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| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
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| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/chottokun/34d8febafb94b011fb768a795273b616/summaryragretriever.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip --quiet install langchain langchain-community openai chromadb tiktoken sentence_transformers huggingface_hub" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "WqMC3BPs6xKk", | |
| "outputId": "dbddf962-95b1-4c2e-e3f1-a5bb4016f53c" | |
| }, | |
| "execution_count": 5, | |
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| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.4/2.5 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m30.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m49.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m423.3/423.3 kB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25h" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "source": [ | |
| "from langchain.vectorstores import Chroma\n", | |
| "from langchain.embeddings import HuggingFaceEmbeddings\n", | |
| "\n", | |
| "# 埋め込みモデルを用意\n", | |
| "embeddings = HuggingFaceEmbeddings(model_name=\"intfloat/multilingual-e5-small\")\n", | |
| "\n", | |
| "# ChromaDBのコレクションを作成（または既存のコレクションを利用）\n", | |
| "vector_store = Chroma(\n", | |
| " collection_name=\"my_collection\",\n", | |
| " embedding_function=embeddings,\n", | |
| " persist_directory=\"./chroma_db\"\n", | |
| ")\n", | |
| "\n", | |
| "# 例としてドキュメントとメタデータを追加\n", | |
| "documents = [\n", | |
| " \"The cat is sleeping on the sofa.\",\n", | |
| " \"Cattle are grazing in the field.\",\n", | |
| " \"The dog is barking loudly.\"\n", | |
| "]\n", | |
| "metadatas = [\n", | |
| " {\"animal\": \"cat\", \"color\": \"black\"},\n", | |
| " {\"animal\": \"cattle\", \"color\": \"brown\"},\n", | |
| " {\"animal\": \"dog\", \"color\": \"white\"}\n", | |
| "]\n", | |
| "ids = [\"doc1\", \"doc2\", \"doc3\"]\n", | |
| "\n", | |
| "vector_store.add_texts(texts=documents, metadatas=metadatas, ids=ids)\n", | |
| "\n", | |
| "# 検索例：animalキーに対してワイルドカード検索「ca*」\n", | |
| "# ChromaDBは$containsや$regexをサポートしていないため、\n", | |
| "# where句で完全一致検索を行い、結果をPython側でフィルタリングします。\n", | |
| "import re\n", | |
| "\n", | |
| "# where句でanimalキーが\"cat\"または\"cattle\"であるものを検索\n", | |
| "# Use the 'filter' argument instead of 'where'\n", | |
| "results = vector_store.similarity_search(query=\"any query\", k=2, filter={\"animal\": {\"$in\": [\"cat\", \"cattle\"]}})\n", | |
| "\n", | |
| "# 複数キーの横断検索例：\n", | |
| "# where句でanimalキーが\"cat\"または\"cattle\"、またはcolorキーが\"brown\"であるものを検索\n", | |
| "# Use the 'filter' argument instead of 'where'\n", | |
| "results = vector_store.similarity_search(query=\"any query\", k=2, filter={\"$or\": [{\"animal\": {\"$in\": [\"cat\", \"cattle\"]}}, {\"color\": {\"$eq\": \"brown\"}}]})" | |
| ], | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "eRcEVq8S95nJ" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "results" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "uH0M7MW4-CLt", | |
| "outputId": "1e26ea6b-c645-48cc-cd9f-71ad1c5c40f3" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[Document(metadata={'animal': 'cattle', 'color': 'brown'}, page_content='Cattle are grazing in the field.'),\n", | |
