feat: add MiniRAG support

flowsettings.py

@@ -318,6 +318,7 @@ SETTINGS_REASONING = {
 }
 
 USE_NANO_GRAPHRAG = config("USE_NANO_GRAPHRAG", default=False, cast=bool)
+USE_MINIRAG = config("USE_MINIRAG", default=False, cast=bool)
 USE_LIGHTRAG = config("USE_LIGHTRAG", default=True, cast=bool)
 USE_MS_GRAPHRAG = config("USE_MS_GRAPHRAG", default=True, cast=bool)
 
@@ -329,6 +330,8 @@ if USE_NANO_GRAPHRAG:
     GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.NanoGraphRAGIndex")
 if USE_LIGHTRAG:
     GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.LightRAGIndex")
+if USE_MINIRAG:
+    GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.MiniRAGIndex")
 
 KH_INDEX_TYPES = [
     "ktem.index.file.FileIndex",

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

@@ -1,5 +1,6 @@
 from .graph_index import GraphRAGIndex
 from .light_graph_index import LightRAGIndex
+from .mini_graph_index import MiniRAGIndex
 from .nano_graph_index import NanoGraphRAGIndex
 
-__all__ = ["GraphRAGIndex", "NanoGraphRAGIndex", "LightRAGIndex"]
+__all__ = ["GraphRAGIndex", "NanoGraphRAGIndex", "LightRAGIndex", "MiniRAGIndex"]

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

@@ -0,0 +1,44 @@
+from typing import Any
+
+from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever
+from .graph_index import GraphRAGIndex
+from .minirag_pipelines import MiniRAGIndexingPipeline, MiniRAGRetrieverPipeline
+
+
+class MiniRAGIndex(GraphRAGIndex):
+    def _setup_indexing_cls(self):
+        self._indexing_pipeline_cls = MiniRAGIndexingPipeline
+
+    def _setup_retriever_cls(self):
+        self._retriever_pipeline_cls = [MiniRAGRetrieverPipeline]
+
+    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 = [
+            MiniRAGRetrieverPipeline(
+                file_ids=file_ids,
+                Index=self._resources["Index"],
+                search_type=search_type,
+            )
+        ]
+
+        return retrievers

