59e89772de
Remove legacy storage implementations and deprecated examples: - Delete FAISS, JSON, Memgraph, Milvus, MongoDB, Nano Vector DB, Neo4j, NetworkX, Qdrant, Redis storage backends - Remove Kubernetes deployment manifests and installation scripts - Delete unofficial examples for deprecated backends and offline deployment docs Streamline core infrastructure: - Consolidate storage layer to PostgreSQL-only implementation - Add full-text search caching with FTS cache module - Implement metrics collection and monitoring pipeline - Add explain and metrics API routes Modernize frontend and tooling: - Switch web UI to Bun with bun.lock, remove npm and pnpm lockfiles - Update Dockerfile for PostgreSQL-only deployment - Add Makefile for common development tasks - Update environment and configuration examples Enhance evaluation and testing capabilities: - Add prompt optimization with DSPy and auto-tuning - Implement ground truth regeneration and variant testing - Add prompt debugging and response comparison utilities - Expand test coverage with new integration scenarios Simplify dependencies and configuration: - Remove offline-specific requirement files - Update pyproject.toml with streamlined dependencies - Add Python version pinning with .python-version - Create project guidelines in CLAUDE.md and AGENTS.md
140 lines
4 KiB
Python
140 lines
4 KiB
Python
import asyncio
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import logging
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import os
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import numpy as np
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from dotenv import load_dotenv
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from openai import AzureOpenAI
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from lightrag import LightRAG, QueryParam
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from lightrag.utils import EmbeddingFunc
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logging.basicConfig(level=logging.INFO)
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load_dotenv()
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AZURE_OPENAI_API_VERSION = os.getenv('AZURE_OPENAI_API_VERSION')
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AZURE_OPENAI_DEPLOYMENT = os.getenv('AZURE_OPENAI_DEPLOYMENT')
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AZURE_OPENAI_API_KEY = os.getenv('AZURE_OPENAI_API_KEY')
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AZURE_OPENAI_ENDPOINT = os.getenv('AZURE_OPENAI_ENDPOINT')
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AZURE_EMBEDDING_DEPLOYMENT = os.getenv('AZURE_EMBEDDING_DEPLOYMENT')
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AZURE_EMBEDDING_API_VERSION = os.getenv('AZURE_EMBEDDING_API_VERSION')
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# Ensure all required environment variables are set
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if not AZURE_OPENAI_API_VERSION:
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raise ValueError('AZURE_OPENAI_API_VERSION is not set')
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if not AZURE_OPENAI_DEPLOYMENT:
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raise ValueError('AZURE_OPENAI_DEPLOYMENT is not set')
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if not AZURE_OPENAI_API_KEY:
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raise ValueError('AZURE_OPENAI_API_KEY is not set')
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if not AZURE_OPENAI_ENDPOINT:
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raise ValueError('AZURE_OPENAI_ENDPOINT is not set')
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if not AZURE_EMBEDDING_DEPLOYMENT:
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raise ValueError('AZURE_EMBEDDING_DEPLOYMENT is not set')
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if not AZURE_EMBEDDING_API_VERSION:
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raise ValueError('AZURE_EMBEDDING_API_VERSION is not set')
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WORKING_DIR = './dickens'
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if os.path.exists(WORKING_DIR):
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import shutil
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shutil.rmtree(WORKING_DIR)
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os.mkdir(WORKING_DIR)
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async def llm_model_func(prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs) -> str:
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if history_messages is None:
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history_messages = []
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client = AzureOpenAI(
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api_key=AZURE_OPENAI_API_KEY,
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api_version=AZURE_OPENAI_API_VERSION,
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azure_endpoint=AZURE_OPENAI_ENDPOINT,
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)
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messages = []
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if system_prompt:
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messages.append({'role': 'system', 'content': system_prompt})
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if history_messages:
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messages.extend(history_messages)
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messages.append({'role': 'user', 'content': prompt})
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chat_completion = client.chat.completions.create(
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model=AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name".
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messages=messages,
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temperature=kwargs.get('temperature', 0),
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top_p=kwargs.get('top_p', 1),
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n=kwargs.get('n', 1),
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)
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return chat_completion.choices[0].message.content
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async def embedding_func(texts: list[str]) -> np.ndarray:
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client = AzureOpenAI(
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api_key=AZURE_OPENAI_API_KEY,
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api_version=AZURE_EMBEDDING_API_VERSION,
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azure_endpoint=AZURE_OPENAI_ENDPOINT,
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)
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embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts)
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embeddings = [item.embedding for item in embedding.data]
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return np.array(embeddings)
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async def test_funcs():
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result = await llm_model_func('How are you?')
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print('Resposta do llm_model_func: ', result)
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result = await embedding_func(['How are you?'])
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print('Resultado do embedding_func: ', result.shape)
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print('Dimensão da embedding: ', result.shape[1])
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asyncio.run(test_funcs())
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embedding_dimension = 3072
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async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=embedding_dimension,
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max_token_size=8192,
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func=embedding_func,
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),
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)
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await rag.initialize_storages() # Auto-initializes pipeline_status
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return rag
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def main():
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rag = asyncio.run(initialize_rag())
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with (
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open('./book_1.txt', encoding='utf-8') as book1,
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open('./book_2.txt', encoding='utf-8') as book2,
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):
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rag.insert([book1.read(), book2.read()])
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query_text = 'What are the main themes?'
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print('Result (Naive):')
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print(rag.query(query_text, param=QueryParam(mode='naive')))
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print('\nResult (Local):')
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print(rag.query(query_text, param=QueryParam(mode='local')))
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print('\nResult (Global):')
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print(rag.query(query_text, param=QueryParam(mode='global')))
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print('\nResult (Hybrid):')
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print(rag.query(query_text, param=QueryParam(mode='hybrid')))
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if __name__ == '__main__':
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main()
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