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Merge pull request #2042 from Giskard-AI/feature/gsk-3835-update-setting-up-llm-client-doc-to-include-embedding
[GSK-3835] Update setting up LLM doc
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‎docs/open_source/scan/scan_llm/index.md‎

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@@ -55,15 +55,19 @@ like this:
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::::::{tab-item} OpenAI
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```python
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import giskard
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import os
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import giskard
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from giskard.llm.client.openai import OpenAIClient
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# Set the OpenAI API key
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os.environ["OPENAI_API_KEY"] = "sk-…"
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# Create a giskard OpenAI client
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openai_client = OpenAIClient(model="gpt-4o")
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# Set the default client
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giskard.llm.set_llm_api("openai")
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oc = OpenAIClient(model="gpt-4-turbo-preview")
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giskard.llm.set_default_client(oc)
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giskard.llm.set_default_client(openai_client)
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```
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::::::
@@ -73,30 +77,38 @@ Require `openai>=1.0.0`
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```python
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import os
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from giskard.llm import set_llm_model
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from giskard.llm.embeddings.openai import set_embedding_model
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import giskard
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# Set the Azure OpenAI API key and endpoint
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os.environ['AZURE_OPENAI_API_KEY'] = '...'
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os.environ['AZURE_OPENAI_ENDPOINT'] = 'https://xxx.openai.azure.com'
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os.environ['OPENAI_API_VERSION'] = '2023-07-01-preview'
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# You'll need to provide the name of the model that you've deployed
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# Beware, the model provided must be capable of using function calls
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set_llm_model('my-gpt-4-model')
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set_embedding_model('my-embedding-model') # Optional
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giskard.llm.set_llm_model('my-gpt-4-model')
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giskard.llm.embeddings.set_embedding_model('my-embedding-model')
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```
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::::::
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::::::{tab-item} Mistral
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```python
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import os
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import giskard
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from giskard.llm.client.mistral import MistralClient
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os.environ["MISTRAL_API_KEY"] = "sk-…"
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# Set the Mistral API key
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os.environ["MISTRAL_API_KEY"] = "…"
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mc = MistralClient()
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giskard.llm.set_default_client(mc)
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# Create a giskard Mistral client
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mistral_client = MistralClient()
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# Set the default client
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giskard.llm.set_default_client(mistral_client)
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# You may also want to set the default embedding model
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# Check the Custom Client code snippet for more details
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```
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::::::
@@ -106,11 +118,18 @@ giskard.llm.set_default_client(mc)
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import giskard
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from openai import OpenAI
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from giskard.llm.client.openai import OpenAIClient
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from giskard.llm.embeddings.openai import OpenAIEmbedding
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# Setup the Ollama client with API key and base URL
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# Setup the OpenAI client with API key and base URL for Ollama
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_client = OpenAI(base_url="http://localhost:11434/v1/", api_key="ollama")
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oc = OpenAIClient(model="gemma:2b", client=_client)
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giskard.llm.set_default_client(oc)
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# Wrap the original OpenAI client with giskard OpenAI client and embedding
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llm_client = OpenAIClient(model="llama3.2", client=_client)
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embed_client = OpenAIEmbedding(model="nomic-embed-text", client=_client)
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# Set the default client and embedding
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giskard.llm.set_default_client(llm_client)
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giskard.llm.embeddings.set_default_embedding(embed_client)
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```
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::::::
@@ -123,13 +142,17 @@ import giskard
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from giskard.llm.client.bedrock import ClaudeBedrockClient
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from giskard.llm.embeddings.bedrock import BedrockEmbedding
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from giskard.llm.embeddings import set_default_embedding
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# Create a Bedrock client
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bedrock_runtime = boto3.client("bedrock-runtime", region_name=os.environ["AWS_DEFAULT_REGION"])
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# Wrap the Beddock client with giskard Bedrock client and embedding
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claude_client = ClaudeBedrockClient(bedrock_runtime, model="anthropic.claude-3-haiku-20240307-v1:0")
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embed_client = BedrockEmbedding(bedrock_runtime, model="amazon.titan-embed-text-v1")
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# Set the default client and embedding
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giskard.llm.set_default_client(claude_client)
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set_default_embedding(embed_client)
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giskard.llm.embeddings.set_default_embedding(embed_client)
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```
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::::::
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```python
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import os
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import giskard
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import google.generativeai as genai
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from giskard.llm.client.gemini import GeminiClient
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# Set the Gemini API key
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os.environ["GEMINI_API_KEY"] = "…"
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# Configure the Gemini API
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genai.configure(api_key=os.environ["GEMINI_API_KEY"])
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giskard.llm.set_default_client(GeminiClient())
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# Create a giskard Gemini client
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gemini_client = GeminiClient()
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# Set the default client
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giskard.llm.set_default_client(gemini_client)
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# You may also want to set the default embedding model
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# Check the Custom Client code snippet for more details
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```
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::::::
@@ -157,7 +189,7 @@ from typing import Sequence, Optional
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from giskard.llm.client import set_default_client
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from giskard.llm.client.base import LLMClient, ChatMessage
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# Create a custom client by extending the LLMClient class
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class MyLLMClient(LLMClient):
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def __init__(self, my_client):
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self._client = my_client
@@ -202,9 +234,17 @@ class MyLLMClient(LLMClient):
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return ChatMessage(role="assistant", message=data["completion"])
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# Create an instance of the custom client
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llm_client = MyLLMClient()
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set_default_client(MyLLMClient())
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# Set the default client
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set_default_client(llm_client)
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# It's also possible to create a custom embedding class extending BaseEmbedding
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# Or you can use FastEmbed for a pre-built embedding model:
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from giskard.llm.embeddings.fastembed import try_get_fastembed_embeddings
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embed_client = try_get_fastembed_embeddings()
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giskard.llm.embeddings.set_default_embedding(embed_client)
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```
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::::::

