{"id":1918,"date":"2024-06-01T19:37:02","date_gmt":"2024-06-01T19:37:02","guid":{"rendered":"https:\/\/www.nicekj.com\/?p=1918"},"modified":"2024-06-01T19:37:02","modified_gmt":"2024-06-01T19:37:02","slug":"aimoxingwuin-context-learningshangxiawenxuexi","status":"publish","type":"post","link":"https:\/\/www.nicekj.com\/aimoxingwuin-context-learningshangxiawenxuexi.html","title":{"rendered":"AI\u6a21\u578b\uff08\u4e94\uff09In-context learning\u4e0a\u4e0b\u6587\u5b66\u4e60"},"content":{"rendered":"

\u4e0a\u4e0b\u6587\u5b66\u4e60In-context learning<\/h1>\n
    \n
  1. \u5e94\u7528\u5927\u8bed\u8a00\u6a21\u578b\u8981\u4ece\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u601d\u7ef4\u5207\u6362\u4e3a\u4e0a\u4e0b\u6587\u5b66\u4e60\u7684\u601d\u8def<\/li>\n
  2. \u4e0a\u4e0b\u6587\u5b66\u4e60\u5305\u62ecZero-shot learning \u548c Few-shot Learning \u4e24\u8005\u5e76\u65e0\u660e\u663e\u754c\u9650\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u8981\u7075\u6d3b\u8fd0\u7528<\/li>\n<\/ol>\n
      \n
    • Zero-shot learning \u53ea\u662f\u63cf\u8ff0\u95ee\u9898\uff0c\u4e0d\u662f\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u5582\u5927\u91cf\u7684\u6570\u636e<\/li>\n
    • Few-shot learning \u53ea\u662f\u7ed9\u51fa\u7b80\u77ed\u7684\u51e0\u4e2a\u793a\u4f8b\uff0c\u8ba9\u5927\u8bed\u8a00\u6a21\u578b\u6309\u7167\u4f8b\u5b50\u51fa<\/li>\n<\/ul>\n

      Zero-shot learning<\/h1>\n
        \n
      • \u610f\u56fe\u8bc6\u522b<\/li>\n<\/ul>\n
        <\/div>
        python<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> openai<\/span>\ndef<\/span> recognize_intent<\/span>(input<\/span><\/span>):<\/span>\n    response = openai.ChatCompletion.create(<\/span>\n        engine=deployment, # engine = \"deployment_name\".<\/span><\/span>\n        temperature = 0<\/span>,<\/span>\n        messages=[<\/span>\n            {\"role\"<\/span>: \"system\"<\/span>, \"content\"<\/span>: \"\"\"<\/span><\/span>\n              Recognize the intent from the user's input and format output as JSON string. <\/span>\n            The output JSON string includes: \"intention\", \"paramters\" \"\"\"},<\/span>\n            {\"role\"<\/span>: \"user\"<\/span>, \"content\"<\/span>: input<\/span>}<\/span>\n        ]<\/span>\n      )<\/span>\n    return<\/span> response.choices[0<\/span>].message.content<\/span>\n<\/code><\/pre>\n
        <\/div>
        python<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>print<\/span>(recognize_intent(\"\u8ba28\u670819\u65e5\u5317\u4eac\u5230\u4e0a\u6d77\u7684\u98de\u673a\"<\/span>))<\/span>\n<\/span>\n<\/span>\n{\"intention\"<\/span>: \"book_flight\"<\/span>, \"parameters\"<\/span>: {\"date\"<\/span>: \"8\u670819\u65e5\"<\/span>, \"departure\"<\/span>: \"\u5317\u4eac\"<\/span>, \"destination\"<\/span>: \"\u4e0a\u6d77\"<\/span>}}<\/span>\n<\/code><\/pre>\n
          \n
        • \u60c5\u611f\u5206\u7c7b<\/li>\n<\/ul>\n
          <\/div>
          python<\/span>
          \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> openai<\/span>\n<\/span>\ndef<\/span> classify_sentiment<\/span>(input<\/span><\/span>):<\/span>\n    response = openai.ChatCompletion.create(<\/span>\n        engine=deployment, # engine = \"deployment_name\".<\/span><\/span>\n        messages=[<\/span>\n            {\"role\"<\/span>: \"system\"<\/span>, \"content\"<\/span>: \"\"\"<\/span><\/span>\n             \u6839\u636e\u7ed9\u5b9a\u6587\u5b57\u6240\u8868\u8fbe\u7684\u60c5\u611f\uff0c\u5c06\u6587\u5b57\u5206\u4e3a\u4e09\u7c7b\uff1a\u6b63\u9762 ,\u4e2d\u7acb\uff0c \u8d1f\u9762\u3002\u8bf7\u5c06\u7528\u6237\u7ed9\u5b9a\u7684\u6587\u5b57\u8fdb\u884c\u5206\u7c7b\uff0c\u5e76\u8f93\u51fa\u5206\u7c7b\u3002<\/span>\n             \"\"\"},   <\/span>\n            {\"role\"<\/span>: \"user\"<\/span>, \"content\"<\/span>: input<\/span>}<\/span>\n        ],<\/span>\n        temperature = 0.9<\/span>, <\/span>\n        max_tokens = 200<\/span><\/span>\n      )<\/span>\n    return<\/span> response.choices[0<\/span>].message.content<\/span>\n<\/code><\/pre>\n
