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@@ -76,93 +76,10 @@ cd llama-cookbook/end-to-end-use-cases/technical_blogger
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Step 2: Set Up Your Python Environment
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-It's highly recommended to use a virtual environment to manage dependencies:
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-
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-Bash
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-
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-python -m venv .venv
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-source .venv/bin/activate # On Windows: .venv\Scripts\activate
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-pip install -r requirements.txt
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-Note: Ensure you have a requirements.txt file in your technical_blogger directory listing all necessary libraries, such as qdrant-client, sentence-transformers, requests, python-dotenv, IPython, etc. If not, create one manually based on your code's imports.
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Step 3: Configure Your API Key
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-For security, your Llama API key must be stored as an environment variable and not directly in your code.
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-
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-Create a .env file: In the root of the technical_blogger directory, create a new file named .env.
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-
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-Add your Llama API Key: Open the .env file and add your Llama API key in the following format:
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-
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-LLAMA_API_KEY="YOUR_LLAMA_API_KEY_HERE"
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-Replace "YOUR_LLAMA_API_KEY_HERE" with your actual API key.
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-
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-Add .env to .gitignore: To prevent accidentally committing your API key, ensure .env is listed in your .gitignore file. If you don't have one, create it and add the line /.env.
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Step 4: Prepare Your Knowledge Base (Data Ingestion)
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-This recipe uses an in-memory Qdrant database, meaning the knowledge base is built each time the script runs. You will need to provide your technical documentation for ingestion.
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-
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-Locate generate_blog function: Open the Technical_Blog_Generator.ipynb (or your main Python script if you converted it) and find the generate_blog function.
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-
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-Update ingest_data_into_qdrant call: Inside generate_blog, there's a section for data ingestion:
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-
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-Python
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-
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-# IMPORTANT: For in-memory Qdrant, you MUST ingest your data every time
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-# the script runs or the client is initialized, as it's not persistent.
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-# Replace this with your actual data loading and chunking.
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-# Example placeholder data:
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-example_data_chunks = [
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- # ... your example data ...
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-]
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-ingest_data_into_qdrant(client, MAIN_COLLECTION_NAME, embedding_model, example_data_chunks)
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-Replace example_data_chunks with your actual code to load your technical documentation (e.g., from mdfiles_latest.txt, 3rd_party_integrations.txt, etc.), chunk it appropriately, and pass it to the ingest_data_into_qdrant function. This step defines the content that Llama will retrieve and use.
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-
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-Example (conceptual - adapt to your file loading logic):
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-
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-Python
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-
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-# Assuming your raw text files are in a 'cookbook_metadata' folder
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-# and you have a function to read and chunk them.
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-from your_data_loader_module import load_and_chunk_docs # You need to implement this
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-
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-all_your_technical_docs_chunks = []
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-# Load from mdfiles_latest.txt, 3rd_party_integrations.txt, Getting_started_files.txt
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-# and split into chunks suitable for embedding.
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-# Example:
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-# with open('cookbook_metadata/mdfiles_latest.txt', 'r') as f:
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-# content = f.read()
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-# all_your_technical_docs_chunks.extend(your_chunking_function(content))
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-# ... repeat for other files ...
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-ingest_data_into_qdrant(client, MAIN_COLLECTION_NAME, embedding_model, all_your_technical_docs_chunks)
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Step 5: Run the Notebook
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-With your environment configured and data ingestion prepared, you can now open the Jupyter notebook and run the blog generator.
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-
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-Start Jupyter:
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-
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-Bash
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-
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-jupyter notebook
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-Open the Notebook: In your browser, navigate to the technical_blogger folder and open Technical_Blog_Generator.ipynb.
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-
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-Run Cells: Execute each cell in the notebook sequentially. This will:
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-
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-Initialize the Llama API client.
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-
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-Set up the in-memory Qdrant database and ingest your provided knowledge base.
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-
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-Load helper functions for querying.
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-
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-Allow you to specify a blog topic.
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-
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-Trigger the RAG process to generate and display the blog post.
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-
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-Customization
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-Knowledge Base: Expand your knowledge base by adding more technical documentation files. Remember to update the data ingestion logic in generate_blog (Step 4) to include these new sources.
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-
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-LLM Model: Experiment with different Llama models by changing the LLAMA_MODEL variable in the configuration.
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-
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-Prompt Engineering: Modify the system_prompt within the generate_blog function to control the tone, structure, depth, and specific requirements for your generated blog posts.
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-
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-RAG Parameters: Adjust top_k in the query_qdrant function to retrieve more or fewer relevant chunks. You can also experiment with different embedding models or reranking models.
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-Output Format: Customize the output formatting if you need something other than Markdown.
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