docs: add llm docs

docs/chat_models.md

@@ -0,0 +1,91 @@
+# UiPath chat models
+
+UiPath provides two chat models `UiPathAzureChatOpenAI` and `UiPathNormalizedChatModel`. These are compatible with langgraph as drop in replacements. You do not need to add tokens from OpenAI or Anthropic, usage of these chat models will consume `AI Units` on your account.
+
+## UiPathAzureChatOpenAI
+
+`UiPathAzureChatOpenAI` can be used as a drop in replacement for `ChatOpenAI` or `AzureChatOpenAI`. 
+
+### Example usage
+
+Here is a code that is using `ChatOpenAI`
+```python
+from langchain_openai import ChatOpenAI
+
+llm = ChatOpenAI(
+    model="gpt-4o",
+    temperature=0,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2,
+    # api_key="...",  # if you prefer to pass api key in directly instaed of using env vars
+    # base_url="...",
+    # organization="...",
+    # other params...
+)
+```
+
+You can simply change `ChatOpenAi` with `UiPathAzureChatOpenAI`, you don't have to provide an `OPEN_AI_TOKEN`
+
+
+```python
+from uipath_langchain.chat.models import UiPathAzureChatOpenAI
+
+llm = UiPathAzureChatOpenAI(
+    model="gpt-4o",
+    temperature=0,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2,
+    # other params...
+)
+```
+
+Currently the following models can be used with `UiPathAzureChatOpenAI` (this list can be updated in the future): 
+- `gpt-4`, `gpt-4-1106-Preview`, `gpt-4-32k`, `gpt-4-turbo-2024-04-09`, `gpt-4-vision-preview`, `gpt-4o-2024-05-13`, `gpt-4o-2024-08-06`, `gpt-4o-mini-2024-07-18`, `o3-mini-2025-01-31`
+
+
+## UiPathNormalizedChatModel
+
+`UiPathNormalizedChatModel` is a more versatile clas that can suport models from diferent vendors including OpenAI.
+
+### Example usage
+
+Given the following code:
+
+```python
+from langchain_anthropic import ChatAnthropic
+
+llm = ChatAnthropic(
+    model="claude-3-5-sonnet-20240620",
+    temperature=0,
+    max_tokens=1024,
+    timeout=None,
+    max_retries=2,
+    # other params...
+)
+```
+
+You can replace it with `UiPathNormalizedChatModel` like so:
+
+```python
+from uipath_langchain.chat.models import UiPathNormalizedChatModel
+
+llm = UiPathNormalizedChatModel(
+    model="anthropic.claude-3-opus-20240229-v1:0",
+    temperature=0,
+    max_tokens=1024,
+    timeout=None,
+    max_retries=2,
+    # other params...
+)
+```
+
+Currently the following models can be used with `UiPathAzureChatOpenAI` (this list can be updated in the future): 
+- `anthropic.claude-3-5-sonnet-20240620-v1:0`, `anthropic.claude-3-5-sonnet-20241022-v2:0`, `anthropic.claude-3-7-sonnet-20250219-v1:0`, `anthropic.claude-3-haiku-20240307-v1:0`, `gemini-1.5-pro-001`, `gemini-2.0-flash-001`, `gpt-4o-2024-05-13`, `gpt-4o-2024-08-06`, `gpt-4o-2024-11-20`, `gpt-4o-mini-2024-07-18`, `o3-mini-2025-01-31`
+
+### Note 
+
+Please note that that you may get errors related to data residency, as some models are not available on all regions.
+
+Example: `[Enforced Region] No model configuration found for product uipath-python-sdk in EU using model anthropic.claude-3-opus-20240229-v1:0`.

docs/context_grounding_chain.md

@@ -0,0 +1,38 @@
+# ContextGroundingVectorStore
+
+`ContextGroundingVectorStore` is a vector store implementation designed for context-aware document retrieval. It allows you to perform semantic searches and create retrieval chains with language models.
+
+## Initialization
+
+You can initialize the vector store with an index name:
+
+```python
+    from uipath_langchain.vectorstores.context_grounding_vectorstore import ContextGroundingVectorStore
+
+    vectorstore = ContextGroundingVectorStore(index_name=index_name)
+```
+
+## Searching Documents
+
+The vector store supports various search methods:
+
+```python
+    # Perform semantic searches with distance scores
+    docs_with_scores = await vectorstore.asimilarity_search_with_score(query=query, k=5)
+
+    # Perform a similarity search with relevance scores
+    docs_with_relevance_scores = await vectorstore.asimilarity_search_with_relevance_scores(query=query, k=5)
+```
+
+## Creating a Retrieval Chain
+
+You can integrate the vector store into a retrieval chain with a language model:
+
+```python
+    # Run a retrieval chain
+    model = UiPathAzureChatOpenAI(model="gpt-4o-2024-08-06", max_retries=3)
+    retrieval_chain = create_retrieval_chain(vectorstore=vectorstore, model=model)
+
+    query = "What is the ECCN for a laptop?"
+    result = retrieval_chain(query)
+```

docs/context_grounding_retriever.md

@@ -0,0 +1,46 @@
+# Context Grounding Retriever
+
+The `ContextGroundingRetriever` is a document retrieval system that uses vector search to efficiently find and retrieve relevant information from your document store.
+
+## Overview
+
+`ContextGroundingRetriever` allows you to:
+- Search through indexed documents using natural language queries
+- Ground LLM responses in your organization's specific information
+- Retrieve context-relevant documents for various applications
+
+
+## Basic Usage
+
+Create a simple retriever by specifying an index name:
+
+```python
+from uipath_langchain.retrievers import ContextGroundingRetriever
+
+retriever = ContextGroundingRetriever(index_name = "Company Policy Context")
+pprint(retriever.invoke("What is the company policy on remote work?"))
+```
+
+## Integration with LangChain Tools
+
+You can easily integrate the retriever with LangChain's tool system:
+
+```python
+from langchain.tools.retriever import create_retriever_tool
+from uipath_langchain.retrievers import ContextGroundingRetriever
+
+retriever = ContextGroundingRetriever(index_name = "Company Policy Context")
+retriever_tool = create_retriever_tool(
+    retriever,
+    "ContextforInvoiceDisputeInvestigation",
+   """
+   Use this tool to search the company internal documents for information about policies around dispute resolution.
+   Use a meaningful query to load relevant information from the documents. Save the citation for later use.
+   """
+)
+```
+
+
+## Advanced Usage
+
+For complex applications, the retriever can be combined with other LangChain components to create robust document QA systems, agents, or knowledge bases.