adding in lorenze feedback

src/crewai/agent.py

@@ -126,7 +126,7 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    # TODO: We need to add in knowledge config (score, top_k, etc)
+    # TODO: Lorenze add knowledge_embedder. Support direct class or config dict.
     _knowledge: Optional[Knowledge] = PrivateAttr(default=None)
 
     @model_validator(mode="after")
@@ -279,10 +279,8 @@ class Agent(BaseAgent):
         if self._knowledge:
             # Query the knowledge base for relevant information
             knowledge_snippets = self._knowledge.query(query=task.prompt())
-            print("knowledge_snippets", knowledge_snippets)
             if knowledge_snippets:
                 formatted_knowledge = "\n".join(knowledge_snippets)
-                print("formatted_knowledge", formatted_knowledge)
                 task_prompt += f"\n\nAdditional Information:\n{formatted_knowledge}"
 
         tools = tools or self.tools or []

src/crewai/knowledge/embedder/ollama.py

@@ -0,0 +1,82 @@
+import os
+from typing import List, Optional
+
+import numpy as np
+from openai import OpenAI
+
+from .base_embedder import BaseEmbedder
+
+
+class OllamaEmbedder(BaseEmbedder):
+    """
+    A wrapper class for text embedding models using Ollama's API
+    """
+
+    def __init__(
+        self,
+        model_name: str,
+        api_key: Optional[str] = None,
+        base_url: str = "http://localhost:11434/v1",
+    ):
+        """
+        Initialize the embedding model
+
+        Args:
+            model_name: Name of the model to use
+            api_key: API key (defaults to 'ollama' or environment variable 'OLLAMA_API_KEY')
+            base_url: Base URL for the Ollama API (default is 'http://localhost:11434/v1')
+        """
+        self.model_name = model_name
+        self.api_key = api_key or os.getenv("OLLAMA_API_KEY") or "ollama"
+        self.base_url = base_url
+        self.client = OpenAI(base_url=self.base_url, api_key=self.api_key)
+
+    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
+        """
+        Generate embeddings for a list of text chunks
+
+        Args:
+            chunks: List of text chunks to embed
+
+        Returns:
+            List of embeddings
+        """
+        return self.embed_texts(chunks)
+
+    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
+        """
+        Generate embeddings for a list of texts
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of embeddings
+        """
+        embeddings = []
+        max_batch_size = 2048  # Adjust batch size if necessary
+        for i in range(0, len(texts), max_batch_size):
+            batch = texts[i : i + max_batch_size]
+            response = self.client.embeddings.create(input=batch, model=self.model_name)
+            batch_embeddings = [np.array(item.embedding) for item in response.data]
+            embeddings.extend(batch_embeddings)
+        return embeddings
+
+    def embed_text(self, text: str) -> np.ndarray:
+        """
+        Generate embedding for a single text
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Embedding array
+        """
+        return self.embed_texts([text])[0]
+
+    @property
+    def dimension(self) -> int:
+        """Get the dimension of the embeddings"""
+        # Embedding dimensions may vary; we'll determine it dynamically
+        test_embed = self.embed_text("test")
+        return len(test_embed)

src/crewai/knowledge/embedder/ollama_embedder.py

@@ -1,82 +0,0 @@
-import os
-from typing import List, Optional
-
-import numpy as np
-from openai import OpenAI
-
-from .base_embedder import BaseEmbedder
-
-
-class OllamaEmbedder(BaseEmbedder):
-    """
-    A wrapper class for text embedding models using Ollama's API
-    """
-
-    def __init__(
-        self,
-        model_name: str,
-        api_key: Optional[str] = None,
-        base_url: str = "http://localhost:11434/v1",
-    ):
-        """
-        Initialize the embedding model
-
-        Args:
-            model_name: Name of the model to use
-            api_key: API key (defaults to 'ollama' or environment variable 'OLLAMA_API_KEY')
-            base_url: Base URL for the Ollama API (default is 'http://localhost:11434/v1')
-        """
-        self.model_name = model_name
-        self.api_key = api_key or os.getenv("OLLAMA_API_KEY") or "ollama"
-        self.base_url = base_url
-        self.client = OpenAI(base_url=self.base_url, api_key=self.api_key)
-
-    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
-        """
-        Generate embeddings for a list of text chunks
-
-        Args:
-            chunks: List of text chunks to embed
-
-        Returns:
-            List of embeddings
-        """
-        return self.embed_texts(chunks)
-
-    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
-        """
-        Generate embeddings for a list of texts
-
-        Args:
-            texts: List of texts to embed
-
-        Returns:
-            List of embeddings
-        """
-        embeddings = []
-        max_batch_size = 2048  # Adjust batch size if necessary
-        for i in range(0, len(texts), max_batch_size):
-            batch = texts[i : i + max_batch_size]
-            response = self.client.embeddings.create(input=batch, model=self.model_name)
-            batch_embeddings = [np.array(item.embedding) for item in response.data]
-            embeddings.extend(batch_embeddings)
-        return embeddings
-
-    def embed_text(self, text: str) -> np.ndarray:
-        """
-        Generate embedding for a single text
-
-        Args:
-            text: Text to embed
-
-        Returns:
-            Embedding array
-        """
-        return self.embed_texts([text])[0]
-
-    @property
-    def dimension(self) -> int:
-        """Get the dimension of the embeddings"""
-        # Embedding dimensions may vary; we'll determine it dynamically
-        test_embed = self.embed_text("test")
-        return len(test_embed)