Add mem0 filter example

examples/filters/mem0_memory_filter_pipeline.py

@@ -0,0 +1,140 @@
+"""
+title: Long Term Memory Filter
+author: Anton Nilsson
+date: 2024-08-23
+version: 1.0
+license: MIT
+description: A filter that processes user messages and stores them as long term memory by utilizing the mem0 framework together with qdrant and ollama
+requirements: pydantic, ollama, mem0ai
+"""
+
+from typing import List, Optional
+from pydantic import BaseModel
+import json
+from mem0 import Memory
+import threading
+
+class Pipeline:
+    class Valves(BaseModel):
+        pipelines: List[str] = []
+        priority: int = 0
+
+        store_cycles: int = 5 # Number of messages from the user before the data is processed and added to the memory
+        mem_zero_user: str = "user" # Memories belongs to this user, only used by mem0 for internal organization of memories
+
+        # Default values for the mem0 vector store
+        vector_store_qdrant_name: str = "memories"
+        vector_store_qdrant_url: str = "host.docker.internal"
+        vector_store_qdrant_port: int = 6333
+        vector_store_qdrant_dims: int = 768 # Need to match the vector dimensions of the embedder model
+
+        # Default values for the mem0 language model
+        ollama_llm_model: str = "llama3.1:latest" # This model need to exist in ollama
+        ollama_llm_temperature: float = 0
+        ollama_llm_tokens: int = 8000
+        ollama_llm_url: str = "http://host.docker.internal:11434"
+
+        # Default values for the mem0 embedding model
+        ollama_embedder_model: str = "nomic-embed-text:latest" # This model need to exist in ollama
+        ollama_embedder_url: str = "http://host.docker.internal:11434"
+
+    def __init__(self):
+        self.type = "filter"
+        self.name = "Memory Filter"
+        self.user_messages = []
+        self.thread = None
+        self.valves = self.Valves(
+            **{
+                "pipelines": ["*"],  # Connect to all pipelines
+            }
+        )
+        self.m = self.init_mem_zero()
+
+    async def on_startup(self):
+        print(f"on_startup:{__name__}")
+        pass
+
+    async def on_shutdown(self):
+        print(f"on_shutdown:{__name__}")
+        pass
+
+    async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
+        print(f"pipe:{__name__}")
+
+        user = self.valves.mem_zero_user
+        store_cycles = self.valves.store_cycles
+
+        if isinstance(body, str):
+            body = json.loads(body)
+
+        all_messages = body["messages"]
+        last_message = all_messages[-1]["content"]
+
+        self.user_messages.append(last_message)
+
+        if len(self.user_messages) == store_cycles:
+
+            message_text = ""
+            for message in self.user_messages:
+                message_text += message + " "
+
+            if self.thread and self.thread.is_alive():
+                print("Waiting for previous memory to be done")
+                self.thread.join()
+
+            self.thread = threading.Thread(target=self.m.add, kwargs={"data":message_text,"user_id":user})
+
+            print("Text to be processed in to a memory:")
+            print(message_text)
+
+            self.thread.start()
+            self.user_messages.clear()
+
+        memories = self.m.search(last_message, user_id=user)
+
+        if(memories):
+            fetched_memory = memories[0]["memory"]
+        else:
+            fetched_memory = ""
+
+        print("Memory added to the context:")
+        print(fetched_memory)
+
+        if fetched_memory:
+            all_messages.insert(0, {"role":"system", "content":"This is your inner voice talking, you remember this about the person you chatting with "+str(fetched_memory)})
+
+        print("Final body to send to the LLM:")
+        print(body)
+
+        return body
+
+    def init_mem_zero(self):
+        config = {
+                "vector_store": {
+                "provider": "qdrant",
+                "config": {
+                    "collection_name": self.valves.vector_store_qdrant_name,
+                    "host": self.valves.vector_store_qdrant_url,
+                    "port": self.valves.vector_store_qdrant_port,
+                    "embedding_model_dims": self.valves.vector_store_qdrant_dims,
+                },
+            },
+            "llm": {
+                "provider": "ollama",
+                "config": {
+                    "model": self.valves.ollama_llm_model,
+                    "temperature": self.valves.ollama_llm_temperature,
+                    "max_tokens": self.valves.ollama_llm_tokens,
+                    "ollama_base_url": self.valves.ollama_llm_url,
+                },
+            },
+            "embedder": {
+                "provider": "ollama",
+                "config": {
+                    "model": self.valves.ollama_embedder_model,
+                    "ollama_base_url": self.valves.ollama_embedder_url,
+                },
+            },
+        }
+
+        return Memory.from_config(config)