Merge pull request #650 from danielaskdd/Add-history-support-for-ollama-api

Add history support for ollama api

lightrag/api/README.md

@@ -94,8 +94,6 @@ For example, chat message "/mix 唐僧有几个徒弟" will trigger a mix mode q
 
 After starting the lightrag-server, you can add an Ollama-type connection in the Open WebUI admin pannel. And then a model named lightrag:latest will appear in Open WebUI's model management interface. Users can then send queries to LightRAG through the chat interface.
 
-To prevent Open WebUI from using LightRAG when generating conversation titles, go to Admin Panel > Interface > Set Task Model and change both Local Models and External Models to any option except "Current Model".
-
 ## Configuration
 
 LightRAG can be configured using either command-line arguments or environment variables. When both are provided, command-line arguments take precedence over environment variables.

lightrag/api/lightrag_server.py

@@ -17,6 +17,7 @@ import shutil
 import aiofiles
 from ascii_colors import trace_exception, ASCIIColors
 import os
+import sys
 import configparser
 
 from fastapi import Depends, Security
@@ -200,8 +201,14 @@ def display_splash_screen(args: argparse.Namespace) -> None:
     ASCIIColors.yellow(f"{args.max_async}")
     ASCIIColors.white("    ├─ Max Tokens: ", end="")
     ASCIIColors.yellow(f"{args.max_tokens}")
-    ASCIIColors.white("    └─ Max Embed Tokens: ", end="")
+    ASCIIColors.white("    ├─ Max Embed Tokens: ", end="")
     ASCIIColors.yellow(f"{args.max_embed_tokens}")
+    ASCIIColors.white("    ├─ Chunk Size: ", end="")
+    ASCIIColors.yellow(f"{args.chunk_size}")
+    ASCIIColors.white("    ├─ Chunk Overlap Size: ", end="")
+    ASCIIColors.yellow(f"{args.chunk_overlap_size}")
+    ASCIIColors.white("    └─ History Turns: ", end="")
+    ASCIIColors.yellow(f"{args.history_turns}")
 
     # System Configuration
     ASCIIColors.magenta("\n🛠️ System Configuration:")
@@ -281,6 +288,9 @@ def display_splash_screen(args: argparse.Namespace) -> None:
 
     ASCIIColors.green("Server is ready to accept connections! 🚀\n")
 
+    # Ensure splash output flush to system log
+    sys.stdout.flush()
+
 
 def parse_args() -> argparse.Namespace:
     """
@@ -294,7 +304,7 @@ def parse_args() -> argparse.Namespace:
         description="LightRAG FastAPI Server with separate working and input directories"
     )
 
-    # Bindings (with env var support)
+    # Bindings configuration
     parser.add_argument(
         "--llm-binding",
         default=get_env_value("LLM_BINDING", "ollama"),
@@ -306,9 +316,6 @@ def parse_args() -> argparse.Namespace:
         help="Embedding binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
     )
 
-    # Parse temporary args for host defaults
-    temp_args, _ = parser.parse_known_args()
-
     # Server configuration
     parser.add_argument(
         "--host",
@@ -335,13 +342,13 @@ def parse_args() -> argparse.Namespace:
     )
 
     # LLM Model configuration
-    default_llm_host = get_env_value(
-        "LLM_BINDING_HOST", get_default_host(temp_args.llm_binding)
-    )
     parser.add_argument(
         "--llm-binding-host",
-        default=default_llm_host,
-        help=f"llm server host URL (default: from env or {default_llm_host})",
+        default=get_env_value("LLM_BINDING_HOST", None),
+        help="LLM server host URL. If not provided, defaults based on llm-binding:\n"
+        + "- ollama: http://localhost:11434\n"
+        + "- lollms: http://localhost:9600\n"
+        + "- openai: https://api.openai.com/v1",
     )
 
     default_llm_api_key = get_env_value("LLM_BINDING_API_KEY", None)
@@ -359,13 +366,13 @@ def parse_args() -> argparse.Namespace:
     )
 
