chapter6/langchain_rag.ipynb

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       "Document(page_content='\\n\\n\\n\\n\\nSitemap | 🦜️🔗 LangChain\\n\\n\\n\\n\\n\\n\\n\\nSkip to main contentComponentsIntegrationsGuidesAPI ReferenceMorePeopleVersioningContributingTemplatesCookbooksTutorialsYouTube🦜️🔗LangSmithLangSmith DocsLangServe GitHubTemplates GitHubTemplates HubLangChain HubJS/TS Docs💬SearchProvidersProvidersAnthropicAWSGoogleHugging FaceMicrosoftOpenAIMoreComponentsChat modelsLLMsEmbedding modelsDocument loadersacreomAirbyteLoaderAirbyte CDK (Deprecated)Airbyte Gong (Deprecated)Airbyte Hubspot (Deprecated)Airbyte JSON (Deprecated)Airbyte Salesforce (Deprecated)Airbyte Shopify (Deprecated)Airbyte Stripe (Deprecated)Airbyte Typeform (Deprecated)Airbyte Zendesk Support (Deprecated)AirtableAlibaba Cloud MaxComputeAmazon TextractApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAstraDBAsync ChromiumAsyncHtmlAthenaAWS S3 DirectoryAWS S3 FileAZLyricsAzure AI DataAzure Blob Storage ContainerAzure Blob Storage FileAzure AI Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessCassandraChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCouchbaseCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDocusaurusDropboxDuckDBEmailEPubEtherscanEverNoteFacebook ChatFaunaFigmaFireCrawlGeopandasGitGitBookGitHubGoogle AlloyDB for PostgreSQLGoogle BigQueryGoogle BigtableGoogle Cloud SQL for SQL serverGoogle Cloud SQL for MySQLGoogle Cloud SQL for PostgreSQLGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle Firestore in Datastore ModeGoogle DriveGoogle El Carro for Oracle WorkloadsGoogle Firestore (Native Mode)Google Memorystore for RedisGoogle SpannerGoogle Speech-to-Text Audio TranscriptsGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebooklakeFSLarkSuite (FeiShu)LLM SherpaMastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft ExcelMicrosoft OneDriveMicrosoft OneNoteMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOracle Autonomous DatabaseOrg-modePandas DataFramePebblo Safe DocumentLoaderPolars DataFramePsychicPubMedPySparkQuipReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleSurrealDBTelegramTencent COS DirectoryTencent COS FileTensorFlow DatasetsTiDB2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLVsdxWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsYuqueDocument transformersVector storesRetrieversToolsToolkitsMemoryGraphsCallbacksChat loadersAdaptersStoresComponentsDocument loadersSitemapOn this pageSitemapExtends from the WebBaseLoader, SitemapLoader loads a sitemap from a\\ngiven URL, and then scrape and load all pages in the sitemap, returning\\neach page as a Document.The scraping is done concurrently. There are reasonable limits to\\nconcurrent requests, defaulting to 2 per second. If you aren’t concerned\\nabout being a good citizen, or you control the scrapped server, or don’t\\ncare about load. Note, while this will speed up the scraping process,\\nbut it may cause the server to block you. Be careful!%pip install --upgrade --quiet  nest_asyncio# fixes a bug with asyncio and jupyterimport nest_asyncionest_asyncio.apply()from langchain_community.document_loaders.sitemap import SitemapLoadersitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")docs = sitemap_loader.load()You can change the requests_per_second parameter to increase the max\\nconcurrent requests. and use requests_kwargs to pass kwargs when send\\nrequests.sitemap_loader.requests_per_second = 2# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issuesitemap_loader.requests_kwargs = {\"verify\": False}docs[0]Document(page_content=\\'\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nLangChain Python API Reference Documentation.\\\\n\\\\n\\\\nYou will be automatically redirected to the new location of this page.\\\\n\\\\n\\', metadata={\\'source\\': \\'https://api.python.langchain.com/en/stable/\\', \\'loc\\': \\'https://api.python.langchain.com/en/stable/\\', \\'lastmod\\': \\'2024-02-09T01:10:49.422114+00:00\\', \\'changefreq\\': \\'weekly\\', \\'priority\\': \\'1\\'})Filtering sitemap URLs\\u200bSitemaps can be massive files, with thousands of URLs. Often you don’t\\nneed every single one of them. You can filter the URLs by passing a list\\nof strings or regex patterns to the filter_urls parameter. Only URLs\\nthat match one of the patterns will be loaded.loader = SitemapLoader(    web_path=\"https://api.python.langchain.com/sitemap.xml\",    filter_urls=[\"https://api.python.langchain.com/en/latest\"],)documents = loader.load()documents[0]Document(page_content=\\'\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nLangChain Python API Reference Documentation.\\\\n\\\\n\\\\nYou will be automatically redirected to the new location of this page.\\\\n\\\\n\\', metadata={\\'source\\': \\'https://api.python.langchain.com/en/latest/\\', \\'loc\\': \\'https://api.python.langchain.com/en/latest/\\', \\'lastmod\\': \\'2024-02-12T05:26:10.971077+00:00\\', \\'changefreq\\': \\'daily\\', \\'priority\\': \\'0.9\\'})Add custom scraping rules\\u200bThe SitemapLoader uses beautifulsoup4 for the scraping process, and\\nit scrapes every element on the page by default. The SitemapLoader\\nconstructor accepts a custom scraping function. This feature can be\\nhelpful to tailor the scraping process to your specific needs; for\\nexample, you might want to avoid scraping headers or navigation\\nelements.The following example shows how to develop and use a custom function to\\navoid navigation and header elements.Import the beautifulsoup4 library and define the custom function.pip install beautifulsoup4from bs4 import BeautifulSoupdef remove_nav_and_header_elements(content: BeautifulSoup) -> str:    # Find all \\'nav\\' and \\'header\\' elements in the BeautifulSoup object    nav_elements = content.find_all(\"nav\")    header_elements = content.find_all(\"header\")    # Remove each \\'nav\\' and \\'header\\' element from the BeautifulSoup object    for element in nav_elements + header_elements:        element.decompose()    return str(content.get_text())Add your custom function to the SitemapLoader object.loader = SitemapLoader(    \"https://api.python.langchain.com/sitemap.xml\",    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],    parsing_function=remove_nav_and_header_elements,)Local Sitemap\\u200bThe sitemap loader can also be used to load local files.sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)docs = sitemap_loader.load()Help us out by providing feedback on this documentation page:PreviousRSTNextSlackFiltering sitemap URLsAdd custom scraping rulesLocal SitemapCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogYouTubeCopyright © 2024 LangChain, Inc.\\n\\n\\n\\n', metadata={'source': 'https://python.langchain.com/docs/integrations/document_loaders/sitemap/', 'loc': 'https://python.langchain.com/docs/integrations/document_loaders/sitemap/', 'changefreq': 'weekly', 'priority': '0.5'})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "from langchain_community.document_loaders import DocusaurusLoader\n",
    "\n",
    "\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()\n",
    "\n",
    "loader = DocusaurusLoader(\"https://python.langchain.com\")\n",
    "docs = loader.load()\n",
    "loader = DocusaurusLoader(\n",
    "    \"https://python.langchain.com\",\n",
    "    filter_urls=[\n",
    "        \"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"\n",
    "    ],\n",
    ")\n",
    "documents = loader.load()\n",
    "documents[0]"
   ]
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      "llama_model_loader: - tensor  112:             blk.13.attn_q.weight q5_0     [  4096,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor  114:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  115:           blk.14.ffn_down.weight q5_0     [ 14336,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor  117:             blk.14.ffn_up.weight q5_0     [  4096, 14336,     1,     1 ]\n",
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      "llama_model_loader: - tensor  123:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  132:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  141:           blk.17.ffn_gate.weight q5_0     [  4096, 14336,     1,     1 ]\n",
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      "llama_model_loader: - tensor  146:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  235:           blk.26.ffn_gate.weight q5_0     [  4096, 14336,     1,     1 ]\n",
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      "llama_model_loader: - tensor  240:                    output.weight q6_K     [  4096, 32032,     1,     1 ]\n",
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      "llama_model_loader: - tensor  245:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  263:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  264:           blk.29.ffn_down.weight q5_0     [ 14336,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor  272:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  275:             blk.30.ffn_up.weight q5_0     [  4096, 14336,     1,     1 ]\n",
      "llama_model_loader: - tensor  276:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  277:             blk.30.attn_k.weight q5_0     [  4096,  1024,     1,     1 ]\n",
      "llama_model_loader: - tensor  278:        blk.30.attn_output.weight q5_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  279:             blk.30.attn_q.weight q5_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  280:             blk.30.attn_v.weight q5_0     [  4096,  1024,     1,     1 ]\n",
      "llama_model_loader: - tensor  281:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  282:           blk.31.ffn_down.weight q5_0     [ 14336,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  283:           blk.31.ffn_gate.weight q5_0     [  4096, 14336,     1,     1 ]\n",
      "llama_model_loader: - tensor  284:             blk.31.ffn_up.weight q5_0     [  4096, 14336,     1,     1 ]\n",
      "llama_model_loader: - tensor  285:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  286:             blk.31.attn_k.weight q5_0     [  4096,  1024,     1,     1 ]\n",
      "llama_model_loader: - tensor  287:        blk.31.attn_output.weight q5_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  288:             blk.31.attn_q.weight q5_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  289:             blk.31.attn_v.weight q5_0     [  4096,  1024,     1,     1 ]\n",
      "llama_model_loader: - tensor  290:               output_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
      "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
      "llama_model_loader: - kv   1:                               general.name str              = jeffq\n",
      "llama_model_loader: - kv   2:                       llama.context_length u32              = 32768\n",
      "llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096\n",
      "llama_model_loader: - kv   4:                          llama.block_count u32              = 32\n",
      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336\n",
      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128\n",
      "llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32\n",
      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 8\n",
