upgraded

configs/config.yaml

@@ -1,5 +1,5 @@
 # =================== Lord Of Large Language Multimodal Systems Configuration file =========================== 
-version: 144
+version: 145
 
 # video viewing and news recovering
 last_viewed_video: null
@@ -280,7 +280,8 @@ audio_silenceTimer: 5000
 
 # Data vectorization
 rag_databases: [] # This is the list of paths to database sources. Each database is a folder containing data
-rag_vectorizer: semantic # possible values semantic, tfidf, openai
+rag_vectorizer: semantic # possible values semantic, tfidf, openai, ollama
+rag_service_url: "http://localhost:11434" # rag service url for ollama
 rag_vectorizer_model:  "BAAI/bge-m3" # The model name if applicable
 rag_vectorizer_parameters: null # Parameters of the model in json format
 rag_chunk_size: 512 # number of tokens per chunk

lollms/configs/config.yaml

@@ -1,5 +1,5 @@
 # =================== Lord Of Large Language Multimodal Systems Configuration file =========================== 
-version: 144
+version: 145
 
 # video viewing and news recovering
 last_viewed_video: null
@@ -280,7 +280,8 @@ audio_silenceTimer: 5000
 
 # Data vectorization
 rag_databases: [] # This is the list of paths to database sources. Each database is a folder containing data
-rag_vectorizer: semantic # possible values semantic, tfidf, openai
+rag_vectorizer: semantic # possible values semantic, tfidf, openai, ollama
+rag_service_url: "http://localhost:11434" # rag service url for ollama
 rag_vectorizer_model:  "BAAI/bge-m3" # The model name if applicable
 rag_vectorizer_parameters: null # Parameters of the model in json format
 rag_chunk_size: 512 # number of tokens per chunk

lollms/databases/discussions_database.py

@@ -783,6 +783,9 @@ class Discussion:
             elif self.lollms.config.rag_vectorizer=="openai":
                 from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                 vectorizer = OpenAIVectorizer(self.lollms.config.rag_vectorizer_model, self.lollms.config.rag_vectorizer_openai_key)
+            elif self.config.rag_vectorizer == "ollama":
+                from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                v = OllamaVectorizer(self.lollms.config.rag_vectorizer_model, self.lollms.config.rag_service_url)
 
             self.vectorizer = VectorDatabase(
                                         self.discussion_rag_folder/"db.sqli",

lollms/databases/skills_database.py

@@ -22,6 +22,10 @@ class SkillsLibrary:
             elif vectorizer == "openai":
                 from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                 v = OpenAIVectorizer()
+            elif self.config.rag_vectorizer == "ollama":
+                from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                v = OllamaVectorizer()
+
         else:
             from lollmsvectordb.lollms_vectorizers.semantic_vectorizer import SemanticVectorizer
             v = SemanticVectorizer("BAAI/bge-m3")

lollms/internet.py

@@ -347,6 +347,9 @@ def internet_search_with_vectorization(query, chromedriver_path=None, internet_n
     elif vectorizer == "openai":
         from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
         v = OpenAIVectorizer()
+    elif vectorizer == "ollama":
+        from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+        v = OllamaVectorizer()
 
     vectorizer = VectorDatabase("", v, TikTokenTokenizer(), internet_vectorization_chunk_size, internet_vectorization_overlap_size)
 

lollms/personality.py

@@ -1379,6 +1379,9 @@ Use this structure:
             elif self.config.rag_vectorizer == "openai":
                 from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                 v = OpenAIVectorizer(api_key=self.config.rag_vectorizer_openai_key)
+            elif self.config.rag_vectorizer == "ollama":
+                from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                v = OllamaVectorizer(self.config.rag_vectorizer_model, self.config.rag_service_url)
 
             self.persona_data_vectorizer = VectorDatabase(self.database_path, v, TikTokenTokenizer(), self.config.rag_chunk_size, self.config.rag_overlap)
 
