【AI大模型实战】从零搭建RAG应用：跳过LangChain，掌握文本分块、向量检索，零基础小白收藏这一篇就够了！！

本文详细介绍了从零开始实现RAG系统的方法，强调自己实现能完全掌控文档处理、向量化和检索流程，精准定位问题并满足特殊业务需求。文章通过文档解析、文本分块、向量数据库搭建、语义检索、LLM接入等步骤，构建了完整的对话式RAG系统，并提供了多种优化技巧，帮助开发者真正理解RAG工作原理，而非仅依赖框架封装函数。

模型优化师

382人浏览 · 2025-09-17 16:39:27

模型优化师 · 2025-09-17 16:39:27 发布

前言

RAG（检索增强生成）本质上就是给AI模型外挂一个知识库。平常用ChatGPT只能基于训练数据回答问题，但RAG可以让它查阅你的专有文档——不管是内部报告、技术文档还是业务资料，都能成为AI的参考资源。

很多人第一反应是用LangChain或LlamaIndex这些现成框架，确实能快速搭起来。但自己实现的核心价值在于：你能清楚知道文档是怎么被切分的、向量是怎么生成的、检索逻辑具体怎么跑的。

当系统出现检索不准确、回答质量差、成本过高这些问题时，你能精确定位到是哪个环节的问题。比如是分块策略不合适，还是embedding模型选择有问题，或者是检索参数需要调整。用框架的话，很多时候只能盲目调参数，治标不治本。

另外业务场景往往有特殊需求：PDF表格要特殊处理、某些文档类型需要提取特定元数据、检索结果要按业务规则重排序等等。自己实现就能在任何环节做针对性优化，而不是被框架的设计限制住。

下面我们开始一步一步的进行：

文档解析：让机器能读懂你的文件

做RAG第一步是把各种格式的文档统一处理成纯文本。PDF、Word、txt这些常见格式各有各的解析方式。

import os  

import PyPDF2  

import docx  

def load_plain_text(file_path: str) -> str:  

    """Load and return the full contents of a .txt file."""  

    with open(file_path, 'r', encoding='utf-8') as fp:  

        return fp.read()  

def extract_text_from_pdf(file_path: str) -> str:  

    """Read every page of a PDF and stitch the text together."""  

    texts = []  

    with open(file_path, 'rb') as fp:  

        reader = PyPDF2.PdfReader(fp)  

        for pg in reader.pages:  

            # ensure we don't end up with None  

            page_txt = pg.extract_text() or ""  

            texts.append(page_txt)  

    # separate pages with a newline  

    return "\n".join(texts)  

def extract_text_from_docx(file_path: str) -> str:  

    """Grab all paragraphs from a .docx document."""  

    doc = docx.Document(file_path)  

    paras = [p.text for p in doc.paragraphs]  

    return "\n".join(paras)

然后写个统一的路由器，根据文件后缀调用对应的解析函数：

import os  

def load_document(file_path: str):  

    """Load a document's text based on its file extension."""  

    _, extension = os.path.splitext(file_path)  

    extension = extension.lower()  

    if extension == '.txt':  

        return read_text_file(file_path)  

    elif extension == '.pdf':  

        return read_pdf_file(file_path)  

    elif extension == '.docx':  

        return read_docx_file(file_path)  

    else:  

        raise ValueError(f"Unsupported file type: {extension}")

这样设计的好处是：文档结构保持完整（用换行符分隔段落），支持各种大小写的文件扩展名，不支持的格式会给出明确提示。

文本分块：把长文档切成合适的片段

因为有上下文的的限制，所以直接把整篇文档丢给LLM就像让人一口吃下整个pizza——不现实。所以需要把文档切成适当大小的块。

def chunk_sentences(text: str, max_length: int = 500) -> list[str]:  

    """Split text into size-limited chunks, breaking only at sentence boundaries."""  

