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| # | |
| # Memory Indexing Utility for LlamaIndex | |
| # | |
| # This script provides functionalities to build and manage memory indices for a conversational AI | |
| # using the LlamaIndex framework. It supports creating structured memory documents from raw | |
| # conversation data and persisting them as vector indices for efficient retrieval. | |
| # | |
| # --- Standard Library Imports --- | |
| import json | |
| import os | |
| # --- Third-Party Imports --- | |
| import tiktoken | |
| from llama_index.core import ( | |
| Document, | |
| PromptHelper, | |
| Settings, # Replaces the deprecated ServiceContext | |
| StorageContext, | |
| VectorStoreIndex, | |
| load_index_from_storage, | |
| ) | |
| from llama_index.llms.openai import OpenAI | |
| # If a specific embedding model is needed (e.g., from OpenAI), uncomment the line below | |
| # from llama_index.embeddings.openai import OpenAIEmbedding | |
| # --- Global Variables --- | |
| # A dictionary to hold the loaded or newly created indices in memory. | |
| index_set = {} | |
| # پشتیبانی هوشمند از Persistent Storage در Hugging Face (/data) یا مسیر محلی | |
| BASE_DIR = "/data" if os.path.exists("/data") else os.path.dirname(os.path.abspath(__file__)) | |
| # تنظیم مسیر دقیق بر اساس ساختار ریپازیتوری (memory_index/llamaindex) | |
| _INDEX_BASE_DIR = os.getenv("EMMA_INDEX_DIR", os.path.join(BASE_DIR, "memory_index", "llamaindex")) | |
| # اطمینان از وجود دایرکتوری اصلی در صورت ساخت ایندکس جدید | |
| os.makedirs(_INDEX_BASE_DIR, exist_ok=True) | |
| # --- Core Functions --- | |
| def setup_global_settings(): | |
| """ | |
| Configures global settings for LlamaIndex (v0.10+). | |
| This function replaces the deprecated `build_service_context` pattern by directly | |
| configuring the global `Settings` object for the LLM, tokenizer, and other components. | |
| """ | |
| # 1. Set Tokenizer (to resolve potential tiktoken errors) | |
| try: | |
| # This encoding is commonly used by OpenAI models like GPT-4. | |
| Settings.tokenizer = tiktoken.get_encoding("cl100k_base").encode | |
| except Exception as e: | |
| print(f"Warning: Could not set the tokenizer. This might lead to issues. Error: {e}") | |
| # 2. Configure the Language Model (LLM) | |
| # Use the LlamaIndex-specific OpenAI class to avoid conflicts with other libraries. | |
| # It automatically reads the API key from the "OPENAI_API_KEY" environment variable. | |
| Settings.llm = OpenAI( | |
| model="gpt-4o", | |
| temperature=1.0, | |
| max_tokens=1024, | |
| api_key=os.environ.get("GAPGPT_API_KEY"), | |
| api_base="https://api.gapgpt.app/v1", | |
| additional_kwargs={ | |
| "top_p": 0.95, | |
| "frequency_penalty": 0.4, | |
| "presence_penalty": 0.2 | |
| } | |
| ) | |
| # 3. Configure the Prompt Helper (with updated parameter names) | |
| # This helps manage context window size, number of outputs, etc. | |
| Settings.prompt_helper = PromptHelper( | |
| context_window=4096, | |
| num_output=256, | |
| chunk_overlap_ratio=0.1 | |
| ) | |
| # 4. Configure Chunk Size for document processing | |
| Settings.chunk_size = 512 | |
| Settings.chunk_overlap = 20 | |
| # Optional: Configure the embedding model if needed. | |
| # If not set, LlamaIndex defaults to a compatible model (e.g., OpenAI's text-embedding-ada-002). | |
| # Settings.embed_model = OpenAIEmbedding(api_key=GAPGPTMASKTOKENlgicihcumgiX0X, api_base=GAPGPTMASKTOKENlgicihcumgiX1X) | |
| def generate_memory_docs(data, language): | |
| """ | |
| Generates structured LlamaIndex Document objects from a nested dictionary of user memories. | |
| This function processes different types of memory (sessions, episodic, semantic) and | |
| formats them into Document objects suitable for indexing. | |
| Args: | |
| data (dict): A dictionary where keys are user names and values contain their memories. | |
| language (str): The language code (e.g., "en"), currently used for formatting. | |
| Returns: | |
| dict: A dictionary where keys are user names and values are dicts containing | |
| lists of Document objects for each memory type. | |
| """ | |
| all_user_memories = {} | |
| for user_name, user_memory in data.items(): | |
| all_user_memories[user_name] = { | |
| "sessions": [], | |
| "episodic_memory": [], | |
| "semantic_memory": [], | |
| } | |
| # Process session history | |
| if "sessions" in user_memory: | |
| for session in user_memory["sessions"]: | |
| date = session["date"] | |
| content = session["conversation"] | |
| memory_str = f"Session on {date}:\n" | |
| for dialog in content: | |
| query = dialog.get("query", "") | |
| response = dialog.get("response", "") | |
| memory_str += f"\n{user_name}: {query.strip()}" | |
| memory_str += f"\nAI: {response.strip()}" | |
| memory_str += "\n" | |
| if "summary" in user_memory and date in user_memory["summary"]: | |
