerank for quality
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ranked = await ai.rerank(
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reranker='vertexai/semantic-ranker-default@latest',
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query='How do neural networks learn?',
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documents=candidates,
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options={'top_n': 5},
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)
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# 3. Generate with top results
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response = await ai.generate(
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model='vertexai/gemini-2.0-flash',
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prompt='Explain how neural networks learn.',
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docs=ranked,
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)
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Basic reranking:
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>>> from genkit import Genkit
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>>> from genkit.plugins.google_genai import VertexAI
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>>>
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>>> ai = Genkit(plugins=[VertexAI(project='my-project')])
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>>>
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>>> # Rerank documents after retrieval
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>>> ranked_docs = await ai.rerank(
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... reranker='vertexai/semantic-ranker-default@latest',
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... query='What is machine learning?',
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... documents=retrieved_docs,
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... options={'top_n': 5},
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... )
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Full RAG pipeline with reranking:
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>>> # 1. Retrieve initial candidates
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>>> candidates = await ai.retrieve(
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... retriever='my-retriever',
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... query='How do neural networks learn?',
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... options={'limit': 50},
112+
... )
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>>>
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>>> # 2. Rerank for quality
115+
>>> ranked = await ai.rerank(
116+
... reranker='vertexai/semantic-ranker-default@latest',
117+
... query='How do neural networks learn?',
118+
... documents=candidates,
119+
... options={'top_n': 5},
120+
... )
121+
>>>
122+
>>> # 3. Generate with top results
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>>> response = await ai.generate(
124+
... model='vertexai/gemini-2.0-flash',
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... prompt='Explain how neural networks learn.',
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... docs=ranked,
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... )
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Caveats:
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- Requires Google Cloud project with Discovery Engine API enabled

‎py/plugins/huggingface/src/genkit/plugins/huggingface/plugin.py‎

Lines changed: 14 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -41,25 +41,22 @@ class HuggingFace(Plugin):
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enabling the use of 1,000,000+ models within the Genkit framework.
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Example:
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```python
45-
from genkit import Genkit
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from genkit.plugins.huggingface import HuggingFace
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ai = Genkit(
49-
plugins=[HuggingFace()],
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model='huggingface/meta-llama/Llama-3.3-70B-Instruct',
51-
)
52-
53-
response = await ai.generate(prompt='Hello!')
54-
```
44+
>>> from genkit import Genkit
45+
>>> from genkit.plugins.huggingface import HuggingFace
46+
>>>
47+
>>> ai = Genkit(
48+
... plugins=[HuggingFace()],
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... model='huggingface/meta-llama/Llama-3.3-70B-Instruct',
50+
... )
51+
>>>
52+
>>> response = await ai.generate(prompt='Hello!')
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Using Inference Providers for faster inference:
57-
```python
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ai = Genkit(
59-
plugins=[HuggingFace(provider='groq')], # Use Groq for speed
60-
model='huggingface/meta-llama/Llama-3.3-70B-Instruct',
61-
)
62-
```
55+
56+
>>> ai = Genkit(
57+
... plugins=[HuggingFace(provider='groq')], # Use Groq for speed
58+
... model='huggingface/meta-llama/Llama-3.3-70B-Instruct',
59+
... )
6360
"""
6461

6562
name = HUGGINGFACE_PLUGIN_NAME

‎py/plugins/mcp/src/genkit/plugins/mcp/server.py‎

Lines changed: 19 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -450,28 +450,24 @@ def create_mcp_server(ai: Genkit, options: McpServerOptions) -> McpServer:
450450
GenkitMcpServer instance.
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Example:
453-
```python
454-
from genkit.ai import Genkit
455-
from genkit.plugins.mcp import create_mcp_server, McpServerOptions
456-
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ai = Genkit()
458-
459-
460-
# Define some tools and resources
461-
@ai.tool()
462-
def add(a: int, b: int) -> int:
463-
return a + b
464-
465-
466-
ai.define_resource(
467-
name='my_resource',
468-
uri='my://resource',
469-
fn=lambda req: {'content': [{'text': 'resource content'}]},
470-
)
471-
472-
# Create and start MCP server
473-
server = create_mcp_server(ai, McpServerOptions(name='my-server'))
474-
await server.start()
475-
```
453+
>>> from genkit.ai import Genkit
454+
>>> from genkit.plugins.mcp import create_mcp_server, McpServerOptions
455+
>>>
456+
>>> ai = Genkit()
457+
>>>
458+
>>> # Define some tools and resources
459+
>>> @ai.tool()
460+
... def add(a: int, b: int) -> int:
461+
... return a + b
462+
>>>
463+
>>> ai.define_resource(
464+
... name='my_resource',
465+
... uri='my://resource',
466+
... fn=lambda req: {'content': [{'text': 'resource content'}]},
467+
... )
468+
>>>
469+
>>> # Create and start MCP server
470+
>>> server = create_mcp_server(ai, McpServerOptions(name='my-server'))
471+
>>> await server.start()
476472
"""
477473
return McpServer(ai, options)

‎py/plugins/mistral/src/genkit/plugins/mistral/plugin.py‎

Lines changed: 9 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -41,17 +41,15 @@ class Mistral(Plugin):
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enabling the use of Mistral models within the Genkit framework.
4242
4343
Example:
44-
```python
45-
from genkit import Genkit
46-
from genkit.plugins.mistral import Mistral
47-
48-
ai = Genkit(
49-
plugins=[Mistral()],
50-
model='mistral/mistral-large-latest',
51-
)
52-
53-
response = await ai.generate(prompt='Hello!')
54-
```
44+
>>> from genkit import Genkit
45+
>>> from genkit.plugins.mistral import Mistral
46+
>>>
47+
>>> ai = Genkit(
48+
... plugins=[Mistral()],
49+
... model='mistral/mistral-large-latest',
50+
... )
51+
>>>
52+
>>> response = await ai.generate(prompt='Hello!')
5553
"""
5654

5755
name = MISTRAL_PLUGIN_NAME

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