ensure embeddings are persisted

src/crewai/cli/cli.py

@@ -136,6 +136,7 @@ def log_tasks_outputs() -> None:
 @click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
+@click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE")
 @click.option(
     "-k",
     "--kickoff-outputs",
@@ -143,7 +144,7 @@ def log_tasks_outputs() -> None:
     help="Reset LATEST KICKOFF TASK OUTPUTS",
 )
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
-def reset_memories(long, short, entities, kickoff_outputs, all):
+def reset_memories(long, short, entities, knowledge, kickoff_outputs, all):
     """
     Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
     """
@@ -153,7 +154,7 @@ def reset_memories(long, short, entities, kickoff_outputs, all):
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, kickoff_outputs, all)
+        reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all)
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 

src/crewai/cli/reset_memories_command.py

@@ -5,9 +5,12 @@ from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
-def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
+def reset_memories_command(
+    long, short, entity, kickoff_outputs, all, knowledge
+) -> None:
     """
     Reset the crew memories.
 
@@ -17,6 +20,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
       entity (bool): Whether to reset the entity memory.
       kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
       all (bool): Whether to reset all memories.
+      knowledge (bool): Whether to reset the knowledge.
     """
 
     try:
@@ -25,6 +29,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             EntityMemory().reset()
             LongTermMemory().reset()
             TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
         else:
             if long:
@@ -40,6 +45,9 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             if kickoff_outputs:
                 TaskOutputStorageHandler().reset()
                 click.echo("Latest Kickoff outputs stored has been reset.")
+            if knowledge:
+                KnowledgeStorage().reset()
+                click.echo("Knowledge has been reset.")
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

src/crewai/knowledge/embedder/fastembed.py

@@ -39,7 +39,7 @@ class FastEmbed(BaseEmbedder):
         if not FASTEMBED_AVAILABLE:
             raise ImportError(
                 "FastEmbed is not installed. Please install it with: "
-                "pip install fastembed or pip install fastembed-gpu for GPU support"
+                "uv pip install fastembed or uv pip install fastembed-gpu for GPU support"
             )
 
         self.model = TextEmbedding(

src/crewai/knowledge/knowledge.py

@@ -1,10 +1,11 @@
-from typing import List
+from typing import List, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.embedder.base_embedder import BaseEmbedder
 from crewai.knowledge.embedder.fastembed import FastEmbed
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
 class Knowledge(BaseModel):
@@ -12,6 +13,8 @@ class Knowledge(BaseModel):
     embedder: BaseEmbedder = Field(default_factory=FastEmbed)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
+    agents: List[str] = Field(default_factory=list)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -19,35 +22,45 @@ class Knowledge(BaseModel):
         for source in self.sources:
             source.add(self.embedder)
 
-    def query(self, query: str, top_k: int = 3) -> List[str]:
+    def query(
+        self, query: str, top_k: int = 3, preference: Optional[str] = None
+    ) -> List[str]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
         """
-        if not self.sources:
-            return []
+        # if not self.sources:
+        #     return []
+
+        results = self.storage.search(
+            [query],
+            top_k,
+            filter={"preference": preference} if preference else None,
+            score_threshold=0.35,
+        )
+        return results
 
         # Collect all chunks and embeddings from all sources
-        all_chunks = []
-        all_embeddings = []
+        # all_chunks = []
+        # all_embeddings = []
 
-        for source in self.sources:
-            all_chunks.extend(source.chunks)
-            all_embeddings.extend(source.get_embeddings())
+        # for source in self.sources:
+        #     all_chunks.extend(source.chunks)
+        #     all_embeddings.extend(source.get_embeddings())
 
-        # Embed the query
-        query_embedding = self.embedder.embed_text(query)
+        # # Embed the query
+        # query_embedding = self.embedder.embed_text(query)
 
