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{
"cells": [
{
"cell_type": "code",
"execution_count": 53,
"id": "408df710-efb3-45e0-94e1-5c4bdac72c06",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.embeddings import OllamaEmbeddings\n",
"from langchain.chains import RetrievalQA\n",
"from langchain.chat_models import ChatOllama\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "b0c1e9a7-85c6-48fb-bc81-b5903b67c044",
"metadata": {},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"with open(\"untitled.txt\", 'r') as f:\n",
" doc = f.read()\n",
"\n",
"docs = [Document(page_content=doc)]\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "b15e44da-6beb-489c-b847-d3276915ce8d",
"metadata": {},
"outputs": [],
"source": [
"splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=50)\n",
"chunks = splitter.split_documents(docs)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "4f6a18b4-cb85-481a-b43f-f38c60959155",
"metadata": {},
"outputs": [],
"source": [
"embeddings = OllamaEmbeddings(model='llama3.2')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "7a7709bc-48a3-4771-af21-5c48e5ae9296",
"metadata": {},
"outputs": [],
"source": [
"vector_store = []\n",
"for i in range(len(chunks)):\n",
" em = embeddings.embed_query(chunks[i].page_content)\n",
" vector_store.append(em)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "f95b010f-c384-4111-af98-43ba51568b08",
"metadata": {},
"outputs": [],
"source": [
"def similarity_search(te):\n",
" result_list = [] # create a list to store all the embeddings\n",
" emb = embeddings.embed_query(te) # create an embedding for our \"te\"\n",
" for i in range(8): # we have created 8 chunks\n",
" result = 0 # initialize the result for each chunk\n",
" for j in range(3072): # we have 3072 dimentional vector as a representation for each chunk and out text\n",
" result += emb[j] * vector_store[i][j]# we then take the dot product\n",
" result = result / 55.42\n",
" result_list.append({f'chunk {i+1}':result}) # and finally append the dot product in our return_list\n",
" return result_list\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "e63c7e94-56a7-4333-804d-e7c2b8697c77",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'chunk 1': 84.98757149361835}, {'chunk 2': 75.60246478296749}, {'chunk 3': 79.17318761328006}, {'chunk 4': 80.69328472997623}, {'chunk 5': 68.01708598133246}, {'chunk 6': 67.64770328462416}, {'chunk 7': 87.4843210948032}, {'chunk 8': 78.75659878926277}]\n"
]
}
],
"source": [
"print(similarity_search(\"Do not share passwords or access credentials. 7. Performance Reviews Formal reviews conducted every 6 months.\"))"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "5418bed3-8cd4-403d-ab6b-8f476cac7f41",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Use company devices for official work only.\\n\\nKeep systems updated and report any security incidents.\\n\\nDo not share passwords or access credentials.\\n\\n7. Performance Reviews\\n\\nFormal reviews conducted every 6 months.\\n\\nFocus on personal growth, goals, and team contributions.\\n\\n8. Learning & Development'"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chunks[5].page_content\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe2342b3-3f82-4ffa-9ad4-c19f12dded21",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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