Multilingual_RAG.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73sQ2LNXOo_9",
        "outputId": "0476a7ba-b6ec-4612-ca5a-242500132ae8"
      },
      "outputs": [],
      "source": [
        "!curl -fsSL https://ollama.com/install.sh | sh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqwMO8lbOv4f",
        "outputId": "13d88045-edac-4501-ee82-f10c20ec442e"
      },
      "outputs": [],
      "source": [
        "!pip install -qU pinecone-client pinecone-datasets langchain-pinecone"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96L-FFfaO4uq",
        "outputId": "a6177df2-cd50-46f0-ab09-85e90d76d5a1"
      },
      "outputs": [],
      "source": [
        "!pip install --quiet langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python langchain-mistralai gpt4all"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXz-pKIDPRkN",
        "outputId": "97c4238d-46eb-4870-8f9b-44475330b2f2"
      },
      "outputs": [],
      "source": [
        "%pip install --quiet sentence_transformers huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxD8PZh3O8NE",
        "outputId": "270bd074-ba1a-44db-b681-196617ee1529"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade --quiet  wikipedia"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sG2D6_edO9wA"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import bs4\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.prompts import ChatPromptTemplate, PromptTemplate\n",
        "from langchain_community.chat_models import ChatOllama\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "import pinecone\n",
        "from pinecone import Pinecone, ServerlessSpec, PodSpec\n",
        "from langchain_community.document_loaders import WikipediaLoader\n",
        "from google.colab import userdata\n",
        "import time\n",
        "from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline\n",
        "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "import sentence_transformers\n",
        "from langchain import hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NMftjygsPASq"
      },
      "outputs": [],
      "source": [
        "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
        "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
        "# In the following two lines either replace the function with\n",
        "# your API keys or save them in the secrets section in google\n",
        "# colab\n",
        "os.environ['LANGCHAIN_API_KEY'] = userdata.get('LANGCHAIN_API_KEY')\n",
        "os.environ['PINECONE_API_KEY'] = userdata.get('PINECONE_API_KEY')\n",
        "pinecone_api_key = os.environ['PINECONE_API_KEY']\n",
        "use_serverless = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2uuBJOsPDNp",
        "outputId": "186320bd-80b4-4d52-8939-9b0058c6c637"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/wikipedia/wikipedia.py:389: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
            "\n",
            "The code that caused this warning is on line 389 of the file /usr/local/lib/python3.10/dist-packages/wikipedia/wikipedia.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
            "\n",
            "  lis = BeautifulSoup(html).find_all('li')\n"
          ]
        }
      ],
      "source": [
        "# Loading Wikipedia articles about Boeing, in this\n",
        "# case we load Google, Microsoft, OpenAI related articles\n",
        "# 24 each\n",
        "french_docs =  WikipediaLoader(query=\"Droits de l'homme\", lang= \"fr\").load()\n",
        "french_docs.extend(WikipediaLoader(query=\"Droits des femmes\", lang= \"fr\").load())\n",
        "french_docs.extend(WikipediaLoader(query=\"Droits de l'enfant\", lang= \"fr\").load())\n",
        "english_docs = WikipediaLoader(query=\"Human rights\", lang= \"en\").load()\n",
        "english_docs.extend(WikipediaLoader(query=\"Women's rights\", lang= \"en\").load())\n",
        "english_docs.extend(WikipediaLoader(query=\"Children's rights\", lang= \"en\").load())\n",
        "german_docs =  WikipediaLoader(query=\"Menschenrechte\", lang= \"de\").load()\n",
        "german_docs.extend(WikipediaLoader(query=\"Frauenrechte\", lang= \"de\").load())\n",
        "german_docs.extend(WikipediaLoader(query=\"Kinderrechte\", lang= \"de\").load())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def remove_duplicates(docs):\n",
        "    # Remove duplicates\n",
        "    unique_docs = []\n",
        "    unique_sources = []\n",
        "    for doc in docs:\n",
        "        if doc.metadata[\"source\"] not in unique_sources:\n",
        "            unique_docs.append(doc)\n",
        "            unique_sources.append(doc.metadata[\"source\"])\n",
        "    return unique_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "french_docs = remove_duplicates(french_docs)\n",
        "english_docs = remove_duplicates(english_docs)\n",
        "german_docs = remove_duplicates(german_docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "eSlteHWzPRF0"
      },
      "outputs": [],
      "source": [
        "def doc_chunk(docs, chunk_size= 100, chunk_overlap= 50):\n",
        "  # Split\n",
        "  text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
        "      chunk_size = chunk_size,\n",
        "      chunk_overlap = chunk_overlap)\n",
        "\n",
        "  # Make splits\n",
        "  splits = text_splitter.split_documents(docs)\n",
        "  return splits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Uya8evCYPj98"
      },
      "outputs": [],
      "source": [
        "french_splits = doc_chunk(french_docs)\n",
        "english_splits = doc_chunk(english_docs)\n",
