Squashed 'packages/tools/' content from commit 78317b9c

git-subtree-dir: packages/tools
git-subtree-split: 78317b9c127f18bd040c1d77e3c0840cdc9a5b38

crewai_tools/tools/contextualai_create_agent_tool/README.md

@@ -0,0 +1,58 @@
+# ContextualAICreateAgentTool
+
+## Description
+This tool is designed to integrate Contextual AI's enterprise-grade RAG agents with CrewAI. This tool enables you to create a new Contextual RAG agent. It uploads your documents to create a datastore and returns the Contextual agent ID and datastore ID.
+
+## Installation
+To incorporate this tool into your project, follow the installation instructions below:
+
+```
+pip install 'crewai[tools]' contextual-client
+```
+
+**Note**: You'll need a Contextual AI API key. Sign up at [app.contextual.ai](https://app.contextual.ai) to get your free API key.
+
+## Example
+
+```python
+from crewai_tools import ContextualAICreateAgentTool
+
+# Initialize the tool
+tool = ContextualAICreateAgentTool(api_key="your_api_key_here")
+
+# Create agent with documents
+result = tool._run(
+    agent_name="Financial Analysis Agent",
+    agent_description="Agent for analyzing financial documents",
+    datastore_name="Financial Reports",
+    document_paths=["/path/to/report1.pdf", "/path/to/report2.pdf"],
+)
+print(result)
+```
+
+## Parameters
+- `api_key`: Your Contextual AI API key
+- `agent_name`: Name for the new agent
+- `agent_description`: Description of the agent's purpose
+- `datastore_name`: Name for the document datastore
+- `document_paths`: List of file paths to upload
+
+Example result: 
+
+```
+Successfully created agent 'Research Analyst' with ID: {created_agent_ID} and datastore ID: {created_datastore_ID}. Uploaded 5 documents.
+```
+
+You can use `ContextualAIQueryTool` with the returned IDs to query the knowledge base and retrieve relevant information from your documents.
+
+## Key Features
+- **Complete Pipeline Setup**: Creates datastore, uploads documents, and configures agent in one operation
+- **Document Processing**: Leverages Contextual AI's powerful parser to ingest complex PDFs and documents
+- **Vector Storage**: Use Contextual AI's datastore for large document collections
+
+## Use Cases
+- Set up new RAG agents from scratch with complete automation
+- Upload and organize document collections into structured datastores
+- Create specialized domain agents for legal, financial, technical, or research workflows
+
+For more detailed information about Contextual AI's capabilities, visit the [official documentation](https://docs.contextual.ai).

crewai_tools/tools/contextualai_create_agent_tool/contextual_create_agent_tool.py

@@ -0,0 +1,71 @@
+from typing import Any, Optional, Type, List
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+import os
+
+
+class ContextualAICreateAgentSchema(BaseModel):
+    """Schema for contextual create agent tool."""
+    agent_name: str = Field(..., description="Name for the new agent")
+    agent_description: str = Field(..., description="Description for the new agent")
+    datastore_name: str = Field(..., description="Name for the new datastore")
+    document_paths: List[str] = Field(..., description="List of file paths to upload")
+
+
+class ContextualAICreateAgentTool(BaseTool):
+    """Tool to create Contextual AI RAG agents with documents."""
+    
+    name: str = "Contextual AI Create Agent Tool"
+    description: str = "Create a new Contextual AI RAG agent with documents and datastore"
+    args_schema: Type[BaseModel] = ContextualAICreateAgentSchema
+    
+    api_key: str
+    contextual_client: Any = None
+    package_dependencies: List[str] = ["contextual-client"]
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        try:
+            from contextual import ContextualAI
+            self.contextual_client = ContextualAI(api_key=self.api_key)
+        except ImportError:
+            raise ImportError(
+                "contextual-client package is required. Install it with: pip install contextual-client"
+            )
+
+    def _run(
+        self,
+        agent_name: str,
+        agent_description: str,
+        datastore_name: str,
+        document_paths: List[str]
+    ) -> str:
+        """Create a complete RAG pipeline with documents."""
+        try:
+            import os
+            
+            # Create datastore
+            datastore = self.contextual_client.datastores.create(name=datastore_name)
+            datastore_id = datastore.id
+            
+            # Upload documents
+            document_ids = []
+            for doc_path in document_paths:
+                if not os.path.exists(doc_path):
+                    raise FileNotFoundError(f"Document not found: {doc_path}")
+                
+                with open(doc_path, 'rb') as f:
+                    ingestion_result = self.contextual_client.datastores.documents.ingest(datastore_id, file=f)
+                    document_ids.append(ingestion_result.id)
+            
+            # Create agent
+            agent = self.contextual_client.agents.create(
+                name=agent_name,
+                description=agent_description,
+                datastore_ids=[datastore_id]
+            )
+            
+            return f"Successfully created agent '{agent_name}' with ID: {agent.id} and datastore ID: {datastore_id}. Uploaded {len(document_ids)} documents."
+            
+        except Exception as e:
+            return f"Failed to create agent with documents: {str(e)}"