@@ -1543,7 +1546,10 @@ Use this structure:
                             self.ShowBlockingMessage("Processing file\nPlease wait ...\nUsing open ai vectorizer")
                             from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                             v = OpenAIVectorizer()
-
+                        elif self.config.rag_vectorizer == "ollama":
+                            self.ShowBlockingMessage("Processing file\nPlease wait ...\nUsing ollama vectorizer")
+                            from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                            v = OllamaVectorizer(self.config.rag_vectorizer_model, self.config.rag_service_url)
                         self.vectorizer = VectorDatabase(
                                     client.discussion.discussion_rag_folder/"db.sqli",
                                     v,
@@ -3741,6 +3747,9 @@ transition-all duration-300 ease-in-out">
         elif vectorizer == "openai":
             from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
             v = OpenAIVectorizer()
+        elif self.config.rag_vectorizer == "ollama":
+            from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+            v = OllamaVectorizer(self.config.rag_vectorizer_model, self.config.rag_service_url)
 
         vectorizer = VectorDatabase("", v, TikTokenTokenizer(), self.config.rag_chunk_size, self.config.rag_overlap)
         vectorizer.add_document(title, text, url)

lollms/server/endpoints/lollms_file_system.py

@@ -142,6 +142,9 @@ def select_rag_database(client) -> Optional[Dict[str, Path]]:
                     elif lollmsElfServer.config.rag_vectorizer == "openai":
                         from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                         v = OpenAIVectorizer(lollmsElfServer.config.rag_vectorizer_openai_key)
+                    elif lollmsElfServer.config.rag_vectorizer == "ollama":
+                        from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                        v = OllamaVectorizer(lollmsElfServer.config.rag_vectorizer_model, lollmsElfServer.config.rag_service_url)
 
                     vdb = VectorDatabase(Path(folder_path)/f"{db_name}.sqlite", v, lollmsElfServer.model if lollmsElfServer.model else TikTokenTokenizer())
                     # Get all files in the folder
@@ -277,6 +280,9 @@ def toggle_mount_rag_database(database_infos: MountDatabase):
                 elif lollmsElfServer.config.rag_vectorizer == "openai":
                     from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                     v = OpenAIVectorizer(lollmsElfServer.config.rag_vectorizer_openai_key)
+                elif lollmsElfServer.config.rag_vectorizer == "ollama":
+                    from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                    v = OllamaVectorizer(lollmsElfServer.config.rag_vectorizer_model, lollmsElfServer.config.rag_service_url)
 
 
                 vdb = VectorDatabase(Path(path)/f"{database_infos.database_name}.sqlite", v, lollmsElfServer.model if lollmsElfServer.model else TikTokenTokenizer(), chunk_size=lollmsElfServer.config.rag_chunk_size, clean_chunks=lollmsElfServer.config.rag_clean_chunks, n_neighbors=lollmsElfServer.config.rag_n_chunks)       
@@ -344,6 +350,9 @@ async def vectorize_folder(database_infos: FolderInfos):
                 elif lollmsElfServer.config.rag_vectorizer == "openai":
                     from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
                     v = OpenAIVectorizer(lollmsElfServer.config.rag_vectorizer_openai_key)
+                elif lollmsElfServer.config.rag_vectorizer == "ollama":
+                    from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+                    v = OllamaVectorizer(lollmsElfServer.config.rag_vectorizer_model, lollmsElfServer.config.rag_service_url)
 
                 vector_db_path = Path(folder_path)/f"{db_name}.sqlite"
 

lollms/server/endpoints/lollms_rag.py

@@ -75,6 +75,9 @@ def get_user_vectorizer(user_key: str):
     elif lollmsElfServer.config.rag_vectorizer == "openai":
         from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
         v = OpenAIVectorizer(lollmsElfServer.config.rag_vectorizer_openai_key)
+    elif lollmsElfServer.config.rag_vectorizer == "ollama":
+        from lollmsvectordb.lollms_vectorizers.ollama_vectorizer import OllamaVectorizer
+        v = OllamaVectorizer(lollmsElfServer.config.rag_vectorizer_model, lollmsElfServer.config.rag_service_url)
 
     return VectorDatabase(
         "",