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‎.gitignore

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*.env
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*.langchain.db
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.vscode
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.DS_STORE
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__pycache__

‎README.md

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# Chainlit Cookbook
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Welcome to the Chainlit Demos repository! Here you'll find a collection of example projects demonstrating how to use Chainlit to create amazing chatbot UIs with ease. Each folder in this repository represents a separate demo project.
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## 🚀 Getting Started
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To run a demo, follow these steps:
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1. Clone this repository:
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```
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git clone https://github.com/Chainlit/cookbook.git chainlit-cookbook
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```
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2. Navigate to the desired demo folder:
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```
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cd chainlit-cookbook/demo-folder-name
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```
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3. Install the required dependencies:
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```
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pip install -r requirements.txt
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```
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4. Create a `.env` file based on the provided `.env.example` file:
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```
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cp .env.example .env
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```
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Modify the `.env` file as needed to include any necessary API keys or configuration settings.
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5. Run the Chainlit app in watch mode:
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```
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chainlit run app.py -w
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```
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Your demo chatbot UI should now be up and running in your browser!
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## 💁 Contributing
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We'd love to see more demos showcasing the power of Chainlit. If you have an idea for a demo or want to contribute one, please feel free to open an issue or create a pull request. Your contributions are highly appreciated!

‎image-gen/.chainlit/config.toml

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[project]
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# Name of the app and chatbot.
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name = "Img Gen"
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# If true (default), the app will be available to anonymous users (once deployed).
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# If false, users will need to authenticate and be part of the project to use the app.
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public = true
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# The project ID (found on https://cloud.chainlit.io).
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# If provided, all the message data will be stored in the cloud.
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# The project ID is required when public is set to false.
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#id = ""
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# Whether to enable telemetry (default: true). No personal data is collected.
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enable_telemetry = true
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# List of environment variables to be provided by each user to use the app.
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user_env = []
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# Hide the chain of thought details from the user in the UI.
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hide_cot = false
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# Link to your github repo. This will add a github button in the UI's header.
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github = "https://github.com/Chainlit/demos"
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# Limit the number of requests per user.
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#request_limit = "10 per day"

‎image-gen/.env.example

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OPENAI_API_KEY=YOUR_OPENAI_API_KEY
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STABILITY_KEY=YOUR_STABILITY_KEY

‎image-gen/app.py

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import chainlit as cl
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from chainlit.action import Action
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from tools import generate_image_tool, edit_image_tool
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from langchain.agents import initialize_agent, AgentType
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import ConversationBufferMemory
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from langchain.agents.structured_chat.prompt import SUFFIX
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@cl.action_callback("Create variation")
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def create_variant(action: Action):
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agent = cl.user_session.get("agent")
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agent_input = f"Create a variation of {action.value}"
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cl.send_message(f"Creating a variation of `{action.value}`.")
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run(agent, agent_input)
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@cl.langchain_rename
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def rename(orig_author):
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mapping = {
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"LLMChain": "Assistant",
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}
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return mapping.get(orig_author, orig_author)
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@cl.langchain_factory
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def main():
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llm = ChatOpenAI(temperature=0, streaming=True)
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tools = [generate_image_tool, edit_image_tool]
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memory = ConversationBufferMemory(memory_key="chat_history")
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_SUFFIX = "Chat history:\n{chat_history}\n\n" + SUFFIX
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agent_executor = initialize_agent(
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tools=tools,
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llm=llm,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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memory=memory,
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agent_kwargs={
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"suffix": _SUFFIX,
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"input_variables": ["input", "agent_scratchpad", "chat_history"],
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},
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)
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return agent_executor
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@cl.langchain_run
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def run(agent_executor, action_input):
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cl.user_session.set("generated_image", None)
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res = agent_executor.run(input=action_input)
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elements = []
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actions = []
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generated_image_name = cl.user_session.get("generated_image")
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generated_image = cl.user_session.get(generated_image_name)
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if generated_image:
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elements = [
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cl.LocalImage(
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content=generated_image, name=generated_image_name, display="inline"
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)
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]
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actions = [cl.Action(name="Create variation", value=generated_image_name)]
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cl.send_message(content=res, elements=elements, actions=actions)

‎image-gen/chainlit.md

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# 🎨 Chainlit Image Gen demo
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Welcome to our creative image generation app built with [Chainlit](https://chainlit.io), [LangChain](https://python.langchain.com/en/latest/index.html), and [Stability AI](https://stability.ai/)! 🌟 This app allows you to create and edit unique images simply by chatting with it. Talk about having an artistic conversation! 🎨🗨️
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## 🎯 How it Works
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It's super simple! Just input your desired image description, and our app will generate a custom image based on your request. You can even edit the image by asking more questions or making modifications. Cool, isn't it? 😎
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Try asking:
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```
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create an image of an astronaut riding a horse on Mars, HD, dramatic lighting
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```
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You can then ask for modifications:
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```
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change the pose of the horse
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```
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Or create new images!