          Few-shot Learning<\/h1>\n
          <\/div>
          python<\/span>
          \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> openai<\/span>\ndef<\/span> book_flight<\/span>(input<\/span><\/span>):<\/span>\n    response = openai.ChatCompletion.create(<\/span>\n        engine=deployment, # engine = \"deployment_name\".<\/span><\/span>\n        temperature = 0<\/span>,<\/span>\n        messages=[<\/span>\n            {\"role\"<\/span>: \"system\"<\/span>, \"content\"<\/span>: \"\"\"<\/span><\/span>\n              \u901a\u8fc7\u7528\u6237\u63cf\u8ff0\uff0c\u63d0\u53d6\u822a\u73ed\u9884\u8ba2\u4fe1\u606f\u5e76\u4ee5JSON\u683c\u5f0f\u8f93\u51fa. <\/span>\n              \u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\uff1a<\/span>\n              \u793a\u4f8b1<\/span>\n              \u8f93\u5165: \"\u8ba28\u67089\u65e5\u4e0a\u6d77\u5230\u5317\u4eac\u7684\u822a\u73ed\uff0c\u4e0a\u5348\u51fa\u53d1\"<\/span>\n              \u8f93\u51fa:\"{\"date\":\"0809\",\"dispatch_time\":\"6-12\",\"from\":\"shanghai\",\"to\":\"beijing\"}\"<\/span>\n              <\/span>\n              \u793a\u4f8b2<\/span>\n              \u8f93\u5165: \"\u8ba28\u67089\u65e5\u4e0a\u6d77\u5230\u5317\u4eac\u7684\u822a\u73ed\uff0c\u4e0b\u5348\u51fa\u53d1\"<\/span>\n              \u8f93\u51fa:\"{\"date\":\"0809\",\"dispatch_time\":\"12-18\",\"from\":\"shanghai\",\"to\":\"beijing\"}\"<\/span>\n              <\/span>\n              \u793a\u4f8b3<\/span>\n              \u8f93\u5165: \"\u8ba28\u67089\u65e5\u4e0a\u6d77\u5230\u5317\u4eac\u7684\u822a\u73ed\uff0c\u665a\u4e0a\u51fa\u53d1\"<\/span>\n              \u8f93\u51fa:\"{\"date\":\"0809\",\"dispatch_time\":\"18-24\",\"from\":\"shanghai\",\"to\":\"beijing\"}\"<\/span>\n              <\/span>\n              \u793a\u4f8b4<\/span>\n              \u8f93\u5165: \"\u8ba28\u67089\u65e5\u4e0a\u6d77\u5230\u5317\u4eac\u7684\u822a\u73ed\"<\/span>\n              \u8f93\u51fa:\"{\"date\":\"0809\",\"dispatch_time\":\"unknown\",\"from\":\"shanghai\",\"to\":\"beijing\"}\"<\/span>\n              \"\"\"},<\/span>\n            {\"role\"<\/span>: \"user\"<\/span>, \"content\"<\/span>: input<\/span>}<\/span>\n        ]<\/span>\n      )<\/span>\n    return<\/span> response.choices[0<\/span>].message.content<\/span>\n<\/code><\/pre>\n
          ReAct Agent<\/h1>\n
            \n
          • \u5b66\u4f1aai\u601d\u8003\u7684\u8fc7\u7a0b\uff0c\u63d0\u95ee\u53ef\u4ee5\u9075\u5faa\u8fd9\u6837\u7684\u63d0<\/li>\n<\/ul>\n
            <\/div>
            python<\/span>
            \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> openai<\/span>\ntool = \"\"\"<\/span><\/span>\n1 tool: python_interpreter, description: use it to execute python code<\/span>\n2 tool: web_access, description: use it to get realtime info, input is the question or query <\/span>\n\"\"\"<\/span>\n<\/span>\nreact_prompt = f\"\"\"<\/span><\/span>\nTry your best to anwser user's question, and use the following format:<\/span>\n<\/span>\nQuestion: the input question you must answer<\/span>\n<\/span>\nThought: you should always think about what to do<\/span>\n<\/span>\nAction: the action to take, should use one of tools in the given tool list:<\/span>\n<\/span>\n[{tool}<\/span>]<\/span>\n<\/span>\nAction Input: the input to the