    # Normalize whitespace and split on basic sentence delimiter  

    segments = text.replace('\n', ' ').split('. ')  

    blocks = []  

    buffer = []  

    buffer_len = 0  

    for segment in segments:  

        seg = segment.strip()  

        if not seg:  

            continue  # skip empty strings  

        # Make sure each segment ends with a period  

        if not seg.endswith('.'):  

            seg += '.'  

        seg_len = len(seg)  

        # If adding this segment would exceed max_length, flush the buffer first  

        if buffer and buffer_len + seg_len > max_length:  

            blocks.append(' '.join(buffer))  

            buffer = [seg]  

            buffer_len = seg_len  

        else:  

            buffer.append(seg)  

            buffer_len += seg_len  

    # Append any remaining sentences  

    if buffer:  

        blocks.append(' '.join(buffer))  

    return blocks

块大小选择是个权衡问题。小块（200-500字符）适合精确匹配，像索引卡一样；中等块（500-1000字符）能保留更多上下文；大块（1000+字符）上下文丰富但可能模糊焦点。技术文档通常用小块效果更好，叙述性内容可以用大一些的块，并且分块还可以有很多策略可选，在以前的文章中都有总结。

搭建向量数据库：用ChromaDB存储语义信息

文档切块后需要存到向量数据库里，这样才能做语义搜索。ChromaDB是个不错的选择，轻量但功能够用。

import chromadb  

from chromadb.utils import embedding_functions  

# Persistent storage - saves data between sessions  

client = chromadb.PersistentClient(path="chroma_db")  

# Our "brain" for understanding text meaning  

sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(  

    model_name="all-MiniLM-L6-v2"  # Compact but powerful  

)  

# Create our knowledge repository  

collection = client.get_or_create_collection(  

    name="documents_collection",  

    embedding_function=sentence_transformer_ef  

)

这里用了几个核心组件：PersistentClient确保数据持久化存储，重启程序后数据还在；SentenceTransformerEmbeddingFunction把文本转成向量，让机器理解语义；all-MiniLM-L6-v2是个轻量级但效果不错的embedding模型。

文档索引：批量处理和元数据管理

接下来要把文档批量处理成可检索的格式。每个文本块都需要唯一ID和元数据，方便后续溯源。

def build_knowledge_units(path: str):  

    """Ingest a file, break it into chunks, and tag each piece with metadata."""  

    try:  

        # Pull in the raw text  

        raw = load_document(path)  

        # Break the text into bite-sized segments  

        segments = partition_text(raw)  

        # Grab just the filename for provenance  

        name = os.path.basename(path)  

        # Assemble metadata dicts for each segment  

        metadata_records = [  

            {"source_file": name, "segment_index": idx}  

            for idx in range(len(segments))  

        ]  

        # Generate a stable ID for each piece  

        unique_keys = [  

            f"{name}_seg_{idx}"  

            for idx in range(len(segments))  

        ]  

        return unique_keys, segments, metadata_records  

    except Exception as err:  

        print(f"Failed to process '{path}': {err}")  

        # Return empty lists so downstream code can continue safely  

        return [], [], []

为了提高效率，批量插入比单条插入快很多：

def batch_insert_into_store(store, record_ids, contents, metadata_list):  

    """Insert items into the vector store in optimized batches."""  

    batch_size = 100  # tuned for ChromaDB throughput  

    for start_idx in range(0, len(contents), batch_size):  

        stop_idx = min(start_idx + batch_size, len(contents))  

        store.add(  

            documents=contents[start_idx:stop_idx],     # text chunks  

            metadatas=metadata_list[start_idx:stop_idx],# chunk metadata  

            ids=record_ids[start_idx:stop_idx]          # unique chunk IDs  

        )  

def ingest_folder(store, directory: str):  

    """Walk a directory, process each file, and load into the store."""  

    # gather only regular files  

    entries = [  

        os.path.join(directory, name)  

        for name in os.listdir(directory)  

        if os.path.isfile(os.path.join(directory, name))  

    ]  

    for path in entries:  

        filename = os.path.basename(path)  

        print(f"► Processing {filename} …")  

        ids, contents, metadata_list = process_document(path)  

        if contents:  

            batch_insert_into_store(store, ids, contents, metadata_list)  

            print(f"✔ Loaded {len(contents)} chunks from {filename}")

实际运行时你会看到这样的输出：

► Processing customer_faqs.pdf …  

✔ Loaded 51 chunks from customer_faqs.pdf  

► Processing onboarding_guide.docx …  

✔ Loaded 20 chunks from onboarding_guide.docx

语义检索：找到最相关的内容块

有了向量数据库，就能进行语义搜索了。不是简单的关键词匹配，而是理解查询的语义含义。

def run_semantic_query(collection, query: str, top_k: int = 2):  

    """Run a semantic search to find the most relevant chunks."""  

    return collection.query(  

        query_texts=[query],      # The actual search query  

        n_results=top_k           # Number of matches to return  

    )  

def build_context_and_citations(results):  