     # Embedding model configuration
-    default_embedding_host = get_env_value(
-        "EMBEDDING_BINDING_HOST", get_default_host(temp_args.embedding_binding)
-    )
     parser.add_argument(
         "--embedding-binding-host",
-        default=default_embedding_host,
-        help=f"embedding server host URL (default: from env or {default_embedding_host})",
+        default=get_env_value("EMBEDDING_BINDING_HOST", None),
+        help="Embedding server host URL. If not provided, defaults based on embedding-binding:\n"
+        + "- ollama: http://localhost:11434\n"
+        + "- lollms: http://localhost:9600\n"
+        + "- openai: https://api.openai.com/v1",
     )
 
     default_embedding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
@@ -383,14 +390,14 @@ def parse_args() -> argparse.Namespace:
 
     parser.add_argument(
         "--chunk_size",
-        default=1200,
-        help="chunk token size default 1200",
+        default=get_env_value("CHUNK_SIZE", 1200),
+        help="chunk chunk size default 1200",
     )
 
     parser.add_argument(
         "--chunk_overlap_size",
-        default=100,
-        help="chunk token size default 1200",
+        default=get_env_value("CHUNK_OVERLAP_SIZE", 100),
+        help="chunk overlap size default 100",
     )
 
     def timeout_type(value):
@@ -470,6 +477,13 @@ def parse_args() -> argparse.Namespace:
         help="Enable automatic scanning when the program starts",
     )
 
+    parser.add_argument(
+        "--history-turns",
+        type=int,
+        default=get_env_value("HISTORY_TURNS", 3, int),
+        help="Number of conversation history turns to include (default: from env or 3)",
+    )
+
     args = parser.parse_args()
 
     return args
@@ -634,8 +648,7 @@ def get_api_key_dependency(api_key: Optional[str]):
 
 
 def create_app(args):
-    # Verify that bindings arer correctly setup
-
+    # Verify that bindings are correctly setup
     if args.llm_binding not in [
         "lollms",
         "ollama",
@@ -648,6 +661,13 @@ def create_app(args):
     if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai"]:
         raise Exception("embedding binding not supported")
 
+    # Set default hosts if not provided
+    if args.llm_binding_host is None:
+        args.llm_binding_host = get_default_host(args.llm_binding)
+
+    if args.embedding_binding_host is None:
+        args.embedding_binding_host = get_default_host(args.embedding_binding)
+
     # Add SSL validation
     if args.ssl:
         if not args.ssl_certfile or not args.ssl_keyfile:
@@ -1442,7 +1462,10 @@ def create_app(args):
 
     @app.post("/api/generate")
     async def generate(raw_request: Request, request: OllamaGenerateRequest):
-        """Handle generate completion requests"""
+        """Handle generate completion requests
+        For compatiblity purpuse, the request is not processed by LightRAG,
+        and will be handled by underlying LLM model.
+        """
         try:
             query = request.prompt
             start_time = time.time_ns()
@@ -1581,15 +1604,22 @@ def create_app(args):
 
     @app.post("/api/chat")
     async def chat(raw_request: Request, request: OllamaChatRequest):
-        """Handle chat completion requests"""
+        """Process chat completion requests.
+        Routes user queries through LightRAG by selecting query mode based on prefix indicators.
+        Detects and forwards OpenWebUI session-related requests (for meta data generation task) directly to LLM.
+        """
         try:
             # Get all messages
             messages = request.messages
             if not messages:
                 raise HTTPException(status_code=400, detail="No messages provided")
 
-            # Get the last message as query
+            # Get the last message as query and previous messages as history
             query = messages[-1].content
+            # Convert OllamaMessage objects to dictionaries
+            conversation_history = [
+                {"role": msg.role, "content": msg.content} for msg in messages[:-1]
+            ]
 
             # Check for query prefix
             cleaned_query, mode = parse_query_mode(query)
@@ -1597,9 +1627,17 @@ def create_app(args):
             start_time = time.time_ns()
             prompt_tokens = estimate_tokens(cleaned_query)
 
-            query_param = QueryParam(
-                mode=mode, stream=request.stream, only_need_context=False
-            )
+            param_dict = {
+                "mode": mode,
+                "stream": request.stream,
+                "only_need_context": False,
+                "conversation_history": conversation_history,
+            }
+
+            if args.history_turns is not None:
+                param_dict["history_turns"] = args.history_turns
+
+            query_param = QueryParam(**param_dict)
 
             if request.stream:
                 from fastapi.responses import StreamingResponse

lightrag/operate.py

@@ -633,11 +633,8 @@ async def kg_query(
     # Process conversation history
     history_context = ""
     if query_param.conversation_history:
-        recent_history = query_param.conversation_history[
-            -query_param.history_window_size :
-        ]
-        history_context = "\n".join(
-            [f"{turn['role']}: {turn['content']}" for turn in recent_history]
+        history_context = get_conversation_turns(
+            query_param.conversation_history, query_param.history_turns
         )
 
     sys_prompt_temp = PROMPTS["rag_response"]