feature: Introducing VoyageAI (#1871)

* Introducing VoyageAI's embedding models

* Adding back the whitespaces

* Adding the whitespaces back

docs/concepts/knowledge.mdx

@@ -288,6 +288,7 @@ 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

docs/concepts/memory.mdx

@@ -293,6 +293,26 @@ my_crew = Crew(
     }
 )
 ```
+### Using VoyageAI embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "voyageai",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "model_name": "<model_name>"
+        }
+    }
+)
+```
 ### Using HuggingFace embeddings
 
 ```python Code

docs/how-to/llm-connections.mdx

@@ -23,6 +23,7 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Azure OpenAI
 - AWS (Bedrock, SageMaker)
 - Cohere
+- VoyageAI
 - Hugging Face
 - Ollama
 - Mistral AI

src/crewai/utilities/embedding_configurator.py

@@ -14,6 +14,7 @@ class EmbeddingConfigurator:
             "vertexai": self._configure_vertexai,
             "google": self._configure_google,
             "cohere": self._configure_cohere,
+            "voyageai": self._configure_voyageai,
             "bedrock": self._configure_bedrock,
             "huggingface": self._configure_huggingface,
             "watson": self._configure_watson,
@@ -163,6 +164,17 @@ class EmbeddingConfigurator:
             api_key=config.get("api_key"),
         )
 
+    @staticmethod
+    def _configure_voyageai(config, model_name):
+        from chromadb.utils.embedding_functions.voyageai_embedding_function import (
+            VoyageAIEmbeddingFunction,
+        )
+
+        return VoyageAIEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
     @staticmethod
     def _configure_bedrock(config, model_name):
         from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (