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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3a800e93-1e4a-40de-a211-4244e8d1a161",
"metadata": {},
"outputs": [],
"source": [
"#!pip install -qU langchain-google-genai"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "764db45e-0ed0-480b-b338-2d7747d7746d",
"metadata": {},
"outputs": [],
"source": [
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain\n",
"import os\n",
"#from google.colab import userdata "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d5b8529b-7206-4f51-804c-0a49b3242310",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f484e386-e7a8-44a9-9ad5-0ec977a8b618",
"metadata": {},
"outputs": [],
"source": [
"#pip install fastapi uvicorn google-generativeai"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bcb32fd9-805c-409c-aa27-2745867daf41",
"metadata": {},
"outputs": [],
"source": [
"from fastapi import FastAPI\n",
"import google.generativeai as genai\n",
"from fastapi.middleware.cors import CORSMiddleware\n",
"\n",
"# Configure Google Gemini API\n",
"from langchain_google_genai import ChatGoogleGenerativeAI"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "34b28dd0-5675-48ac-b07a-5ee27b5a04dd",
"metadata": {},
"outputs": [],
"source": [
"google_api_key = os.environ[\"MY_SECRET_KEY\"]\n",
"\n",
"# Check if the API key was found\n",
"if google_api_key:\n",
" # Set the environment variable if the API key was found\n",
" os.environ[\"GOOGLE_API_KEY\"] = google_api_key\n",
"\n",
" llm = ChatGoogleGenerativeAI(\n",
" model=\"gemini-pro\", # Specify the model name\n",
" google_api_key=os.environ[\"GOOGLE_API_KEY\"]\n",
" )\n",
"else:\n",
" print(\"Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.\")\n",
"\n",
"\n",
"\n",
"genai.configure(api_key=google_api_key)\n",
"model = genai.GenerativeModel(\"gemini-pro\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c452755f-fad6-455f-9822-7c66b36d724f",
"metadata": {},
"outputs": [],
"source": [
"# Initialize FastAPI\n",
"app = FastAPI()\n",
"\n",
"# Enable CORS for frontend access\n",
"app.add_middleware(\n",
" CORSMiddleware,\n",
" allow_origins=[\"*\"],\n",
" allow_credentials=True,\n",
" allow_methods=[\"*\"],\n",
" allow_headers=[\"*\"],\n",
")\n",
"\n",
"@app.get(\"/chat\")\n",
"def chat(query: str):\n",
" response = model.generate_content(query)\n",
" return {\"response\": response.text}\n",
"\n",
"# Run the server: uvicorn backend:app --reload"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cde9c8d0-80a4-4943-a78a-a75ca4825e34",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_11301/2071011701.py:7: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
" chain = LLMChain(llm=llm, prompt=prompt)\n",
"/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_11301/2071011701.py:11: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
" response = chain.run(query=query)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chatbot Response: The capital of France is Paris.\n"
]
}
],
"source": [
"prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
"\n",
"# Initialize LLM\n",
"llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
"\n",
"# Create an LLM Chain\n",
"chain = LLMChain(llm=llm, prompt=prompt)\n",
"\n",
"# Run chatbot\n",
"query = \"What is the capital of France?\"\n",
"response = chain.run(query=query)\n",
"print(\"Chatbot Response:\", response)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "da1d9899-328b-47c2-87a5-4175519bacdc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chatbot Response: The capital of India is **New Delhi**.\n"
]
}
],
"source": [
"# Run chatbot\n",
"query = \"What is the capital of India?\"\n",
"response = chain.run(query=query)\n",
"print(\"Chatbot Response:\", response)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "d42c4cf2-8b96-4310-8692-350d0d9c85b4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/saurabhverma/GENAI'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "161cd070-2ac4-422a-8199-2a7cc42ac335",
"metadata": {},
"outputs": [],
"source": [
"# UI with Gradio\n",
"def chat_interface(question):\n",
" return rag_pipeline(question)\n",
"\n",
"ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
"ui.launch()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cc81ff71-c152-4780-bb90-31f1df623f7e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" Attempting uninstall: tomlkit\n",
" Found existing installation: tomlkit 0.11.1\n",
" Uninstalling tomlkit-0.11.1:\n",
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]
}
],
"source": [
"#pip install -U langchain-community\n",
