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"cells": [
{
"cell_type": "code",
"execution_count": 24,
"id": "2bf551ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting langchain-chroma\n",
" Downloading langchain_chroma-1.1.0-py3-none-any.whl.metadata (1.9 kB)\n",
"Requirement already satisfied: chromadb<2.0.0,>=1.3.5 in c:\\parth files\\programming projects\\ollama-test\\venv\\lib\\site-packages (from langchain-chroma) (1.5.5)\n",
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"Downloading langchain_chroma-1.1.0-py3-none-any.whl (12 kB)\n",
"Installing collected packages: langchain-chroma\n",
"Successfully installed langchain-chroma-1.1.0\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"[notice] A new release of pip is available: 25.1.1 -> 26.0.1\n",
"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
]
}
],
"source": [
"%pip install langchain-chroma"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7b591154",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.llms import Ollama\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_huggingface import HuggingFaceEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "08b54e9b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading weights: 100%|██████████| 103/103 [00:00<00:00, 6966.50it/s]\n",
"\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/all-MiniLM-L6-v2\n",
"Key | Status | | \n",
"------------------------+------------+--+-\n",
"embeddings.position_ids | UNEXPECTED | | \n",
"\n",
"\u001b[3mNotes:\n",
"- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n"
]
}
],
"source": [
"embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
"vectorstore = Chroma(\n",
" persist_directory=\"chroma_db\",\n",
" embedding_function=embeddings,\n",
" collection_name=\"langchain\"\n",
")\n",
"\n",
"template = \"\"\"You are an expert coding assistant. Use the following documentation excerpts to answer the question accurately. If the answer is not in the context, say \"I don't know.\"\n",
"\n",
"Context:\n",
"{context}\n",
"\n",
"Question: {question}\n",
"\n",
"Answer:\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"context\", \"question\"],\n",
" template=template\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "08c9933f",
"metadata": {},
"outputs": [],
"source": [
"llm = Ollama(model=\"llama3\", temperature=0.1)\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 3})\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "30cb5d17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== ASSISTANT'S ANSWER ===\n",
"I can help you with that!\n",
"\n",
"According to the documentation, Python's dictionaries are ordered by default since Python 3.7, so you can use them as is. There is no specific function or method to use dictionaries in Python, as they are a built-in data structure.\n",
"\n",
"Here's a simple example of how you can use a dictionary in Python:\n",
"\n",
"```\n",
"my_dict = {'name': 'John', 'age': 30}\n",
"print(my_dict['name']) # Output: John\n",
"print(my_dict['age']) # Output: 30\n",
"```\n",
"\n",
"In this example, we create a dictionary called `my_dict` with two key-value pairs: `name` with the value `'John'` and `age` with the value `30`. Then, we access the values using their corresponding keys.\n",
"\n",
"=== SOURCES USED ===\n",
"1. ..\\..\\docs\\dict.html\n",
"2. ..\\..\\docs\\dict.html\n",
"3. ..\\..\\docs\\dict.html\n"
]
}
],
"source": [
"question = \"How do I use dictionary in python?\"\n",
"answer = rag_chain.invoke(question)\n",
"source_docs = retriever.invoke(question)\n",
"print(\"=== ASSISTANT'S ANSWER ===\")\n",
"print(answer)\n",
" \n",
"print(\"\\n=== SOURCES USED ===\")\n",
"for idx, doc in enumerate(source_docs):\n",
" source_file = doc.metadata.get(\"source\", \"Unknown file\")\n",
" print(f\"{idx + 1}. {source_file}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98d2af72",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv (3.13.5)",
"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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|