feat: add graphrag modes

flowsettings.py

@@ -302,7 +302,7 @@
 
 if USE_NANO_GRAPHRAG:
     GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.NanoGraphRAGIndex")
-elif USE_LIGHTRAG:
+if USE_LIGHTRAG:
     GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.LightRAGIndex")
 
 KH_INDEX_TYPES = [

libs/ktem/ktem/assets/css/main.css

@@ -204,13 +204,24 @@ mark {
   right: 15px;
 }
 
+/* prevent overflow of html info panel */
+#html-info-panel {
+  overflow-x: auto !important;
+}
+
 #chat-expand-button {
   position: absolute;
   top: 6px;
   right: -10px;
   z-index: 1;
 }
 
+#save-setting-btn {
+  width: 150px;
+  height: 30px;
+  min-width: 100px !important;
+}
+
 #quick-setting-labels {
   margin-top: 5px;
   margin-bottom: -10px;

libs/ktem/ktem/assets/js/main.js

@@ -21,6 +21,11 @@ function run() {
   let chat_column = document.getElementById("main-chat-bot");
   let conv_column = document.getElementById("conv-settings-panel");
 
+  // move setting close button
+  let setting_tab_nav_bar = document.querySelector("#settings-tab .tab-nav");
+  let setting_close_button = document.getElementById("save-setting-btn");
+  setting_tab_nav_bar.appendChild(setting_close_button);
+
   let default_conv_column_min_width = "min(300px, 100%)";
   conv_column.style.minWidth = default_conv_column_min_width
 

libs/ktem/ktem/index/file/graph/light_graph_index.py

@@ -1,6 +1,6 @@
 from typing import Any
 
-from ..base import BaseFileIndexRetriever
+from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever
 from .graph_index import GraphRAGIndex
 from .lightrag_pipelines import LightRAGIndexingPipeline, LightRAGRetrieverPipeline
 
@@ -12,14 +12,32 @@ def _setup_indexing_cls(self):
     def _setup_retriever_cls(self):
         self._retriever_pipeline_cls = [LightRAGRetrieverPipeline]
 
+    def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing:
+        pipeline = super().get_indexing_pipeline(settings, user_id)
+        # indexing settings
+        prefix = f"index.options.{self.id}."
+        striped_settings = {
+            key[len(prefix) :]: value
+            for key, value in settings.items()
+            if key.startswith(prefix)
+        }
+        # set the prompts
+        pipeline.prompts = striped_settings
+        return pipeline
+
     def get_retriever_pipelines(
         self, settings: dict, user_id: int, selected: Any = None
     ) -> list["BaseFileIndexRetriever"]:
         _, file_ids, _ = selected
+        # retrieval settings
+        prefix = f"index.options.{self.id}."
+        search_type = settings.get(prefix + "search_type", "local")
+
         retrievers = [
             LightRAGRetrieverPipeline(
                 file_ids=file_ids,
                 Index=self._resources["Index"],
+                search_type=search_type,
             )
         ]
 

libs/ktem/ktem/index/file/graph/lightrag_pipelines.py

@@ -70,7 +70,7 @@ async def llm_func(
             if if_cache_return is not None:
                 return if_cache_return["return"]
 
-        output = model(input_messages).text
+        output = (await model.ainvoke(input_messages)).text
 
         print("-" * 50)
         print(output, "\n", "-" * 50)
@@ -220,7 +220,37 @@ def build_graphrag(working_dir, llm_func, embedding_func):
 class LightRAGIndexingPipeline(GraphRAGIndexingPipeline):
     """GraphRAG specific indexing pipeline"""
 
+    prompts: dict[str, str] = {}
+
+    @classmethod
+    def get_user_settings(cls) -> dict:
+        try:
+            from lightrag.prompt import PROMPTS
+
+            blacklist_keywords = ["default", "response", "process"]
+            return {
+                prompt_name: {
+                    "name": f"Prompt for '{prompt_name}'",
+                    "value": content,
+                    "component": "text",
+                }
+                for prompt_name, content in PROMPTS.items()
+                if all(
+                    keyword not in prompt_name.lower() for keyword in blacklist_keywords
+                )
+            }
+        except ImportError as e:
+            print(e)
+            return {}
+
     def call_graphrag_index(self, graph_id: str, docs: list[Document]):
+        from lightrag.prompt import PROMPTS
+
+        # modify the prompt if it is set in the settings
+        for prompt_name, content in self.prompts.items():
+            if prompt_name in PROMPTS:
+                PROMPTS[prompt_name] = content
+
         _, input_path = prepare_graph_index_path(graph_id)
         input_path.mkdir(parents=True, exist_ok=True)
 
