add milvus vector store

TODO

@@ -1 +1,10 @@
-RAG忘了触发保存了！
+RAG忘了触发保存了！
+
+
+刘博寅： 用llama index 实现 RAG 文档向量化
+    RAG代码参考：
+        crazy_functions/rag_fns/llama_index_worker.py
+        crazy_functions/rag_fns/milvus_worker.py
+        crazy_functions/rag_fns/vector_store_index.py
+    读取文件的代码参考（使用glob）：
+        crazy_functions/SourceCode_Analyse.py

crazy_functions/Rag_Interface.py

@@ -1,7 +1,14 @@
 from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
+
+VECTOR_STORE_TYPE = "Milvus"
+
+if VECTOR_STORE_TYPE == "Simple":
+    from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
+if VECTOR_STORE_TYPE == "Milvus":
+    from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
+
 
 RAG_WORKER_REGISTER = {}
 

crazy_functions/rag_fns/llama_index_worker.py

@@ -1,4 +1,7 @@
 import llama_index
+import os
+import atexit
+from typing import List
 from llama_index.core import Document
 from llama_index.core.schema import TextNode
 from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
@@ -38,6 +41,7 @@ class SaveLoad():
         return True
 
     def save_to_checkpoint(self, checkpoint_dir=None):
+        print(f'saving vector store to: {checkpoint_dir}')
         if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
         self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
 
@@ -65,7 +69,8 @@ class LlamaIndexRagWorker(SaveLoad):
         if auto_load_checkpoint:
             self.vs_index = self.load_from_checkpoint(checkpoint_dir)
         else:
-            self.vs_index = self.create_new_vs()
+            self.vs_index = self.create_new_vs(checkpoint_dir)
+        atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
 
     def assign_embedding_model(self):
         pass
@@ -117,6 +122,3 @@ class LlamaIndexRagWorker(SaveLoad):
         buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
         if self.debug_mode: print(buf)
         return buf
-
-
-

crazy_functions/rag_fns/milvus_worker.py

@@ -0,0 +1,94 @@
+import llama_index
+import os
+import atexit
+from typing import List
+from llama_index.core import Document
+from llama_index.core.schema import TextNode
+from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
+from shared_utils.connect_void_terminal import get_chat_default_kwargs
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
+from llama_index.core.ingestion import run_transformations
+from llama_index.core import PromptTemplate
+from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.core import StorageContext
+from llama_index.vector_stores.milvus import MilvusVectorStore
+from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
+
+DEFAULT_QUERY_GENERATION_PROMPT = """\
+Now, you have context information as below:
+---------------------
+{context_str}
+---------------------
+Answer the user request below (use the context information if necessary, otherwise you can ignore them):
+---------------------
+{query_str}
+"""
+
+QUESTION_ANSWER_RECORD = """\
+{{
+    "type": "This is a previous conversation with the user",
+    "question": "{question}",
+    "answer": "{answer}",
+}}
+"""
+
+
+class MilvusSaveLoad():
+
+    def does_checkpoint_exist(self, checkpoint_dir=None):
+        import os, glob
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if not os.path.exists(checkpoint_dir): return False
+        if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
+        return True
+
+    def save_to_checkpoint(self, checkpoint_dir=None):
+        print(f'saving vector store to: {checkpoint_dir}')
+        # if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        # self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
+
+    def load_from_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
+            print('loading checkpoint from disk')
+            from llama_index.core import StorageContext, load_index_from_storage
+            storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
+            try:
+                self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
+                return self.vs_index
+            except:
+                return self.create_new_vs(checkpoint_dir)
+        else:
+            return self.create_new_vs(checkpoint_dir)
+
+    def create_new_vs(self, checkpoint_dir):
+        vector_store = MilvusVectorStore(
+            uri=os.path.join(checkpoint_dir, "milvus_demo.db"), 
+            dim=self.embed_model.embedding_dimension()
+        )
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+        index = GptacVectorStoreIndex.default_vector_store(storage_context=storage_context, embed_model=self.embed_model)
+        return index
+
+
+class MilvusRagWorker(LlamaIndexRagWorker):
+
+
+    def inspect_vector_store(self):
+        # This function is for debugging
+        try:
+            self.vs_index.storage_context.index_store.to_dict()
+            docstore = self.vs_index.storage_context.docstore.docs
+            if not docstore.items():
+                raise ValueError("cannot inspect")
+            vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
+        except:
+            dummy_retrieve_res: List["NodeWithScore"] = self.vs_index.as_retriever().retrieve(' ')
+            vector_store_preview = "\n".join(
+                [f"{node.id_} | {node.text}" for node in dummy_retrieve_res]
+            )
+        print('\n++ --------inspect_vector_store begin--------')
+        print(vector_store_preview)
+        print('oo --------inspect_vector_store end--------')
+        return vector_store_preview

request_llms/embed_models/openai_embed.py

@@ -71,7 +71,13 @@ class OpenAiEmbeddingModel(EmbeddingModel):
             embedding = res.data[0].embedding
         return embedding
 
-    def embedding_dimension(self, llm_kwargs):
+    def embedding_dimension(self, llm_kwargs=None):
+        # load kwargs
+        if llm_kwargs is None:
+            llm_kwargs = self.llm_kwargs
+        if llm_kwargs is None:
+            raise RuntimeError("llm_kwargs is not provided!")
+
         from .bridge_all_embed import embed_model_info
         return embed_model_info[llm_kwargs['embed_model']]['embed_dimension']
 

requirements.txt

@@ -7,7 +7,9 @@ tiktoken>=0.3.3
 requests[socks]
 pydantic==2.5.2
 llama-index==0.10.47
-protobuf==3.18
+llama-index-vector-stores-milvus==0.1.16
+pymilvus==2.4.2
+protobuf==3.20
 transformers>=4.27.1,<4.42
 scipdf_parser>=0.52
 anthropic>=0.18.1