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Browse files- .chainlit/config.toml +97 -0
- Dockerfile +11 -0
- README.md +19 -4
- app.py +40 -0
- chainlit.md +14 -0
- requirements.txt +7 -0
- storage_mini/default__vector_store.json +0 -0
- storage_mini/docstore.json +0 -0
- storage_mini/index_store.json +1 -0
.chainlit/config.toml
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[project]
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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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# Duration (in seconds) during which the session is saved when the connection is lost
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session_timeout = 3600
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# Enable third parties caching (e.g LangChain cache)
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cache = false
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# Authorized origins
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allow_origins = ["*"]
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# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
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# follow_symlink = false
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[features]
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# Show the prompt playground
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prompt_playground = true
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# Process and display HTML in messages. This can be a security risk (see https://stackoverflow.com/questions/19603097/why-is-it-dangerous-to-render-user-generated-html-or-javascript)
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unsafe_allow_html = false
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# Process and display mathematical expressions. This can clash with "$" characters in messages.
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latex = false
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# Authorize users to upload files with messages
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multi_modal = true
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# Allows user to use speech to text
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[features.speech_to_text]
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enabled = false
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# See all languages here https://github.com/JamesBrill/react-speech-recognition/blob/HEAD/docs/API.md#language-string
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# language = "en-US"
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[UI]
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# Name of the app and chatbot.
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name = "Chatbot"
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# Show the readme while the thread is empty.
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show_readme_as_default = true
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# Description of the app and chatbot. This is used for HTML tags.
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# description = ""
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# Large size content are by default collapsed for a cleaner ui
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default_collapse_content = true
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# The default value for the expand messages settings.
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default_expand_messages = false
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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 = ""
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# Specify a CSS file that can be used to customize the user interface.
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# The CSS file can be served from the public directory or via an external link.
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# custom_css = "/public/test.css"
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# Specify a Javascript file that can be used to customize the user interface.
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# The Javascript file can be served from the public directory.
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# custom_js = "/public/test.js"
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# Specify a custom font url.
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# custom_font = "https://fonts.googleapis.com/css2?family=Inter:wght@400;500;700&display=swap"
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# Override default MUI light theme. (Check theme.ts)
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[UI.theme]
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#font_family = "Inter, sans-serif"
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[UI.theme.light]
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#background = "#FAFAFA"
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#paper = "#FFFFFF"
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[UI.theme.light.primary]
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#main = "#F80061"
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#dark = "#980039"
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#light = "#FFE7EB"
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# Override default MUI dark theme. (Check theme.ts)
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[UI.theme.dark]
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#background = "#FAFAFA"
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#paper = "#FFFFFF"
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[UI.theme.dark.primary]
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#main = "#F80061"
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#dark = "#980039"
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#light = "#FFE7EB"
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[meta]
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generated_by = "1.0.401"
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Dockerfile
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FROM python:3.9
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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WORKDIR $HOME/app
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COPY --chown=user . $HOME/app
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COPY ./requirements.txt ~/app/requirements.txt
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RUN pip install -r requirements.txt
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COPY . .
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CMD ["chainlit", "run", "app.py", "--port", "7860"]
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README.md
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RAG use-case implementation using Llamaindex
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Supercharge your RAG pipeline with the following:
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- Framework -> [Llamaindex](https://docs.llamaindex.ai/en/stable/index.html)
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- Loader -> [SimpleDirectoryLoader](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html)
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- Chunking -> [Semantic Chunking](https://docs.llamaindex.ai/en/stable/examples/node_parsers/semantic_chunking.html)
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- Embeddings -> [Gemini Embeddings](https://docs.llamaindex.ai/en/stable/examples/node_parsers/semantic_chunking.html)
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- Reranking -> [Cohere Rerank Model](https://docs.llamaindex.ai/en/stable/examples/node_postprocessor/CohereRerank.html)
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- LLM -> [Groq Mistral](https://docs.llamaindex.ai/en/stable/examples/llm/groq.html#groq)
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For index creation follow notebook file ```rag.ipynb```
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To run application:
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```
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pip install -r requirements.txt
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chainlit run app.py
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```
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app.py
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from llama_index.core import StorageContext, ServiceContext, load_index_from_storage
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from llama_index.core.callbacks.base import CallbackManager
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.llms.groq import Groq
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import os
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from dotenv import load_dotenv
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load_dotenv()
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import chainlit as cl
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GROQ_API_KEY = os.getenv("GROQ_API_KEY")
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@cl.on_chat_start
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async def factory():
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storage_context = StorageContext.from_defaults(persist_dir="./storage_mini")
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embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
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llm = Groq(model="llama3-70b-8192", api_key=GROQ_API_KEY)
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service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm,
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callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
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)
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index = load_index_from_storage(storage_context, service_context=service_context)
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chat_engine = index.as_chat_engine(service_context=service_context)
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cl.user_session.set("chat_engine", chat_engine)
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@cl.on_message
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async def main(message: cl.Message):
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chat_engine = cl.user_session.get("chat_engine")
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response = await cl.make_async(chat_engine.chat)(message.content)
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response_message = cl.Message(content="")
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for token in response.response:
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await response_message.stream_token(token=token)
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await response_message.send()
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chainlit.md
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# Welcome to Chainlit! ππ€
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Hi there, Developer! π We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
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## Useful Links π
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- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) π
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- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! π¬
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We can't wait to see what you create with Chainlit! Happy coding! π»π
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## Welcome screen
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To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.
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requirements.txt
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llama-index==0.10.18
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llama-index-llms-groq==0.1.3
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chainlit==1.0.401
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groq==0.4.2
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python-dotenv==1.0.1
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llama-index-embeddings-gemini==0.1.4
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llama-index-postprocessor-cohere-rerank==0.1.2
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storage_mini/default__vector_store.json
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storage_mini/docstore.json
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storage_mini/index_store.json
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{"index_store/data": {"f42a6af7-0775-453f-9b3b-3a2ae2f23eab": {"__type__": "vector_store", "__data__": "{\"index_id\": \"f42a6af7-0775-453f-9b3b-3a2ae2f23eab\", \"summary\": null, \"nodes_dict\": {\"f719fbb6-e0ed-40ac-839a-c0d6a6505b4c\": \"f719fbb6-e0ed-40ac-839a-c0d6a6505b4c\", \"9c3dc423-ed76-48d2-b81c-0590b366d4ea\": \"9c3dc423-ed76-48d2-b81c-0590b366d4ea\", \"e8dc3a2b-45e9-4a4d-968f-df887afb4193\": \"e8dc3a2b-45e9-4a4d-968f-df887afb4193\", \"36830b4c-c762-45dc-bb77-2693fc244f1d\": \"36830b4c-c762-45dc-bb77-2693fc244f1d\", \"15b83726-755a-4f0c-b035-fe7b7be94d29\": \"15b83726-755a-4f0c-b035-fe7b7be94d29\", \"9c3c3031-2dfe-4b0c-ae9a-ec6fa4178520\": \"9c3c3031-2dfe-4b0c-ae9a-ec6fa4178520\", \"1e00f16c-0f83-4d24-9122-0e3c5dd43381\": \"1e00f16c-0f83-4d24-9122-0e3c5dd43381\", \"18302cce-5e0a-4808-8d63-8eab892c9136\": \"18302cce-5e0a-4808-8d63-8eab892c9136\", \"eb32dd9c-263c-4b5a-a3f1-2090aad558ce\": \"eb32dd9c-263c-4b5a-a3f1-2090aad558ce\", \"95fe93ab-e60b-471d-b80a-4dfa7d35d665\": 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