{"id":91400,"date":"2026-08-07T20:57:13","date_gmt":"2026-08-07T12:57:13","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/91400.html"},"modified":"2026-08-07T20:57:13","modified_gmt":"2026-08-07T12:57:13","slug":"%e5%9f%ba%e4%ba%8erag%e4%b8%8ellm%e7%9a%84%e4%bc%81%e4%b8%9a%e7%9f%a5%e8%af%86%e5%ba%93%e5%90%8e%e7%ab%af%e7%b3%bb%e7%bb%9f%e8%ae%be%e8%ae%a1%e4%b8%8e%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/91400.html","title":{"rendered":"\u57fa\u4e8eRAG\u4e0eLLM\u7684\u4f01\u4e1a\u77e5\u8bc6\u5e93\u540e\u7aef\u7cfb\u7edf\u8bbe\u8ba1\u4e0e\u5b9e\u73b0"},"content":{"rendered":"

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u5728\u4f01\u4e1a\u7ea7\u5e94\u7528\u4e2d，\u5982\u4f55\u9ad8\u6548\u7ba1\u7406\u548c\u68c0\u7d22\u6d77\u91cf\u6587\u6863\u77e5\u8bc6（\u5982\u5458\u5de5\u624b\u518c\u3001\u653f\u7b56\u6587\u4ef6\u3001\u5c97\u4f4d\u4fe1\u606f\u7b49）\u662f\u4e00\u4e2a\u5e38\u89c1\u9700\u6c42\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u4e00\u4e2a\u57fa\u4e8e\u68c0\u7d22\u589e\u5f3a\u751f\u6210（RAG） \u548c\u5927\u8bed\u8a00\u6a21\u578b（LLM） \u7684\u540e\u7aef\u7cfb\u7edf，\u8be5\u7cfb\u7edf\u901a\u8fc7FastAPI\u63d0\u4f9b\u670d\u52a1，\u7ed3\u5408\u5411\u91cf\u6570\u636e\u5e93\u3001\u6587\u672c\u5206\u5757\u3001\u5de5\u5177\u8c03\u7528\u7b49\u6280\u672f，\u5b9e\u73b0\u4f01\u4e1a\u77e5\u8bc6\u7684\u667a\u80fd\u68c0\u7d22\u4e0e\u95ee\u7b54\u3002
\n\u4e00\u3001\u6280\u672f\u6808\u6982\u89c8
\n\u672c\u7cfb\u7edf\u4e3b\u8981\u4f9d\u8d56\u4ee5\u4e0b\u6280\u672f\u7ec4\u4ef6：
\n\u7f16\u7a0b\u8bed\u8a00：Python 3.8+
\nWeb\u6846\u67b6：FastAPI（\u63d0\u4f9bRESTful API）
\n\u5411\u91cf\u6570\u636e\u5e93：ChromaDB（\u5b58\u50a8\u6587\u672c\u5411\u91cf，\u652f\u6301\u9ad8\u6548\u76f8\u4f3c\u6027\u68c0\u7d22）
\nRAG\u6846\u67b6：LlamaIndex（\u6587\u6863\u5904\u7406\u3001\u7d22\u5f15\u6784\u5efa\u3001\u67e5\u8be2\u5f15\u64ce）
\nLLM\/Embedding\u6a21\u578b：\u963f\u91cc\u4e91Qwen\u7cfb\u5217（qwen-plus\u3001qwen3.7-text-embedding）\u3001DeepSeek（\u5907\u9009LLM）
\n\u7f13\u5b58：Redis（\u7f13\u5b58\u5b66\u5386\u9a8c\u8bc1\u7ed3\u679c，\u63d0\u5347\u6027\u80fd）
\n\u6587\u6863\u5904\u7406：python-docx（\u89e3\u6790Word\u6587\u6863）
\nORM：Tortoise（\u5f02\u6b65\u6570\u636e\u5e93\u64cd\u4f5c，\u67e5\u8be2\u5c97\u4f4d\u4fe1\u606f<\/p>\n