"!pip install gradio"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8c816fe4-5454-4e30-bbf9-cda7214943f5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_16004/3095841870.py:27: LangChainDeprecationWarning: The class `OpenAIEmbeddings` was deprecated in LangChain 0.0.9 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-openai package and should be used instead. To use it run `pip install -U :class:`~langchain-openai` and import as `from :class:`~langchain_openai import OpenAIEmbeddings``.\n",
" vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n"
]
},
{
"ename": "ValidationError",
"evalue": "1 validation error for OpenAIEmbeddings\n Value error, Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. [type=value_error, input_value={'model_kwargs': {}, 'cli...20, 'http_client': None}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/value_error",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[11], line 27\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Create Vector Database\u001b[39;00m\n\u001b[1;32m 26\u001b[0m docs \u001b[38;5;241m=\u001b[39m load_docs()\n\u001b[0;32m---> 27\u001b[0m vector_store \u001b[38;5;241m=\u001b[39m Chroma\u001b[38;5;241m.\u001b[39mfrom_documents(docs, embedding\u001b[38;5;241m=\u001b[39mOpenAIEmbeddings())\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# RAG Pipeline: Retrieve and Generate\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrag_pipeline\u001b[39m(query):\n",
"File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:214\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.finalize.<locals>.warn_if_direct_instance\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 212\u001b[0m warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 213\u001b[0m emit_warning()\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapped(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pydantic/main.py:214\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 213\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 214\u001b[0m validated_self \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pydantic_validator__\u001b[38;5;241m.\u001b[39mvalidate_python(data, self_instance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m validated_self:\n\u001b[1;32m 216\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA custom validator is returning a value other than `self`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturning anything other than `self` from a top level model validator isn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt supported when validating via `__init__`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 220\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 221\u001b[0m )\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for OpenAIEmbeddings\n Value error, Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. [type=value_error, input_value={'model_kwargs': {}, 'cli...20, 'http_client': None}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/value_error"
]
}
],
"source": [
"import google.generativeai as genai\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.schema import Document\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain_community.document_loaders import TextLoader\n",
"import gradio as gr\n",
"\n",
"# Configure Google Gemini API\n",
"GOOGLE_API_KEY = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n",
"genai.configure(api_key=GOOGLE_API_KEY)\n",
"model = genai.GenerativeModel(\"gemini-pro\")\n",
"\n",
"# Load and process documents\n",
"def load_docs():\n",
" raw_text = \"\"\"\n",
" Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time.\n",
" Supervised learning uses labeled data, while unsupervised learning finds hidden patterns.\n",
" Reinforcement learning is based on rewards and penalties.\n",
" \"\"\"\n",
" text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)\n",
" docs = [Document(page_content=text) for text in text_splitter.split_text(raw_text)]\n",
" return docs\n",
"\n",
"# Create Vector Database\n",
"docs = load_docs()\n",
"vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n",
"\n",
"# RAG Pipeline: Retrieve and Generate\n",
"def rag_pipeline(query):\n",
" results = vector_store.similarity_search(query, k=2)\n",
" context = \" \".join([doc.page_content for doc in results])\n",
" \n",
" # Pass context + query to Gemini\n",
" full_prompt = f\"Context: {context}\\n\\nQuestion: {query}\\nAnswer:\"\n",
" response = model.generate_content(full_prompt)\n",
" \n",
" return response.text\n",
"\n",
"# UI with Gradio\n",
"def chat_interface(question):\n",
" return rag_pipeline(question)\n",
"\n",
"ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
"ui.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93bcb8cd-9202-45c1-98e3-9f9b06387fc2",
"metadata": {},
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"source": []
}
],
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|