‎docs/open_source/setting_up/index.md‎

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Original file line numberDiff line numberDiff line change
@@ -11,60 +11,78 @@ This guide focuses primarily on configuring and using various LLM clients suppor
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## OpenAI GPT-4 Client Setup
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```python
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import giskard
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import os
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import giskard
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from giskard.llm.client.openai import OpenAIClient
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18+
# Set the OpenAI API key
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os.environ["OPENAI_API_KEY"] = "sk-…"
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21+
# Create a giskard OpenAI client
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openai_client = OpenAIClient(model="gpt-4o")
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# Set the default client
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giskard.llm.set_llm_api("openai")
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oc = OpenAIClient(model="gpt-4-turbo-preview")
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giskard.llm.set_default_client(oc)
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giskard.llm.set_default_client(openai_client)
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```
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## Azure OpenAI Client Setup
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```python
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import os
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from giskard.llm import set_llm_model
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from giskard.llm.embeddings.openai import set_embedding_model
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import giskard
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35+
# Set the Azure OpenAI API key and endpoint
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os.environ['AZURE_OPENAI_API_KEY'] = '...'
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os.environ['AZURE_OPENAI_ENDPOINT'] = 'https://xxx.openai.azure.com'
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os.environ['OPENAI_API_VERSION'] = '2023-07-01-preview'
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# You'll need to provide the name of the model that you've deployed
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# Beware, the model provided must be capable of using function calls
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set_llm_model('my-gpt-4-model')
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set_embedding_model('my-embedding-model') # Optional
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giskard.llm.set_llm_model('my-gpt-4-model')
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giskard.llm.embeddings.set_embedding_model('my-embedding-model')
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```
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## Mistral Client Setup
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```python
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import os
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import giskard
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from giskard.llm.client.mistral import MistralClient
4852

