Merge pull request #40 from UiPath/feature/add_llm_doc

docs: add llm docs

Author: ionmincu

docs/llms_and_embeddings.md

@@ -0,0 +1,162 @@
+# UiPath LLMs and Embeddings
+
+This guide covers the UiPath-integrated Large Language Models (LLMs) and embedding models available in the UiPath LlamaIndex SDK.
+
+## Overview
+
+The UiPath LlamaIndex SDK provides pre-configured LLM and embedding classes that integrate seamlessly with UiPath. These classes handle authentication, routing, and configuration automatically, allowing you to focus on building your agents.
+
+## Prerequisites
+
+Before using these classes, ensure you have:
+
+- Authenticated with UiPath using `uipath auth`
+- Set up your environment variables (automatically configured after authentication)
+
+## UiPathOpenAI
+
+The `UiPathOpenAI` class is a pre-configured Azure OpenAI client that routes requests through UiPath.
+
+### Available Models
+
+The following OpenAI models are available through the `OpenAIModel` enum:
+
+- `GPT_4_1_2025_04_14`
+- `GPT_4_1_MINI_2025_04_14`
+- `GPT_4_1_NANO_2025_04_14`
+- `GPT_4O_2024_05_13`
+- `GPT_4O_2024_08_06`
+- `GPT_4O_2024_11_20`
+- `GPT_4O_MINI_2024_07_18` (default)
+- `O3_MINI_2025_01_31`
+- `TEXT_DAVINCI_003`
+
+### Basic Usage
+
+```python
+from uipath_llamaindex.llms import UiPathOpenAI
+from llama_index.core.llms import ChatMessage
+
+# Create an LLM instance with default settings
+llm = UiPathOpenAI()
+
+# Create chat messages
+messages = [
+    ChatMessage(
+        role="system", content="You are a pirate with colorful personality."
+    ),
+    ChatMessage(role="user", content="Hello"),
+]
+
+# Generate a response
+response = llm.chat(messages)
+print(response)
+```
+
+### Custom Model Configuration
+
+```python
+from uipath_llamaindex.llms import UiPathOpenAI, OpenAIModel
+
+# Use a specific model
+llm = UiPathOpenAI(model=OpenAIModel.GPT_4O_2024_11_20)
+
+# Or use a model string directly
+llm = UiPathOpenAI(model="gpt-4o-2024-11-20")
+```
+
+## UiPathOpenAIEmbedding
+
+The `UiPathOpenAIEmbedding` class provides text embedding capabilities using OpenAI's embedding models through UiPath.
+
+### Available Embedding Models
+
+The following embedding models are available through the `OpenAIEmbeddingModel` enum:
+
+- `TEXT_EMBEDDING_ADA_002` (default)
+- `TEXT_EMBEDDING_3_LARGE`
+
+### Basic Usage
+
+```python
+from uipath_llamaindex.embeddings import UiPathOpenAIEmbedding
+
+# Create an embedding model instance
+embed_model = UiPathOpenAIEmbedding()
+
+# Get embeddings for a single text
+result = embed_model.get_text_embedding("the quick brown fox jumps over the lazy dog")
+print(f"Embedding dimension: {len(result)}")
+```
+
+### Batch Embeddings
+
+```python
+from uipath_llamaindex.embeddings import UiPathOpenAIEmbedding
+
+embed_model = UiPathOpenAIEmbedding()
+
+# Get embeddings for multiple texts
+texts = [
+    "Hello world",
+    "How are you?",
+    "This is a test"
+]
+
+embeddings = embed_model.get_text_embedding_batch(texts)
+print(f"Number of embeddings: {len(embeddings)}")
+```
+
+
+## Integration with LlamaIndex
+
+Both classes integrate seamlessly with LlamaIndex components:
+
+### Using with Agents
+
+```python
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from uipath_llamaindex.llms import UiPathOpenAI
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two integers and returns the result."""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+# Create agent with UiPath LLM
+agent = ReActAgent.from_tools(
+    [multiply_tool], 
+    llm=UiPathOpenAI(model=OpenAIModel.GPT_4O_2024_11_20)
+)
+
+response = agent.chat("What is 21 multiplied by 2?")
+```
+
+### Using with VectorStoreIndex
+
+```python
+from llama_index.core import VectorStoreIndex, Document
+from uipath_llamaindex.llms import UiPathOpenAI
+from uipath_llamaindex.embeddings import UiPathOpenAIEmbedding
+
+# Create documents
+documents = [
+    Document(text="This is a sample document about artificial intelligence."),
+    Document(text="Machine learning is a subset of AI that focuses on algorithms."),
+]
+
+# Create index with UiPath models
+index = VectorStoreIndex.from_documents(
+    documents,
+    embed_model=UiPathOpenAIEmbedding()
+)
+
+# Create query engine with UiPath LLM
+query_engine = index.as_query_engine(
+    llm=UiPathOpenAI(model=OpenAIModel.GPT_4O_2024_11_20)
+)
+
+response = query_engine.query("What is machine learning?")
+```