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Merge branch 'main' into feat/trace-ui-exec-3

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This commit is contained in:
Eduardo Chiarotti
2025-04-11 12:06:31 -04:00
committed by GitHub
parent aa9f35bdbc 0cd524af86
commit 12054b63db
2 changed files with 6 additions and 2 deletions
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4
docs/guides/flows/first-flow.mdx
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@@ -263,6 +263,7 @@ Let's create our flow in the `main.py` file:
```python
#!/usr/bin/env python
import json
import os
from typing import List, Dict
from pydantic import BaseModel, Field
from crewai import LLM
@@ -341,6 +342,9 @@ class GuideCreatorFlow(Flow[GuideCreatorState]):
outline_dict = json.loads(response)
self.state.guide_outline = GuideOutline(**outline_dict)
# Ensure output directory exists before saving
os.makedirs("output", exist_ok=True)
# Save the outline to a file
with open("output/guide_outline.json", "w") as f:
json.dump(outline_dict, f, indent=2)

4
docs/tools/weaviatevectorsearchtool.mdx
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@@ -25,7 +25,7 @@ uv add weaviate-client
To effectively use the `WeaviateVectorSearchTool`, follow these steps:
1. **Package Installation**: Confirm that the `crewai[tools]` and `weaviate-client` packages are installed in your Python environment.
2. **Weaviate Setup**: Set up a Weaviate cluster. You can follow the [Weaviate documentation](https://weaviate.io/developers/wcs/connect) for instructions.
2. **Weaviate Setup**: Set up a Weaviate cluster. You can follow the [Weaviate documentation](https://weaviate.io/developers/wcs/manage-clusters/connect) for instructions.
3. **API Keys**: Obtain your Weaviate cluster URL and API key.
4. **OpenAI API Key**: Ensure you have an OpenAI API key set in your environment variables as `OPENAI_API_KEY`.
@@ -161,4 +161,4 @@ rag_agent = Agent(
## Conclusion
The `WeaviateVectorSearchTool` provides a powerful way to search for semantically similar documents in a Weaviate vector database. By leveraging vector embeddings, it enables more accurate and contextually relevant search results compared to traditional keyword-based searches. This tool is particularly useful for applications that require finding information based on meaning rather than exact matches.
The `WeaviateVectorSearchTool` provides a powerful way to search for semantically similar documents in a Weaviate vector database. By leveraging vector embeddings, it enables more accurate and contextually relevant search results compared to traditional keyword-based searches. This tool is particularly useful for applications that require finding information based on meaning rather than exact matches.