Comments (3)
I implemented LlamaIndex and created a CondensedQuestionChatEngine in combination with a RetrieverQueryEngine, which efficiently retrieve relevant context from a knowledge base when given a query. The RetrieverQueryEngine is based on a VectorIndecRetriever to retrieve the relevant nodes of a VectorIndex. The nodes are parsed with marvin ai_model as metadataExtractor, the metadata is used by the retriever to find the relevant nodes.
In general the Concepts "Decoupling Chunks Used for Retrieval" and "Chunks Used for Synthesis" are both used here:
- The CondenseQuestionChatEngine is responsible for synthesis
- while the RetrieverQueryEngine handles retrieval.
This allows for more efficient and accurate retrieval of relevant documents before retrieving the specific chunks needed for synthesis.
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Implemented code for the Document and ChatEngine classes in script.py, functionality is tested in if _name_ == "_main_"
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Great! @mt7180, you nailed the issue commenting game :)
Such comments can be made either in the PR or the issue, depending on the company's preferences.
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Related Issues (20)
- use pathlib (in backend) to avoid breaking paths
- Provide adequate tests for backend
- Set up code quality checks HOT 3
- add buttons to clear chat history and vector store HOT 1
- implement tests for qa on html
- implement quiz function on texts/ html HOT 1
- Conduct PyBites Learning Path: Beginner
- create test on upload route functionality processing file AND str
- implementing api statistics in the forms of used tokens
- persist token usage (per user) and load into st.session_state
- bug fix: delete document files (*.txt) when context deleted
- set up GitHub Actions HOT 4
- deploy to fly HOT 3
- implement login to protect api key after deploying app
- Deploy FastAPI backend to AWS using Pulumi HOT 6
- Implement better website Reader for complex/ dynamic websites
- update pre-commit hooks
- fix streamlit frontend with new streamlit version
- fix url website reader in backend
- fix MARVIN_OPENAI_API_KEY error
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