Merge pull request #1292 from qdrant/crewai

docs: CrewAI integration example

qdrant-landing/content/documentation/frameworks/_index.md

@@ -11,6 +11,7 @@ partition: build
 | [AutoGen](/documentation/frameworks/autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.                             |
 | [Canopy](/documentation/frameworks/canopy/)                   | Framework from Pinecone for building RAG applications using LLMs and knowledge bases.                                |
 | [Cheshire Cat](/documentation/frameworks/cheshire-cat/)       | Framework to create personalized AI assistants using custom data.                                                    |
+| [CrewAI](/documentation/frameworks/crewai/)                   | CrewAI is a framework to build automated workflows using multiple AI agents that perform complex tasks.              |
 | [DocArray](/documentation/frameworks/docarray/)               | Python library for managing data in multi-modal AI applications.                                                     |
 | [DSPy](/documentation/frameworks/dspy/)                       | Framework for algorithmically optimizing LM prompts and weights.                                                     |
 | [Feast](/documentation/frameworks/feast/)                     | Open-source feature store to operate production ML systems at scale as a set of features.                            |

qdrant-landing/content/documentation/frameworks/crewai.md

@@ -0,0 +1,125 @@
+---
+title: CrewAI
+---
+
+# CrewAI
+
+[CrewAI](https://www.crewai.com) is a framework for orchestrating role-playing, autonomous AI agents. By leveraging collaborative intelligence, CrewAI allows agents to work together seamlessly, tackling complex tasks.
+
+The framework has a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system aids agents to remember, reason, and learn from past interactions. You can use Qdrant to store short-term memory and entity memories of CrewAI agents.
+
+- Short-Term Memory
+
+Temporarily stores recent interactions and outcomes using RAG, enabling agents to recall and utilize information relevant to their current context during the current executions.
+
+- Entity Memory
+
+Entity Memory Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses RAG for storing entity information.
+
+## Usage with Qdrant
+
+We'll learn how to customize CrewAI's default memory storage to use Qdrant.
+
+### Installation
+
+First, install CrewAI and Qdrant client packages:
+
+```shell
+pip install 'crewai[tools]' 'qdrant-client[fastembed]'
+```
+
+### Setup a CrewAI Project
+
+You can learn to set up a CrewAI project [here](https://docs.crewai.com/installation#create-a-new-crewai-project). Let's assume the project was name `mycrew`.
+
+### Define the Qdrant storage
+
+> src/mycrew/storage.py
+
+```python
+from typing import Any, Dict, List, Optional
+
+from crewai.memory.storage.rag_storage import RAGStorage
+from qdrant_client import QdrantClient
+
+
+class QdrantStorage(RAGStorage):
+    """
+    Extends Storage to handle embeddings for memory entries using Qdrant.
+
+    """
+
+    def __init__(self, type, allow_reset=True, embedder_config=None, crew=None):
+        super().__init__(type, allow_reset, embedder_config, crew)
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0,
+    ) -> List[Any]:
+        points = self.client.query(
+            self.type,
+            query_text=query,
+            query_filter=filter,
+            limit=limit,
+            score_threshold=score_threshold,
+        )
+        results = [
+            {
+                "id": point.id,
+                "metadata": point.metadata,
+                "context": point.document,
+                "score": point.score,
+            }
+            for point in points
+        ]
+
+        return results
+
+    def reset(self) -> None:
+        self.client.delete_collection(self.type)
+
+    def _initialize_app(self):
+        self.client = QdrantClient()
+        if not self.client.collection_exists(self.type):
+            self.client.create_collection(
+                collection_name=self.type,
+                vectors_config=self.client.get_fastembed_vector_params(),
+                sparse_vectors_config=self.client.get_fastembed_sparse_vector_params(),
+            )
+
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        self.client.add(self.type, documents=[value], metadata=[metadata or {}])
+```
+
+The `add` AND `query` methods use [FastEmbed](https://github.com/qdrant/fastembed/) to vectorize data. You can however customize it if required.
+
+### Instantiate your crew
+
+You can learn about setting up agents and tasks for your crew [here](https://docs.crewai.com/quickstart). We can update the instantiation of `Crew` to use our storage mechanism.
+
+> src/mycrew/crew.py
+
+```python
+from crewai import Crew
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+
+from mycrew.storage import QdrantStorage
+
+Crew(
+    # Import the agents and tasks here.
+    memory=True,
+    entity_memory=EntityMemory(storage=QdrantStorage("entity")),
+    short_term_memory=ShortTermMemory(storage=QdrantStorage("short-term")),
+)
+```
+
+You can now run your Crew workflow with `crew run`. It'll use Qdrant for memory ingestion and retrieval.
+
+## Further Reading
+
+- [CrewAI Documentation](https://docs.crewai.com/introduction)
+- [CrewAI Examples](https://github.com/crewAIInc/crewAI-examples)