‎image-gen/requirements.txt

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chainlit
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langchain
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pillow
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stability_sdk==0.8.0

‎image-gen/tools.py

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import os
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import io
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import chainlit as cl
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from stability_sdk import client
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import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
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from PIL import Image
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from langchain.tools import Tool, StructuredTool
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os.environ["STABILITY_HOST"] = "grpc.stability.ai:443"
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def get_image_name():
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image_count = cl.user_session.get("image_count")
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if image_count is None:
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image_count = 0
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else:
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image_count += 1
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cl.user_session.set("image_count", image_count)
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return f"image-{image_count}"
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def _generate_image(prompt: str, init_image=None):
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# Set up our connection to the API.
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stability_api = client.StabilityInference(
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key=os.environ["STABILITY_KEY"], # API Key reference.
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verbose=True, # Print debug messages.
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engine="stable-diffusion-xl-beta-v2-2-2", # Set the engine to use for generation.
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# Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0
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# stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-diffusion-xl-beta-v2-2-2 stable-inpainting-v1-0 stable-inpainting-512-v2-0
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)
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start_schedule = 0.8 if init_image else 1
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# Set up our initial generation parameters.
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answers = stability_api.generate(
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prompt=prompt,
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init_image=init_image,
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start_schedule=start_schedule,
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seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
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# What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
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# Note: This isn't quite the case for CLIP Guided generations, which we tackle in the CLIP Guidance documentation.
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steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
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cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
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# Setting this value higher increases the strength in which it tries to match your prompt.
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# Defaults to 7.0 if not specified.
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width=512, # Generation width, defaults to 512 if not included.
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height=512, # Generation height, defaults to 512 if not included.
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samples=1, # Number of images to generate, defaults to 1 if not included.
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sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with.
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# Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
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# (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m, k_dpmpp_sde)
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)
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# Set up our warning to print to the console if the adult content classifier is tripped.
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# If adult content classifier is not tripped, save generated images.
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for resp in answers:
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for artifact in resp.artifacts:
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if artifact.finish_reason == generation.FILTER:
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raise ValueError(
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"Your request activated the API's safety filters and could not be processed."
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"Please modify the prompt and try again."
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)
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if artifact.type == generation.ARTIFACT_IMAGE:
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name = get_image_name()
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cl.user_session.set(name, artifact.binary)
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cl.user_session.set("generated_image", name)
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return name
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else:
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raise ValueError(
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f"Your request did not generate an image. Please modify the prompt and try again. Finish reason: {artifact.finish_reason}"
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)
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def generate_image(prompt: str):
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image_name = _generate_image(prompt)
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return f"Here is {image_name}."
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def edit_image(init_image_name: str, prompt: str):
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init_image_bytes = cl.user_session.get(init_image_name)
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if init_image_bytes is None:
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raise ValueError(f"Could not find image `{init_image_name}`.")
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init_image = Image.open(io.BytesIO(init_image_bytes))
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image_name = _generate_image(prompt, init_image)
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return f"Here is {image_name} based on {init_image_name}."
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generate_image_tool = Tool.from_function(
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func=generate_image,
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name="GenerateImage",
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description="Useful to create an image from a text prompt.",
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return_direct=True,
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)
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edit_image_tool = StructuredTool.from_function(
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func=edit_image,
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name="EditImage",
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description="Useful to edit an image with a prompt. Works well with commands such as 'replace', 'add', 'change', 'remove'.",
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return_direct=True,
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)

‎langchain-aiplugins/.chainlit/config.toml

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[project]
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# Name of the app and chatbot.
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name = "Klarna plugin"
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# If true (default), the app will be available to anonymous users (once deployed).
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# If false, users will need to authenticate and be part of the project to use the app.
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public = true
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# The project ID (found on https://cloud.chainlit.io).
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# If provided, all the message data will be stored in the cloud.
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# The project ID is required when public is set to false.
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#id = ""
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# List of environment variables to be provided by each user to use the app.
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user_env = []
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# Hide the chain of thought details from the user in the UI.
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hide_cot = false
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# Limit the number of requests per user.
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request_limit = "20 per day"
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github = "https://github.com/Chainlit/demos"

‎langchain-aiplugins/.env.example

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OPENAI_API_KEY=YOUR_OPENAI_API_KEY

‎langchain-aiplugins/app.py

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from langchain.chat_models import ChatOpenAI
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from langchain.agents import load_tools, initialize_agent
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from langchain.agents import AgentType
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from langchain.tools import AIPluginTool
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from chainlit import langchain_factory
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@langchain_factory
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def load():
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tool = AIPluginTool.from_plugin_url(
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"https://www.klarna.com/.well-known/ai-plugin.json"
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)
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llm = ChatOpenAI(temperature=0, streaming=True)
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tools = load_tools(["requests_all"])
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tools += [tool]
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return initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)

‎langchain-aiplugins/chainlit.md

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# Welcome to Chainlit! 🚀🤖
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This is a demo using langchain and the Klarna plugin!
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## Test it
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Try typing
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```what t shirts are available in klarna?```
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‎langchain-aiplugins/requirements.txt

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langchain==0.0.157
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chainlit

‎pdf-qa/.chainlit/config.toml

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[project]
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# Name of the app and chatbot.
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name = "PDF QA"
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# If true (default), the app will be available to anonymous users (once deployed).
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# If false, users will need to authenticate and be part of the project to use the app.
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public = true
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# The project ID (found on https://cloud.chainlit.io).
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# If provided, all the message data will be stored in the cloud.
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# The project ID is required when public is set to false.
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#id = ""
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# List of environment variables to be provided by each user to use the app.
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user_env = []
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# Hide the chain of thought details from the user in the UI.
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hide_cot = false
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# Limit the number of requests per user.
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request_limit = "20 per day"
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github = "https://github.com/Chainlit/demos"

‎pdf-qa/.env.example

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PINECONE_API_KEY=YOUR_PINECONE_API_KEY
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PINECONE_ENV=YOUR_PINECONE_ENV
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OPENAI_API_KEY=YOUR_OPENAI_API_KEY

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