action<\/span>\n<\/span>\nHere, you should pause the process and return to wait the outside observation. <\/span>\n<\/span>\nObservation: the result of the action<\/span>\n... <\/span>(this Thought\/Action\/Action Input\/Observation can repeat N times)<\/span>\n<\/span>\nThought: I now know the final answer<\/span>\n<\/span>\nFinal Answer: the final answer to the original input question<\/span>\n\"\"\"<\/span>\n<\/span>\ndef<\/span> react_demo<\/span>(request<\/span>):<\/span>\n    response = openai.ChatCompletion.create(<\/span>\n        engine=deployment, # engine = \"deployment_name\".<\/span><\/span>\n        temperature = 0<\/span>,<\/span>\n        messages=[<\/span>\n            {\"role\"<\/span>: \"system\"<\/span>, \"content\"<\/span>: react_prompt,<\/span>\n            },<\/span>\n            {\"role\"<\/span>: \"user\"<\/span>, \"content\"<\/span>: request}<\/span>\n        ]<\/span>\n      )<\/span>\n    print<\/span>(response.choices[0<\/span>].message.content)<\/span>\n<\/code><\/pre>\n
            <\/div>
            python<\/span>
            \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>print<\/span>(react_demo(\"\u4eca\u5929\u5317\u4eac\u9002\u5408\u7a7f\u4ec0\u4e48\uff1f\"<\/span>))<\/span>\n<\/code><\/pre>\n
            <\/div>
            python<\/span>
            \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div># run the web_access tool to get the Beijing's weather today. For example, <\/span><\/span>\nweather = \"The weather today in Beijing is 33\u00b0C, with 0% precipitation, 31% humidity, and a wind speed of 4 mph.\"<\/span><\/span>\n<\/span>\nnext_prompt = \"\"\"<\/span><\/span>\nQuestion: \u4eca\u5929\u5317\u4eac\u9002\u5408\u7a7f\u4ec0\u4e48\uff1f<\/span>\n<\/span>\nThought: I need to check the current weather in Beijing to give a proper clothing suggestion.<\/span>\n<\/span>\nAction: web_access<\/span>\n<\/span>\nAction Input: \u4eca\u5929\u5317\u4eac\u5929\u6c14\u5982\u4f55\uff1f<\/span>\n<\/span>\nObservation: \"\"\" + weather<\/span>\n<\/span>\nprint<\/span>(react_demo(next_prompt))<\/span>\n<\/code><\/pre>\n
            \u6587\u672c\u5411\u91cf\u5316embedding<\/h1>\n
            <\/div>
            python<\/span>
            \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.embeddings.openai import<\/span> OpenAIEmbeddings<\/span>\n# embedding = OpenAIEmbeddings() #\u5982\u679c\u76f4\u63a5\u4f7f\u7528OpenAI\u7684GPT\u670d\u52a1<\/span><\/span>\nembedding = OpenAIEmbeddings(deployment=embedding_deployment) #deployment\u662f\u4f60\u5728Azure\u4e2d\u7684 embedding \u6a21\u578b\u7684\u90e8\u7f72\u540d\u5b57<\/span><\/span>\nsentence1 = \"\u6211\u662f\u4e00\u540d\u8f6f\u4ef6\u5de5\u7a0b\u5e08\"<\/span><\/span>\nsentence2 = \"\u5c0f\u5f20\u4ece\u4e8b\u6cd5\u5f8b\u5de5\u4f5c\"<\/span><\/span>\nsentence3 = \"\u6211\u662f\u4e00\u540d\u7a0b\u5e8f\u5458\"<\/span><\/span>\n<\/span>\nembedding1 = embedding.embed_query(sentence1)<\/span>\nembedding2 = embedding.embed_query(sentence2)<\/span>\nembedding3 = embedding.embed_query(sentence3)<\/span>\n<\/code><\/pre>\n
              \n
            • \u7528\u70b9\u96c6\u8861\u91cf\u5411\u91cf\u4e4b\u95f4\u7684\u8ddd\u79bb<\/li>\n<\/ul>\n
              <\/div>
              python<\/span>
              \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> numpy as<\/span> np<\/span>\nprint<\/span>(np.dot(embedding1,embedding2))<\/span>\nprint<\/span>(np.dot(embedding2,embedding3))<\/span>\nprint<\/span>(np.dot(embedding1,embedding3))<\/span>\n<\/code><\/pre>\n
              0.7987662070931474
              \n0.7992461569419606
              \n0.9592105615669977<\/p>\n