-        # Calculate similarities
-        similarities = []
-        for idx, embedding in enumerate(all_embeddings):
-            similarity = query_embedding.dot(embedding)
-            similarities.append((similarity, idx))
+        # # Calculate similarities
+        # similarities = []
+        # for idx, embedding in enumerate(all_embeddings):
+        #     similarity = query_embedding.dot(embedding)
+        #     similarities.append((similarity, idx))
 
-        # Sort by similarity
-        similarities.sort(reverse=True, key=lambda x: x[0])
+        # # Sort by similarity
+        # similarities.sort(reverse=True, key=lambda x: x[0])
 
-        # Get top_k results
-        top_chunks = [all_chunks[idx] for _, idx in similarities[:top_k]]
+        # # Get top_k results
+        # top_chunks = [all_chunks[idx] for _, idx in similarities[:top_k]]
 
-        return top_chunks
+        # return top_chunks

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -3,6 +3,7 @@ from pathlib import Path
 from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from typing import Dict, Any
 
 
 class BaseFileKnowledgeSource(BaseKnowledgeSource):
@@ -22,3 +23,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource):
         if not self.file_path.is_file():
             raise ValueError(f"Path is not a file: {self.file_path}")
         return ""
+
+    def _save_documents(self, metadata: Dict[str, Any]):
+        """Save the documents to the storage."""
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/base_knowledge_source.py

@@ -1,10 +1,12 @@
 from abc import ABC, abstractmethod
-from typing import List
+from typing import List, Optional
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.embedder.base_embedder import BaseEmbedder
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from typing import Dict, Any
 
 
 class BaseKnowledgeSource(BaseModel, ABC):
@@ -16,6 +18,8 @@ class BaseKnowledgeSource(BaseModel, ABC):
     chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
 
     @abstractmethod
     def load_content(self):
@@ -37,3 +41,10 @@ class BaseKnowledgeSource(BaseModel, ABC):
             text[i : i + self.chunk_size]
             for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
         ]
+
+    def _save_documents(self, metadata: Dict[str, Any]):
+        """
+        Save the documents to the storage.
+        This method should be called after the chunks and embeddings are generated.
+        """
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -29,6 +29,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
         new_embeddings = embedder.embed_chunks(new_chunks)
         # Save the embeddings
         self.chunk_embeddings.extend(new_embeddings)
+        self._save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -39,6 +39,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
         new_embeddings = embedder.embed_chunks(new_chunks)
         # Save the embeddings
         self.chunk_embeddings.extend(new_embeddings)
+        self._save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/json_knowledge_source.py

@@ -41,6 +41,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
         new_embeddings = embedder.embed_chunks(new_chunks)
         # Save the embeddings
         self.chunk_embeddings.extend(new_embeddings)
+        self._save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -41,6 +41,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
         new_embeddings = embedder.embed_chunks(new_chunks)
         # Save the embeddings
         self.chunk_embeddings.extend(new_embeddings)
+        self._save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/string_knowledge_source.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Dict, Any
 
 from pydantic import Field
 
@@ -28,6 +28,8 @@ class StringKnowledgeSource(BaseKnowledgeSource):
         new_embeddings = embedder.embed_chunks(new_chunks)
         # Save the embeddings
         self.chunk_embeddings.extend(new_embeddings)
+        print("adding")
+        self._save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -24,6 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
         new_embeddings = embedder.embed_chunks(new_chunks)
         # Save the embeddings
         self.chunk_embeddings.extend(new_embeddings)
+        self._save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/storage/knowledge_storage.py