| " Document(metadata={'color': 'black', 'animal': 'cat'}, page_content='The cat is sleeping on the sofa.')]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "source": [ | |
| "from langchain.vectorstores import Chroma\n", | |
| "from langchain.embeddings import HuggingFaceEmbeddings\n", | |
| "import re\n", | |
| "\n", | |
| "\n", | |
| "# 検索例：animalキーに対してワイルドカード検索「ca*」\n", | |
| "def wildcard_search(query, key, pattern):\n", | |
| " \"\"\"\n", | |
| " Performs a wildcard search on the specified key using a regular expression pattern.\n", | |
| "\n", | |
| " Args:\n", | |
| " query: The query string for similarity search.\n", | |
| " key: The metadata key to search on (e.g., \"animal\").\n", | |
| " pattern: The regular expression pattern for the wildcard search (e.g., \"ca.*\").\n", | |
| "\n", | |
| " Returns:\n", | |
| " A list of search results matching the wildcard pattern.\n", | |
| " \"\"\"\n", | |
| " # Perform similarity search to get all results\n", | |
| " all_results = vector_store.similarity_search(query=query, k=10) # Adjust 'k' as needed\n", | |
| "\n", | |
| " # Filter results based on the wildcard pattern\n", | |
| " filtered_results = [\n", | |
| " doc for doc in all_results\n", | |
| " if doc.metadata and key in doc.metadata and re.match(pattern, doc.metadata[key])\n", | |
| " ]\n", | |
| "\n", | |
| " return filtered_results\n", | |
| "\n", | |
| "# Example usage:\n", | |
| "results = wildcard_search(query=\"any query\", key=\"animal\", pattern=\"ca.*\")" | |
| ], | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "IObJVMCh-ccG" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "results" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "iDx58Ig9-eTw", | |
| "outputId": "afb1c43b-1999-4f71-cf59-813ac656529b" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[Document(metadata={'animal': 'cattle', 'color': 'brown'}, page_content='Cattle are grazing in the field.'),\n", | |
| " Document(metadata={'color': 'black', 'animal': 'cat'}, page_content='The cat is sleeping on the sofa.')]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "results" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "yapnrYq_I4Vh", | |
| "outputId": "c5bca1b8-2139-4f1a-8e67-79ab3793568d" | |
| }, | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 18 | |
| } | |
| ] | |
| }, | |
| { | |
| "source": [ | |
| "from langchain.vectorstores import Chroma\n", | |
| "from langchain.embeddings import HuggingFaceEmbeddings\n", | |
| "from langchain.schema import Document # Import Document\n", | |
| "import re\n", | |
| "\n", | |
| "# ... (rest of your code) ...\n", | |
| "\n", | |
| "def metadata_search(query=None, metadata_filter=None, k=10):\n", | |
| " \"\"\"\n", | |
| " Performs a search based on metadata, optionally with a query for content.\n", | |
| "\n", | |
| " Args:\n", | |
| " query: The query string for content similarity search (optional).\n", | |
| " metadata_filter: A dictionary specifying metadata filters.\n", | |
| " Use a simple key-value structure for exact match\n", | |
| " or a list of values for \"OR\" search (e.g., {\"animal\": [\"cat\", \"cattle\"]}).\n", | |
| " k: The maximum number of results to return.\n", | |
| "\n", | |
| " Returns:\n", | |
| " A list of search results matching the criteria.\n", | |
| " \"\"\"\n", | |
| " if query:\n", | |
| " # If a query is provided, perform a similarity search first, applying filters if available\n", | |
| " if metadata_filter:\n", | |
