information-extraction-langchain.ipynb

{"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q newspaper3k==0.2.8 python-dotenv","metadata":{"id":"bNnSbi-hOmEd","execution":{"iopub.status.busy":"2023-07-22T07:39:44.926713Z","iopub.execute_input":"2023-07-22T07:39:44.927360Z","iopub.status.idle":"2023-07-22T07:40:15.674216Z","shell.execute_reply.started":"2023-07-22T07:39:44.927301Z","shell.execute_reply":"2023-07-22T07:40:15.672616Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"!pip install langchain deeplake openai tiktoken","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FnwP62m5OtmB","outputId":"d74e1210-21e7-4b9b-f012-15adc5a9e2db","execution":{"iopub.status.busy":"2023-07-22T07:40:15.676734Z","iopub.execute_input":"2023-07-22T07:40:15.677120Z","iopub.status.idle":"2023-07-22T07:40:46.333588Z","shell.execute_reply.started":"2023-07-22T07:40:15.677084Z","shell.execute_reply":"2023-07-22T07:40:46.332062Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting langchain\n  Downloading langchain-0.0.239-py3-none-any.whl (1.4 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting deeplake\n  Downloading deeplake-3.6.12.tar.gz (527 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta 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\u001b[?25ldone\n\u001b[?25h  Created wheel for deeplake: filename=deeplake-3.6.12-py3-none-any.whl size=642184 sha256=85727cf251eb2050114c2485660d28da171227b6ed8acae07d5fc762d8f0977b\n  Stored in directory: /root/.cache/pip/wheels/2e/3c/7c/7f7952a6c967017bb37799c005253a2d07cc1812e34a3b0fb8\nSuccessfully built deeplake\nInstalling collected packages: numcodecs, tiktoken, openapi-schema-pydantic, langsmith, humbug, botocore, openai, aiobotocore, boto3, langchain, aioboto3, deeplake\n  Attempting uninstall: botocore\n    Found existing installation: botocore 1.29.161\n    Uninstalling botocore-1.29.161:\n      Successfully uninstalled botocore-1.29.161\n  Attempting uninstall: aiobotocore\n    Found existing installation: aiobotocore 2.5.2\n    Uninstalling aiobotocore-2.5.2:\n      Successfully uninstalled aiobotocore-2.5.2\n  Attempting uninstall: boto3\n    Found existing installation: boto3 1.26.100\n    Uninstalling boto3-1.26.100:\n      Successfully uninstalled boto3-1.26.100\nSuccessfully installed aioboto3-11.2.0 aiobotocore-2.5.0 boto3-1.26.76 botocore-1.29.76 deeplake-3.6.12 humbug-0.3.2 langchain-0.0.239 langsmith-0.0.14 numcodecs-0.11.0 openai-0.27.8 openapi-schema-pydantic-1.2.4 tiktoken-0.4.0\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\n\nos.environ[\"OPENAI_API_KEY\"] = \"<openai key\"\nos.environ[\"ACTIVELOOP_TOKEN\"] = \"<activeloop key>\"","metadata":{"id":"1P-ChDcCO73f","execution":{"iopub.status.busy":"2023-07-22T07:51:34.117819Z","iopub.execute_input":"2023-07-22T07:51:34.118270Z","iopub.status.idle":"2023-07-22T07:51:34.124275Z","shell.execute_reply.started":"2023-07-22T07:51:34.118236Z","shell.execute_reply":"2023-07-22T07:51:34.123106Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"import requests\nfrom newspaper import Article # https://github.com/codelucas/newspaper\nimport time\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'\n}\n\narticle_urls = [\n          \"https://www.britannica.com/place/India/History\",\n    \"https://en.wikipedia.org/wiki/History_of_India\"                # add as many source you want\n]\n\nsession = requests.Session()\npages_content = [] # where we save the scraped articles\n\nfor url in article_urls:\n    try:\n        time.sleep(2) # sleep two seconds for gentle scraping\n        response = session.get(url, headers=headers, timeout=10)\n\n        if response.status_code == 200:\n            article = Article(url)\n            article.download() # download HTML of webpage\n            article.parse() # parse HTML to extract the article text\n            pages_content.append({ \"url\": url, \"text\": article.text })\n        else:\n            print(f\"Failed to fetch article at {url}\")\n    except Exception as e:\n        print(f\"Error occurred while fetching