fix: Moss integration bug with QueryOptions (#20815)

* fix: moss querying fix + update

* fix: moss querying fix + update

* fix: moss querying fix + update

* fix: pytest issue

* fixing coverage test

Author: CoderOMaster

docs/examples/tools/moss_agent.ipynb …xamples/tools/moss_with_llamaindex.ipynbdocs/examples/tools/moss_agent.ipynb renamed to docs/examples/tools/moss_with_llamaindex.ipynb docs/examples/tools/moss_agent.ipynb renamed to docs/examples/tools/moss_with_llamaindex.ipynb

@@ -5,13 +5,9 @@
    "id": "3e37307f",
    "metadata": {},
    "source": [
-    "# Moss Tool Example\n",
+    "# Moss + LlamaIndex\n",
     "\n",
-    "This notebook demonstrates how to use the `MossToolSpec` with LlamaIndex.\n",
-    "\n",
-    "## Installation\n",
-    "\n",
-    "First, you need to install the package (and LlamaIndex core if not present)."
+    "[Moss](https://moss.dev) is a search runtime for voice agents, copilots, and multimodal apps. Sub-10ms lookups, zero infrastructure. Built in Rust and WebAssembly. Moss runs search inside your agent runtime. Sub-10ms lookups, always-current data, without rebuilding your infra."
    ]
   },
   {
@@ -31,7 +27,7 @@
    "source": [
     "## Setup\n",
     "\n",
-    "Import the necessary classes and initialize the Moss client."
+    "Import the necessary classes and initialize the Moss client with your project credentials. We'll configure query options to tune the search behavior (`top_k` for result count, `alpha` for the semantic/keyword blend)."
    ]
   },
   {
@@ -75,7 +71,7 @@
    "source": [
     "## Indexing Data\n",
     "\n",
-    "Let's add some sample data to our index."
+    "Create sample documents with `DocumentInfo` (each with `id`, `text`, and `metadata`). Then call `index_docs()` to build the Moss index — this creates or replaces the index with your documents and makes them searchable."
    ]
   },
   {
@@ -110,7 +106,7 @@
    "source": [
     "## Using with an Agent\n",
     "\n",
-    "Now we can wrap the tool for use with a LlamaIndex agent."
+    "Now we'll expose the Moss tool's methods (`query`, `list_indexes`, `delete_index`) to a `ReActAgent`. The agent will autonomously decide which tool to call based on the user's question and the search results it retrieves."
    ]
   },
   {

llama-index-integrations/tools/llama-index-tools-moss/examples/moss_agent.py

@@ -1,71 +1,80 @@
 import asyncio
 import os
 from typing import List
-
+from dotenv import load_dotenv
+load_dotenv()
 from llama_index.core.agent import ReActAgent
 from llama_index.llms.openai import OpenAI
 from llama_index.tools.moss import MossToolSpec, QueryOptions
 from inferedge_moss import MossClient, DocumentInfo
 
 
 async def main():
-    print("--- Moss Tool with ReAct Agent Example ---\n")
-
     # 1. Initialize Client
-    # Ensure you have your environment variables set or pass credentials directly.
-    MOSS_PROJECT_KEY = os.getenv('MOSS_PROJECT_KEY')
-    MOSS_PROJECT_ID = os.getenv('MOSS_PROJECT_ID')
+    MOSS_PROJECT_KEY = os.getenv("MOSS_PROJECT_KEY")
+    MOSS_PROJECT_ID = os.getenv("MOSS_PROJECT_ID")
     client = MossClient(project_id=MOSS_PROJECT_ID, project_key=MOSS_PROJECT_KEY)
-    # 2. Configure query settings - Instantiate QueryOptions (Optional)
-    # If skipped, the tool will use its own defaults.
-    query_options = QueryOptions(top_k=12, alpha=0.9)
+
+    # 2. Configure query settings (optional — defaults: top_k=5, alpha=0.5, model_id="moss-minilm")
+    query_options = QueryOptions(top_k=5, alpha=0.5, model_id="moss-minilm")
+
     # 3. Initialize Tool
-    print("Initializing MossToolSpec...")
     moss_tool = MossToolSpec(
         client=client,
-        index_name="knowledge_base",
-        query_options=query_options
+        index_name="knowledge_base_new",
+        query_options=query_options,
     )
 
-    # 4. Index Documents (Optional step)
-    print("\n[Step 4] Indexing Documents...")
-    docs = [
+    # 4. List existing indexes before indexing
+    print("\n[Step 4] Listing existing indexes...")
+    print(await moss_tool.list_indexes())
+
+    # 5. Index Documents
+    print("\n[Step 5] Indexing Documents...")
+    docs: List[DocumentInfo] = [
         DocumentInfo(
+            id="123",
             text="LlamaIndex is a data framework for LLM-based applications.",
-            metadata={"source": "docs", "category": "framework"}
+            metadata={"source": "docs", "category": "framework"},
         ),
         DocumentInfo(
+            id="124",
             text="Moss is a real-time semantic search engine optimized for speed.",
-            metadata={"source": "moss_website", "category": "engine"}
+            metadata={"source": "moss_website", "category": "engine"},
         ),
     ]
     await moss_tool.index_docs(docs)
     print(f"Indexed {len(docs)} documents.")
 