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

@@ -0,0 +1,340 @@
+import glob
+import logging
+import os
+from pathlib import Path
+from typing import Generator
+
+import numpy as np
+from ktem.db.models import engine
+from ktem.embeddings.manager import embedding_models_manager as embeddings
+from ktem.llms.manager import llms
+from sqlalchemy.orm import Session
+from tenacity import (
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+from theflow.settings import settings
+
+from kotaemon.base import Document, Param, RetrievedDocument
+from kotaemon.base.schema import AIMessage, HumanMessage, SystemMessage
+
+from ..pipelines import BaseFileIndexRetriever
+from .pipelines import GraphRAGIndexingPipeline
+
+try:
+    from minirag import MiniRAG, QueryParam
+    from minirag.utils import EmbeddingFunc, compute_args_hash
+
+except ImportError:
+    print(
+        (
+            "MiniRAG dependencies not installed. "
+            "Try `pip install git+https://github.com/HKUDS/MiniRAG.git` to install. "
+            "MiniRAG retriever pipeline will not work properly."
+        )
+    )
+
+
+logging.getLogger("minirag").setLevel(logging.INFO)
+
+
+filestorage_path = Path(settings.KH_FILESTORAGE_PATH) / "minirag"
+filestorage_path.mkdir(parents=True, exist_ok=True)
+
+INDEX_BATCHSIZE = 4
+
+
+def get_llm_func(model):
+    @retry(
+        stop=stop_after_attempt(3),
+        wait=wait_exponential(multiplier=1, min=4, max=10),
+        retry=retry_if_exception_type((Exception,)),
+        after=lambda retry_state: logging.warning(
+            f"LLM API call attempt {retry_state.attempt_number} failed. Retrying..."
+        ),
+    )
+    async def _call_model(model, input_messages):
+        return (await model.ainvoke(input_messages)).text
+
+    async def llm_func(
+        prompt, system_prompt=None, history_messages=[], **kwargs
+    ) -> str:
+        input_messages = [SystemMessage(text=system_prompt)] if system_prompt else []
+
+        hashing_kv = kwargs.pop("hashing_kv", None)
+        if history_messages:
+            for msg in history_messages:
+                if msg.get("role") == "user":
+                    input_messages.append(HumanMessage(text=msg["content"]))
+                else:
+                    input_messages.append(AIMessage(text=msg["content"]))
+
+        input_messages.append(HumanMessage(text=prompt))
+
+        if hashing_kv is not None:
+            args_hash = compute_args_hash("model", input_messages)
+            if_cache_return = await hashing_kv.get_by_id(args_hash)
+            if if_cache_return is not None:
+                return if_cache_return["return"]
+
+        print("-" * 50)
+        print(prompt, "\n", "-" * 50)
+
+        try:
+            output = await _call_model(model, input_messages)
+        except Exception as e:
+            logging.error(f"Failed to call LLM API after 3 retries: {str(e)}")
+            raise
+
+        print("-" * 50)
+        print(prompt, "\n", "-" * 50)
+        print(output, "\n", "-" * 50)
+
+        if hashing_kv is not None:
+            await hashing_kv.upsert({args_hash: {"return": output, "model": "model"}})
+
+        return output
+
+    return llm_func
+
+
+def get_embedding_func(model):
+    async def embedding_func(texts: list[str]) -> np.ndarray:
+        outputs = model(texts)
+        embedding_outputs = np.array([doc.embedding for doc in outputs])
+
+        return embedding_outputs
+
+    return embedding_func
+
+
+def get_default_models_wrapper():
+    # setup model functions
+    default_embedding = embeddings.get_default()
+    default_embedding_dim = len(default_embedding(["Hi"])[0].embedding)
+    embedding_func = EmbeddingFunc(
+        embedding_dim=default_embedding_dim,
+        max_token_size=8192,
+        func=get_embedding_func(default_embedding),
+    )
+    print("GraphRAG embedding dim", default_embedding_dim)
+
+    default_llm = llms.get_default()
+    llm_func = get_llm_func(default_llm)
+
+    return llm_func, embedding_func, default_llm, default_embedding
+
+
+def prepare_graph_index_path(graph_id: str):
+    root_path = Path(filestorage_path) / graph_id
+    input_path = root_path / "input"
+
+    return root_path, input_path
+
+
+def build_graphrag(working_dir, llm_func, embedding_func):
+    graphrag_func = MiniRAG(
+        working_dir=working_dir,
+        llm_model_func=llm_func,
+        llm_model_max_token_size=2048,
+        embedding_func=embedding_func,
+    )
+    return graphrag_func
+
+
+class MiniRAGIndexingPipeline(GraphRAGIndexingPipeline):
+    """GraphRAG specific indexing pipeline"""
+
+    prompts: dict[str, str] = {}
+
+    @classmethod
+    def get_user_settings(cls) -> dict:
+        try:
+            from minirag.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
+                )
+                and isinstance(content, str)
+            }
+        except ImportError as e:
+            print(e)
+            return {}
+
+    def call_graphrag_index(self, graph_id: str, docs: list[Document]):
+        from minirag.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)