@@ -0,0 +1,110 @@
+from crewai.memory.storage.base_rag_storage import BaseRAGStorage
+from crewai.utilities.paths import db_storage_path
+from typing import Optional, List
+
+import chromadb
+import numpy as np
+from typing import Dict, Any
+import uuid
+import contextlib
+import io
+import logging
+
+
+@contextlib.contextmanager
+def suppress_logging(
+    logger_name="chromadb.segment.impl.vector.local_persistent_hnsw",
+    level=logging.ERROR,
+):
+    logger = logging.getLogger(logger_name)
+    original_level = logger.getEffectiveLevel()
+    logger.setLevel(level)
+    with (
+        contextlib.redirect_stdout(io.StringIO()),
+        contextlib.redirect_stderr(io.StringIO()),
+        contextlib.suppress(UserWarning),
+    ):
+        yield
+    logger.setLevel(original_level)
+
+
+class KnowledgeStorage(BaseRAGStorage):
+    """
+    Extends Storage to handle embeddings for memory entries, improving
+    search efficiency.
+    """
+
+    collection: Optional[chromadb.Collection] = None
+
+    def __init__(self, embedder_config=None):
+        self.embedder_config = (
+            embedder_config or self._create_default_embedding_function()
+        )
+        self._initialize_app()
+
+    def _sanitize_role(self, role: str) -> str:
+        return role.replace(" ", "_")
+
+    def search(
+        self,
+        query: List[str],
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Dict[str, Any]]:
+        with suppress_logging():
+            if self.collection:
+                fetched = self.collection.query(
+                    query_texts=query,
+                    n_results=limit,
+                    where=filter,
+                )
+                print("Fetched", fetched)
+                results = []
+                for i in range(len(fetched["ids"][0])):
+                    result = {
+                        "id": fetched["ids"][0][i],
+                        "metadata": fetched["metadatas"][0][i],
+                        "context": fetched["documents"][0][i],
+                        "score": fetched["distances"][0][i],
+                    }
+                    if result["score"] >= score_threshold:
+                        results.append(result)
+                return results
+            else:
+                raise Exception("Collection not initialized")
+
+    def _initialize_app(self):
+        import chromadb
+        from chromadb.config import Settings
+
+        chroma_client = chromadb.PersistentClient(
+            path=f"{db_storage_path()}/knowledge",
+            settings=Settings(allow_reset=True),
+        )
+
+        self.app = chroma_client
+
+        try:
+            self.collection = self.app.get_or_create_collection(name="knowledge")
+        except Exception:
+            raise Exception("Failed to create or get collection")
+
+    def reset(self):
+        if self.app:
+            self.app.reset()
+
+    def save(self, documents: List[str], metadata: Dict[str, Any]):
+        if self.collection:
+            self.collection.add(
+                documents=documents,
+                metadatas=metadata,
+                ids=[str(uuid.uuid4())],
+            )
+        else:
+            raise Exception("Collection not initialized")
+
+    def _create_default_embedding_function(self):
+        from crewai.knowledge.embedder.fastembed import FastEmbed
+
+        return FastEmbed().embed_texts

src/crewai/memory/contextual/contextual_memory.py

@@ -102,3 +102,11 @@ class ContextualMemory:
             f"- {result['memory']}" for result in user_memories
         )
         return f"User memories/preferences:\n{formatted_memories}"
+
+    # TODO: set this up
+    # def _fetch_knowledge_context(self, query: str) -> str:
+    #     """
+    #     Fetches relevant knowledge from Knowledge Storage.
+    #     """
+    #     knowledge_results = self.knowledge.query(query)
+    #     return "\n".join([result["context"] for result in knowledge_results])

src/crewai/memory/storage/base_rag_storage.py

@@ -55,12 +55,12 @@ class BaseRAGStorage(ABC):
         """Reset the storage."""
         pass
 
-    @abstractmethod
-    def _generate_embedding(
-        self, text: str, metadata: Optional[Dict[str, Any]] = None
-    ) -> Any:
-        """Generate an embedding for the given text and metadata."""
-        pass
+    # @abstractmethod
+    # def _generate_embedding(
+    #     self, text: str, metadata: Optional[Dict[str, Any]] = None
+    # ) -> Any:
+    #     """Generate an embedding for the given text and metadata."""
+    #     pass
 
     @abstractmethod
     def _initialize_app(self):