Merge branch 'main' into main

.readthedocs.yaml

@@ -7,7 +7,7 @@ build:
   jobs:
     post_create_environment:
       - pip install pdm
-      - pdm export --without-hashes -G doc,hub -o requirements-sphinx.txt
+      - pdm export --without-hashes -G doc,llm -o requirements-sphinx.txt
 
 sphinx:
   configuration: docs/conf.py

docs/open_source/scan/scan_llm/index.md

@@ -55,15 +55,19 @@ like this:
 ::::::{tab-item} OpenAI
 
 ```python
-import giskard
 import os
+import giskard
 from giskard.llm.client.openai import OpenAIClient
 
+# Set the OpenAI API key
 os.environ["OPENAI_API_KEY"] = "sk-…"
 
+# Create a giskard OpenAI client
+openai_client = OpenAIClient(model="gpt-4o")
+
+# Set the default client
 giskard.llm.set_llm_api("openai")
-oc = OpenAIClient(model="gpt-4-turbo-preview")
-giskard.llm.set_default_client(oc)
+giskard.llm.set_default_client(openai_client)
 ```
 
 ::::::
@@ -73,30 +77,38 @@ Require `openai>=1.0.0`
 
 ```python
 import os
-from giskard.llm import set_llm_model
-from giskard.llm.embeddings.openai import set_embedding_model
+import giskard
 
+# Set the Azure OpenAI API key and endpoint
 os.environ['AZURE_OPENAI_API_KEY'] = '...'
 os.environ['AZURE_OPENAI_ENDPOINT'] = 'https://xxx.openai.azure.com'
 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
-set_llm_model('my-gpt-4-model')
-set_embedding_model('my-embedding-model') # Optional
+giskard.llm.set_llm_model('my-gpt-4-model')
+giskard.llm.embeddings.set_embedding_model('my-embedding-model')
 ```
 
 ::::::
 ::::::{tab-item} Mistral
 
 ```python
 import os
+import giskard
 from giskard.llm.client.mistral import MistralClient
 
-os.environ["MISTRAL_API_KEY"] = "sk-…"
+# Set the Mistral API key
+os.environ["MISTRAL_API_KEY"] = "…"
 
-mc = MistralClient()
-giskard.llm.set_default_client(mc)
+# Create a giskard Mistral client
+mistral_client = MistralClient()
+
+# Set the default client
+giskard.llm.set_default_client(mistral_client)
+
+# You may also want to set the default embedding model
+# Check the Custom Client code snippet for more details
 ```
 
 ::::::
@@ -106,11 +118,18 @@ giskard.llm.set_default_client(mc)
 import giskard
 from openai import OpenAI
 from giskard.llm.client.openai import OpenAIClient
+from giskard.llm.embeddings.openai import OpenAIEmbedding
 
-# Setup the Ollama client with API key and base URL
+# Setup the OpenAI client with API key and base URL for Ollama
 _client = OpenAI(base_url="http://localhost:11434/v1/", api_key="ollama")
-oc = OpenAIClient(model="gemma:2b", client=_client)
-giskard.llm.set_default_client(oc)
+
+# Wrap the original OpenAI client with giskard OpenAI client and embedding
+llm_client = OpenAIClient(model="llama3.2", client=_client)
+embed_client = OpenAIEmbedding(model="nomic-embed-text", client=_client)
+
+# Set the default client and embedding
+giskard.llm.set_default_client(llm_client)
+giskard.llm.embeddings.set_default_embedding(embed_client)
 ```
 
 ::::::
@@ -123,13 +142,17 @@ import giskard
 
 from giskard.llm.client.bedrock import ClaudeBedrockClient
 from giskard.llm.embeddings.bedrock import BedrockEmbedding
-from giskard.llm.embeddings import set_default_embedding
 
+# Create a Bedrock client
 bedrock_runtime = boto3.client("bedrock-runtime", region_name=os.environ["AWS_DEFAULT_REGION"])
+
+# Wrap the Beddock client with giskard Bedrock client and embedding
 claude_client = ClaudeBedrockClient(bedrock_runtime, model="anthropic.claude-3-haiku-20240307-v1:0")
 embed_client = BedrockEmbedding(bedrock_runtime, model="amazon.titan-embed-text-v1")
+
+# Set the default client and embedding
 giskard.llm.set_default_client(claude_client)
-set_default_embedding(embed_client)
+giskard.llm.embeddings.set_default_embedding(embed_client)
 ```
 