@@ -302,6 +332,19 @@ class LightRAGRetrieverPipeline(BaseFileIndexRetriever):
 
     Index = Param(help="The SQLAlchemy Index table")
     file_ids: list[str] = []
+    search_type: str = "local"
+
+    @classmethod
+    def get_user_settings(cls) -> dict:
+        return {
+            "search_type": {
+                "name": "Search type",
+                "value": "local",
+                "choices": ["local", "global", "hybrid"],
+                "component": "dropdown",
+                "info": "Whether to use local or global search in the graph.",
+            }
+        }
 
     def _build_graph_search(self):
         file_id = self.file_ids[0]
@@ -326,7 +369,8 @@ def _build_graph_search(self):
             llm_func=llm_func,
             embedding_func=embedding_func,
         )
-        query_params = QueryParam(mode="local", only_need_context=True)
+        print("search_type", self.search_type)
+        query_params = QueryParam(mode=self.search_type, only_need_context=True)
 
         return graphrag_func, query_params
 
@@ -381,20 +425,40 @@ def run(
             return []
 
         graphrag_func, query_params = self._build_graph_search()
-        entities, relationships, sources = asyncio.run(
-            lightrag_build_local_query_context(graphrag_func, text, query_params)
-        )
 
-        documents = self.format_context_records(entities, relationships, sources)
-        plot = self.plot_graph(relationships)
-
-        return documents + [
-            RetrievedDocument(
-                text="",
-                metadata={
-                    "file_name": "GraphRAG",
-                    "type": "plot",
-                    "data": plot,
-                },
-            ),
-        ]
+        # only local mode support graph visualization
+        if query_params.mode == "local":
+            entities, relationships, sources = asyncio.run(
+                lightrag_build_local_query_context(graphrag_func, text, query_params)
+            )
+            documents = self.format_context_records(entities, relationships, sources)
+            plot = self.plot_graph(relationships)
+            documents += [
+                RetrievedDocument(
+                    text="",
+                    metadata={
+                        "file_name": "GraphRAG",
+                        "type": "plot",
+                        "data": plot,
+                    },
+                ),
+            ]
+        else:
+            context = graphrag_func.query(text, query_params)
+
+            # account for missing ``` for closing code block
+            context += "\n```"
+
+            documents = [
+                RetrievedDocument(
+                    text=context,
+                    metadata={
+                        "file_name": "GraphRAG {} Search".format(
+                            query_params.mode.capitalize()
+                        ),
+                        "type": "table",
+                    },
+                )
+            ]
+
+        return documents

libs/ktem/ktem/index/file/graph/nano_graph_index.py

@@ -1,6 +1,6 @@
 from typing import Any
 
-from ..base import BaseFileIndexRetriever
+from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever
 from .graph_index import GraphRAGIndex
 from .nano_pipelines import NanoGraphRAGIndexingPipeline, NanoGraphRAGRetrieverPipeline
 
@@ -12,14 +12,32 @@ def _setup_indexing_cls(self):
     def _setup_retriever_cls(self):
         self._retriever_pipeline_cls = [NanoGraphRAGRetrieverPipeline]
 
+    def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing:
+        pipeline = super().get_indexing_pipeline(settings, user_id)
+        # indexing settings
+        prefix = f"index.options.{self.id}."
+        striped_settings = {
+            key[len(prefix) :]: value
+            for key, value in settings.items()
+            if key.startswith(prefix)
+        }
+        # set the prompts
+        pipeline.prompts = striped_settings
+        return pipeline
+
     def get_retriever_pipelines(
         self, settings: dict, user_id: int, selected: Any = None
     ) -> list["BaseFileIndexRetriever"]:
         _, file_ids, _ = selected
+        # retrieval settings
+        prefix = f"index.options.{self.id}."
+        search_type = settings.get(prefix + "search_type", "local")
+
         retrievers = [
             NanoGraphRAGRetrieverPipeline(
                 file_ids=file_ids,
                 Index=self._resources["Index"],
+                search_type=search_type,
             )
         ]