| " results = vector_store.similarity_search(query=query, k=k, filter=metadata_filter)\n", | |
| " else:\n", | |
| " results = vector_store.similarity_search(query=query, k=k)\n", | |
| " else:\n", | |
| " # If no query, perform metadata-only search\n", | |
| " all_docs = vector_store.get()\n", | |
| " results = []\n", | |
| " for doc, meta in zip(all_docs['documents'], all_docs['metadatas']):\n", | |
| " # Check if the document's metadata matches the filter\n", | |
| " match = True\n", | |
| " if metadata_filter:\n", | |
| " for key, val in metadata_filter.items():\n", | |
| " if key in meta:\n", | |
| " if isinstance(val, list):\n", | |
| " # If val is a list, it's an \"OR\" search\n", | |
| " if meta[key] not in val:\n", | |
| " match = False\n", | |
| " break # Stop checking if one condition fails\n", | |
| " else:\n", | |
| " # Otherwise, it's an exact match search\n", | |
| " if meta[key] != val:\n", | |
| " match = False\n", | |
| " break # Stop checking if one condition fails\n", | |
| " else:\n", | |
| " # If the key is not in metadata, it's not a match\n", | |
| " match = False\n", | |
| " break # Stop checking if one condition fails\n", | |
| "\n", | |
| " if match:\n", | |
| " # Create a Document object directly\n", | |
| " results.append(Document(page_content=doc, metadata=meta))\n", | |
| "\n", | |
| " # Limit the results to k\n", | |
| " results = results[:k]\n", | |
| "\n", | |
| " return results\n", | |
| "\n", | |
| "# Example usage:\n", | |
| "# 1. Metadata cross-search with content query:\n", | |
| "results = metadata_search(query=\"sleeping\", metadata_filter={\"animal\": \"cat\"})\n", | |
| "\n", | |
| "# 2. Metadata-only search:\n", | |
| "results = metadata_search(metadata_filter={\"animal\": [\"cat\", \"cattle\"]}) # \"OR\" search for animal\n", | |
| "\n", | |
| "# 3. Wildcard search (using regex in Python):\n", | |
| "def wildcard_search(query, key, pattern):\n", | |
| " results = metadata_search(query=query) # Get all results or filter by query if provided\n", | |
| " return [doc for doc in results if doc.metadata and key in doc.metadata and re.match(pattern, doc.metadata[key])]\n", | |
| "\n", | |
| "results = wildcard_search(query=\"any query\", key=\"animal\", pattern=\"ca.*\")" | |
| ], | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "iezVBwR7Jvym" | |
| }, | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "results" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "H1Wspw0fJ0UT", | |
| "outputId": "e72e8b80-c44d-4021-c9d4-bdbe99bc8593" | |
| }, | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[Document(metadata={'animal': 'cattle', 'color': 'brown'}, page_content='Cattle are grazing in the field.'),\n", | |
| " Document(metadata={'animal': 'cat', 'color': 'black'}, page_content='The cat is sleeping on the sofa.')]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 24 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import re\n", | |
| "from langchain.vectorstores import Chroma\n", | |
| "from langchain.embeddings import HuggingFaceEmbeddings\n", | |
| "from langchain.schema import Document\n", | |
| "\n", | |
| "# 1. EmbeddingおよびChromaの初期化\n", | |
| "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", | |
| "vector_store = Chroma(\n", | |
| " collection_name=\"summary_index\",\n", | |
| " embedding_function=embeddings,\n", | |
| " persist_directory=\"./chroma_db\"\n", | |
| ")\n", | |
| "\n", | |
| "# 2. ドキュメント登録用データ例\n", | |
| "# 各ドキュメントは、全文(full_text)とサマリー(summary)およびメタデータを持つ\n", | |
| "documents_data = [\n", | |
| " {\n", | |
| " \"full_text\": \"The cat is sleeping on the sofa. Detailed description of the environment...\",\n", | |
| " \"summary\": \"cat sleeping on sofa\",\n", | |
| " \"metadata\": {\"animal\": \"cat\", \"source\": \"doc1\"}\n", | |
| " },\n", | |
| " {\n", | |
| " \"full_text\": \"Cattle are grazing in the vast field. The cattle move slowly under the sun.\",\n", | |
| " \"summary\": \"cattle grazing in field\",\n", | |
| " \"metadata\": {\"animal\": \"cattle\", \"source\": \"doc2\"}\n", | |
| " },\n", | |
| " {\n", | |
| " \"full_text\": \"The dog is barking loudly at strangers near the park.\",\n", | |
| " \"summary\": \"dog barking at park\",\n", | |
| " \"metadata\": {\"animal\": \"dog\", \"source\": \"doc3\"}\n", | |
| " }\n", | |
| "]\n", | |
| "\n", | |
| "# 3. 登録処理\n", | |
| "# 検索用のサマリーを使ってベクトル化し、全文はメタデータに保持する\n", | |
| "texts = [doc[\"summary\"] for doc in documents_data]\n", | |
| "metadatas = [\n", | |
| " {**doc[\"metadata\"], \"full_text\": doc[\"full_text\"]}\n", | |
| " for doc in documents_data\n", | |
| "]\n", | |
| "ids = [f\"doc_{i}\" for i in range(len(documents_data))]\n", | |
| "\n", | |
| "# 登録（既存コレクションに追加）\n", | |
| "vector_store.add_texts(texts=texts, metadatas=metadatas, ids=ids)\n", | |
| "\n", | |
| "# 4. metadata_search関数（全文・サマリー両方の検索に対応）\n", | |
| "def metadata_search(query=None, metadata_filter=None, k=10):\n", | |
| " \"\"\"\n", | |
| " Metadataとオプションのクエリに基づき検索する関数。\n", | |
| " クエリがある場合は、類似検索（サマリーのベクトル検索）を実施し、フィルタ条件を適用。\n", | |
| " クエリがなければ、全ドキュメントからメタデータでフィルタリングを行う。\n", | |
| " \"\"\"\n", | |
| " if query:\n", | |
| " if metadata_filter:\n", | |
| " results = vector_store.similarity_search(query=query, k=k, filter=metadata_filter)\n", | |
| " else:\n", | |
| " results = vector_store.similarity_search(query=query, k=k)\n", | |
| " else:\n", | |
| " # メタデータのみで検索（全ドキュメント取得してフィルタリング）\n", | |
| " all_docs = vector_store.get()\n", | |
| " results = []\n", | |
| " for doc, meta in zip(all_docs['documents'], all_docs['metadatas']):\n", | |
| " match = True\n", | |
| " if metadata_filter:\n", | |
| " for key, val in metadata_filter.items():\n", | |
| " if key in meta:\n", | |
| " if isinstance(val, list):\n", | |
| " if meta[key] not in val:\n", | |
| " match = False\n", | |
| " break\n", | |
| " else:\n", | |
| " if meta[key] != val:\n", | |
| " match = False\n", | |
| " break\n", | |
| " else:\n", | |
| " match = False\n", | |
| " break\n", | |
| " if match:\n", | |
| " results.append(Document(page_content=doc, metadata=meta))\n", | |
| " results = results[:k]\n", | |
| " return results\n", | |
| "\n", | |
| "# 5. Wildcard search（正規表現利用）\n", | |
| "def wildcard_search(query, key, pattern):\n", | |
| " base_results = metadata_search(query=query) # クエリがあれば類似検索実施\n", | |
| " return [doc for doc in base_results if doc.metadata and key in doc.metadata and re.match(pattern, doc.metadata[key])]\n", | |
| "\n", | |
| "# 6. Summary-based RAG Retriever\n", | |
| "# サマリー検索で上位の結果を得た後、各結果のメタデータからfull_textを取得してDocumentに再構成\n", | |
| "class SummaryRAGRetriever:\n", | |
| " def __init__(self, vector_store, embeddings):\n", | |
| " self.vector_store = vector_store\n", | |
| " self.embeddings = embeddings\n", | |
| "\n", | |
| " def retrieve(self, query, metadata_filter=None, k=4):\n", | |