49-
os.environ["MISTRAL_API_KEY"] = "sk-…"
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# Set the Mistral API key
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os.environ["MISTRAL_API_KEY"] = "…"
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51-
mc = MistralClient()
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giskard.llm.set_default_client(mc)
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# Create a giskard Mistral client
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mistral_client = MistralClient()
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# Set the default client
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giskard.llm.set_default_client(mistral_client)
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# You may also want to set the default embedding model
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# Check the Custom Client Setup section for more details
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```
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## Ollama Client Setup
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The Ollama setup involves configuring an OpenAI client customized for the Ollama API:
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```python
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import giskard
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from openai import OpenAI
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from giskard.llm.client.openai import OpenAIClient
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from giskard.llm.client.mistral import MistralClient
74+
from giskard.llm.embeddings.openai import OpenAIEmbedding
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64-
# Setup the Ollama client with API key and base URL
76+
# Setup the OpenAI client with API key and base URL for Ollama
6577
_client = OpenAI(base_url="http://localhost:11434/v1/", api_key="ollama")
66-
oc = OpenAIClient(model="gemma:2b", client=_client)
67-
giskard.llm.set_default_client(oc)
78+
79+
# Wrap the original OpenAI client with giskard OpenAI client and embedding
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llm_client = OpenAIClient(model="llama3.2", client=_client)
81+
embed_client = OpenAIEmbedding(model="nomic-embed-text", client=_client)
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83+
# Set the default client and embedding
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giskard.llm.set_default_client(llm_client)
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giskard.llm.embeddings.set_default_embedding(embed_client)
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```
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## Claude 3 Client Setup
@@ -78,28 +96,41 @@ import giskard
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from giskard.llm.client.bedrock import ClaudeBedrockClient
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from giskard.llm.embeddings.bedrock import BedrockEmbedding
81-
from giskard.llm.embeddings import set_default_embedding
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# Create a Bedrock client
83101
bedrock_runtime = boto3.client("bedrock-runtime", region_name=os.environ["AWS_DEFAULT_REGION"])
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# Wrap the Beddock client with giskard Bedrock client and embedding
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claude_client = ClaudeBedrockClient(bedrock_runtime, model="anthropic.claude-3-haiku-20240307-v1:0")
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embed_client = BedrockEmbedding(bedrock_runtime, model="amazon.titan-embed-text-v1")
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107+
# Set the default client and embedding
86108
giskard.llm.set_default_client(claude_client)
87-
set_default_embedding(embed_client)
109+
giskard.llm.embeddings.set_default_embedding(embed_client)
88110
```
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## Gemini Client Setup
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```python
93115
import os
94116
import giskard
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96117
import google.generativeai as genai
97-
98118
from giskard.llm.client.gemini import GeminiClient
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120+
# Set the Gemini API key
121+
os.environ["GEMINI_API_KEY"] = "…"
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123+
# Configure the Gemini API
100124
genai.configure(api_key=os.environ["GEMINI_API_KEY"])
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102-
giskard.llm.set_default_client(GeminiClient())
126+
# Create a giskard Gemini client
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gemini_client = GeminiClient()
128+
129+
# Set the default client
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giskard.llm.set_default_client(gemini_client)
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132+
# You may also want to set the default embedding model
133+
# Check the Custom Client Setup section for more details
103134
```
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## Custom Client Setup
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110141
from giskard.llm.client import set_default_client
111142
from giskard.llm.client.base import LLMClient, ChatMessage
112143

113-
144+
# Create a custom client by extending the LLMClient class
114145
class MyLLMClient(LLMClient):
115146
def __init__(self, my_client):
116147
self._client = my_client
@@ -155,7 +186,17 @@ class MyLLMClient(LLMClient):
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156187
return ChatMessage(role="assistant", message=data["completion"])
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set_default_client(MyLLMClient())
189+
# Create an instance of the custom client
190+
llm_client = MyLLMClient()
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192+
# Set the default client
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set_default_client(llm_client)
194+
195+
# It's also possible to create a custom embedding class extending BaseEmbedding
196+
# Or you can use FastEmbed for a pre-built embedding model:
197+
from giskard.llm.embeddings.fastembed import try_get_fastembed_embeddings
198+
embed_client = try_get_fastembed_embeddings()
199+
giskard.llm.embeddings.set_default_embedding(embed_client)
159200
```
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If you run into any issues configuring the LLM client, don't hesitate to [ask us on Discord](https://discord.com/invite/ABvfpbu69R) or open a new issue on [our GitHub repo](https://github.com/Giskard-AI/giskard).

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