Merge pull request #44 from UiPath/fix/docs_cleanup

feat(docs): update docs

Author: ionmincu

docs/chat_models.md

@@ -81,7 +81,7 @@ llm = UiPathNormalizedChatModel(
 )
 ```
 
-Currently the following models can be used with `UiPathAzureChatOpenAI` (this list can be updated in the future): 
+Currently the following models can be used with `UiPathNormalizedChatModel` (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 

docs/context_grounding_chain.md

@@ -1,4 +1,4 @@
-# Context Grounding Retriever
+# ContextGroundingRetriever
 
 The `ContextGroundingRetriever` is a document retrieval system that uses vector search to efficiently find and retrieve relevant information from your document store.
 
@@ -10,6 +10,8 @@ The `ContextGroundingRetriever` is a document retrieval system that uses vector
 - Retrieve context-relevant documents for various applications
 
 
+You will need to create an index in `Context Grounding` to use this feature. To create an index go to organization `Admin` -> `AI Trust Layer` -> `Context Grounding`. There you can create a new index and add documents to it. See the full documentation [here](https://docs.uipath.com/automation-cloud/automation-cloud/latest/admin-guide/about-context-grounding) for more details.
+
 ## Basic Usage
 
 Create a simple retriever by specifying an index name:
@@ -18,14 +20,15 @@ Create a simple retriever by specifying an index name:
 from uipath_langchain.retrievers import ContextGroundingRetriever
 
 retriever = ContextGroundingRetriever(index_name = "Company Policy Context")
-pprint(retriever.invoke("What is the company policy on remote work?"))
+print(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.agents import create_react_agent
 from langchain.tools.retriever import create_retriever_tool
 from uipath_langchain.retrievers import ContextGroundingRetriever
 
@@ -38,6 +41,11 @@ retriever_tool = create_retriever_tool(
    Use a meaningful query to load relevant information from the documents. Save the citation for later use.
    """
 )
+
+# You can use the tool in your agents
+model = OpenAI()
+tools = [retriever_tool]
+agent = create_react_agent(model, tools, prompt="Answer user questions as best as you can using the search tool.")
 ```
 
 

docs/context_grounding_retriever.md

@@ -0,0 +1,35 @@
+# 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.
+
+You will need to create an index in `Context Grounding` to use this feature. To create an index go to organization `Admin` -> `AI Trust Layer` -> `Context Grounding`. There you can create a new index and add documents to it. See the full documentation [here](https://docs.uipath.com/automation-cloud/automation-cloud/latest/admin-guide/about-context-grounding) for more details.
+
+
+## Searching Documents
+
+The vector store supports various search methods:
+
+```python
+from uipath_langchain.vectorstores.context_grounding_vectorstore import ContextGroundingVectorStore
+
+vectorstore = ContextGroundingVectorStore(index_name="Company policy")
+
+# Perform semantic searches with distance scores
+docs_with_scores = vectorstore.asimilarity_search_with_score(query="What is the company policy on data storage?", 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)
+```