docs: SmolAgents - Qdrant example (#1366)

* docs: SmolAgent example

Signed-off-by: Anush008 <anushshetty90@gmail.com>

* Update smolagents.md

* Update smolagents.md

---------

Signed-off-by: Anush008 <anushshetty90@gmail.com>

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

@@ -34,6 +34,7 @@ partition: build
 | [Ragbits](/documentation/frameworks/ragbits/)                 | Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications. |
 | [Rig-rs](/documentation/frameworks/rig-rs/)                   | Rust library for building scalable, modular, and ergonomic LLM-powered applications.                                 |
 | [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search.                             |
+| [SmolAgents](/documentation/frameworks/smolagents/)           | Barebones library for agents. Agents write python code to call tools and orchestrate other agent.                    |
 | [Spring AI](/documentation/frameworks/spring-ai/)             | Java AI framework for building with Spring design principles such as portability and modular design.                 |
 | [Superduper](/documentation/frameworks/superduper/)           | Framework for building flexible, compositional AI apps which may be applied directly to databases.                   |
 | [Swarm](/documentation/frameworks/swarm/)                     | Python framework for managing multiple AI agents that can work together.                                             |

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

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+---
+title: SmolAgents
+---
+
+# SmolAgents
+
+HuggingFace [SmolAgents](https://github.com/huggingface/smolagents) is a Python library for building AI agents. These agents write Python code to call tools and orchestrate other agents.
+
+It uses `CodeAgent`. An LLM engine that writes its actions in code. SmolAgents suggests that this approach is demonstrated to work better than the current industry practice of letting the LLM output a dictionary of the tools it wants to call: [uses 30% fewer steps](https://huggingface.co/papers/2402.01030) (thus 30% fewer LLM calls)
+and [reaches higher performance on difficult benchmarks](https://huggingface.co/papers/2411.01747).
+
+## Usage with Qdrant
+
+We'll demonstrate how you can pair SmolAgents with Qdrant's retrieval by building a movie recommendation agent.
+
+### Installation
+
+```shell
+pip install smolagents qdrant-client fastembed
+```
+
+### Setup a Qdrant tool
+
+We'll build a SmolAgents tool that can query a Qdrant collection. This tool will vectorise queries locally using [FastEmbed](https://github.com/qdrant/fastembed).
+
+Initially, we'll be populating a Qdrant collection with information about 1000 movies from IMDb that we can search across.
+
+```py
+from fastembed import TextEmbedding
+from qdrant_client import QdrantClient
+from smolagents import Tool
+
+
+class QdrantQueryTool(Tool):
+    name = "qdrant_query"
+    description = "Uses semantic search to retrieve movies from a Qdrant collection."
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "The query to perform. This should be semantically close to your target documents.",
+        }
+    }
+    output_type = "string"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.collection_name = "smolagents"
+        self.client = QdrantClient()
+
+        if not self.client.collection_exists(self.collection_name):
+            self.client.recover_snapshot(
+                collection_name=self.collection_name,
+                location="https://snapshots.qdrant.io/imdb-1000-jina.snapshot",
+            )
+        self.embedder = TextEmbedding(model_name="jinaai/jina-embeddings-v2-base-en")
+
+    def forward(self, query: str) -> str:
+        points = self.client.query_points(
+            self.collection_name, query=next(self.embedder.query_embed(query)), limit=5
+        ).points
+        docs = "Retrieved documents:\n" + "".join(
+            [
+                f"== Document {str(i)} ==\n"
+                + f"MOVIE TITLE: {point.payload['movie_name']}\n"
+                + f"MOVIE SUMMARY: {point.payload['description']}\n"
+                for i, point in enumerate(points)
+            ]
+        )
+
+        return docs
+```
+
+### Define the agent
+
+We can now set up `CodeAgent` to use our `QdrantQueryTool`.
+
+```python
+from smolagents import CodeAgent, HfApiModel
+import os
+
+# HuggingFace Access Token
+# https://huggingface.co/docs/hub/en/security-tokens
+os.environ["HF_TOKEN"] = "----------"
+
+agent = CodeAgent(
+    tools=[QdrantQueryTool()], model=HfApiModel(), max_iterations=4, verbose=True
+)
+```
+
+Finally, we can run the agent with a user query.
+
+```python
+agent_output = agent.run("Movie about people taking a strong action for justice")
+print(agent_output)
+```
+
+We should results similar to:
+
+```console
+[...truncated]
+
+Out - Final answer: Jai Bhim
+[Step 1: Duration 0.25 seconds| Input tokens: 4,497 | Output tokens: 134]
+Jai Bhim
+```
+
+## Further Reading
+
+- [SmolAgents Blog](https://huggingface.co/blog/smolagents#code-agents)
+- [SmolAgents Source](https://github.com/huggingface/smolagents)