      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
      "llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000\n",
      "llama_model_loader: - kv  11:                          general.file_type u32              = 8\n",
      "llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama\n",
      "llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32032]   = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
      "llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32032]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
      "llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32032]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
      "llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 1\n",
      "llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 32000\n",
      "llama_model_loader: - kv  18:               tokenizer.ggml.add_bos_token bool             = true\n",
      "llama_model_loader: - kv  19:               tokenizer.ggml.add_eos_token bool             = false\n",
      "llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {% for message in messages %}{{'<|im_...\n",
      "llama_model_loader: - kv  21:               general.quantization_version u32              = 2\n",
      "llama_model_loader: - type  f32:   65 tensors\n",
      "llama_model_loader: - type q5_0:  225 tensors\n",
      "llama_model_loader: - type q6_K:    1 tensors\n",
      "llm_load_vocab: special tokens definition check successful ( 291/32032 ).\n",
      "llm_load_print_meta: format           = GGUF V3 (latest)\n",
      "llm_load_print_meta: arch             = llama\n",
      "llm_load_print_meta: vocab type       = SPM\n",
      "llm_load_print_meta: n_vocab          = 32032\n",
      "llm_load_print_meta: n_merges         = 0\n",
      "llm_load_print_meta: n_ctx_train      = 32768\n",
      "llm_load_print_meta: n_embd           = 4096\n",
      "llm_load_print_meta: n_head           = 32\n",
      "llm_load_print_meta: n_head_kv        = 8\n",
      "llm_load_print_meta: n_layer          = 32\n",
      "llm_load_print_meta: n_rot            = 128\n",
      "llm_load_print_meta: n_gqa            = 4\n",
      "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
      "llm_load_print_meta: f_norm_rms_eps   = 1.0e-05\n",
      "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
      "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
      "llm_load_print_meta: n_ff             = 14336\n",
      "llm_load_print_meta: n_expert         = 0\n",
      "llm_load_print_meta: n_expert_used    = 0\n",
      "llm_load_print_meta: rope scaling     = linear\n",
      "llm_load_print_meta: freq_base_train  = 10000.0\n",
      "llm_load_print_meta: freq_scale_train = 1\n",
      "llm_load_print_meta: n_yarn_orig_ctx  = 32768\n",
      "llm_load_print_meta: rope_finetuned   = unknown\n",
      "llm_load_print_meta: model type       = 7B\n",
      "llm_load_print_meta: model ftype      = Q5_0\n",
      "llm_load_print_meta: model params     = 7.24 B\n",
      "llm_load_print_meta: model size       = 4.65 GiB (5.52 BPW) \n",
      "llm_load_print_meta: general.name     = jeffq\n",
      "llm_load_print_meta: BOS token        = 1 '<s>'\n",
      "llm_load_print_meta: EOS token        = 32000 '<|im_end|>'\n",
      "llm_load_print_meta: UNK token        = 0 '<unk>'\n",
      "llm_load_print_meta: LF token         = 13 '<0x0A>'\n",
      "llm_load_tensors: ggml ctx size =    0.11 MiB\n",
      "llm_load_tensors: mem required  = 4765.79 MiB\n",
      "...................................................................................................\n",
      "llama_new_context_with_model: n_ctx      = 512\n",
      "llama_new_context_with_model: freq_base  = 10000.0\n",
      "llama_new_context_with_model: freq_scale = 1\n",
      "llama_new_context_with_model: KV self size  =   64.00 MiB, K (f16):   32.00 MiB, V (f16):   32.00 MiB\n",
      "llama_build_graph: non-view tensors processed: 676/676\n",
      "AVX = 1 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n",
      "llama_new_context_with_model: compute buffer total size = 4.33 MiB\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from langchain_community.embeddings import LlamaCppEmbeddings\n",
    "\n",
    "llama_embedings = LlamaCppEmbeddings(model_path=os.path.expanduser(\"~/Downloads/Hermes-2-Pro-Mistral-7B.Q5_0.gguf\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e014eb47-e99d-4fb2-9c81-474bf7a9f846",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_chroma import Chroma\n",
    "\n",
    "vectorstore = Chroma.from_documents(documents=docs, embedding=llama_embedings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0d3865a-500c-4c7d-b7a2-dcfcfaae6edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_vertexai import VertexAI\n",
    "\n",
    "llm = VertexAI(model_name=\"gemini-pro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7000aba-e64d-406d-880a-c3dc29fec3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import hub\n",
    "\n",
    "retriever = vectorstore.as_retriever()\n",
    "prompt = hub.pull(\"rlm/rag-prompt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b45d2b7-b566-45c6-b356-33385155ec89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.runnables import RunnablePassthrough\n",
    "\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "rag_chain = (\n",
    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "rag_chain.invoke(\"What is Task Decomposition?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b49471e2-b7e7-42bf-ab03-d31de8a72271",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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