| " # サマリー用の類似検索を実施（metadata_filterがあれば適用）\n", | |
| " if metadata_filter:\n", | |
| " results = self.vector_store.similarity_search(query=query, k=k, filter=metadata_filter)\n", | |
| " else:\n", | |
| " results = self.vector_store.similarity_search(query=query, k=k)\n", | |
| " # 検索結果の各Documentのmetadataから全文(full_text)を取り出して再構成\n", | |
| " final_results = []\n", | |
| " for doc in results:\n", | |
| " full_text = doc.metadata.get(\"full_text\", doc.page_content)\n", | |
| " new_doc = Document(page_content=full_text, metadata=doc.metadata)\n", | |
| " final_results.append(new_doc)\n", | |
| " return final_results\n", | |
| "\n", | |
| "# 7. Retrieverの利用例\n", | |
| "retriever = SummaryRAGRetriever(vector_store=vector_store, embeddings=embeddings)\n", | |
| "\n", | |
| "# (1) クエリ＋メタデータフィルタによる検索例：\n", | |
| "results1 = retriever.retrieve(query=\"sleeping\", metadata_filter={\"animal\": \"cat\"}, k=2)\n", | |
| "print(\"=== クエリ＋メタデータフィルタ ===\")\n", | |
| "for doc in results1:\n", | |
| " print(f\"* {doc.page_content}\\n {doc.metadata}\\n\")\n", | |
| "\n", | |
| "# (2) メタデータのみ検索（類似検索無し）例：\n", | |
| "results2 = metadata_search(metadata_filter={\"animal\": [\"cat\", \"cattle\"]})\n", | |
| "print(\"=== メタデータのみ検索 ===\")\n", | |
| "for doc in results2:\n", | |
| " # full_textはメタデータから取得\n", | |
| " full_text = doc.metadata.get(\"full_text\", doc.page_content)\n", | |
| " print(f\"* {full_text}\\n {doc.metadata}\\n\")\n", | |
| "\n", | |
| "# (3) ワイルドカード検索例（\"animal\"が \"ca.*\"にマッチするもの）\n", | |
| "results3 = wildcard_search(query=\"any query\", key=\"animal\", pattern=\"ca.*\")\n", | |
| "print(\"=== ワイルドカード検索 ===\")\n", | |
| "for doc in results3:\n", | |
| " print(f\"* {doc.page_content}\\n {doc.metadata}\\n\")\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 699, | |
| "referenced_widgets": [ | |
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| "text": [ | |
| "=== クエリ＋メタデータフィルタ ===\n", | |
| "* The cat is sleeping on the sofa. Detailed description of the environment...\n", | |
| " {'animal': 'cat', 'source': 'doc1', 'full_text': 'The cat is sleeping on the sofa. Detailed description of the environment...'}\n", | |
| "\n", | |
| "=== メタデータのみ検索 ===\n", | |
| "* The cat is sleeping on the sofa. Detailed description of the environment...\n", | |
| " {'source': 'doc1', 'full_text': 'The cat is sleeping on the sofa. Detailed description of the environment...', 'animal': 'cat'}\n", | |
| "\n", | |
| "* Cattle are grazing in the vast field. The cattle move slowly under the sun.\n", | |
| " {'source': 'doc2', 'animal': 'cattle', 'full_text': 'Cattle are grazing in the vast field. The cattle move slowly under the sun.'}\n", | |
| "\n", | |
| "=== ワイルドカード検索 ===\n", | |
| "* cattle grazing in field\n", | |
| " {'source': 'doc2', 'animal': 'cattle', 'full_text': 'Cattle are grazing in the vast field. The cattle move slowly under the sun.'}\n", | |
| "\n", | |
| "* cat sleeping on sofa\n", | |
| " {'animal': 'cat', 'full_text': 'The cat is sleeping on the sofa. Detailed description of the environment...', 'source': 'doc1'}\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from langchain.vectorstores import Chroma\n", | |
| "from langchain.embeddings import HuggingFaceEmbeddings\n", | |
| "from langchain.schema import Document\n", | |
| "import re\n", | |
| "\n", | |
| "# EmbeddingとChromaの初期化\n", | |