article at {url}: {e}\")\n\n","metadata":{"id":"FeGZlWlwQjhO","execution":{"iopub.status.busy":"2023-07-22T07:51:34.232626Z","iopub.execute_input":"2023-07-22T07:51:34.234497Z","iopub.status.idle":"2023-07-22T07:51:42.591366Z","shell.execute_reply.started":"2023-07-22T07:51:34.234421Z","shell.execute_reply":"2023-07-22T07:51:42.590152Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"from langchain.embeddings.openai import OpenAIEmbeddings\nfrom langchain.vectorstores import DeepLake\n\nembeddings = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n\n# TODO: use your organization id here. (by default, org id is your username)\nmy_activeloop_org_id = \"<org_id>\"\nmy_activeloop_dataset_name = \"langchain_course_qabot_with_source2\"\ndataset_path = f\"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}\"\n\ndb = DeepLake(dataset_path=dataset_path, embedding_function=embeddings)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c-0Phu6nQ76m","outputId":"626c5bbe-4dae-43c3-93d4-40edb3e5680b","execution":{"iopub.status.busy":"2023-07-22T07:51:42.593493Z","iopub.execute_input":"2023-07-22T07:51:42.593874Z","iopub.status.idle":"2023-07-22T07:51:50.557369Z","shell.execute_reply.started":"2023-07-22T07:51:42.593842Z","shell.execute_reply":"2023-07-22T07:51:50.555764Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Deep Lake Dataset in hub://kumarrupesh2002/langchain_course_qabot_with_source2 already exists, loading from the storage\n","output_type":"stream"}]},{"cell_type":"code","source":"from langchain.text_splitter import RecursiveCharacterTextSplitter\n\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n\nall_texts, all_metadatas = [], []\nfor d in pages_content:\n    chunks = text_splitter.split_text(d[\"text\"])\n    for chunk in chunks:\n        all_texts.append(chunk)\n        all_metadatas.append({ \"source\": d[\"url\"] })","metadata":{"id":"Z9RKwdGyRARY","execution":{"iopub.status.busy":"2023-07-22T07:51:50.558820Z","iopub.execute_input":"2023-07-22T07:51:50.559565Z","iopub.status.idle":"2023-07-22T07:51:50.582594Z","shell.execute_reply.started":"2023-07-22T07:51:50.559530Z","shell.execute_reply":"2023-07-22T07:51:50.580991Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"db.add_texts(all_texts, all_metadatas)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6KgLL9eGRWBx","outputId":"3c2a66dc-26ea-4af4-80a6-3686d31357fe","execution":{"iopub.status.busy":"2023-07-22T07:51:50.585253Z","iopub.execute_input":"2023-07-22T07:51:50.585733Z","iopub.status.idle":"2023-07-22T07:52:06.672720Z","shell.execute_reply.started":"2023-07-22T07:51:50.585691Z","shell.execute_reply":"2023-07-22T07:52:06.671514Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stderr","text":"\\","output_type":"stream"},{"name":"stdout","text":"Dataset(path='hub://kumarrupesh2002/langchain_course_qabot_with_source2', tensors=['embedding', 'id', 'metadata', 'text'])\n\n  tensor      htype       shape      dtype  compression\n  -------    -------     -------    -------  ------- \n embedding  embedding  (185, 1536)  float32   None   \n    id        text      (185, 1)      str     None   \n metadata     json      (185, 1)      str     None   \n   text       text      (185, 1)      str     None   \n","output_type":"stream"},{"name":"stderr","text":" \r","output_type":"stream"},{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"['9de48bd4-2864-11ee-8205-0242ac130202',\n '9de48d3c-2864-11ee-8205-0242ac130202',\n '9de48dd2-2864-11ee-8205-0242ac130202',\n '9de48e22-2864-11ee-8205-0242ac130202',\n '9de48e68-2864-11ee-8205-0242ac130202',\n '9de48eae-2864-11ee-8205-0242ac130202',\n '9de48efe-2864-11ee-8205-0242ac130202',\n '9de490d4-2864-11ee-8205-0242ac130202',\n '9de4914c-2864-11ee-8205-0242ac130202',\n '9de491a6-2864-11ee-8205-0242ac130202',\n '9de491ec-2864-11ee-8205-0242ac130202',\n '9de49232-2864-11ee-8205-0242ac130202',\n '9de49278-2864-11ee-8205-0242ac130202',\n '9de492d2-2864-11ee-8205-0242ac130202',\n '9de49318-2864-11ee-8205-0242ac130202',\n '9de4935e-2864-11ee-8205-0242ac130202',\n '9de493ae-2864-11ee-8205-0242ac130202',\n '9de493fe-2864-11ee-8205-0242ac130202',\n '9de49444-2864-11ee-8205-0242ac130202',\n 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'9de4b604-2864-11ee-8205-0242ac130202',\n '9de4b64a-2864-11ee-8205-0242ac130202',\n '9de4b744-2864-11ee-8205-0242ac130202',\n '9de4b7a8-2864-11ee-8205-0242ac130202',\n '9de4b7ee-2864-11ee-8205-0242ac130202',\n '9de4b834-2864-11ee-8205-0242ac130202',\n '9de4b8d4-2864-11ee-8205-0242ac130202',\n '9de4b924-2864-11ee-8205-0242ac130202',\n '9de4b96a-2864-11ee-8205-0242ac130202',\n '9de4b9b0-2864-11ee-8205-0242ac130202',\n '9de4b9f6-2864-11ee-8205-0242ac130202',\n '9de4ba3c-2864-11ee-8205-0242ac130202',\n '9de4ba82-2864-11ee-8205-0242ac130202',\n '9de4bac8-2864-11ee-8205-0242ac130202',\n '9de4bb0e-2864-11ee-8205-0242ac130202',\n '9de4bb68-2864-11ee-8205-0242ac130202',\n '9de4bbae-2864-11ee-8205-0242ac130202',\n '9de4bbf4-2864-11ee-8205-0242ac130202',\n '9de4bc3a-2864-11ee-8205-0242ac130202',\n '9de4bc80-2864-11ee-8205-0242ac130202',\n '9de4bcc6-2864-11ee-8205-0242ac130202',\n '9de4bd0c-2864-11ee-8205-0242ac130202',\n '9de4bd52-2864-11ee-8205-0242ac130202',\n '9de4bd98-2864-11ee-8205-0242ac130202']"},"metadata":{}}]},{"cell_type":"code","source":"from langchain.chains import RetrievalQAWithSourcesChain\nfrom langchain import OpenAI\n\nllm = OpenAI(model_name=\"text-davinci-003\", temperature=0)\n\nchain = RetrievalQAWithSourcesChain.from_chain_type(llm= llm,\n                                                    chain_type=\"stuff\",\n                                                    retriever=db.as_retriever()\n                                                     )","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":519},"id":"w3pmfboYRYCo","outputId":"0fdacb18-0012-4a0f-b46c-c35781a4a22b","execution":{"iopub.status.busy":"2023-07-22T07:52:06.674169Z","iopub.execute_input":"2023-07-22T07:52:06.674519Z","iopub.status.idle":"2023-07-22T07:52:06.681130Z","shell.execute_reply.started":"2023-07-22T07:52:06.674489Z","shell.execute_reply":"2023-07-22T07:52:06.680118Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"d_response = chain({\"question\": \"create bullet points of Indian History after 1800\"})\n\nprint(\"Response:\")\nprint(d_response[\"answer\"])\nprint(\"Sources:\")\nfor source in d_response[\"sources\"].split(\", \"):\n    print(\"- \" + source)","metadata":{"id":"QJHgQghuRb7F","execution":{"iopub.status.busy":"2023-07-22T07:52:06.682577Z","iopub.execute_input":"2023-07-22T07:52:06.683570Z","iopub.status.idle":"2023-07-22T07:52:12.776123Z","shell.execute_reply.started":"2023-07-22T07:52:06.683534Z","shell.execute_reply":"2023-07-22T07:52:12.774762Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Response:\n\nBullet points of Indian History after 1800:\n- East India Company gradually annexed large regions of India from mid-18th century to mid-19th century\n- Indian Rebellion of 1857 led to dissolution of the company and India was ruled directly by the British Crown in the British Raj\n- Nationwide struggle for independence launched by Indian National Congress led by Mahatma Gandhi\n- All-India Muslim League advocated for a separate Muslim-majority nation state\n- British Indian Empire was partitioned in August 1947 into the Dominion of India and Dominion of Pakistan\n- Colonial government strengthened and expanded infrastructure via court system, legal procedures, and statutes\n- Indian Penal Code came into being\n- Thomas Babington Macaulay made schooling a priority for the Raj and English was used as the medium of instruction\n- Indian economy grew at about 1% per year from 1880 to 1920\n- Indian private industry began to grow significantly\n- India built a modern railway system in the late 19th century\n- British Raj invested heavily in infrastructure\n- Various parts of India were ruled by numerous dynasties, including the Gupta Empire in the 4-6th centuries CE\n- Various dynasties declared independence and had a significant impact on the history of South India\nSOURC\nSources:\n- \n","output_type":"stream"}]}]}