Merge pull request #34 from UiPath/feat/vectorstore

feat: add retriever and query engine implementations

Author: radu-mocanu

docs/context_grounding.md

@@ -0,0 +1,73 @@
+# Context Grounding
+
+Context Grounding Service 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
+
+
+You will need to create an index in `Context Grounding` to use this feature. To create an index go to organization `Orchestrator` -> the folder where you'd like to create an index -> `Indexes`. There you can create a new index from a storage bucket which you've added documents to. See the full documentation [here](https://docs.uipath.com/automation-cloud/automation-cloud/latest/admin-guide/about-context-grounding) for more details.
+
+
+## ContextGroundingRetriever
+
+The `ContextGroundingRetriever` is a document retrieval system that uses vector search to efficiently find and retrieve relevant information from your document store.
+
+### Basic Usage
+
+Create a simple retriever by specifying an index name:
+
+```python
+from uipath_llamaindex.retrievers import ContextGroundingRetriever
+
+retriever = ContextGroundingRetriever(index_name = "Company Policy Context")
+print(retriever.retrieve("What is the company policy on remote work?"))
+```
+
+## ContextGroundingQueryEngine
+
+Query engines are interfaces that allows you to ask question over your data. The `ContextGroundingQueryEngine` is a query engine system that leverages the `ContextGroundingRetriever`.
+
+### Basic Usage
+
+Create a simple query engine by specifying an index name and a synthesizer strategy:
+
+```python
+from uipath_llamaindex.query_engines import ContextGroundingQueryEngine
+from llama_index.core.response_synthesizers.type import ResponseMode
+from llama_index.core import get_response_synthesizer
+
+synthesizer = get_response_synthesizer(ResponseMode.SIMPLE_SUMMARIZE)
+query_engine = ContextGroundingQueryEngine(index_name = "Company Policy Context", response_synthesizer=synthesizer)
+print(query_engine.query("What is the company policy on remote work?"))
+```
+
+### Integration with LlamaIndex Tools
+
+You can easily integrate the query engine with LlamaIndex's tool system:
+
+```python
+from uipath_llamaindex.query_engines import ContextGroundingQueryEngine
+from llama_index.core.response_synthesizers.type import ResponseMode
+from llama_index.core import get_response_synthesizer
+
+query_engine = ContextGroundingQueryEngine(
+    index_name="Company Policy Context",
+    response_synthesizer=get_response_synthesizer(ResponseMode.REFINE),
+)
+query_engine_tools = [QueryEngineTool(
+            query_engine=query_engine,
+            metadata=ToolMetadata(
+                name="Company policy",
+                description="Information about general company policy",
+            )
+        )]
+# You can use the tool in your agents
+react_agent = ReActAgent.from_tools(query_engine_tools)
+response = react_agent.chat("Answer user questions as best as you can using the query engine tool.")
+```
+
+
+
+> **HINT:** Check our [travel-helper-RAG-agent sample](https://github.com/UiPath/uipath-llamaindex-python/tree/main/samples/travel-helper-RAG-agent) to see context grounding query engines in action.