adding ragas to dev
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parent
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commit
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@ -30,5 +30,6 @@ dev = [
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"langfuse>=3.14.4",
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"langfuse>=3.14.4",
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"mteb>=2.8.8",
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"mteb>=2.8.8",
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"polars>=1.38.1",
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"polars>=1.38.1",
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"ragas>=0.4.3",
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"ruff>=0.15.1",
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"ruff>=0.15.1",
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]
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]
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@ -0,0 +1,302 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "efbabfec",
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"metadata": {},
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"source": [
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"# Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e06aaf8c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.config import ELASTICSEARCH_INDEX, OPENAI_API_KEY, OPENAI_MODEL, BASE_URL, MODEL_NAME"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8488db9f",
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'ragas.testset.generator'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_core\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdocuments\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Document\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_ollama\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ChatOllama, OllamaEmbeddings\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mragas\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtestset\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgenerator\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TestsetGenerator\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mragas\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mllms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LangchainLLMWrapper\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'ragas.testset.generator'"
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]
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}
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],
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"source": [
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"from langchain_core.embeddings import Embeddings \n",
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"from ragas.testset import TestsetGenerator \n",
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"from langchain_community.document_loaders import DirectoryLoader \n",
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"from openai import OpenAI \n",
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"from ragas.llms import llm_factory \n",
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"from langchain_core.documents import Document"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "816afb58",
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"metadata": {},
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"outputs": [],
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"source": [
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"class ElasticsearchEmbeddings(Embeddings): \n",
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" \"\"\"Fetch precomputed embeddings from Elasticsearch.\"\"\" \n",
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" def __init__(self, es_client, index_name, id_field=\"_id\", embedding_field=\"embedding_vector\"): \n",
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" self.es = es_client \n",
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" self.index = index_name \n",
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" self.id_field = id_field \n",
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" self.embedding_field = embedding_field \n",
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" \n",
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" def embed_query(self, text: str): \n",
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" # Example: hash text to a doc ID or perform a text lookup in ES \n",
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" doc_id = self._text_to_doc_id(text) # implement your mapping \n",
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" resp = self.es.get(index=self.index, id=doc_id) \n",
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" return resp[\"_source\"][self.embedding_field] \n",
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" \n",
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" def embed_documents(self, texts): \n",
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" return [self.embed_query(t) for t in texts] \n",
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" \n",
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" # Optional: implement async variants if Ragas calls them"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "65a7c504",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = DirectoryLoader(\"./my_code_repo\", glob=\"**/*.py\", recursive=True) \n",
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"docs = loader.load() \n",
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" \n",
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"# 2) Ollama LLM via OpenAI-compatible client \n",
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"ollama_client = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\") \n",
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"generator_llm = llm_factory(\"codellama\", provider=\"openai\", client=ollama_client) \n",
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" \n",
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"# 3) Custom ES embeddings wrapper \n",
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"from elasticsearch import Elasticsearch \n",
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"es_client = Elasticsearch(\"http://localhost:9200\") \n",
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"es_embeddings = ElasticsearchEmbeddings(es_client, index_name=\"my_docs_embeddings\") \n",
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" \n",
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"# 4) Ragas TestsetGenerator using the ES embeddings \n",
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"generator = TestsetGenerator.from_langchain(generator_llm, es_embeddings) \n",
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" \n",
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"# 5) Generate testset \n",
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"dataset = generator.generate_with_langchain_docs(docs, testset_size=20) \n",
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" \n",
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"# 6) Export \n",
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"df = dataset.to_pandas() \n",
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"print(df[[\"user_input\", \"reference\"]].head())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "06f41852",
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"metadata": {},
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"source": [
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"# Functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d24262b0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def fetch_documents_from_elasticsearch(\n",
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" es_url: str, \n",
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" index_name: str, \n",
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" size: int = 100\n",
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") -> List[Document]:\n",
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" \"\"\"\n",
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" Retrieve documents from Elasticsearch and convert to LangChain Document objects.\n",
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" \n",
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" Args:\n",
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" es_url: Elasticsearch URL\n",
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" index_name: Index name to query\n",
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" size: Number of documents to retrieve\n",
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" \n",
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" Returns:\n",
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" List of LangChain Document objects with metadata\n",
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" \"\"\"\n",
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" es = Elasticsearch(es_url)\n",
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" \n",
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" # Query all documents from the index\n",
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" response = es.search(\n",
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" index=index_name,\n",
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" body={\"query\": {\"match_all\": {}}, \"size\": size}\n",
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" )\n",
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" \n",
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" documents = []\n",
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" for hit in response[\"hits\"][\"hits\"]:\n",
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" source = hit.get(\"_source\", {})\n",
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" \n",
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" # Extract text content (field name is 'text' based on your ES setup)\n",
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" page_content = source.get(\"text\", \"\")\n",
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" \n",
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" if not page_content:\n",
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" continue\n",
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" \n",
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" # Extract metadata from the document\n",
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" metadata = {\n",
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" \"source\": source.get(\"source\", \"unknown\"),\n",
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" \"doc_id\": source.get(\"doc_id\", hit.get(\"_id\", \"\")),\n",
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" \"chunk_id\": source.get(\"chunk_id\", \"\"),\n",
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" \"title\": source.get(\"title\", \"Untitled\"),\n",
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" \"es_id\": hit.get(\"_id\") # Store the ES document ID for reference\n",
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" }\n",
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" \n",
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" # Create LangChain Document object\n",
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" doc = Document(page_content=page_content, metadata=metadata)\n",
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" documents.append(doc)\n",
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" \n",
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" print(f\"✓ Retrieved {len(documents)} documents from Elasticsearch\")\n",
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" return documents\n",
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"\n",
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"\n",
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"# Fetch documents from Elasticsearch\n",
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"documents = fetch_documents_from_elasticsearch(ELASTICSEARCH_URL, ELASTICSEARCH_INDEX, size=100)\n",
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"\n",
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"if documents:\n",
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" print(f\"\\nFirst document sample:\")\n",
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" print(f\" Content length: {len(documents[0].page_content)} chars\")\n",
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" print(f\" Metadata: {documents[0].metadata}\")\n",
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"else:\n",
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" print(\"⚠️ No documents found in Elasticsearch. Check index name and ES connection.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf8797ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"if not documents:\n",
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" print(\"❌ Cannot proceed: No documents to generate testset from.\")\n",
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" print(\"Ensure Elasticsearch is running and the index contains documents.\")\n",
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"else:\n",
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" print(\"\\n\" + \"=\" * 60)\n",
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" print(\"Initializing Ragas TestSetGenerator\")\n",
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" print(\"=\" * 60)\n",
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" \n",
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" # Initialize LLMs for test generation\n",
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" generator_llm = LangchainLLMWrapper(\n",
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" ChatOllama(base_url=BASE_URL, model=MODEL_NAME)\n",
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" )\n",
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" \n",
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" # Initialize embeddings for semantic similarity evaluation\n",
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" embeddings = OllamaEmbeddings(base_url=BASE_URL, model=MODEL_NAME)\n",
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" \n",
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" # Create the TestsetGenerator\n",
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" generator = TestsetGenerator.from_langchain(\n",
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" llm=generator_llm,\n",
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" embedding_model=embeddings\n",
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" )\n",
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" \n",
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" print(\"✓ TestsetGenerator initialized successfully\")\n",
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" print(f\" - LLM Model: {MODEL_NAME}\")\n",
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" print(f\" - Using {len(documents)} documents for generation\")\n",
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" print(\"\\n⏳ Generating synthetic test set (this may take a few minutes)...\")\n",
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" \n",
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" # Generate synthetic test set\n",
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" # Adjust testset_size based on your needs and token budget\n",
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" testset_size = min(50, len(documents) * 2) # Generate up to 50 test cases or 2x the docs\n",
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" \n",
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" testset = generator.generate_with_langchain_docs(\n",
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" documents=documents,\n",
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" testset_size=testset_size,\n",
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" raise_exceptions=False\n",
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" )\n",
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" \n",
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" print(f\"✓ Test set generated with {len(testset)} examples\")\n",
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" \n",
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" # Convert to DataFrame for easy viewing and exporting\n",
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" df = testset.to_pandas()\n",
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" \n",
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" print(\"\\n\" + \"=\" * 60)\n",
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" print(\"Test Set Summary\")\n",
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" print(\"=\" * 60)\n",
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" print(f\"Total test cases: {len(df)}\")\n",
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" print(f\"\\nColumns: {list(df.columns)}\")\n",
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" print(f\"\\nFirst few rows:\")\n",
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" print(df.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8dfd3472",
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"from pathlib import Path\n",
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"\n",
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"# Define output directory\n",
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"output_dir = Path(\"data/interim/synthetic_testsets\")\n",
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"output_dir.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"# Generate filename with timestamp\n",
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"timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
