Markdown Documentation for Pinecone, LangChain, Fireworks-AI, and Cohere-AI Integration

This documentation provides an overview and explanation of the code that integrates Pinecone, LangChain, Fireworks-AI, and Cohere-AI to generate data from given documents.

Table of Contents

1. Introduction
2. Environment Variables
3. Pinecone Index Creation
4. Updating Pinecone Index
5. Querying Pinecone Vector Store and LLM
6. Embedding Documents
7. Conclusion

Introduction

This software integrates various AI and machine learning libraries to process and generate data from given documents. The main components used are:

• Pinecone: A vector database for efficient similarity search and retrieval. [1]
• LangChain: A framework for developing applications powered by language models. [2]
• Fireworks-AI: An AI platform that provides embeddings for text data. [3]
• Cohere-AI: A natural language processing platform for building AI-powered applications. [4]

The code demonstrates how to create and update a Pinecone index, query the index, and generate responses using Cohere-AI's language model.

Environment Variables

The code assumes that the following environment variables are set in the .env.local.example file:

• FIREWORKSAI_API_KEY: The API key for Fireworks-AI.
• COHERE_API_KEY: The API key for Cohere-AI.

Make sure to replace these placeholders with your actual API keys.

Pinecone Index Creation

The createPineconeIndex function checks if a Pinecone index with the given name already exists. If the index doesn't exist, it creates a new index with the specified configuration [1]. The function takes the following parameters:

• client: The Pinecone client instance.
• indexName: The name of the index to create.
• vectorDimension: The dimension of the vectors to be stored in the index.

The function uses the listIndexes method to retrieve the existing indexes and checks if the desired index already exists. If not, it creates a new index using the createIndex method with the provided configuration.

Updating Pinecone Index

The updatePineconeIndex function updates the Pinecone index with new data [1]. It takes the following parameters:

• client: The Pinecone client instance.
• indexName: The name of the index to update.
• docs: An array of documents to be indexed.

For each document, the function splits the text into chunks using the RecursiveCharacterTextSplitter from LangChain [2]. It then embeds the chunks using Fireworks-AI's embeddings [3] and creates vectors with the embedded values and metadata. The vectors are inserted into the Pinecone index in batches using the upsert method.

Querying Pinecone Vector Store and LLM

The queryPineconeVectorStoreAndQueryLLM function queries the Pinecone vector store and generates a response using Cohere-AI's language model [4]. It takes the following parameters:

• client: The Pinecone client instance.
• indexName: The name of the index to query.
• question: The question to ask the language model.

The function retrieves the Pinecone index and creates a query embedding using Fireworks-AI's embeddings [3]. It then queries the index with the query embedding and retrieves the top-k most similar matches. If matches are found, it concatenates the page content of the matches and creates a new document. The document is passed to Cohere-AI's language model using the loadQAStuffChain function from LangChain [2], along with the question. The generated response is returned.

Embedding Documents

The getEmbeddings function is a helper function that takes an array of text chunks and returns their embeddings using Fireworks-AI's embeddings [3]. It creates an instance of FireworksEmbeddings with the provided API key and calls the embedDocuments method to embed the text chunks.

Conclusion

This code demonstrates how to integrate Pinecone, LangChain, Fireworks-AI, and Cohere-AI to create a powerful system for generating data from documents. By creating and updating a Pinecone index, querying the index, and using Cohere-AI's language model, the system can provide relevant and accurate responses to questions based on the indexed documents.

References

[1] Pinecone Documentation: https://docs.pinecone.io/ [2] LangChain Documentation: https://python.langchain.com/ [3] Fireworks-AI: https://www.fireworks.ai/ [4] Cohere-AI: https://cohere.ai/