\u4e8c\u3001\u6838\u5fc3\u529f\u80fd\u6a21\u5757\u8be6\u89e3
\n2.1 \u6587\u6863\u5904\u7406\u4e0e\u6587\u672c\u5206\u5757
\n\u6587\u6863\u5904\u7406\u662fRAG\u7cfb\u7edf\u7684\u57fa\u7840，\u9700\u5c06\u539f\u59cb\u6587\u6863（\u5982Word\u3001TXT）\u8f6c\u6362\u4e3a\u53ef\u5411\u91cf\u5316\u7684\u6587\u672c\u7247\u6bb5\u3002\u7cfb\u7edf\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0：
\n\u6587\u6863\u52a0\u8f7d：\u4f7f\u7528python-docx\u8bfb\u53d6Word\u6587\u6863\u5185\u5bb9，\u6216\u901a\u8fc7LlamaIndex\u7684SimpleDirectoryReader\u6279\u91cf\u52a0\u8f7d\u76ee\u5f55\u4e0b\u7684\u6587\u6863（\u652f\u6301\u9012\u5f52\u8bfb\u53d6）\u3002
\n\u6587\u672c\u5206\u5757：\u5c06\u957f\u6587\u672c\u6309\u56fa\u5b9a\u957f\u5ea6（\u5982200-500 token）\u62c6\u5206，\u540c\u65f6\u4fdd\u7559\u91cd\u53e0\u90e8\u5206（\u598250-150 token），\u907f\u514d\u8bed\u4e49\u65ad\u88c2\u3002\u4f8b\u5982：<\/p>\n

def split_text(all_text):
\n\u00a0 \u00a0 chunks = []
\n\u00a0 \u00a0 # \u6309200\u5b57\u7b26\u62c6\u5206，\u53ef\u6839\u636etoken\u957f\u5ea6\u8c03\u6574
\n\u00a0 \u00a0 for i in range(0, len(all_text), 200):
\n\u00a0 \u00a0 \u00a0 \u00a0 chunks.append(all_text[i:i+200])
\n\u00a0 \u00a0 return chunks<\/p>\n

\n\u6216\u4f7f\u7528LlamaIndex\u7684SentenceSplitter（\u66f4\u667a\u80fd\u7684token\u7ea7\u5206\u5757）：<\/p>\n

Settings.text_splitter = SentenceSplitter(
\n\u00a0 \u00a0 chunk_size=500, \u00a0# \u6bcf\u5757\u7ea6500 token
\n\u00a0 \u00a0 chunk_overlap=150 \u00a0# \u91cd\u53e0150 token
\n)<\/p>\n

\n2.2 \u6587\u672c\u5411\u91cf\u5316\u4e0e\u5411\u91cf\u5b58\u50a8
\n\u6587\u672c\u5206\u5757\u540e，\u9700\u901a\u8fc7Embedding\u6a21\u578b\u8f6c\u6362\u4e3a\u5411\u91cf，\u5e76\u5b58\u50a8\u5230\u5411\u91cf\u6570\u636e\u5e93\u4e2d，\u4ee5\u4fbf\u540e\u7eed\u68c0\u7d22\u3002
\n\u5411\u91cf\u5316：\u4f7f\u7528Qwen3.7-text-embedding\u6a21\u578b\u751f\u6210\u6587\u672c\u5411\u91cf，\u652f\u6301\u6279\u91cf\u5904\u7406\u4ee5\u63d0\u5347\u6548\u7387：<\/p>\n