        "german_splits = doc_chunk(german_docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "8t2TPtP03gL4",
        "outputId": "32d2a0d1-9ac8-42c9-a78a-d2ec90a2c1d9"
      },
      "outputs": [],
      "source": [
        "EMBEDDING_MODEL_NAME = \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\"\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(\n",
        "    model_name=EMBEDDING_MODEL_NAME\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbdT9ysL7IGr",
        "outputId": "668b9349-e3d8-4cc3-c291-8086e22b86e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "384"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(embeddings.embed_query(\"Hello World\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "jPLN-n2WPolJ"
      },
      "outputs": [],
      "source": [
        "pc = Pinecone(api_key=pinecone_api_key)\n",
        "\n",
        "def create_pinecone_index(pc, index_name, embedding_dim = 1024, use_serverless = True, metric = \"dotproduct\"):\n",
        "    if use_serverless:\n",
        "        spec = ServerlessSpec(cloud='aws', region='us-west-2')\n",
        "    else:\n",
        "        # if not using a starter index, you should specify a pod_type too\n",
        "        spec = PodSpec()\n",
        "    # check for and delete index if already exists\n",
        "    if index_name in pc.list_indexes().names():\n",
        "        pc.delete_index(index_name)\n",
        "    # create a new index\n",
        "    pc.create_index(\n",
        "        index_name,\n",
        "        dimension= embedding_dim,  # dimensionality of the embedding model\n",
        "        metric= metric,\n",
        "        spec=spec\n",
        "    )\n",
        "    # wait for index to be initialized\n",
        "    while not pc.describe_index(index_name).status['ready']:\n",
        "        time.sleep(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oIVbrgA4PthK"
      },
      "outputs": [],
      "source": [
        "create_pinecone_index(pc, \"french-db\", embedding_dim=384)\n",
        "create_pinecone_index(pc, \"english-db\", embedding_dim=384)\n",
        "create_pinecone_index(pc, \"german-db\", embedding_dim=384)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "i2Q2N_uSeGMG"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores import Pinecone\n",
        "def create_pinecone_vectorstore(splits, embeddings, index_name):\n",
        "  vectorstore = Pinecone.from_documents(splits, embeddings, index_name = index_name)\n",
        "  retriever = vectorstore.as_retriever()\n",
        "  return vectorstore, retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "HhwAmfPbeK57"
      },
      "outputs": [],
      "source": [
        "french_vectorstore, french_retriever = create_pinecone_vectorstore(french_splits, embeddings, \"french-db\")\n",
        "english_vectorstore, english_retriever = create_pinecone_vectorstore(english_splits, embeddings, \"english-db\")\n",
        "german_vectorstore, german_retriever = create_pinecone_vectorstore(german_splits, embeddings, \"german-db\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "nvfYSkRPenNW"
      },
      "outputs": [],
      "source": [
        "routing_local_llm = \"stablelm2\"\n",
        "\n",
        "routing_llm = ChatOllama(model=routing_local_llm, temperature=0.75)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2spttcchRcs",
        "outputId": "3b10754b-2b27-4f5c-e385-73f9f9338246"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The origin language of the given query \"When was the human rights treaty made?\" can be identified as English, as it is a widely spoken and understood language. This query refers to searching for information about when the Universal Declaration of Human Rights (UDHR) or other related human rights treaties were established.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "prompt = \"\"\"\n",
        "Answer in one line\n",
        "what is the origin language of the following query:\n",
        "{question}\n",
        "\n",
        "The possible languages are:\n",
        "- French\n",
        "- English\n",
        "- German\n",
        "\"\"\"\n",
        "\n",
        "prompt_template = PromptTemplate(\n",
        "    input_variables=[\"question\"],\n",
        "    template=prompt,\n",
        ")\n",
        "\n",
        "router = prompt_template | routing_llm | StrOutputParser()\n",
        "\n",
        "question = \"\"\"\n",
        "When was the human rights treaty made\n",
        "\"\"\"\n",
        "\n",
        "result = router.invoke({'question': question})\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "sc-MTDHVhZOv"
      },
      "outputs": [],
      "source": [
        "def hyde_doc_generation(llm, hyde_template):\n",
        "  # HyDE document genration\n",
        "  # template = \"\"\"Please write a scientific paper passage to answer the question\n",
        "  # Question: {question}\n",
        "  # Passage:\"\"\"\n",
        "  prompt_hyde = ChatPromptTemplate.from_template(hyde_template)\n",
        "\n",
        "  generate_docs_for_retrieval = (\n",
        "      prompt_hyde | llm | StrOutputParser()\n",
        "  )\n",
        "\n",
        "  return generate_docs_for_retrieval\n",
        "\n",
        "def hyde_docs(generate_docs_for_retrieval, retriever):\n",
        "  retrieval_chain = generate_docs_for_retrieval | retriever\n",
        "  retrieved_docs = retrieval_chain.invoke({\"question\":question})\n",
        "  return retrieved_docs\n",
        "\n",
        "def hyde_rag(llm, rag_template):\n",
        "  # template = \"\"\"Answer the following question based on this context:\n",
        "\n",
        "  # {context}\n",
