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* docs: SmolAgent example Signed-off-by: Anush008 <anushshetty90@gmail.com> * Update smolagents.md * Update smolagents.md --------- Signed-off-by: Anush008 <anushshetty90@gmail.com>
111 lines
3.5 KiB
Markdown
111 lines
3.5 KiB
Markdown
---
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title: SmolAgents
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---
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# SmolAgents
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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.
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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)
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and [reaches higher performance on difficult benchmarks](https://huggingface.co/papers/2411.01747).
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## Usage with Qdrant
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We'll demonstrate how you can pair SmolAgents with Qdrant's retrieval by building a movie recommendation agent.
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### Installation
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```shell
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pip install smolagents qdrant-client fastembed
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```
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### Setup a Qdrant tool
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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).
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Initially, we'll be populating a Qdrant collection with information about 1000 movies from IMDb that we can search across.
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```py
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from fastembed import TextEmbedding
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from qdrant_client import QdrantClient
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from smolagents import Tool
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class QdrantQueryTool(Tool):
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name = "qdrant_query"
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description = "Uses semantic search to retrieve movies from a Qdrant collection."
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inputs = {
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"query": {
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"type": "string",
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"description": "The query to perform. This should be semantically close to your target documents.",
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}
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}
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output_type = "string"
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.collection_name = "smolagents"
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self.client = QdrantClient()
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if not self.client.collection_exists(self.collection_name):
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self.client.recover_snapshot(
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collection_name=self.collection_name,
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location="https://snapshots.qdrant.io/imdb-1000-jina.snapshot",
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)
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self.embedder = TextEmbedding(model_name="jinaai/jina-embeddings-v2-base-en")
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def forward(self, query: str) -> str:
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points = self.client.query_points(
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self.collection_name, query=next(self.embedder.query_embed(query)), limit=5
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).points
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docs = "Retrieved documents:\n" + "".join(
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[
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f"== Document {str(i)} ==\n"
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+ f"MOVIE TITLE: {point.payload['movie_name']}\n"
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+ f"MOVIE SUMMARY: {point.payload['description']}\n"
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for i, point in enumerate(points)
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]
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)
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return docs
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```
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### Define the agent
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We can now set up `CodeAgent` to use our `QdrantQueryTool`.
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```python
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from smolagents import CodeAgent, HfApiModel
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import os
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# HuggingFace Access Token
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# https://huggingface.co/docs/hub/en/security-tokens
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os.environ["HF_TOKEN"] = "----------"
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agent = CodeAgent(
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tools=[QdrantQueryTool()], model=HfApiModel(), max_iterations=4, verbose=True
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)
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```
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Finally, we can run the agent with a user query.
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```python
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agent_output = agent.run("Movie about people taking a strong action for justice")
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print(agent_output)
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```
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We should results similar to:
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```console
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[...truncated]
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Out - Final answer: Jai Bhim
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[Step 1: Duration 0.25 seconds| Input tokens: 4,497 | Output tokens: 134]
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Jai Bhim
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```
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## Further Reading
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- [SmolAgents Blog](https://huggingface.co/blog/smolagents#code-agents)
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- [SmolAgents Source](https://github.com/huggingface/smolagents)
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