feat(docker): containerize application and add TTS integration
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gameloader
195
haystack_rag/rag_pipeline.py
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195
haystack_rag/rag_pipeline.py
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# rag_pipeline.py
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from haystack import Pipeline
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from haystack import Document # 导入 Document
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from milvus_haystack import MilvusDocumentStore
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from .data_handling import initialize_milvus_lite
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from .embedding import initialize_text_embedder
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from .retrieval import initialize_vector_retriever
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from .llm_integration import initialize_llm_and_prompt_builder
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from haystack.utils import Secret
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def build_rag_pipeline(user_id: str) -> tuple[Pipeline, MilvusDocumentStore]:
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"""
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为指定用户构建并返回 RAG 查询管道和对应的 DocumentStore。
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"""
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print(f"\n--- Building RAG Pipeline for User: {user_id} ---")
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# 1. 初始化该用户的 DocumentStore
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document_store = initialize_milvus_lite(user_id)
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# 2. 初始化共享组件（可以在应用启动时初始化一次，这里为简单起见每次都创建）
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text_embedder = initialize_text_embedder()
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vector_retriever = initialize_vector_retriever(document_store)
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llm, prompt_builder = initialize_llm_and_prompt_builder()
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# 3. 创建 Haystack Pipeline
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rag_pipeline = Pipeline()
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# 4. 向管道添加组件，并指定名称
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rag_pipeline.add_component(instance=text_embedder, name="text_embedder")
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rag_pipeline.add_component(instance=vector_retriever, name="retriever")
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rag_pipeline.add_component(instance=prompt_builder, name="prompt_builder")
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rag_pipeline.add_component(instance=llm, name="llm")
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# 5. 连接管道组件
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# - 将用户问题文本输入到 text_embedder
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# - 将 text_embedder 输出的嵌入向量连接到 retriever 的查询嵌入输入
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# - 将 retriever 输出的文档连接到 prompt_builder 的文档输入
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# - 将用户问题文本也连接到 prompt_builder 的问题输入
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# - 将 prompt_builder 输出的完整提示连接到 llm 的提示输入
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rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
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rag_pipeline.connect("prompt_builder.prompt", "llm.prompt")
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print("--- RAG Pipeline Built Successfully ---")
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# 返回管道和文档存储实例，因为主程序需要用文档存储来写入数据
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return rag_pipeline, document_store
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# --- Corrected Test Block ---
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if __name__ == "__main__":
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import os # Needed for API Key check
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# We need OpenAIDocumentEmbedder to index test documents
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from haystack.components.embedders import OpenAIDocumentEmbedder
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# Import necessary config for initializing the Document Embedder
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from config import (
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OPENAI_API_KEY_FROM_CONFIG,
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OPENAI_API_BASE_URL_CONFIG,
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OPENAI_EMBEDDING_MODEL,
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)
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# --- Configuration ---
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test_user = "test_user"
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test_query = "Haystack是什么？"
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# Sample documents to index for testing
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docs_to_index = [
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Document(
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content="Haystack是一个用于构建 NLP 应用程序（如问答系统、语义搜索）的开源框架。",
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meta={"user_id": test_user, "source": "test_doc_1"},
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),
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Document(
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content="你可以使用 Haystack 连接不同的组件，如文档存储、检索器和生成器。",
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meta={"user_id": test_user, "source": "test_doc_2"},
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),
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Document(
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content="Milvus 是一个流行的向量数据库，常用于 RAG 系统中存储嵌入。",
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meta={"user_id": test_user, "source": "test_doc_3"},
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),
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]
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print(f"--- Running Test for RAG Pipeline (User: {test_user}) ---")
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# --- 1. Check API Key Availability ---
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# Pipeline execution requires OpenAI API calls
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api_key_configured = (
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OPENAI_API_KEY_FROM_CONFIG and "YOUR_API_KEY" not in OPENAI_API_KEY_FROM_CONFIG
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)
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if not api_key_configured:
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print("\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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print("! WARNING: OpenAI API Key not configured in config.py. !")
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print("! Skipping RAG pipeline test execution. !")
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print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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exit() # Exit script if key is missing for test run
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else:
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print("\n[Test Setup] OpenAI API Key found in config.")
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# --- 2. Build the RAG Pipeline and get the Document Store ---
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# This function initializes the store (potentially dropping old data)
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# and builds the *querying* pipeline.
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try:
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pipeline, store = build_rag_pipeline(test_user)
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except Exception as e:
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print(f"\nError building RAG pipeline: {e}")
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import traceback
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traceback.print_exc()
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exit()
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# --- 3. Index Test Documents (with embeddings) ---
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print("\n[Test Setup] Initializing Document Embedder for indexing test data...")
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try:
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# Initialize the Document Embedder directly here for the test
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document_embedder = OpenAIDocumentEmbedder(
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api_key=Secret.from_token(OPENAI_API_KEY_FROM_CONFIG),
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model=OPENAI_EMBEDDING_MODEL,
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api_base_url=OPENAI_API_BASE_URL_CONFIG,
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)
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print("[Test Setup] Document Embedder initialized.")
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print("[Test Setup] Embedding test documents...")
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embedding_result = document_embedder.run(docs_to_index)
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embedded_docs = embedding_result.get("documents", [])
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if embedded_docs:
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print(
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f"[Test Setup] Writing {len(embedded_docs)} embedded documents to Milvus..."
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)
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store.write_documents(embedded_docs)
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print("[Test Setup] Test documents written successfully.")
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# Optional: Verify count
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# print(f"[Test Setup] Document count in store: {store.count_documents()}")
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documents_indexed = True
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else:
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print("[Test Setup] ERROR: Failed to embed test documents.")
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documents_indexed = False
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except Exception as e:
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print(f"\nError during test data indexing: {e}")
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import traceback
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traceback.print_exc()
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documents_indexed = False
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# --- 4. Run the RAG Pipeline (if setup succeeded) ---
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if documents_indexed:
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print(f"\n[Test Run] Running RAG pipeline for query: '{test_query}'")
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# Prepare input for the RAG pipeline instance built by build_rag_pipeline
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pipeline_input = {
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"text_embedder": {"text": test_query}, # Input for the query embedder
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"prompt_builder": {
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"query": test_query
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}, # Input for the prompt builder template
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}
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try:
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results = pipeline.run(pipeline_input)
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print("\n[Test Run] Pipeline Results:")
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# Process and print the generator's answer
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if "llm" in results and results["llm"]["replies"]:
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answer = results["llm"]["replies"][0]
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print(f"\nGenerated Answer: {answer}")
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else:
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print("\n[Test Run] Could not extract answer from generator.")
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print(
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"Full Pipeline Output:", results
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) # Print full output for debugging
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except Exception as e:
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print(f"\n[Test Run] Error running RAG pipeline: {e}")
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import traceback
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traceback.print_exc()
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else:
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print("\n[Test Run] Skipping RAG pipeline execution due to indexing failure.")
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# --- 5. Cleanup Note ---
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# Optional: Add instructions or commented-out code for cleaning up the test Milvus data
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print(
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f"\n[Test Cleanup] Test finished. Consider manually removing data in: ./milvus_user_data_openai_fixed/{test_user}"
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)
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# import shutil
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# from pathlib import Path
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# from config import MILVUS_PERSIST_BASE_DIR
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# test_db_path = MILVUS_PERSIST_BASE_DIR / test_user
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# if test_db_path.exists():
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# print(f"\nAttempting to clean up test data at {test_db_path}...")
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# # shutil.rmtree(test_db_path) # Use with caution
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print("\n--- RAG Pipeline Test Complete ---")
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