        "\n",
        "  # Question: {question}\n",
        "  # \"\"\"\n",
        "\n",
        "  prompt = rag_template\n",
        "\n",
        "  final_rag_chain = (\n",
        "      prompt\n",
        "      | llm\n",
        "      | StrOutputParser()\n",
        "  )\n",
        "\n",
        "  return final_rag_chain\n",
        "\n",
        "def rag_wrapper(llm, retriever, hyde_template, rag_template):\n",
        "  generate_docs_for_retrieval = hyde_doc_generation(llm, hyde_template)\n",
        "  retrieved_docs = hyde_docs(generate_docs_for_retrieval, retriever)\n",
        "  rag_chain = hyde_rag(llm, rag_template)\n",
        "  return rag_chain, retrieved_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Haz0UbMCqarD"
      },
      "outputs": [],
      "source": [
        "def call_hugging_face_pipeline(model_id):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_id)\n",
        "    pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=10)\n",
        "    hf = HuggingFacePipeline(pipeline=pipe)\n",
        "    return hf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "cniZD2rRmS-a"
      },
      "outputs": [],
      "source": [
        "def choose_route(result):\n",
        "    if \"French\" in result:\n",
        "        french_hyde_template = \"\"\"Veuillez rédiger un paragraphe scientifique pour répondre à la question\n",
        "        Question: {question}\n",
        "        Paragraphe:\"\"\"\n",
        "        french_rag_template = \"\"\"Répondez à la question suivante en fonction de ce contexte:\n",
        "\n",
        "        {context}\n",
        "\n",
        "        Question: {question}\n",
        "        \"\"\"\n",
        "        french_rag_template = ChatPromptTemplate.from_template(french_rag_template)\n",
        "        french_model_id = \"paulml/Hermes-2-Pro-French\"\n",
        "        french_hyde_llm = call_hugging_face_pipeline(french_model_id)\n",
        "        french_chain, retrieved_docs = rag_wrapper(french_hyde_llm,\n",
        "                                                    french_retriever,\n",
        "                                                    french_hyde_template,\n",
        "                                                    french_rag_template)\n",
        "        return french_chain, retrieved_docs\n",
        "    elif \"English\" in result:\n",
        "        hyde_local_llm = \"mistral-openorca\"\n",
        "\n",
        "        english_hyde_llm = ChatOllama(model=hyde_local_llm, temperature=0.75)\n",
        "        english_hyde_template = \"\"\"Please write a scientific paper passage to answer the question\n",
        "        Question: {question}\n",
        "        Passage:\"\"\"\n",
        "        english_rag_template = hub.pull(\"rlm/rag-prompt\")\n",
        "        english_chain, retrieved_docs = rag_wrapper(english_hyde_llm,\n",
        "                                                    english_retriever,\n",
        "                                                    english_hyde_template,\n",
        "                                                    english_rag_template)\n",
        "        return english_chain, retrieved_docs\n",
        "    else:\n",
        "        german_model_id = \"mayflowergmbh/Wiedervereinigung-7b-dpo\"\n",
        "        german_hyde_llm = call_hugging_face_pipeline(german_model_id)\n",
        "        german_hyde_template = \"\"\"Bitte verfassen Sie eine wissenschaftliche Textpassage zur Beantwortung der Frage\n",
        "        Frage: {question}\n",
        "        Textpassage:\"\"\"\n",
        "        german_rag_template = hub.pull(\"askmewhy/rag-prompt-de\")\n",
        "        german_chain, retrieved_docs = rag_wrapper(german_hyde_llm,\n",
        "                                                    german_retriever,\n",
        "                                                    german_hyde_template,\n",
        "                                                    german_rag_template)\n",
        "        return german_chain, retrieved_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "ZTzwnHMiDU8S"
      },
      "outputs": [],
      "source": [
        "final_chain, retrieved_docs = choose_route(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "CtlQzwOGhfRb"
      },
      "outputs": [],
      "source": [
        "answer = final_chain.invoke({\"context\":retrieved_docs,\"question\":question})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrbvtDd-y88F",
        "outputId": "36a5f0fe-0466-4273-c098-ab83869443e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ChatPromptTemplate(input_variables=['context', 'question'], metadata={'lc_hub_owner': 'rlm', 'lc_hub_repo': 'rag-prompt', 'lc_hub_commit_hash': '50442af133e61576e74536c6556cefe1fac147cad032f4377b60c436e6cdcb6e'}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: {question} \\nContext: {context} \\nAnswer:\"))])\n",
              "| ChatOllama(model='mistral-openorca', temperature=0.75)\n",
              "| StrOutputParser()"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_chain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gOhIzP9y0oWO",
        "outputId": "f70774f2-70e8-446f-d7c6-d5eb89277f97"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' The Universal Declaration of Human Rights was adopted on 10 December 1948.<|im_end|>'"
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          },
          "execution_count": 44,
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      ],
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        "answer"
      ]
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