 ::::::
@@ -138,14 +161,23 @@ set_default_embedding(embed_client)
 ```python
 import os
 import giskard
-
 import google.generativeai as genai
-
 from giskard.llm.client.gemini import GeminiClient
 
+# Set the Gemini API key
+os.environ["GEMINI_API_KEY"] = "…"
+
+# Configure the Gemini API
 genai.configure(api_key=os.environ["GEMINI_API_KEY"])
 
-giskard.llm.set_default_client(GeminiClient())
+# Create a giskard Gemini client
+gemini_client = GeminiClient()
+
+# Set the default client
+giskard.llm.set_default_client(gemini_client)
+
+# You may also want to set the default embedding model
+# Check the Custom Client code snippet for more details
 ```
 
 ::::::
@@ -157,7 +189,7 @@ from typing import Sequence, Optional
 from giskard.llm.client import set_default_client
 from giskard.llm.client.base import LLMClient, ChatMessage
 
-
+# Create a custom client by extending the LLMClient class
 class MyLLMClient(LLMClient):
     def __init__(self, my_client):
         self._client = my_client
@@ -202,9 +234,17 @@ class MyLLMClient(LLMClient):
 
         return ChatMessage(role="assistant", message=data["completion"])
 
+# Create an instance of the custom client
+llm_client = MyLLMClient()
 
-set_default_client(MyLLMClient())
+# Set the default client
+set_default_client(llm_client)
 
+# It's also possible to create a custom embedding class extending BaseEmbedding
+# Or you can use FastEmbed for a pre-built embedding model:
+from giskard.llm.embeddings.fastembed import try_get_fastembed_embeddings
+embed_client = try_get_fastembed_embeddings()
+giskard.llm.embeddings.set_default_embedding(embed_client)
 ```
 
 ::::::

docs/open_source/setting_up/index.md

@@ -11,60 +11,78 @@ This guide focuses primarily on configuring and using various LLM clients suppor
 ## OpenAI GPT-4 Client Setup
 
 ```python
-import giskard
 import os
+import giskard
 from giskard.llm.client.openai import OpenAIClient
 
+# Set the OpenAI API key
 os.environ["OPENAI_API_KEY"] = "sk-…"
 
+# Create a giskard OpenAI client
+openai_client = OpenAIClient(model="gpt-4o")
+
+# Set the default client
 giskard.llm.set_llm_api("openai")
-oc = OpenAIClient(model="gpt-4-turbo-preview")
-giskard.llm.set_default_client(oc)
+giskard.llm.set_default_client(openai_client)
 ```
 
 ## Azure OpenAI Client Setup
 
 ```python
 import os
-from giskard.llm import set_llm_model
-from giskard.llm.embeddings.openai import set_embedding_model
+import giskard
 
+# Set the Azure OpenAI API key and endpoint
 os.environ['AZURE_OPENAI_API_KEY'] = '...'
 os.environ['AZURE_OPENAI_ENDPOINT'] = 'https://xxx.openai.azure.com'
 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
-set_llm_model('my-gpt-4-model')
-set_embedding_model('my-embedding-model') # Optional
+giskard.llm.set_llm_model('my-gpt-4-model')
+giskard.llm.embeddings.set_embedding_model('my-embedding-model')
 ```
 
 ## Mistral Client Setup
 
 ```python
 import os
+import giskard
 from giskard.llm.client.mistral import MistralClient
 
-os.environ["MISTRAL_API_KEY"] = "sk-…"
+# Set the Mistral API key
+os.environ["MISTRAL_API_KEY"] = "…"
 
