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Introducing VoyageAI's embedding models

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fzowl
2025-01-09 19:29:18 +01:00
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docs/concepts/knowledge.mdx
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@@ -45,7 +45,7 @@ CrewAI supports various types of knowledge sources out of the box:
## Quickstart Example
<Tip>
For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project.
For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project.
Also, use relative paths from the `knowledge` directory when creating the source.
</Tip>
@@ -91,7 +91,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
```
Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more.
Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more.
```python Code
from crewai import LLM, Agent, Crew, Process, Task
@@ -253,7 +253,7 @@ crew = Crew(
### Chunking Configuration
Knowledge sources automatically chunk content for better processing.
Knowledge sources automatically chunk content for better processing.
You can configure chunking behavior in your knowledge sources:
```python
@@ -273,7 +273,7 @@ The chunking configuration helps in:
### Embeddings Configuration
You can also configure the embedder for the knowledge store.
You can also configure the embedder for the knowledge store.
This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
The `embedder` parameter supports various embedding model providers that include:
- `openai`: OpenAI's embedding models
@@ -282,6 +282,7 @@ The `embedder` parameter supports various embedding model providers that include
- `ollama`: Local embeddings with Ollama
- `vertexai`: Google Cloud VertexAI embeddings
- `cohere`: Cohere's embedding models
- `voyageai`: VoyageAI's embedding models
- `bedrock`: AWS Bedrock embeddings
- `huggingface`: Hugging Face models
- `watson`: IBM Watson embeddings
@@ -347,7 +348,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
## Task: Answer the following questions about the user: What city does John live in and how old is he?
# Agent: About User
## Final Answer:
## Final Answer:
John is 30 years old and lives in San Francisco.
```
</CodeGroup>
@@ -603,7 +604,7 @@ recent_news = SpaceNewsKnowledgeSource(
</Accordion>
<Accordion title="Performance Tips">
- Adjust chunk sizes based on content complexity
- Adjust chunk sizes based on content complexity
- Configure appropriate embedding models
- Consider using local embedding providers for faster processing
</Accordion>