https://github.com/giovaneiwamoto/capiara-code-mentor
🦜 CapIAra Code Mentor - Learn to think, not just code! Develop real problem-solving skills by mastering algorithmic logic step by step. Strengthen your reasoning, break down complex challenges, and build a solid foundation for programming without relying on ready-made code.
https://github.com/giovaneiwamoto/capiara-code-mentor
agent code langchain llm maritalk mentor streamlit
Last synced: 3 months ago
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🦜 CapIAra Code Mentor - Learn to think, not just code! Develop real problem-solving skills by mastering algorithmic logic step by step. Strengthen your reasoning, break down complex challenges, and build a solid foundation for programming without relying on ready-made code.
- Host: GitHub
- URL: https://github.com/giovaneiwamoto/capiara-code-mentor
- Owner: GiovaneIwamoto
- License: mit
- Created: 2025-02-28T04:27:23.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-06-09T17:07:51.000Z (4 months ago)
- Last Synced: 2025-06-09T18:23:27.943Z (4 months ago)
- Topics: agent, code, langchain, llm, maritalk, mentor, streamlit
- Language: Python
- Homepage:
- Size: 703 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Installation Guide
- Install required dependencies
```shell
pip install -r requirements.txt
```
- Run Streamlit
```shell
streamlit run app.py
```
# Documentation - Langchain
- Conceptual Guide
https://python.langchain.com/v0.2/docs/concepts/
- Callbacks
https://python.langchain.com/v0.2/docs/concepts/#callbacks
- Streaming
https://python.langchain.com/v0.2/docs/concepts/#streaming
- Message Persistence
https://python.langchain.com/docs/tutorials/chatbot/#message-persistence
- Managing Conversation History
https://python.langchain.com/docs/tutorials/chatbot/#managing-conversation-history
- Trim Messages
https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html
- Retrieval Augmented Generation
https://python.langchain.com/docs/tutorials/rag/#setup
https://python.langchain.com/docs/tutorials/qa_chat_history/
- Retrieval Concept
https://python.langchain.com/docs/concepts/retrieval/
- Retrievers
https://python.langchain.com/docs/concepts/retrievers/
- Vector Store
https://python.langchain.com/docs/concepts/vectorstores/
- Embedding Models
https://python.langchain.com/docs/concepts/embedding_models/
- Ollama Embeddings
https://python.langchain.com/docs/integrations/text_embedding/ollama/
- Pinecone Vector Store
https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html#langchain_pinecone.vectorstores.PineconeVectorStore
- Recursive Text Splitter
https://python.langchain.com/docs/how_to/recursive_text_splitter/
- Document Base
https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html
- Tool Calling
https://python.langchain.com/docs/concepts/tool_calling/
https://blog.langchain.dev/tool-calling-with-langchain/
- Tool Artifacts
https://python.langchain.com/docs/how_to/tool_artifacts/
- Structured Output
https://python.langchain.com/docs/how_to/structured_output/
- Chat OpenAI
https://python.langchain.com/docs/integrations/chat/openai/
# Documentation - Maritalk
- Models
https://docs.maritaca.ai/pt/modelos
- Rate Limits
https://docs.maritaca.ai/pt/rate-limits
- Embedding and RAG
https://docs.maritaca.ai/pt/embeddings+Sabia-3+RAG
- Tool Calling
https://docs.maritaca.ai/pt/chamada-funcao
# Documentation - Ollama
- Meta Llama 3
https://ollama.com/library/llama3/blobs/6a0746a1ec1a
```shell
ollama run llama3
```
- Nomic Embed Text
https://ollama.com/library/nomic-embed-text
```shell
ollama pull nomic-embed-text
```
# Documentation - Nomic
- HuggingFace
https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
- Blog
https://www.nomic.ai/blog/posts/nomic-embed-text-v1
# Documentation - LangSmith
https://docs.smith.langchain.com
# Info - AI Powered Search
- The Basics of AI-Powered Vector Search
https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2
- LLM Powered Autonomous Agents
https://lilianweng.github.io/posts/2023-06-23-agent/
# Documentation - Prompt Engineering
- AWS Prompt Engineering Best Practices
https://docs.aws.amazon.com/prescriptive-guidance/latest/llm-prompt-engineering-best-practices/introduction.html
- AWS Common prompt injection attacks
https://docs.aws.amazon.com/prescriptive-guidance/latest/llm-prompt-engineering-best-practices/common-attacks.html
- IBM Prompt Injection
https://www.ibm.com/think/topics/prompt-injection
# About
| **Use Case** | **chunk_size** | **chunk_overlap** | **Justification** |
|-----------------------|-------------------|-------------------|--------------------|
| **Short Queries** | 500 - 1000 | 100 - 200 | Improves semantic search accuracy without overly fragmenting the context. |
| **Long Documents** | 1000 - 2000 | 200 - 300 | Prevents context loss when splitting large documents. |
| **Source Code** | 300 - 600 | 50 - 100 | Smaller chunks help avoid losing critical information. |