def text_embedding(chunks):
\n\u00a0 \u00a0 client = OpenAI(
\n\u00a0 \u00a0 \u00a0 \u00a0 api_key=os.getenv("DASHSCOPE_API_KEY"),
\n\u00a0 \u00a0 \u00a0 \u00a0 base_url="https:\/\/ws-wj4y279gkc4bd0x9.cn-beijing.maas.aliyuncs.com\/compatible-mode\/v1"
\n\u00a0 \u00a0 )
\n\u00a0 \u00a0 embeddings = []
\n\u00a0 \u00a0 batch_size = 20 \u00a0# \u6279\u91cf\u5904\u740620\u4e2a\u6587\u672c\u5757
\n\u00a0 \u00a0 for i in range(0, len(chunks), batch_size):
\n\u00a0 \u00a0 \u00a0 \u00a0 batch_chunks = chunks[i:i+batch_size]
\n\u00a0 \u00a0 \u00a0 \u00a0 completion = client.embeddings.create(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model="qwen3.7-text-embedding",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 input=batch_chunks
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0 batch_embeddings = [item.embedding for item in completion.data]
\n\u00a0 \u00a0 \u00a0 \u00a0 embeddings.extend(batch_embeddings)
\n\u00a0 \u00a0 return embeddings<\/p>\n

\n\u5411\u91cf\u5b58\u50a8：\u4f7f\u7528ChromaDB\u6301\u4e45\u5316\u5b58\u50a8\u5411\u91cf，\u652f\u6301\u521b\u5efa\/\u52a0\u8f7d\u96c6\u5408（Collection）：<\/p>\n

def build_and_save_index(documents, persist_dir=".\/storage"):
\n\u00a0 \u00a0 # \u8fde\u63a5ChromaDB
\n\u00a0 \u00a0 chroma_client = chromadb.PersistentClient(path=persist_dir)
\n\u00a0 \u00a0 chroma_collection = chroma_client.get_or_create_collection(name="bank_policy_collection")
\n\u00a0 \u00a0 vector_store = ChromaVectorStore(chroma_collection=chroma_collection)<\/p>\n

\u00a0 \u00a0 # \u6784\u5efa\u7d22\u5f15\u5e76\u6301\u4e45\u5316
\n\u00a0 \u00a0 storage_context = StorageContext.from_defaults(vector_store=vector_store)
\n\u00a0 \u00a0 index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
\n\u00a0 \u00a0 index.storage_context.persist(persist_dir=persist_dir)
\n\u00a0 \u00a0 print(f"\u2705 \u7d22\u5f15\u5df2\u4fdd\u5b58，\u5411\u91cf\u6570: {chroma_collection.count()}")
\n\u00a0 \u00a0 return index<\/p>\n

\n2.3 LLM\u96c6\u6210\u4e0e\u67e5\u8be2\u5f15\u64ce
\n\u67e5\u8be2\u65f6，\u7cfb\u7edf\u5148\u5c06\u7528\u6237\u95ee\u9898\u5411\u91cf\u5316，\u4eceChromaDB\u68c0\u7d22\u76f8\u4f3c\u6587\u672c\u7247\u6bb5，\u518d\u5c06\u7247\u6bb5\u4f5c\u4e3a\u4e0a\u4e0b\u6587\u8f93\u5165LLM\u751f\u6210\u7b54\u6848\u3002
\n\u7d22\u5f15\u52a0\u8f7d：\u4eceChromaDB\u52a0\u8f7d\u5df2\u6784\u5efa\u7684\u7d22\u5f15：<\/p>\n

