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Upload 6 files
Browse files- .dockerignore +9 -0
- .gitignore +6 -0
- Dockerfile +7 -0
- Readme.md +55 -0
- docker-compose.yaml +19 -0
- requirements.txt +12 -0
.dockerignore
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myenv/
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/app/__pycache__/
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./app/__pycache__/
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./app/__pycache__/
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*.env
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*.env.*
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env.*
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.gitignore
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./myenv
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.env
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./env
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./.env
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/myenv
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myenv
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Dockerfile
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FROM python:3.8.10-slim
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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COPY ./app /code
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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Readme.md
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# FastAPI for interacting with langchain and GPT-3.5 based chatbot, with Redis database as the vector store-backed retriever memory.
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## How to run
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### Using virtual environment:
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1. Set up a virtual environment:
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<code>python -m venv myenv</code>
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2. Create a .env file and add 'OPENAI_API_KEY', 'REDIS_URL', and 'HUGGINGFACEHUB_API_TOKEN as variables
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3. Navigate to the app directory:
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<code>cd app</code>
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4. Install the required dependencies:
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<code>pip install -r requirements.txt</code>
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5. Run the FastAPI server with uvicorn:
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<code>uvicorn main:app --reload --port=8000 --host=0.0.0.0</code>
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### Using Docker Compose:
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1. Build the Docker images:
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<code>docker-compose build</code>
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2. Start the Docker containers:
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<code>docker-compose up</code>
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## API Documentation
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### Changing User for Redis Vector Store
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To change the Redis vector store retriever memory to a specific user, send a request to the following endpoint:
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<code>localhost:8000/api/{username}</code>
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Replace `{username}` with the desired username. This action ensures that the chatbot will only retrieve data from the Redis database specific to that user.
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### Accessing API Documentation
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For detailed documentation on how to interact with the APIs in the application, visit:
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<code>localhost:8000/docs</code>
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This endpoint provides comprehensive guidance on utilizing the APIs effectively.
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---
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You can seamlessly integrate this backend into your existing application, providing your users with access to a dedicated vector-based database chatbot. Remember to generate the repsective API keys.
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docker-compose.yaml
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version: '3'
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services:
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app:
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build:
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context: .
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dockerfile: Dockerfile
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command: sh -c "uvicorn main:app --reload --port=8000 --host=0.0.0.0"
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ports:
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- "8000:8000"
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volumes:
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- ./app:/code/app
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environment:
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- OPENAI_API_KEY=YPUR-OPENAI_API_KEY
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- REDIS_URL=YOUR-REDIS_URL
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- HUGGINGFACEHUB_API_TOKEN=YOUR-HUGGINGFACEHUB_API_TOKEN
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requirements.txt
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langchain
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openai
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tiktoken
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langchain_openai
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redis[hiredis]
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python-dotenv
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huggingface_hub
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langchainhub
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langserve[all]
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fastapi
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pydantic==1.10.13
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uvicorn
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