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"output_file = output_dir / f\"testset_{timestamp}.csv\"\n",
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"\n",
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"# Save to CSV\n",
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"df.to_csv(output_file, index=False)\n",
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"print(f\"✓ Test set saved to: {output_file}\")\n",
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"\n",
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"# Optional: Save as JSON for better structure preservation\n",
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"json_file = output_dir / f\"testset_{timestamp}.json\"\n",
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"testset.save_as_json(str(json_file))\n",
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"print(f\"✓ Test set saved (JSON) to: {json_file}\")\n",
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"\n",
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"# Display statistics\n",
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"print(\"\\n\" + \"=\" * 60)\n",
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"print(\"Test Set Distribution\")\n",
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"print(\"=\" * 60)\n",
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"if \"question_type\" in df.columns:\n",
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" print(\"\\nQuestion types:\")\n",
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" print(df[\"question_type\"].value_counts())\n",
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"\n",
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"if \"retrieval_context\" in df.columns:\n",
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" avg_context_len = df[\"retrieval_context\"].apply(lambda x: len(x) if x else 0).mean()\n",
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" print(f\"\\nAverage context length: {avg_context_len:.0f} characters\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "assistance-engine",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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337
uv.lock
337
uv.lock
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version = 1
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version = 1
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revision = 3
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revision = 2
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requires-python = ">=3.11"
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requires-python = ">=3.11"
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resolution-markers = [
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resolution-markers = [
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"python_full_version >= '3.14' and sys_platform == 'win32'",
|
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||||||
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{ url = "https://files.pythonhosted.org/packages/81/10/b8523105c590c5b8349f2587e2fdfe51a69544bd5a76295fc20f2374f470/tiktoken-0.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:ffc5288f34a8bc02e1ea7047b8d041104791d2ddbf42d1e5fa07822cbffe16bd", size = 878694, upload-time = "2025-10-06T20:21:59.876Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/00/61/441588ee21e6b5cdf59d6870f86beb9789e532ee9718c251b391b70c68d6/tiktoken-0.12.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:775c2c55de2310cc1bc9a3ad8826761cbdc87770e586fd7b6da7d4589e13dab3", size = 1050802, upload-time = "2025-10-06T20:22:00.96Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/1f/05/dcf94486d5c5c8d34496abe271ac76c5b785507c8eae71b3708f1ad9b45a/tiktoken-0.12.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a01b12f69052fbe4b080a2cfb867c4de12c704b56178edf1d1d7b273561db160", size = 993995, upload-time = "2025-10-06T20:22:02.788Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/a0/70/5163fe5359b943f8db9946b62f19be2305de8c3d78a16f629d4165e2f40e/tiktoken-0.12.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:01d99484dc93b129cd0964f9d34eee953f2737301f18b3c7257bf368d7615baa", size = 1128948, upload-time = "2025-10-06T20:22:03.814Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/0c/da/c028aa0babf77315e1cef357d4d768800c5f8a6de04d0eac0f377cb619fa/tiktoken-0.12.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:4a1a4fcd021f022bfc81904a911d3df0f6543b9e7627b51411da75ff2fe7a1be", size = 1151986, upload-time = "2025-10-06T20:22:05.173Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/a0/5a/886b108b766aa53e295f7216b509be95eb7d60b166049ce2c58416b25f2a/tiktoken-0.12.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:981a81e39812d57031efdc9ec59fa32b2a5a5524d20d4776574c4b4bd2e9014a", size = 1194222, upload-time = "2025-10-06T20:22:06.265Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/f4/f8/4db272048397636ac7a078d22773dd2795b1becee7bc4922fe6207288d57/tiktoken-0.12.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9baf52f84a3f42eef3ff4e754a0db79a13a27921b457ca9832cf944c6be4f8f3", size = 1255097, upload-time = "2025-10-06T20:22:07.403Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/8e/32/45d02e2e0ea2be3a9ed22afc47d93741247e75018aac967b713b2941f8ea/tiktoken-0.12.0-cp313-cp313-win_amd64.whl", hash = "sha256:b8a0cd0c789a61f31bf44851defbd609e8dd1e2c8589c614cc1060940ef1f697", size = 879117, upload-time = "2025-10-06T20:22:08.418Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/ce/76/994fc868f88e016e6d05b0da5ac24582a14c47893f4474c3e9744283f1d5/tiktoken-0.12.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:d5f89ea5680066b68bcb797ae85219c72916c922ef0fcdd3480c7d2315ffff16", size = 1050309, upload-time = "2025-10-06T20:22:10.939Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/f6/b8/57ef1456504c43a849821920d582a738a461b76a047f352f18c0b26c6516/tiktoken-0.12.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b4e7ed1c6a7a8a60a3230965bdedba8cc58f68926b835e519341413370e0399a", size = 993712, upload-time = "2025-10-06T20:22:12.115Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/72/90/13da56f664286ffbae9dbcfadcc625439142675845baa62715e49b87b68b/tiktoken-0.12.