---Ingestion

• Loading the medium blog (TextLoader)
• Splitting the blog into smaller chunks (TextSplitter)
• Embed the chunks and get vectors (OpenAIEmbeddings)
• Store the embeddings in Pinecone Vectorstore (PineconeVectorstore)

---Retrival

• Get the embeddings from Pinecone Vectorstore, Embedding User Query
• Semantic Search (Relevant Vectors)
• Prompt Augmentation
• Generation

This script retrieves information using language models and vector stores based on a given query. It combines different chains to retrieve relevant documents and provide concise answers to questions.

• Description: Formats a list of documents into a single string, separating each document by two newline characters.
• Parameters:
  • docs: List of documents to be formatted.
• Returns:
  • A string containing the formatted documents.

1. Retrieving Information:

   • The script begins by initializing language models and loading the necessary environment variables.

2. Creating Chains:

   • Embeddings and Language Models:
     • OpenAI embeddings and ChatOpenAI language model are initialized.
   • Prompt Template:
     • A prompt template is created from the query "What is Pinecone in Machine Learning?" using the PromptTemplate class.
   • Vector Store:
     • A Pinecone vector store is initialized with the specified index name and embeddings.
   • Retrieval Chains:
     • Retrieval chains are created using the create_stuff_documents_chain and create_retrieval_chain functions, which combine language models and vector stores to retrieve relevant documents based on the query.

3. Invoking Chains:

   • The retrieval chain is invoked with the query input, which retrieves relevant documents.
   • A custom RAG prompt template is created to provide helpful answers to the question.
   • The RAG chain is invoked with the query to generate a response using the retrieved documents and the specified question.

4. Output:

   • The retrieved documents and the response generated by the RAG chain are printed to the console.