    """Combine matched chunks and reference their original sources."""  

    combined_text = "\n\n".join(results['documents'][0])  

    references = [  

        f"{meta['source']} (chunk {meta['chunk']})"  

        for meta in results['metadatas'][0]  

    ]  

    return combined_text, references

想了解底层发生了什么，可以看看搜索结果的详细信息：

def display_search_hits(results):  

    """Nicely formatted display of search output for readability."""  

    print("\nTop Matches\n" + "=" * 50)  

    hits = results['documents'][0]  

    metadata = results['metadatas'][0]  

    scores = results['distances'][0]  

    for idx in range(len(hits)):  

        snippet = hits[idx]  

        info = metadata[idx]  

        score = scores[idx]  

        print(f"\nMatch #{idx + 1}")  

        print(f"From: {info['source']} — Chunk {info['chunk']}")  

        print(f"Similarity Score: {1 - score:.2f} / 1.00")  

        print(f"Excerpt: {snippet[:150]}...\n")

搜索结果会显示相似度分数和来源文档，帮你判断检索质量。

接入LLM：让模型基于检索内容回答

检索到相关内容后，就要让LLM基于这些内容生成回答。关键是构造好的prompt，确保模型只基于提供的上下文回答。

import os  

from openai import OpenAI  

# Initialize the OpenAI client  

client = OpenAI()  

# Set your OpenAI API key  

os.environ["OPENAI_API_KEY"] = "your_api_key"  # Replace this with your actual key  

def build_prompt(context: str, question: str) -> str:  

    """Construct a focused prompt using context and a user question."""  

    return f"""You are a helpful assistant. Use only the context provided below to answer.   

If the answer cannot be found in the context, reply with "I don't have that information."  

Context:  

{context}  

Question: {question}  

Answer:"""  

def ask_openai(question: str, context: str) -> str:  

    """Send the prompt to OpenAI and return the generated response."""  

    prompt = build_prompt(context, question)  

    try:  

        reply = client.chat.completions.create(  

            model="gpt-4-turbo",  

            messages=[  

                {"role": "system", "content": "You answer based strictly on the context provided."},  

                {"role": "user", "content": prompt}  

            ],  

            temperature=0.3,  

            max_tokens=300  

        )  

        return reply.choices[0].message.content  

    except Exception as err:  

        return f"Error: {str(err)}"

temperature参数控制生成的随机性：0.0最保守只输出事实，0.5平衡事实和表达，1.0最有创造性。对RAG来说，0.0到0.3比较合适，保证回答基于文档内容。

对话记忆：让AI记住聊天历史

ChatGPT能记住对话上下文，我们的RAG系统也需要这个能力。实现起来其实不复杂，就是维护一个会话状态。

import uuid  

from datetime import datetime  

# In-memory chat log (swap with persistent storage in production)  

chat_sessions = {}  

def start_new_session() -> str:  

    """Initialize a fresh conversation session with a unique ID."""  

    session_id = str(uuid.uuid4())  

    chat_sessions[session_id] = []  

    return session_id  

def log_message(session_id: str, sender: str, message: str):  

    """Add a message to the session history."""  

    if session_id not in chat_sessions:  

        chat_sessions[session_id] = []  

    chat_sessions[session_id].append({  

        "role": sender,  

        "content": message,  

        "timestamp": datetime.now().isoformat()  

    })  

def fetch_recent_messages(session_id: str, limit: int = 5):  

    """Return the last few messages from a session."""  

    msgs = chat_sessions.get(session_id, [])  

    return msgs[-limit:]  

def prepare_history_for_model(messages: list) -> str:  

    """Convert messages into a single formatted string."""  

    return "\n".join(  

        f"{msg['role'].capitalize()}: {msg['content']}"  

        for msg in messages  

    )

这样设计后，每个用户的对话都有独立的session_id，每条消息都会记录到历史中，生成回答时可以参考最近的几条消息作为上下文。

解决指代消解：理解"它"、"那个"指的是什么

用户经常会问一些不完整的问题，比如先问"LaunchPad项目是什么"，接着问"它什么时候开始的"。这里的"它"显然指LaunchPad，但AI不知道。需要把后续问题重写成独立完整的问题。

def rewrite_query_with_context(query: str, chat_log: str, client: OpenAI) -> str:  

    """Rewrites a follow-up query as a full standalone question using prior conversation."""  

    prompt = f"""Rephrase follow-up questions to be fully self-contained.  