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:fc530a28591a2d74bce821d10b418b26a094bf33839e69042a6e86ddb7a7fb27", size = 1128725, upload-time = "2025-10-06T20:22:13.541Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/05/df/4f80030d44682235bdaecd7346c90f67ae87ec8f3df4a3442cb53834f7e4/tiktoken-0.12.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:06a9f4f49884139013b138920a4c393aa6556b2f8f536345f11819389c703ebb", size = 1151875, upload-time = "2025-10-06T20:22:14.559Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/22/1f/ae535223a8c4ef4c0c1192e3f9b82da660be9eb66b9279e95c99288e9dab/tiktoken-0.12.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:04f0e6a985d95913cabc96a741c5ffec525a2c72e9df086ff17ebe35985c800e", size = 1194451, upload-time = "2025-10-06T20:22:15.545Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/78/a7/f8ead382fce0243cb625c4f266e66c27f65ae65ee9e77f59ea1653b6d730/tiktoken-0.12.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:0ee8f9ae00c41770b5f9b0bb1235474768884ae157de3beb5439ca0fd70f3e25", size = 1253794, upload-time = "2025-10-06T20:22:16.624Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/93/e0/6cc82a562bc6365785a3ff0af27a2a092d57c47d7a81d9e2295d8c36f011/tiktoken-0.12.0-cp313-cp313t-win_amd64.whl", hash = "sha256:dc2dd125a62cb2b3d858484d6c614d136b5b848976794edfb63688d539b8b93f", size = 878777, upload-time = "2025-10-06T20:22:18.036Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/72/05/3abc1db5d2c9aadc4d2c76fa5640134e475e58d9fbb82b5c535dc0de9b01/tiktoken-0.12.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:a90388128df3b3abeb2bfd1895b0681412a8d7dc644142519e6f0a97c2111646", size = 1050188, upload-time = "2025-10-06T20:22:19.563Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/e3/7b/50c2f060412202d6c95f32b20755c7a6273543b125c0985d6fa9465105af/tiktoken-0.12.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:da900aa0ad52247d8794e307d6446bd3cdea8e192769b56276695d34d2c9aa88", size = 993978, upload-time = "2025-10-06T20:22:20.702Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/14/27/bf795595a2b897e271771cd31cb847d479073497344c637966bdf2853da1/tiktoken-0.12.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:285ba9d73ea0d6171e7f9407039a290ca77efcdb026be7769dccc01d2c8d7fff", size = 1129271, upload-time = "2025-10-06T20:22:22.06Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/f5/de/9341a6d7a8f1b448573bbf3425fa57669ac58258a667eb48a25dfe916d70/tiktoken-0.12.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:d186a5c60c6a0213f04a7a802264083dea1bbde92a2d4c7069e1a56630aef830", size = 1151216, upload-time = "2025-10-06T20:22:23.085Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/75/0d/881866647b8d1be4d67cb24e50d0c26f9f807f994aa1510cb9ba2fe5f612/tiktoken-0.12.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:604831189bd05480f2b885ecd2d1986dc7686f609de48208ebbbddeea071fc0b", size = 1194860, upload-time = "2025-10-06T20:22:24.602Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/b3/1e/b651ec3059474dab649b8d5b69f5c65cd8fcd8918568c1935bd4136c9392/tiktoken-0.12.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:8f317e8530bb3a222547b85a58583238c8f74fd7a7408305f9f63246d1a0958b", size = 1254567, upload-time = "2025-10-06T20:22:25.671Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/80/57/ce64fd16ac390fafde001268c364d559447ba09b509181b2808622420eec/tiktoken-0.12.0-cp314-cp314-win_amd64.whl", hash = "sha256:399c3dd672a6406719d84442299a490420b458c44d3ae65516302a99675888f3", size = 921067, upload-time = "2025-10-06T20:22:26.753Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/ac/a4/72eed53e8976a099539cdd5eb36f241987212c29629d0a52c305173e0a68/tiktoken-0.12.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:c2c714c72bc00a38ca969dae79e8266ddec999c7ceccd603cc4f0d04ccd76365", size = 1050473, upload-time = "2025-10-06T20:22:27.775Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/e6/d7/0110b8f54c008466b19672c615f2168896b83706a6611ba6e47313dbc6e9/tiktoken-0.12.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cbb9a3ba275165a2cb0f9a83f5d7025afe6b9d0ab01a22b50f0e74fee2ad253e", size = 993855, upload-time = "2025-10-06T20:22:28.799Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/5f/77/4f268c41a3957c418b084dd576ea2fad2e95da0d8e1ab705372892c2ca22/tiktoken-0.12.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:dfdfaa5ffff8993a3af94d1125870b1d27aed7cb97aa7eb8c1cefdbc87dbee63", size = 1129022, upload-time = "2025-10-06T20:22:29.981Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/4e/2b/fc46c90fe5028bd094cd6ee25a7db321cb91d45dc87531e2bdbb26b4867a/tiktoken-0.12.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:584c3ad3d0c74f5269906eb8a659c8bfc6144a52895d9261cdaf90a0ae5f4de0", size = 1150736, upload-time = "2025-10-06T20:22:30.996Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/28/c0/3c7a39ff68022ddfd7d93f3337ad90389a342f761c4d71de99a3ccc57857/tiktoken-0.12.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:54c891b416a0e36b8e2045b12b33dd66fb34a4fe7965565f1b482da50da3e86a", size = 1194908, upload-time = "2025-10-06T20:22:32.073Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/ab/0d/c1ad6f4016a3968c048545f5d9b8ffebf577774b2ede3e2e352553b685fe/tiktoken-0.12.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5edb8743b88d5be814b1a8a8854494719080c28faaa1ccbef02e87354fe71ef0", size = 1253706, upload-time = "2025-10-06T20:22:33.385Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/af/df/c7891ef9d2712ad774777271d39fdef63941ffba0a9d59b7ad1fd2765e57/tiktoken-0.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f61c0aea5565ac82e2ec50a05e02a6c44734e91b51c10510b084ea1b8e633a71", size = 920667, upload-time = "2025-10-06T20:22:34.444Z" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "tinycss2"
|
name = "tinycss2"
|
||||||
version = "1.4.0"
|
version = "1.4.0"
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue