llm-template

.gitignore

@@ -192,3 +192,12 @@ data/*
 **/output/*.csv
 !**/output/.gitkeep
 !**/data/.gitkeep
+
+# ignore macos files
+.DS_Store
+
+# ignore llmops
+app/llmops/*
+
+# ignore wandb files
+**/wandb/

app/llm-template/Pipfile

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+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+langchain = "*"
+google-genai = "*"
+chromadb = "*"
+SQLAlchemy = "*"
+wandb = "*"
+langchain-google-genai = "*"
+openai = "*"
+pydantic = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.10"

app/llm-template/README.md

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+# LLM Project Template
+
+This template provides a starting point for building LLM-powered applications with Langchain, AI Agents, RAG, Summarization, Question-Answering with SQL, and LLMOps.
+
+## Requirements
+
+- Python 3.9+
+- Pipenv
+
+## Installation
+
+```bash
+pipenv install
+```
+
+## Usage
+
+1.  Set the `OPENAI_API_KEY`, `WANDB_API_KEY`, `GOOGLE_API_KEY`, and `LANGSMITH_API_KEY` environment variables in `config.py`.
+2.  Run the `main.py` file:
+
+```bash
+pipenv run python main.py
+```

app/llm-template/agents/agent1.py

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+# agent1.py
+
+# Add your agent implementation here

app/llm-template/agents/agent2.py

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+# agent2.py
+
+# Add your agent implementation here

app/llm-template/chains/qa_chain.py

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+# qa_chain.py
+
+# Add your question-answering chain implementation here

app/llm-template/chains/rag_chain.py

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+# rag_chain.py
+
+# Add your RAG chain implementation here

app/llm-template/chains/sql_chain.py

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+# sql_chain.py
+
+# Add your question-answering with SQL chain implementation here

app/llm-template/chains/summarization_chain.py

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+# summarization_chain.py
+
+# Add your summarization chain implementation here

app/llm-template/config.py

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+# config.py
+
+# Add your configuration variables here
+OPENAI_API_KEY = "YOUR_OPENAI_API_KEY"
+WANDB_API_KEY = "df1c4099caa8afae3fc83140689c7e799fb0cdad"
+GOOGLE_API_KEY = "AIzaSyCIRXC8q5-gKjT5TjiqLxEdKFEmOEzebNQ"
+LANGSMITH_API_KEY = "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"
+
+LANGCHAIN_PROJECT = 'llmops-demo'

app/llm-template/llmops/deployment.py

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+# deployment.py
+
+# Add your deployment scripts and configurations here

app/llm-template/llmops/evaluation.py

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+# evaluation.py
+
+# Add your evaluation metrics and scripts here

app/llm-template/llmops/monitoring.py

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+# monitoring.py
+
+# Add your monitoring and logging implementation here

app/llm-template/main.py

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+# main.py
+import os
+import wandb
+from config import GOOGLE_API_KEY, WANDB_API_KEY, LANGSMITH_API_KEY, LANGCHAIN_PROJECT
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.callbacks import LangChainTracer
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+# Set LangSmith environment variables
+os.environ["LANGCHAIN_TRACING"] = "true"
+os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+os.environ["LANGCHAIN_PROJECT"] = LANGCHAIN_PROJECT
+os.environ["LANGCHAIN_API_KEY"] = LANGSMITH_API_KEY
+
+# Initialize Weights & Biases
+wandb.login(key=WANDB_API_KEY)
+run = wandb.init(project=LANGCHAIN_PROJECT, entity="aimingmed")
+
+# Initialize Gemini API
+tracer = LangChainTracer()
+llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001", google_api_key=GOOGLE_API_KEY, callbacks=[tracer])
+
+# Example usage of Gemini API
+prompt_template = PromptTemplate(template="Write a short poem about the sun.", input_variables=[])
+chain = LLMChain(llm=llm, prompt=prompt_template)
+response = chain.run({})
+
+print(response)
+
+wandb.finish()

app/llm-template/requirements.txt

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+langchain
+google-generativeai
+chromadb
+SQLAlchemy
+weights&biases
+langsmith

app/llm-template/utils/data_loader.py

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+# data_loader.py
+
+# Add your data loading functions here

app/llm-template/utils/embedding_utils.py

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+# embedding_utils.py
+
+# Add your embedding utilities here