def load_index(persist_dir=".\/storage"):
\n\u00a0 \u00a0 chroma_client = chromadb.PersistentClient(path=persist_dir)
\n\u00a0 \u00a0 chroma_collection = chroma_client.get_collection("bank_policy_collection")
\n\u00a0 \u00a0 vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
\n\u00a0 \u00a0 storage_context = StorageContext.from_defaults(vector_store=vector_store, persist_dir=persist_dir)
\n\u00a0 \u00a0 index = load_index_from_storage(storage_context)
\n\u00a0 \u00a0 return index
\n\u67e5\u8be2\u6267\u884c：\u901a\u8fc7LlamaIndex\u7684\u67e5\u8be2\u5f15\u64ce\u5b9e\u73b0\u68c0\u7d22\u589e\u5f3a\u751f\u6210：
\ndef query_policy(index, user_query, top_k=3):
\n\u00a0 \u00a0 query_engine = index.as_query_engine(similarity_top_k=top_k) \u00a0# \u68c0\u7d22top 3\u76f8\u4f3c\u7247\u6bb5
\n\u00a0 \u00a0 response = query_engine.query(user_query)
\n\u00a0 \u00a0 # \u6253\u5370\u68c0\u7d22\u5230\u7684\u7247\u6bb5（\u7528\u4e8e\u8c03\u8bd5）
\n\u00a0 \u00a0 for i, node in enumerate(response.source_nodes, 1):
\n\u00a0 \u00a0 \u00a0 \u00a0 print(f"[{i}] \u76f8\u4f3c\u5ea6: {node.score:.4f} | \u6765\u6e90: {node.metadata.get('source')}")
\n\u00a0 \u00a0 return response.response
\n2.4 FastAPI\u670d\u52a1\u4e0e\u5de5\u5177\u8c03\u7528
\n\u7cfb\u7edf\u901a\u8fc7FastAPI\u63d0\u4f9b\u5bf9\u5916\u63a5\u53e3，\u5e76\u652f\u6301LLM\u5de5\u5177\u8c03\u7528（\u5982\u5b66\u5386\u9a8c\u8bc1\u3001\u5c97\u4f4d\u67e5\u8be2\u3001\u77e5\u8bc6\u5e93\u68c0\u7d22），\u5b9e\u73b0\u591a\u80fd\u529b\u96c6\u6210\u3002
\n\u5de5\u5177\u5b9a\u4e49：\u6309OpenAI\u51fd\u6570\u8c03\u7528\u683c\u5f0f\u5b9a\u4e49\u5de5\u5177，\u4f8b\u5982\u5c97\u4f4d\u67e5\u8be2\u5de5\u5177：
\nasync def query_job_tool(arguments):
\n\u00a0 \u00a0 job_id = arguments["job_id"]
\n\u00a0 \u00a0 job = await Job.get_or_none(id=job_id) \u00a0# \u5f02\u6b65\u67e5\u8be2\u6570\u636e\u5e93
\n\u00a0 \u00a0 if job is None:
\n\u00a0 \u00a0 \u00a0 \u00a0 return "\u5c97\u4f4d\u4e0d\u5b58\u5728"
\n\u00a0 \u00a0 return f"\u5c97\u4f4d\u540d\u79f0: {job.job_name}\\\\n\u804c\u8d23: {job.job_desc}"<\/p>\n

\n\u5de5\u5177\u8c03\u7528\u6d41\u7a0b：LLM\u6839\u636e\u7528\u6237\u95ee\u9898\u81ea\u52a8\u9009\u62e9\u5de5\u5177，\u6267\u884c\u540e\u5c06\u7ed3\u679c\u4f5c\u4e3a\u4e0a\u4e0b\u6587\u7ee7\u7eed\u751f\u6210\u7b54\u6848：<\/p>\n

@llm_day05_router.post("\/llm_chat")
\nasync def llm_chat(request: LLMCase3):
\n\u00a0 \u00a0 messages = [{"role": "system", "content": system_prompt}] \u00a0# \u7cfb\u7edf\u63d0\u793a：\u6307\u5b9a\u5de5\u5177\u8c03\u7528\u89c4\u5219
\n\u00a0 \u00a0 messages.append({"role": "user", "content": request.user_query})<\/p>\n

\u00a0 \u00a0 completion = get_ai_response(messages) \u00a0# \u8c03\u7528LLM，\u83b7\u53d6\u5de5\u5177\u8c03\u7528\u6307\u4ee4
\n\u00a0 \u00a0 if completion.choices[0].message.tool_calls:
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6267\u884c\u5de5\u5177\u8c03\u7528
\n\u00a0 \u00a0 \u00a0 \u00a0 for tool in completion.choices[0].message.tool_calls:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 func_name = tool.function.name
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 func_args = json.loads(tool.function.arguments)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 tool_result = func_mapping[func_name](func_args) \u00a0# \u8c03\u7528\u5bf9\u5e94\u5de5\u5177\u51fd\u6570
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if hasattr(tool_result, "__await__"): \u00a0# \u5904\u7406\u5f02\u6b65\u5de5\u5177
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 tool_result = await tool_result
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 messages.append({"role": "tool", "content": tool_result})
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4e8c\u6b21\u8c03\u7528LLM，\u751f\u6210\u6700\u7ec8\u7b54\u6848
\n\u00a0 \u00a0 \u00a0 \u00a0 completion = get_ai_response(messages)
\n\u00a0 \u00a0 return {"code": 1, "data": completion.choices[0].message.content}<\/p>\n

