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crewai_tools/tools/llamaindex_tool/README.md

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+# LlamaIndexTool Documentation
+
+## Description
+This tool is designed to be a general wrapper around LlamaIndex tools and query engines, enabling you to leverage LlamaIndex resources
+in terms of RAG/agentic pipelines as tools to plug into CrewAI agents.
+
+## Installation
+To incorporate this tool into your project, follow the installation instructions below:
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+The following example demonstrates how to initialize the tool and execute a search with a given query:
+
+```python
+from crewai_tools import LlamaIndexTool
+
+# Initialize the tool from a LlamaIndex Tool
+
+## Example 1: Initialize from FunctionTool
+from llama_index.core.tools import FunctionTool
+
+your_python_function = lambda ...: ...
+og_tool = FunctionTool.from_defaults(your_python_function, name="<name>", description='<description>')
+tool = LlamaIndexTool.from_tool(og_tool)
+
+## Example 2: Initialize from LlamaHub Tools
+from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
+wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
+wolfram_tools = wolfram_spec.to_tool_list()
+tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
+
+
+# Initialize Tool from a LlamaIndex Query Engine
+
+## NOTE: LlamaIndex has a lot of query engines, define whatever query engine you want
+query_engine = index.as_query_engine() 
+query_tool = LlamaIndexTool.from_query_engine(
+    query_engine,
+    name="Uber 2019 10K Query Tool",
+    description="Use this tool to lookup the 2019 Uber 10K Annual Report"
+)
+
+```
+
+## Steps to Get Started
+To effectively use the `LlamaIndexTool`, follow these steps:
+
+1. **Install CrewAI**: Confirm that the `crewai[tools]` package is installed in your Python environment.
+2. **Install and use LlamaIndex**: Follow LlamaIndex documentation (https://docs.llamaindex.ai/) to setup a RAG/agent pipeline.
+
+

crewai_tools/tools/llamaindex_tool/llamaindex_tool.py

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+from typing import Any, Optional, Type, cast
+
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class LlamaIndexTool(BaseTool):
+    """Tool to wrap LlamaIndex tools/query engines."""
+
+    llama_index_tool: Any
+
+    def _run(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        """Run tool."""
+        from llama_index.core.tools import BaseTool as LlamaBaseTool
+
+        tool = cast(LlamaBaseTool, self.llama_index_tool)
+
+        if self.result_as_answer:
+            return tool(*args, **kwargs).content
+
+        return tool(*args, **kwargs)
+
+    @classmethod
+    def from_tool(cls, tool: Any, **kwargs: Any) -> "LlamaIndexTool":
+        from llama_index.core.tools import BaseTool as LlamaBaseTool
+
+        if not isinstance(tool, LlamaBaseTool):
+            raise ValueError(f"Expected a LlamaBaseTool, got {type(tool)}")
+        tool = cast(LlamaBaseTool, tool)
+
+        if tool.metadata.fn_schema is None:
+            raise ValueError(
+                "The LlamaIndex tool does not have an fn_schema specified."
+            )
+        args_schema = cast(Type[BaseModel], tool.metadata.fn_schema)
+
+        return cls(
+            name=tool.metadata.name,
+            description=tool.metadata.description,
+            args_schema=args_schema,
+            llama_index_tool=tool,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_query_engine(
+        cls,
+        query_engine: Any,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        return_direct: bool = False,
+        **kwargs: Any,
+    ) -> "LlamaIndexTool":
+        from llama_index.core.query_engine import BaseQueryEngine
+        from llama_index.core.tools import QueryEngineTool
+
+        if not isinstance(query_engine, BaseQueryEngine):
+            raise ValueError(f"Expected a BaseQueryEngine, got {type(query_engine)}")
+
+        # NOTE: by default the schema expects an `input` variable. However this
+        # confuses crewAI so we are renaming to `query`.
+        class QueryToolSchema(BaseModel):
+            """Schema for query tool."""
+
+            query: str = Field(..., description="Search query for the query tool.")
+
+        # NOTE: setting `resolve_input_errors` to True is important because the schema expects `input` but we are using `query`
+        query_engine_tool = QueryEngineTool.from_defaults(
+            query_engine,
+            name=name,
+            description=description,
+            return_direct=return_direct,
+            resolve_input_errors=True,
+        )
+        # HACK: we are replacing the schema with our custom schema
+        query_engine_tool.metadata.fn_schema = QueryToolSchema
+
+        return cls.from_tool(query_engine_tool, **kwargs)