从零搭建RAG应用：跳过LangChain，掌握文本分块、向量检索、指代消解等核心技术实现

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。

https://avoid.overfit.cn/post/a9251c8e996b4c24b1b9536537b0c936

作者：Abdur Rahman