Langchain + Graph RAG + GPT-4o Python Project - Easy AI/Chat for your Website

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AI Nuggets

Based on the provided YouTube video transcript, here is an outline of the detailed instructions, CLI commands, website URLs, and tips extracted from the text:

Outline of Instructions from Video Transcript

Step 1: Install Lang Chain in Python

• Install Lang Chain using pip or conda.
• Recommended to frequently upgrade the packages.
• CLI Command:

  pip install langchain  
  

  or

  conda install langchain  
  

Step 2: Set Up Neo4j

• Start a free instance on Neo4j Aura for cloud instances of the Neo4j database.
• Alternatively, set up a local instance by downloading the Neo4j Desktop application and creating a local database instance.
• Neo4j Aura URL: https://neo4j.com/cloud/aura/
• Neo4j Desktop URL: https://neo4j.com/download/

Step 3: Extract Structured Information with OpenAI Functions

• Use OpenAI functions to extract structured information from natural language.
• Define data structures for the Knowledge Graph, specifying properties for each node, relationship, and Knowledge Graph classes.
• Create helper functions to format keys, convert lists to dictionaries, and custom nodes to base nodes.

Step 4: Extract and Store Knowledge Graph

• Create a function called get_extraction_chain to build a chain that extracts information from text.
• Use optional parameters allowed_nodes and allowed_rels to specify which types of nodes and relationships are allowed.
• Create a structured output chain using the given prompt and the language model.
• Add an option to limit which node or relationship types should be extracted.
• Define the information extraction pipeline as a single function.
• Use a large chunk size value for better context in core reference resolution.
• Call the extract_and_store_graph function for each document to save the extracted knowledge graph to the Neo4j database.
• Use the tqdm library to display processing progress.

Step 5: Browse Information with Graph Cipher Question Answer Chain

• Construct Cipher statements to browse information in a Knowledge Graph, similar to SQL for relational databases.
• Handle queries by searching by vectors and fetching related information.
• Explore different queries and convert them to Cipher.

Additional Tips and Observations

• Knowledge graphs are less prone to hallucinate and provide more accurate and factual text generation.
• Graph databases use nodes and edges to represent relationships between information.
• Graph RAG (Retrieval-Augmented Generation) uses graph database technology to search and generate information.
• Combining knowledge graphs with RAG could be promising in the future.

Conclusion

• The video concludes with an encouragement to subscribe for upcoming content.

Please note that the above outline is based on the text provided and does not include any additional context or information that may be present in the actual video or its description.