| summary = f'The summary of the conversation on {date} is: {user_memory["summary"][date]}' | |
| memory_str += summary | |
| # Use the 'text' argument for the Document object | |
| all_user_memories[user_name]["sessions"].append(Document(text=memory_str)) | |
| # Process episodic memory | |
| if "episodic_memory" in user_memory: | |
| episodic_str = "Recent experiences:\n" | |
| for event in user_memory["episodic_memory"]: | |
| episodic_str += f"- {event}\n" | |
| all_user_memories[user_name]["episodic_memory"].append(Document(text=episodic_str)) | |
| # Process semantic memory (long-term facts and personality) | |
| if "semantic_memory" in user_memory: | |
| semantic_str = "Long-term personality traits and facts:\n" | |
| if isinstance(user_memory["semantic_memory"], dict): | |
| for trait, value in user_memory["semantic_memory"].items(): | |
| semantic_str += f"- {trait}: {value}\n" | |
| elif isinstance(user_memory["semantic_memory"], list): | |
| for item in user_memory["semantic_memory"]: | |
| semantic_str += f"- {item}\n" | |
| else: | |
| print(f"WARNING: semantic_memory for user '{user_name}' has an unexpected format: {type(user_memory['semantic_memory'])}") | |
| all_user_memories[user_name]["semantic_memory"].append(Document(text=semantic_str)) | |
| return all_user_memories | |
| def build_memory_index(all_user_memories, data_args, name=None): | |
| """ | |
| Build and persist memory indices for each user and memory type. | |
| """ | |
| structured_docs = generate_memory_docs( | |
| all_user_memories, data_args.language | |
| ) | |
| setup_global_settings() | |
| for user_name, memories_by_type in structured_docs.items(): | |
| if name and user_name != name: | |
| continue | |
| print(f"Building indices for user '{user_name}'") | |
| base_path = os.path.join(_INDEX_BASE_DIR, user_name) | |
| path_map = { | |
| "sessions": os.path.join(base_path, "sessions"), | |
| "episodic_memory": os.path.join(base_path, "episodic_memory"), | |
| "semantic_memory": os.path.join(base_path, "semantic_memory"), | |
| } | |
| for memory_type, docs in memories_by_type.items(): | |
| if not docs: | |
| print(f" -> Skipping '{memory_type}' index (no documents found).") | |
| continue | |
| if memory_type not in path_map: | |
| print(f" -> Skipping unknown memory type: '{memory_type}'") | |
| continue | |
| print(f" -> Building '{memory_type}' index...") | |
| cur_index = VectorStoreIndex.from_documents(docs) | |
| index_dir = path_map[memory_type] | |
| os.makedirs(index_dir, exist_ok=True) | |
| cur_index.storage_context.persist(persist_dir=index_dir) | |
| print(f" + Saved '{memory_type}' index to: {index_dir}") | |
| index_set[f"{user_name}_{memory_type}"] = cur_index | |
| # --- Deprecated Functions (Kept for reference) --- | |
| def generate_memory_docs_old(data, language): | |
| """ | |
| DEPRECATED: An older version of the document generation function. | |
| This function had a simpler structure and contained Chinese string literals. | |
| It is replaced by `generate_memory_docs`. | |
| """ | |
| all_user_memories = {} | |
| for user_name, user_memory in data.items(): | |
| all_user_memories[user_name] = [] | |
| if "history" not in user_memory: | |
| continue | |
| for date, content in user_memory["history"].items(): | |
| memory_str = f"Conversation on {date}:" | |
| for dialog in content: | |
| query = dialog.get("query", "") | |
| response = dialog.get("response", "") | |
| memory_str += f"\n{user_name}: {query.strip()}" | |
| memory_str += f"\nAI: {response.strip()}" | |
| memory_str += "\n" | |
| if "summary" in user_memory and date in user_memory["summary"]: | |
| summary = f'The summary of the conversation on {date} is: {user_memory["summary"][date]}' | |
| memory_str += summary | |
| all_user_memories[user_name].append(Document(text=memory_str)) | |
| return all_user_memories | |
| def build_memory_index_old(all_user_memories, data_args, name=None): | |
| """ | |
| DEPRECATED: An older version of the index building function. | |
| This function used a flatter memory structure and is replaced by the more | |
| comprehensive `build_memory_index`. | |
| """ | |
| # The function name is a typo in the original code, corrected here for clarity. | |
| all_user_memories_docs = generate_memory_docs( | |
| all_user_memories, data_args.language | |
| ) | |
| # Apply global settings | |
| setup_global_settings() | |
| for user_name, memories in all_user_memories_docs.items(): | |
| if name and user_name != name: | |
| continue | |
| print(f"Building index for user {user_name} (using old method)") | |
| # The service_context argument is removed in newer versions. | |
| cur_index = VectorStoreIndex.from_documents(memories) | |
| index_set[user_name] = cur_index | |
| # استفاده از مسیر داینامیک و امن | |
| save_dir = os.path.join(_INDEX_BASE_DIR, f"{user_name}_store") | |
| os.makedirs(save_dir, exist_ok=True) | |
| cur_index.storage_context.persist(persist_dir=save_dir) | |