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Merge branch 'main' into dep/batch-upgrade-2024-11-18
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henchaves authored Nov 18, 2024
2 parents ac2c157 + 285fd6c commit 6550111
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2 changes: 1 addition & 1 deletion docs/open_source/scan/scan_llm/index.md
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Expand Up @@ -87,7 +87,7 @@ os.environ['OPENAI_API_VERSION'] = '2023-07-01-preview'
# You'll need to provide the name of the model that you've deployed
# Beware, the model provided must be capable of using function calls
giskard.llm.set_llm_model('my-gpt-4-model')
giskard.llm.embeddings.set_embedding_model('my-embedding-model')
giskard.llm.embeddings.openai.set_embedding_model('my-embedding-model')
```

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2 changes: 1 addition & 1 deletion docs/open_source/setting_up/index.md
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Expand Up @@ -40,7 +40,7 @@ os.environ['OPENAI_API_VERSION'] = '2023-07-01-preview'
# You'll need to provide the name of the model that you've deployed
# Beware, the model provided must be capable of using function calls
giskard.llm.set_llm_model('my-gpt-4-model')
giskard.llm.embeddings.set_embedding_model('my-embedding-model')
giskard.llm.embeddings.openai.set_embedding_model('my-embedding-model')
```

## Mistral Client Setup
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Expand Up @@ -121,7 +121,7 @@ os.environ['OPENAI_API_VERSION'] = '2023-07-01-preview'
# You'll need to provide the name of the model that you've deployed
# Beware, the model provided must be capable of using function calls
giskard.llm.set_llm_model('my-gpt-4-model')
giskard.llm.embeddings.set_embedding_model('my-embedding-model')
giskard.llm.embeddings.openai.set_embedding_model('my-embedding-model')
```

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2 changes: 1 addition & 1 deletion giskard/rag/metrics/ragas_metrics.py
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Expand Up @@ -108,7 +108,7 @@ def __call__(self, question_sample: dict, answer: AgentAnswer) -> dict:
if self.requires_context and answer.documents is None:
logger.warning(
f"No retrieved documents are passed to the evaluation function, computation of {self.name} cannot be done without it."
"Make sure you pass 'retrieved_documents' to the evaluate function or that the 'answer_fn' return documents alongside the answer."
"Make sure that the 'answer_fn' return documents alongside the answer, wrapped by the 'AgentAnswer' class."
)
return {self.name: 0}

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