-mc = MistralClient()
-giskard.llm.set_default_client(mc)
+# Create a giskard Mistral client
+mistral_client = MistralClient()
+
+# Set the default client
+giskard.llm.set_default_client(mistral_client)
+
+# You may also want to set the default embedding model
+# Check the Custom Client Setup section for more details
 ```
 
 ## Ollama Client Setup
 
 The Ollama setup involves configuring an OpenAI client customized for the Ollama API:
 
 ```python
+import giskard
 from openai import OpenAI
 from giskard.llm.client.openai import OpenAIClient
-from giskard.llm.client.mistral import MistralClient
+from giskard.llm.embeddings.openai import OpenAIEmbedding
 
-# Setup the Ollama client with API key and base URL
+# Setup the OpenAI client with API key and base URL for Ollama
 _client = OpenAI(base_url="http://localhost:11434/v1/", api_key="ollama")
-oc = OpenAIClient(model="gemma:2b", client=_client)
-giskard.llm.set_default_client(oc)
+
+# Wrap the original OpenAI client with giskard OpenAI client and embedding
+llm_client = OpenAIClient(model="llama3.2", client=_client)
+embed_client = OpenAIEmbedding(model="nomic-embed-text", client=_client)
+
+# Set the default client and embedding
+giskard.llm.set_default_client(llm_client)
+giskard.llm.embeddings.set_default_embedding(embed_client)
 ```
 
 ## Claude 3 Client Setup
@@ -78,28 +96,41 @@ import giskard
 
 from giskard.llm.client.bedrock import ClaudeBedrockClient
 from giskard.llm.embeddings.bedrock import BedrockEmbedding
-from giskard.llm.embeddings import set_default_embedding
 
+# Create a Bedrock client
 bedrock_runtime = boto3.client("bedrock-runtime", region_name=os.environ["AWS_DEFAULT_REGION"])
+
+# Wrap the Beddock client with giskard Bedrock client and embedding
 claude_client = ClaudeBedrockClient(bedrock_runtime, model="anthropic.claude-3-haiku-20240307-v1:0")
 embed_client = BedrockEmbedding(bedrock_runtime, model="amazon.titan-embed-text-v1")
+
+# Set the default client and embedding
 giskard.llm.set_default_client(claude_client)
-set_default_embedding(embed_client)
+giskard.llm.embeddings.set_default_embedding(embed_client)
 ```
 
 ## Gemini Client Setup
 
 ```python
 import os
 import giskard
-
 import google.generativeai as genai
-
 from giskard.llm.client.gemini import GeminiClient
 
+# Set the Gemini API key
+os.environ["GEMINI_API_KEY"] = "…"
+
+# Configure the Gemini API
 genai.configure(api_key=os.environ["GEMINI_API_KEY"])
 
-giskard.llm.set_default_client(GeminiClient())
+# Create a giskard Gemini client
+gemini_client = GeminiClient()
+
+# Set the default client
+giskard.llm.set_default_client(gemini_client)
+
+# You may also want to set the default embedding model
+# Check the Custom Client Setup section for more details
 ```
 
 ## Custom Client Setup
@@ -110,7 +141,7 @@ from typing import Sequence, Optional
 from giskard.llm.client import set_default_client
 from giskard.llm.client.base import LLMClient, ChatMessage
 
-
+# Create a custom client by extending the LLMClient class
 class MyLLMClient(LLMClient):
     def __init__(self, my_client):
         self._client = my_client
@@ -155,7 +186,17 @@ class MyLLMClient(LLMClient):
 
         return ChatMessage(role="assistant", message=data["completion"])
 
-set_default_client(MyLLMClient())
+# Create an instance of the custom client
+llm_client = MyLLMClient()
+
+# Set the default client
+set_default_client(llm_client)
+
+# It's also possible to create a custom embedding class extending BaseEmbedding
+# Or you can use FastEmbed for a pre-built embedding model:
+from giskard.llm.embeddings.fastembed import try_get_fastembed_embeddings
+embed_client = try_get_fastembed_embeddings()
+giskard.llm.embeddings.set_default_embedding(embed_client)
 ```
 
 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).