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🇨🇳readme cn version

book intro:

runing environment:

  • using .env to save some sensitive informations
  • here are the variables you need to define in .env.
    OLLAMA_API=http://localhost:11434
    OLLAMA_MODEL=mistral:latest
    MONGO_URI=mongodb://root:root@localhost
    PG_URI=postgresql://yourname:password@localhost:5433/db_name
    LLAMA_CLOUD_API_KEY=
    OPENAI_API_KEY=
  • you need to create a .env file mannually under the source code directory.

notifications

  • the recommendions version of python is 3.11, otherwise it might has some incompatible issues.
  • env.py is the basic settings for using local ollama
  • 01.foundations.ipynb convered the knowledges of the book chapters from 1 to 5 , including:
    • reader,
    • splitter,
    • phaser,
    • meta-data extractor,
    • pipepine data ingestion.
    • indexing
    • storage context
    • doc & nodes & index persistence.
  • 02.retriever-query.ipynb convered the 6th and 7th chapters.
    • retrievers
      • VectorStoreIndex retrivers
      • DocumentSummaryIndex Retrivers
      • TreeIndex Retrivers
      • KnowledgeGraphIndex Retrivers
    • buiding more advanced retrival machanisms
      • native retrieval method
      • implementing metadata filters
      • using selector for more advanced decesion logic
      • transforming and rewriting queries
      • more specific sub-queries
    • Understanding the concepts of dense and sparse retrieval
      • dense retrival
      • sparse retrival
    • using postprocessor to rerank, transform and fiter nodes
      • how postprocessor works
      • SimilarlyPostProcessor
      • KeywordNodePostProcessor
      • PrevNextNodePostProcessor
      • LongContextRecorder
      • PIINodePostProcessor
      • NERPIINodePostProcessor
      • MetadataReplacementPostProcessor
      • SentenceEmbeddingPostProcessor
      • Time-based post processors
      • Re-ranking post processors
      • final thoughts about ndoe post processors
    • understanding response synthesizers
    • implementing output parsing techniques
      • extracting structured outputs using output parsers
      • extracting structured outputs using Pydantic Programs
    • building and using query engine
      • exploring different methods to building query engines
      • advanced uses of the QueryEngine interface.
  • 03.chat-agent.ipynb introduces how to build a chat system working with agent.
    • chat
      • discovering chatengine
      • understanding the different chat modes
    • agent
      • building tools and Tookspec classes for our agent
      • understanding reasoning loops
      • OpenAIAgent
      • ReActAgent
      • how do we interact with agents
      • enhancing our agents with the help of utility tools
      • using the LLMCompiler agent for more advanced scenarios
      • using the low-level-agent protocal API
  • 04.customizing-deploying.ipynb
  • 05.propmpt-engineering.ipynb