\n\u4e09\u3001\u7cfb\u7edf\u5de5\u4f5c\u6d41\u7a0b
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u6570\u636e\u51c6\u5907：\u901a\u8fc7case7.py\u6216build_index.py\u52a0\u8f7d\u6587\u6863\u2192\u5206\u5757\u2192\u5411\u91cf\u5316\u2192\u5b58\u50a8\u5230ChromaDB\u3002
\n\u7528\u6237\u67e5\u8be2：\u7528\u6237\u901a\u8fc7FastAPI\u63a5\u53e3（\/llm_chat）\u63d0\u4ea4\u95ee\u9898\u3002
\n\u5de5\u5177\u8c03\u7528\u5224\u65ad：LLM\u6839\u636e\u7cfb\u7edf\u63d0\u793a\u5224\u65ad\u662f\u5426\u9700\u8981\u8c03\u7528\u5de5\u5177（\u5982\u5b66\u5386\u9a8c\u8bc1\u3001\u5c97\u4f4d\u67e5\u8be2）\u3002
\n\u77e5\u8bc6\u68c0\u7d22：\u5982\u9700\u67e5\u8be2\u4f01\u4e1a\u77e5\u8bc6\u5e93，\u8c03\u7528query_knowledge_tool\u4eceChromaDB\u68c0\u7d22\u76f8\u5173\u6587\u672c\u7247\u6bb5\u3002
\n\u7b54\u6848\u751f\u6210：LLM\u7ed3\u5408\u5de5\u5177\u8fd4\u56de\u7ed3\u679c\u6216\u68c0\u7d22\u5230\u7684\u77e5\u8bc6，\u751f\u6210\u6700\u7ec8\u7b54\u6848\u5e76\u8fd4\u56de\u3002
\n\u56db\u3001\u603b\u7ed3\u4e0e\u5c55\u671b
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u672c\u7cfb\u7edf\u901a\u8fc7RAG\u6280\u672f\u89e3\u51b3\u4e86\u4f01\u4e1a\u77e5\u8bc6\u68c0\u7d22\u7684\u51c6\u786e\u6027\u95ee\u9898，\u7ed3\u5408LLM\u5de5\u5177\u8c03\u7528\u5b9e\u73b0\u4e86\u591a\u80fd\u529b\u96c6\u6210（\u6570\u636e\u5e93\u67e5\u8be2\u3001\u7b2c\u4e09\u65b9API\u8c03\u7528\u7b49）\u3002\u672a\u6765\u53ef\u8fdb\u4e00\u6b65\u4f18\u5316：
\n\u5206\u5757\u7b56\u7565：\u4f7f\u7528\u8bed\u4e49\u611f\u77e5\u5206\u5757（\u5982\u57fa\u4e8e\u6587\u6863\u7ed3\u6784）\u63d0\u5347\u68c0\u7d22\u7cbe\u5ea6\u3002
\n\u591a\u6a21\u6001\u652f\u6301：\u6269\u5c55\u81f3PDF\u3001\u56fe\u7247\u7b49\u975e\u6587\u672c\u683c\u5f0f\u6587\u6863\u5904\u7406\u3002
\n\u6027\u80fd\u4f18\u5316：\u901a\u8fc7\u5411\u91cf\u7d22\u5f15\u4f18\u5316（\u5982FAISS）\u63d0\u5347\u68c0\u7d22\u901f\u5ea6，\u589e\u52a0\u7f13\u5b58\u5c42\u51cf\u5c11\u91cd\u590d\u8ba1\u7b97\u3002
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