+
+        (
+            llm_func,
+            embedding_func,
+            default_llm,
+            default_embedding,
+        ) = get_default_models_wrapper()
+        print(
+            f"Indexing GraphRAG with LLM {default_llm} "
+            f"and Embedding {default_embedding}..."
+        )
+
+        all_docs = [
+            doc.text
+            for doc in docs
+            if doc.metadata.get("type", "text") == "text" and len(doc.text.strip()) > 0
+        ]
+
+        yield Document(
+            channel="debug",
+            text="[GraphRAG] Creating index... This can take a long time.",
+        )
+
+        # remove all .json files in the input_path directory (previous cache)
+        json_files = glob.glob(f"{input_path}/*.json")
+        for json_file in json_files:
+            os.remove(json_file)
+
+        # indexing
+        graphrag_func = build_graphrag(
+            input_path,
+            llm_func=llm_func,
+            embedding_func=embedding_func,
+        )
+        # output must be contain: Loaded graph from
+        # ..input/graph_chunk_entity_relation.graphml with xxx nodes, xxx edges
+        total_docs = len(all_docs)
+        process_doc_count = 0
+        yield Document(
+            channel="debug",
+            text=f"[GraphRAG] Indexed {process_doc_count} / {total_docs} documents.",
+        )
+
+        for doc_id in range(0, len(all_docs), INDEX_BATCHSIZE):
+            cur_docs = all_docs[doc_id : doc_id + INDEX_BATCHSIZE]
+            combined_doc = "\n".join(cur_docs)
+
+            graphrag_func.insert(combined_doc)
+            process_doc_count += len(cur_docs)
+            yield Document(
+                channel="debug",
+                text=(
+                    f"[GraphRAG] Indexed {process_doc_count} "
+                    f"/ {total_docs} documents."
+                ),
+            )
+
+        yield Document(
+            channel="debug",
+            text="[GraphRAG] Indexing finished.",
+        )
+
+    def stream(
+        self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs
+    ) -> Generator[
+        Document, None, tuple[list[str | None], list[str | None], list[Document]]
+    ]:
+        file_ids, errors, all_docs = yield from super().stream(
+            file_paths, reindex=reindex, **kwargs
+        )
+
+        return file_ids, errors, all_docs
+
+
+class MiniRAGRetrieverPipeline(BaseFileIndexRetriever):
+    """GraphRAG specific retriever pipeline"""
+
+    Index = Param(help="The SQLAlchemy Index table")
+    file_ids: list[str] = []
+    search_type: str = "mini"
+
+    @classmethod
+    def get_user_settings(cls) -> dict:
+        return {
+            "search_type": {
+                "name": "Search type",
+                "value": "mini",
+                "choices": ["mini", "light"],
+                "component": "dropdown",
+                "info": "Search type in the graph.",
+            }
+        }
+
+    def _build_graph_search(self):
+        file_id = self.file_ids[0]
+
+        # retrieve the graph_id from the index
+        with Session(engine) as session:
+            graph_id = (
+                session.query(self.Index.target_id)
+                .filter(self.Index.source_id == file_id)
+                .filter(self.Index.relation_type == "graph")
+                .first()
+            )
+            graph_id = graph_id[0] if graph_id else None
+            assert graph_id, f"GraphRAG index not found for file_id: {file_id}"
+
+        _, input_path = prepare_graph_index_path(graph_id)
+        input_path.mkdir(parents=True, exist_ok=True)
+
+        llm_func, embedding_func, _, _ = get_default_models_wrapper()
+        graphrag_func = build_graphrag(
+            input_path,
+            llm_func=llm_func,
+            embedding_func=embedding_func,
+        )
+        print("search_type", self.search_type)
+        query_params = QueryParam(mode=self.search_type, only_need_context=True)
+
+        return graphrag_func, query_params
+
+    def _to_document(self, header: str, context_text: str) -> RetrievedDocument:
+        return RetrievedDocument(
+            text=context_text,
+            metadata={
+                "file_name": header,
+                "type": "table",
+                "llm_trulens_score": 1.0,
+            },
+            score=1.0,
+        )
+
+    def run(
+        self,
+        text: str,
+    ) -> list[RetrievedDocument]:
+        if not self.file_ids:
+            return []
+
+        graphrag_func, query_params = self._build_graph_search()
+
+        # only support non-graph visualization for now
+        context = graphrag_func.query(text, query_params)
+
+        documents = [
+            RetrievedDocument(
+                text=context,
+                metadata={
+                    "file_name": "GraphRAG {} Search".format(
+                        query_params.mode.capitalize()
+                    ),
+                    "type": "table",
+                },
+            )
+        ]
+
+        return documents