Refer to the chat history as needed. Return only the rewritten question.  

Chat History:  

{chat_log}  

Follow-up: {query}  

Standalone Question:"""  

    try:  

        response = client.chat.completions.create(  

            model="gpt-3.5-turbo",  # Fast, cheap, reliable  

            messages=[{"role": "user", "content": prompt}],  

            temperature=0  # Keep output consistent  

        )  

        return response.choices[0].message.content  

    except Exception as err:  

        print(f"Failed to contextualize query: {err}")  

        return query  # Return original if there's an error

这样"它什么时候开始的"就能自动变成"LaunchPad项目什么时候开始的"，确保搜索能找到正确的内容。

完整的对话流程：把所有组件串起来

最后把所有功能整合成一个完整的对话式RAG系统：

def handle_conversational_query(  

    collection,  

    query: str,  

    session_id: str,  

    n_chunks: int = 3  

):  

    """Orchestrates the full RAG-based QA flow in a chat session."""  

    # Step 1: Pull session history and prep it for context injection  

    chat_log = get_conversation_history(session_id)  

    prior_messages = format_history(chat_log)  

    # Step 2: Resolve pronouns or unclear references in the query  

    refined_query = contextualize_query(query, prior_messages, client)  

    print(f"[Refined Query] {refined_query}")  

    # Step 3: Retrieve relevant knowledge from the vector DB  

    search_results = run_semantic_query(collection, refined_query, n_chunks)  

    retrieved_text, citations = build_context_and_citations(search_results)  

    # Step 4: Generate an answer grounded in retrieved content  

    answer = generate_response(refined_query, retrieved_text)  

    # Step 5: Save both user input and AI reply into memory  

    add_message(session_id, "user", query)  

    add_message(session_id, "assistant", answer)  

    return answer, citations

实际使用时的对话流程：

session = start_conversation()  

# 初始查询  

q1 = "LaunchPad是做什么的？"  

reply, refs = smart_retrieval(collection, q1, session)  

print(f"Answer: {reply}\nSources: {refs}")  

# 后续查询  

q2 = "它什么时候开始的？"  

reply, refs = smart_retrieval(collection, q2, session)  

print(f"Answer: {reply}\nSources: {refs}")

系统会自动处理指代消解，输出类似这样：

Contextualized：LaunchPad是做什么的？  

Answer：LaunchPad帮助初创公司快速原型设计和验证产品想法。  

Sources：['program_overview.pdf (chunk 1)']  

Contextualized：LaunchPad项目什么时候开始的？  

Answer：LaunchPad项目始于2018年。  

Sources：['timeline_notes.txt (chunk 3)']

几个实用的优化技巧

做完基础功能后，还有一些进阶优化可以考虑。

1、混合搜索能结合语义搜索和元数据过滤：

# 在语义搜索中添加元数据过滤  

collection.query(  

    query_texts=[query],  

    n_results=5,  

    where={"department": "HR"}  # 只搜索HR部门的文档  

)

2、自动添加来源引用让用户知道答案的出处：

# 在回答后自动添加来源链接  

def enhance_response(response, sources):  

    return f"{response}\n\n来源:\n" + "\n".join(  

        f"- {source}" for source in sources  

    )

3、根据文档类型调整分块策略：

# 根据文档类型动态调整块大小  

if "financial" in file_name:  

    chunks = split_text(content, chunk_size=300)  # 财务文档用小块  

else:  

    chunks = split_text(content, chunk_size=600)  # 其他文档用大块

4、长对话需要压缩历史：

# 当对话历史太长时自动摘要  

def summarize_history(history):  

    prompt = f"总结以下对话的关键信息:\n{history}"  

    return client.chat.completions.create(/*...*/).choices[0].message.content

总结

从零实现RAG系统确实比用现成框架麻烦一些，但带来的好处很明显。

你对每个环节都有完全控制权，可以根据具体需求精确调优。出了问题能快速定位，不用在框架的抽象层里瞎猜。成本也更透明，每个API调用、每个token都在你掌控之中。更重要的是你真正理解了RAG的工作原理，而不是只会调用几个封装好的函数。这种理解在遇到复杂场景时价值巨大。

虽然初期投入的时间多一些，但长期来看绝对值得。特别是对于有特定需求的业务场景，自实现的灵活性是框架无法比拟的。

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