docs: new algorithm migration guides + memory evaluation (#4811)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
Co-authored-by: Saket Aryan <saketaryan2002@gmail.com>

docs/components/rerankers/config.mdx

@@ -91,7 +91,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4.1-nano-2025-04-14"
+            "model": "gpt-5-mini"
         }
     },
     "reranker": {

docs/components/rerankers/custom-prompts.mdx

@@ -189,7 +189,7 @@ for i, prompt in enumerate(prompts):
     config["reranker"]["config"]["scoring_prompt"] = prompt
     memory = Memory.from_config(config)
 
-    results = memory.search("test query", user_id="test_user")
+    results = memory.search("test query", filters={"user_id": "test_user"})
     print(f"Prompt {i+1} results: {results}")
 ```
 

docs/components/rerankers/models/cohere.mdx

@@ -35,7 +35,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4.1-nano-2025-04-14"
+            "model": "gpt-5-mini"
         }
     },
     "reranker": {
@@ -95,7 +95,7 @@ messages = [
 memory.add(messages, user_id="bob")
 
 # Search with reranking
-results = memory.search("What is the user's profession?", user_id="bob")
+results = memory.search("What is the user's profession?", filters={"user_id": "bob"})
 
 for result in results['results']:
     print(f"Memory: {result['memory']}")

docs/components/rerankers/models/huggingface.mdx

@@ -175,7 +175,7 @@ queries = [
 
 results = []
 for query in queries:
-    result = m.search(query, user_id="alice", rerank=True)
+    result = m.search(query, filters={"user_id": "alice"}, rerank=True)
     results.append(result)
 ```
 

docs/components/rerankers/models/llm.mdx

@@ -111,7 +111,7 @@ messages = [
 memory.add(messages, user_id="david")
 
 # Search with LLM reranking
-results = memory.search("What programming topics is the user studying?", user_id="david")
+results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
 
 for result in results['results']:
     print(f"Memory: {result['memory']}")

docs/components/rerankers/models/llm_reranker.mdx

@@ -283,12 +283,12 @@ for result in results["results"]:
 def safe_llm_rerank_search(query, user_id, max_retries=3):
     for attempt in range(max_retries):
         try:
-            return m.search(query, user_id=user_id, rerank=True)
+            return m.search(query, filters={"user_id": user_id}, rerank=True)
         except Exception as e:
             print(f"Attempt {attempt + 1} failed: {e}")
             if attempt == max_retries - 1:
                 # Fall back to vector search
-                return m.search(query, user_id=user_id, rerank=False)
+                return m.search(query, filters={"user_id": user_id}, rerank=False)
 
 # Use the safe function
 results = safe_llm_rerank_search("What are my preferences?", "alice")
@@ -376,19 +376,19 @@ class RobustLLMReranker:
         # Try primary LLM reranker
         for attempt in range(max_retries):
             try:
-                return self.primary.search(query, user_id=user_id, rerank=True)
+                return self.primary.search(query, filters={"user_id": user_id}, rerank=True)
             except Exception as e:
                 print(f"Primary reranker attempt {attempt + 1} failed: {e}")
 
         # Try fallback reranker
         if self.fallback:
             try:
-                return self.fallback.search(query, user_id=user_id, rerank=True)
+                return self.fallback.search(query, filters={"user_id": user_id}, rerank=True)
             except Exception as e:
                 print(f"Fallback reranker failed: {e}")
 
         # Final fallback: vector search only
-        return self.primary.search(query, user_id=user_id, rerank=False)
+        return self.primary.search(query, filters={"user_id": user_id}, rerank=False)
 
 # Usage
 primary_config = {

docs/components/rerankers/models/sentence_transformer.mdx

@@ -101,7 +101,7 @@ messages = [
 memory.add(messages, user_id="charlie")
 
 # Search with local reranking
-results = memory.search("What books does the user like?", user_id="charlie")
+results = memory.search("What books does the user like?", filters={"user_id": "charlie"})
 
 for result in results['results']:
     print(f"Memory: {result['memory']}")

docs/components/rerankers/models/zero_entropy.mdx

@@ -86,7 +86,7 @@ messages = [
 memory.add(messages, user_id="alice")
 
 # Search with reranking
-results = memory.search("What Italian food does the user like?", user_id="alice")
+results = memory.search("What Italian food does the user like?", filters={"user_id": "alice"})
 
 for result in results['results']:
     print(f"Memory: {result['memory']}")

docs/components/rerankers/optimization.mdx

@@ -153,7 +153,7 @@ def measure_reranker_performance(config, queries, user_id):
     latencies = []
     for query in queries:
         start_time = time.time()
-        results = memory.search(query, user_id=user_id)
+        results = memory.search(query, filters={"user_id": user_id})
         latency = time.time() - start_time
         latencies.append(latency)
 
@@ -191,7 +191,7 @@ class CachedReranker:
 
     @lru_cache(maxsize=1000)
     def search_cached(self, query_hash, user_id):
-        return self.memory.search(query, user_id=user_id)
+        return self.memory.search(query, filters={"user_id": user_id})
 
     def search(self, query, user_id):
         query_hash = hashlib.md5(f"{query}_{user_id}".encode()).hexdigest()