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from pymongo import MongoClient |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.vectorstores import MongoDBAtlasVectorSearch |
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from langchain.document_loaders import DirectoryLoader |
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from langchain.llms import OpenAI |
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from langchain.chains import RetrievalQA |
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import gradio as gr |
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from gradio.themes.base import Base |
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import os |
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mongo_uri = os.getenv("MONGO_URI") |
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openai_api_key = os.getenv("OPENAI_API_KEY") |
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client = MongoClient(mongo_uri) |
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dbName = "langchain_demo" |
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collectionName = "collection_of_text_blobs" |
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collection = client[dbName][collectionName] |
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loader = DirectoryLoader( './sample_files', glob="./*.txt", show_progress=True) |
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data = loader.load() |
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embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) |
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vectorStore = MongoDBAtlasVectorSearch.from_documents( data, embeddings, collection=collection, index_name="default" ) |
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