              \u5904\u7406\u5927\u6587\u672c<\/h1>\n
              \u601d\u8def<\/h3>\n
                \n
              • \n
                  \n
                1. \u628a\u6587\u7ae0\u5207\u5272\u90e8\u5206\uff0c\u5206\u522b\u5411\u91cf\u5316<\/li>\n<\/ol>\n<\/li>\n
                2. \n
                    \n
                  1. \u5728\u7528\u7528\u6237\u7ed9\u5b9a\u7684\u5173\u952e\u8bcd\uff0c\u7528 numpy \u7684\u70b9\u96c6 dot\uff0c\u67e5\u627e\u8ba1\u7b97\u5173\u952e\u8bcd\u76f8\u4f3c\u7684\u6bb5\u843d<\/li>\n<\/ol>\n<\/li>\n
                  2. \n
                      \n
                    1. \u518d\u628a\u6bb5\u843d\u4ea4\u7ed9gpt\uff0c\u603b\u7ed3\u51fa\u901a\u987a\u7684\u8bdd<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n
                      \u505a\u6cd5<\/h3>\n
                        \n
                      • \u5411\u91cf\u5316<\/li>\n<\/ul>\n
                        <\/div>
                        python<\/span>
                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.embeddings.openai import<\/span> OpenAIEmbeddings<\/span>\n<\/span>\ndef<\/span> embed<\/span>(chunks<\/span>): <\/span>\n    # embedding = OpenAIEmbeddings()#\u5982\u679c\u76f4\u63a5\u4f7f\u7528OpenAI\u7684GPT\u670d\u52a1<\/span><\/span>\n    embedding = OpenAIEmbeddings(deployment=embedding_deployment)<\/span>\n    return<\/span> [embedding.embed_query(chunk) for<\/span> chunk in<\/span> chunks]<\/span>\n        <\/span>\n        <\/span>\n<\/span>\nchunks = split_file_into_chunks(\"spotmax_intro.txt\"<\/span>,100<\/span>)<\/span>\nembeddeds = embed(chunks)<\/span>\n<\/code><\/pre>\n
                          \n
                        • \u5411\u91cf\u68c0\u7d22<\/li>\n<\/ul>\n
                          <\/div>
                          python<\/span>
                          \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def<\/span> find_k_largest_indices<\/span>(input_list, k<\/span>):<\/span>\n    sorted_indices = sorted<\/span>(range<\/span>(len<\/span>(input_list)), key=lambda<\/span> i: input_list[i], reverse=True<\/span>)<\/span>\n    print<\/span>(input_list)<\/span>\n    print<\/span>(sorted_indices)<\/span>\n    return<\/span> sorted_indices[:k]<\/span>\n<\/span>\ndef<\/span> search<\/span>(chunks,embeddeds,top_k, txt<\/span>):<\/span>\n    embedding = OpenAIEmbeddings(deployment=\"embedding\"<\/span>,chunk_size=1<\/span>)<\/span>\n    embedded_txt = embedding.embed_query(txt)<\/span>\n    distances = [np.dot(embedded_txt,embedded) for<\/span> embedded in<\/span> embeddeds]<\/span>\n    ret_idx = find_k_largest_indices(distances, top_k)<\/span>\n    return<\/span> [chunks[i] for<\/span> i in<\/span> ret_idx]<\/span>\n<\/code><\/pre>\n
                            \n
                          • \u4f7f\u7528<\/li>\n<\/ul>\n
                            <\/div>
                            python<\/span>
                            \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>search(chunks,embeddeds,2<\/span>, \"\u63d0\u9ad8\u7cfb\u7edf\u53ef\u7528\u6027\"<\/span>)<\/span>\n<\/code><\/pre>\n
                              \n
                            • gpt\u603b\u7ed3<\/li>\n<\/ul>\n
                              <\/div>
                              python<\/span>
                              \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> openai<\/span>\nimport<\/span> numpy as<\/span> np<\/span>\ndef<\/span> anwser_question_with_doc<\/span>(question, chunks, embeddeds<\/span>):<\/span>\n    relevent_chunks = search(chunks,embeddeds,2<\/span>, question)<\/span>\n    prompt = \"\"\"<\/span><\/span>\n    \u4ec5\u901a\u8fc7\u603b\u7ed3\u4ee5\u4e0b\u7684\u6587\u5b57\u7247\u6bb5\u56de\u7b54\u7528\u6237\u95ee\u9898, \u6ce8\u610f\u4fdd\u6301\u56de\u7b54\u7684\u8bed\u8a00\u901a\u987a\uff08\u5b57\u6570\u572830\u5b57\u4ee5\u5185\uff09<\/span>\n    --- <\/span>\n    \"\"\"<\/span>\n    for<\/span> rchunks in<\/span> relevent_chunks:<\/span>\n        prompt = prompt + \"\\n'\"<\/span> + rchunks + \"'\"<\/span><\/span>\n    <\/span>\n    response = openai.ChatCompletion.create(<\/span>\n    engine=deployment, # engine = \"deployment_name\".<\/span><\/span>\n    messages=[<\/span>\n            {\"role\"<\/span>: \"system\"<\/span>, \"content\"<\/span>: prompt},   <\/span>\n            {\"role\"<\/span>: \"user\"<\/span>, \"content\"<\/span>: question}<\/span>\n        ],<\/span>\n        temperature = 0.9<\/span>, <\/span>\n        max_tokens = 200<\/span><\/span>\n   )<\/span>\n    return<\/span> response.choices[0<\/span>].message.content<\/span>\n<\/code><\/pre>\n
                                \n
                              • \u6700\u7ec8\u4f7f\u7528<\/li>\n<\/ul>\n
                                <\/div>
                                python<\/span>
                                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>print<\/span>(anwser_question_with_doc(\"\u5982\u4f55\u63d0\u9ad8\u53ef\u7528\u6027\"<\/span>, chunks,embeddeds))<\/span>\n<\/code><\/pre>\n
                                \u7528 LangChain \u8fde\u63a5\u5185\u90e8\u6570\u636e\u548c\u5927\u6a21\u578b<\/h1>\n
                                \u5411\u91cf\u6570\u636e\u5e93 Chroma<\/h2>\n
                                <\/div>
                                python<\/span>
                                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>!pip install chromadb<\/span>\n<\/code><\/pre>\n
                                  \n
                                • \u52a0\u8f7d\u6587\u6863 \uff08Document Loader\uff09<\/li>\n<\/ul>\n
                                  <\/div>
                                  python<\/span>
                                  \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.document_loaders import<\/span> DirectoryLoader<\/span>\nloader = DirectoryLoader('.\/'<\/span>, glob=\"spotmax_intro.txt\"<\/span>)<\/span>\ndocs = loader.load()<\/span>\n<\/code><\/pre>\n
                                  <\/div>
                                  python<\/span>
                                  \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>pip install pypdf<\/span>\n<\/code><\/pre>\n
                                  <\/div>
                                  python<\/span>
                                  \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.document_loaders import<\/span> PyPDFLoader<\/span>\n<\/span>\nloader = PyPDFLoader(\"Spotmax_intro_cn_2020.pdf\"<\/span>)<\/span>\ndocs = loader.load()<\/span>\n<\/code><\/pre>\n
                                    \n
                                  • \u5207\u5272\u6587\u6863\n
                                      \n
                                    1. \u8fd9\u4e2a\u6587\u672c\u5206\u5272\u5668\u662f\u901a\u7528\u6587\u672c\u7684\u63a8\u8350\u5206\u5272\u5668\u3002\u5b83\u7531\u4e00\u4e2a\u5b57\u7b26\u5217\u8868\u53c2\u6570\u5316\u3002\u5b83\u4f1a\u6309\u987a\u5e8f\u5c1d\u8bd5\u5728\u8fd9\u4e9b\u5b57\u7b26\u4e0a\u8fdb\u884c\u5206\u5272\uff0c\u76f4\u5230\u5206\u5757\u8db3\u591f\u5c0f\u3002\u9ed8\u8ba4\u5217\u8868\u662f[“\\n\\n”, “\\n”, ” “, \u201c”]\u3002\u8fd9\u6837\u505a\u7684\u6548\u679c\u662f\u5c3d\u53ef\u80fd\u5730\u4fdd\u6301\u6240\u6709\u6bb5\u843d\uff08\u7136\u540e\u662f\u53e5\u5b50\uff0c\u7136\u540e\u662f\u5355\u8bcd\uff09\u5728\u4e00\u8d77\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n
                                      <\/div>
                                      python<\/span>
                                      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.text_splitter import<\/span> RecursiveCharacterTextSplitter<\/span>\ntext_splitter = RecursiveCharacterTextSplitter(<\/span>\n    chunk_size = 200<\/span>,<\/span>\n    chunk_overlap = 10<\/span><\/span>\n)<\/span>\n<\/code><\/pre>\n
                                      <\/div>
                                      python<\/span>
                                      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>splits = text_splitter.split_documents(docs)<\/span>\n<\/span>\n<\/span>\npersist_directory = 'data\/'<\/span><\/span>\n!rm -rf .\/data <\/span>\n<\/code><\/pre>\n
                                        \n
                                      • \u5c06\u6570\u636e\u5b58\u5165\u5411\u91cf\u6570\u636e\u5e93<\/li>\n<\/ul>\n
                                        <\/div>
                                        python<\/span>
                                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.embeddings.openai import<\/span> OpenAIEmbeddings<\/span>\nfrom<\/span> langchain.vectorstores import<\/span> Chroma<\/span>\n# embedding = OpenAIEmbeddings() #\u5982\u4f55\u76f4\u63a5\u8c03\u7528OpenAI\u7684 GPT \u670d\u52a1<\/span><\/span>\nembedding = OpenAIEmbeddings(deployment=embedding_deployment) #\u901a\u8fc7Azure\u7684OpenAI GPT\u670d\u52a1<\/span><\/span>\nvectordb = Chroma.from_documents(<\/span>\n    documents=splits,<\/span>\n    collection_name=\"spotmax\"<\/span>,<\/span>\n    embedding=embedding,<\/span>\n    persist_directory=persist_directory<\/span>\n)<\/span>\nvectordb.persist()<\/span>\nprint<\/span>(vectordb._collection.count())<\/span>\n<\/code><\/pre>\n
                                        <\/div>
                                        python<\/span>
                                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>docs = vectordb.similarity_search(\"\u5982\u4f55\u63d0\u9ad8\u53ef\u7528\u6027\"<\/span>,k=2<\/span>)<\/span>\n<\/code><\/pre>\n
                                        \u5229\u7528\u5411\u91cf\u6570\u636e\u5e93\u8fdb\u884cQA<\/h2>\n
                                        <\/div>
                                        python<\/span>
                                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> langchain.chains import<\/span> RetrievalQA<\/span>\nfrom<\/span> langchain.chat_models import<\/span> AzureChatOpenAI<\/span>\n# from langchain.chat_models import ChatOpenAI #\u76f4\u63a5\u8bbf\u95eeOpenAI\u7684GPT\u670d\u52a1<\/span><\/span>\n<\/span>\n#llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0) #\u76f4\u63a5\u8bbf\u95eeOpenAI\u7684GPT\u670d\u52a1<\/span><\/span>\nllm = AzureChatOpenAI(deployment_name = deployment, model_name=model, temperature=0.7<\/span>, max_tokens=1000<\/span>) #\u901a\u8fc7Azure\u7684OpenAI\u670d\u52a1<\/span><\/span>\nretriever=vectordb.as_retriever(<\/span>\n    search_type=\"mmr\"<\/span>, search_kwargs={\"k\"<\/span>: 3<\/span>}<\/span>\n    #search_type=\"similarity\", search_kwargs={\"k\": 3}<\/span><\/span>\n)<\/span>\nqa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"refine\"<\/span>, retriever=retriever,<\/span>\n                                 return_source_documents=False<\/span>, verbose=True<\/span>)<\/span>\n<\/span>\n<\/code><\/pre>\n
                                        <\/div>
                                        python<\/span>
                                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>result = qa({\"query\"<\/span>: \"\u4ec0\u4e48\u662fSpotMax?\"<\/span>})<\/span>\nprint<\/span>(result[\"result\"<\/span>])<\/span>\n<\/span>\n<\/code><\/pre>\n
                                        \u4ece\u6587\u672c\u5230\u56fe\u7247<\/h1>\n
                                        <\/div>
                                        python<\/span>
                                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import<\/span> os<\/span>\nimport<\/span> http.client<\/span>\nimport<\/span> requests<\/span>\nimport<\/span> time<\/span>\n<\/span>\ndef<\/span> create_image<\/span>(prompt<\/span>):<\/span>\n  api_base = os.getenv(\"OPENAI_API_BASE\"<\/span>)<\/span>\n  api_key = os.getenv(\"OPENAI_API_KEY\"<\/span>)<\/span>\n  api_version = api_version = '2022-08-03-preview'<\/span><\/span>\n<\/span>\n  url = \"{}dalle\/text-to-image?api-version={}\"<\/span>.format<\/span>(api_base, api_version)<\/span>\n  headers= { \"api-key\"<\/span>: api_key, \"Content-Type\"<\/span>: \"application\/json\"<\/span> }<\/span>\n  body = {<\/span>\n      \"caption\"<\/span>: prompt,<\/span>\n      \"resolution\"<\/span>: \"512x512\"<\/span><\/span>\n  }<\/span>\n  submission = requests.post(url, headers=headers, json=body)<\/span>\n  operation_location = submission.headers['Operation-Location'<\/span>]<\/span>\n  retry_after = submission.headers['Retry-after'<\/span>]<\/span>\n  status = \"\"<\/span><\/span>\n  while<\/span> (status != \"Succeeded\"<\/span>):<\/span>\n      time.sleep(int<\/span>(retry_after))<\/span>\n      response = requests.get(operation_location, headers=headers)<\/span>\n      status = response.json()['status'<\/span>]<\/span>\n  image_url = response.json()['result'<\/span>]['contentUrl'<\/span>]<\/span>\n  #display(Image(url=image_url))<\/span><\/span>\n  return<\/span> \"\\n![image](\"<\/span>+image_url+\")\"<\/span><\/span>\n<\/code><\/pre>\n
                                        <\/div>
                                        python<\/span>
                                        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>print<\/span> (create_image(\"A dog on the street.\"<\/span>))<\/span>\n<\/code><\/pre>\n
                                        \u6784\u5efa\u591a\u6a21\u6001Chatbot,\u5305\u62ec\u6709\u8bb0\u5fc6\u7684Agent<\/h1>\n","protected":false},"excerpt":{"rendered":"
                                        \u4e0a\u4e0b\u6587\u5b66\u4e60In-context learning \u5e94\u7528\u5927\u8bed\u8a00\u6a21\u578b\u8981\u4ece\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u601d\u7ef4\u5207\u6362\u4e3a\u4e0a\u4e0b\u6587\u5b66\u4e60\u7684\u601d\u8def \u4e0a\u4e0b\u6587\u5b66\u4e60\u5305\u62ecZero-shot learning \u548c Few-shot Learning <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":"","views":"3","footnotes":""},"categories":[3],"tags":[126,127,128,129,136],"collection":[],"class_list":["post-1918","post","type-post","status-publish","format-standard","hentry","category-fenlei2","tag-gpt","tag-ai","tag-128","tag-129","tag-136"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1918","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/comments?post=1918"}],"version-history":[{"count":0,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1918\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/media?parent=1918"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/categories?post=1918"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/tags?post=1918"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/collection?post=1918"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}