| "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", | |
| "vector_store = Chroma(\n", | |
| " collection_name=\"summary_index\",\n", | |
| " embedding_function=embeddings,\n", | |
| " persist_directory=\"./chroma_db\"\n", | |
| ")\n", | |
| "\n", | |
| "# ドキュメントデータ例（全文とサマリー、メタデータを保持）\n", | |
| "documents_data = [\n", | |
| " {\n", | |
| " \"full_text\": \"The cat is sleeping on the sofa. Detailed description of the environment...\",\n", | |
| " \"summary\": \"cat sleeping on sofa\",\n", | |
| " \"metadata\": {\"animal\": \"cat\", \"source\": \"doc1\"}\n", | |
| " },\n", | |
| " {\n", | |
| " \"full_text\": \"Cattle are grazing in the vast field. The cattle move slowly under the sun.\",\n", | |
| " \"summary\": \"cattle grazing in field\",\n", | |
| " \"metadata\": {\"animal\": \"cattle\", \"source\": \"doc2\"}\n", | |
| " },\n", | |
| " {\n", | |
| " \"full_text\": \"The dog is barking loudly at strangers near the park.\",\n", | |
| " \"summary\": \"dog barking at park\",\n", | |
| " \"metadata\": {\"animal\": \"dog\", \"source\": \"doc3\"}\n", | |
| " }\n", | |
| "]\n", | |
| "\n", | |
| "# 登録処理：検索用サマリーでインデックスを作成し、全文はmetadataに保持する\n", | |
| "texts = [doc[\"summary\"] for doc in documents_data]\n", | |
| "metadatas = [\n", | |
| " {**doc[\"metadata\"], \"full_text\": doc[\"full_text\"]}\n", | |
| " for doc in documents_data\n", | |
| "]\n", | |
| "ids = [f\"doc_{i}\" for i in range(len(documents_data))]\n", | |
| "\n", | |
| "vector_store.add_texts(texts=texts, metadatas=metadatas, ids=ids)\n", | |
| "\n", | |
| "def metadata_search(query=None, metadata_filter=None, k=10):\n", | |
| " \"\"\"\n", | |
| " クエリとメタデータフィルタに基づいて検索を実施します。\n", | |
| " クエリがある場合は類似検索を、ない場合は全件取得してフィルタリングを行います。\n", | |
| " \"\"\"\n", | |
| " if query:\n", | |
| " if metadata_filter:\n", | |
| " results = vector_store.similarity_search(query=query, k=k, filter=metadata_filter)\n", | |
| " else:\n", | |
| " results = vector_store.similarity_search(query=query, k=k)\n", | |
| " else:\n", | |
| " all_docs = vector_store.get()\n", | |
| " results = []\n", | |
| " for doc, meta in zip(all_docs['documents'], all_docs['metadatas']):\n", | |
| " match = True\n", | |
| " if metadata_filter:\n", | |
| " for key, val in metadata_filter.items():\n", | |
| " if key in meta:\n", | |
| " if isinstance(val, list):\n", | |
| " if meta[key] not in val:\n", | |
| " match = False\n", | |
| " break\n", | |
| " else:\n", | |
| " if meta[key] != val:\n", | |
| " match = False\n", | |
| " break\n", | |
| " else:\n", | |
| " match = False\n", | |
| " break\n", | |
| " if match:\n", | |
| " results.append(Document(page_content=doc, metadata=meta))\n", | |
| " results = results[:k]\n", | |
| " return results\n", | |
| "\n", | |
| "def wildcard_search(query, key, pattern):\n", | |
| " base_results = metadata_search(query=query)\n", | |
| " return [doc for doc in base_results if doc.metadata and key in doc.metadata and re.match(pattern, doc.metadata[key])]\n", | |
| "\n", | |
| "# Retriever実装：検索結果の各Documentのmetadataに\"full_text\"があればそれをpage_contentに設定して返す\n", | |
| "class SummaryRAGRetriever:\n", | |
| " def __init__(self, vector_store, embeddings):\n", | |
| " self.vector_store = vector_store\n", | |
| " self.embeddings = embeddings\n", | |
| "\n", | |
| " def retrieve(self, query, metadata_filter=None, k=4):\n", | |
| " if metadata_filter:\n", | |
| " results = self.vector_store.similarity_search(query=query, k=k, filter=metadata_filter)\n", | |