-    # 5. Create an agent (Using OpenAI llm for demonstration)
-    print("\n[Step 5] Creating Agent...")
-    os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-here')
-    llm = OpenAI()
-    agent = ReActAgent.from_tools(
-        moss_tool.to_tool_list(),
+    # 6. List indexes again to confirm creation
+    print("\n[Step 6] Listing indexes after indexing...")
+    print(await moss_tool.list_indexes())
+
+    # 7. Create agent with all exposed tools (query, list_indexes, delete_index)
+    print("\n[Step 7] Creating Agent...")
+    llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+    agent = ReActAgent(
+        tools=moss_tool.to_tool_list(),
         llm=llm,
-        verbose=True
+        verbose=True,
     )
 
-    # 6. Run Agent
-    print("\n[Step 6] Querying...")
-    # This query would trigger the tool usage in a real scenario
-    response = await agent.achat("What is Moss?")
+    # 8. Run Agent — natural language query triggers the query tool
+    print("\n[Step 8] Querying via Agent...")
+    response = await agent.run(user_msg="What is Moss?")
     print("\nAgent Response:")
     print(response)
 
+    # 9. Run Agent — ask it to list available indexes
+    print("\n[Step 9] Listing indexes via Agent...")
+    response = await agent.run(user_msg="What indexes are available?")
+    print("\nAgent Response:")
+    print(response)
+
+    # 10. Clean up — delete the index directly (not via agent to avoid accidental deletion)
+    print("\n[Step 10] Cleaning up...")
+    print(await moss_tool.delete_index("knowledge_base"))
+
 
 if __name__ == "__main__":
-    # Ensure we catch ImportError for better user experience if run without deps
-    try:
-        asyncio.run(main())
-    except ImportError as e:
-        print(f"Error: {e}")
-        print("Please install required dependencies: pip install llama-index-tools-moss llama-index-core llama-index-llms-openai inferedge-moss")
-    except Exception as e:
-        print(f"An error occurred: {e}")
+    asyncio.run(main())

llama-index-integrations/tools/llama-index-tools-moss/llama_index/tools/moss/base.py

@@ -4,6 +4,7 @@
 from dataclasses import dataclass
 from llama_index.core.tools.tool_spec.base import BaseToolSpec
 from inferedge_moss import MossClient, DocumentInfo
+from inferedge_moss import QueryOptions as MossQueryOptions
 
 
 @dataclass
@@ -14,7 +15,7 @@ class QueryOptions:
     Attributes:
         top_k (int): Number of results to return from queries. Defaults to 5.
         alpha (float): Hybrid search weight (0.0=keyword, 1.0=semantic). Defaults to 0.5.
-        model_id (str): The model ID to use for embeddings. Defaults to "moss-minilm".
+        model_id (str): The embedding model ID used when creating the index. Defaults to "moss-minilm".
 
     """
 
@@ -31,7 +32,7 @@ class MossToolSpec(BaseToolSpec):
     and query for relevant information.
     """
 
-    spec_functions: tuple[str, ...] = ("query",)
+    spec_functions: tuple[str, ...] = ("query", "list_indexes", "delete_index")
 
     def __init__(
         self,
@@ -58,10 +59,10 @@ def __init__(
 
         self.top_k: int = opt.top_k
         self.alpha: float = opt.alpha
+        self.model_id: str = opt.model_id
         self.client: MossClient = client
         self.index_name: str = index_name
         self._index_loaded: bool = False
-        self.model_id: str = opt.model_id
 
     async def index_docs(self, docs: List[DocumentInfo]) -> None:
         await self.client.create_index(self.index_name, docs, model_id=self.model_id)
@@ -93,7 +94,7 @@ async def query(self, query: str) -> str:
             await self._load_index()
 
         results = await self.client.query(
-            self.index_name, query, self.top_k, self.alpha
+            self.index_name, query, MossQueryOptions(top_k=self.top_k, alpha=self.alpha)
         )
         answer = f"Search results for: '{query}'\n\n"
 
@@ -111,3 +112,40 @@ async def query(self, query: str) -> str:
             answer += "-" * 20 + "\n\n"
 
         return answer
+
+    async def list_indexes(self) -> str:
+        """
+        List all available indexes in the Moss project.
+
+        Use this tool to discover what indexes exist before querying or managing them.
+
+        Returns:
+            str: A formatted list of all index names in the project.
+
+        """
+        indexes = await self.client.list_indexes()
+        if not indexes:
+            return "No indexes found."
+
+        result = "Available indexes:\n"
+        for idx in indexes:
+            result += f"  - {idx.name} (docs: {idx.doc_count}, status: {idx.status})\n"
+        return result
+
+    async def delete_index(self, index_name: str) -> str:
+        """
+        Delete an index from the Moss project.
+
+        Use this tool to remove an index and all its documents when it is no longer needed.
+
+        Args:
+            index_name (str): The name of the index to delete.
+
+        Returns:
+            str: A confirmation message indicating the index was deleted.
+
+        """
+        await self.client.delete_index(index_name)
+        if index_name == self.index_name:
+            self._index_loaded = False
+        return f"Index '{index_name}' has been deleted."

llama-index-integrations/tools/llama-index-tools-moss/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "llama-index-tools-moss"
-version = "0.1.0"
+version = "0.2.0"
 description = "llama-index tools moss integration"
 authors = [{name = "Roaim Khan", email = "roaimkhan459@gmail.com"}]
 requires-python = ">=3.9,<4.0"