| " else:\n", | |
| " results = self.vector_store.similarity_search(query=query, k=k)\n", | |
| " final_results = []\n", | |
| " for doc in results:\n", | |
| " # 必ず metadata に \"full_text\" があるかチェック\n", | |
| " if \"full_text\" in doc.metadata:\n", | |
| " new_content = doc.metadata[\"full_text\"]\n", | |
| " else:\n", | |
| " # 存在しない場合は警告表示や例外を投げることも検討可能\n", | |
| " new_content = doc.page_content\n", | |
| " # LangChainのRetriever互換にするため、返すDocumentのpage_contentはfull_text\n", | |
| " new_doc = Document(page_content=new_content, metadata=doc.metadata)\n", | |
| " final_results.append(new_doc)\n", | |
| " return final_results\n", | |
| "\n", | |
| "# Retrieverの利用例\n", | |
| "retriever = SummaryRAGRetriever(vector_store=vector_store, embeddings=embeddings)\n", | |
| "\n", | |
| "# (1) クエリ＋メタデータフィルタによる検索例\n", | |
| "results1 = retriever.retrieve(query=\"sleeping\", metadata_filter={\"animal\": \"cat\"}, k=3)\n", | |
| "print(\"=== クエリ＋メタデータフィルタ ===\")\n", | |
| "for doc in results1:\n", | |
| " print(f\"* {doc.page_content}\\n {doc.metadata}\\n\")\n", | |
| "\n", | |
| "# (2) メタデータのみ検索（類似検索無し）例\n", | |
| "results2 = metadata_search(metadata_filter={\"animal\": [\"cat\", \"cattle\"]})\n", | |
| "print(\"=== メタデータのみ検索 ===\")\n", | |
| "for doc in results2:\n", | |
| " # metadataにあるfull_textをそのまま出力\n", | |
| " print(f\"* {doc.metadata.get('full_text', doc.page_content)}\\n {doc.metadata}\\n\")\n", | |
| "\n", | |
| "# (3) ワイルドカード検索例（\"animal\"が \"ca.*\"にマッチするもの）\n", | |
| "results3 = wildcard_search(query=\"any query\", key=\"animal\", pattern=\"ca.*\")\n", | |
| "print(\"=== ワイルドカード検索 ===\")\n", | |
| "for doc in results3:\n", | |
| " print(f\"* {doc.metadata.get('full_text', doc.page_content)}\\n {doc.metadata}\\n\")\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "I_fov52LWdim", | |
| "outputId": "42fad925-4d31-41a8-9fd7-77a52e96ed31" | |
| }, | |
| "execution_count": 29, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "=== クエリ＋メタデータフィルタ ===\n", | |
| "* The cat is sleeping on the sofa. Detailed description of the environment...\n", | |
| " {'animal': 'cat', 'source': 'doc1', 'full_text': 'The cat is sleeping on the sofa. Detailed description of the environment...'}\n", | |
| "\n", | |
| "=== メタデータのみ検索 ===\n", | |
| "* The cat is sleeping on the sofa. Detailed description of the environment...\n", | |
| " {'source': 'doc1', 'full_text': 'The cat is sleeping on the sofa. Detailed description of the environment...', 'animal': 'cat'}\n", | |
| "\n", | |
| "* Cattle are grazing in the vast field. The cattle move slowly under the sun.\n", | |
| " {'animal': 'cattle', 'full_text': 'Cattle are grazing in the vast field. The cattle move slowly under the sun.', 'source': 'doc2'}\n", | |
| "\n", | |
| "=== ワイルドカード検索 ===\n", | |
| "* Cattle are grazing in the vast field. The cattle move slowly under the sun.\n", | |
| " {'animal': 'cattle', 'source': 'doc2', 'full_text': 'Cattle are grazing in the vast field. The cattle move slowly under the sun.'}\n", | |
| "\n", | |
| "* The cat is sleeping on the sofa. Detailed description of the environment...\n", | |
| " {'animal': 'cat', 'source': 'doc1', 'full_text': 'The cat is sleeping on the sofa. Detailed description of the environment...'}\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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