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coleam00 / bolt.new-any-llm

Prompt, run, edit, and deploy full-stack web applications using any LLM you want!
https://bolt.new
MIT License
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Browsing feature for the LLM #326

Open ed23x opened 3 days ago

ed23x commented 3 days ago

Give LLM the ability to browse the Web and searech for Information it needs to fulfill the users request

SujalXplores commented 1 day ago

To enable an LLM to browse the web and search for information to answer user requests, you need to integrate a "Retrieval Augmented Generation" (RAG) system, which essentially allows the LLM to query a search engine in real-time to retrieve relevant information before generating a response.

Key components of a RAG system:

Search Engine API: Connect the LLM to a search engine like Google Search, Bing, DuckDuckGo, or a specialized search API using their provided developer tools.

Query Generation Module: When the user asks a question, the LLM needs to translate that into a well-structured search query that will return the most relevant results from the search engine.

Information Retrieval Module: This component retrieves the top search results based on the generated query and extracts the most relevant information from the retrieved pages.

Contextual Understanding Module: The LLM should be able to understand the context of the user's question and the retrieved information to generate a coherent and accurate response.

How it works:

  1. User Input: The user asks a question.
  2. Query Generation: The LLM parses the user's question and generates a search query that is suitable for the chosen search engine.
  3. Search Engine Query: The query is sent to the search engine API, which returns a list of relevant web pages.
  4. Information Extraction: The LLM extracts key information from the retrieved web pages, often using techniques like named entity recognition and text summarization.
  5. Response Generation: The LLM combines the extracted information with its existing knowledge base to generate a comprehensive and informative response to the user.

Technical considerations:

API Keys and Rate Limits: Accessing search engines requires API keys and managing potential rate limits to avoid being throttled by the provider.

Data Filtering and Quality Control: Implementing mechanisms to filter out irrelevant or low-quality information from the retrieved web pages.

Privacy Concerns: Be mindful of user privacy when accessing information from the web, especially when dealing with sensitive topics.

Examples of existing solutions:

LangChain: A popular open-source framework that provides tools for building RAG systems with various search engine integrations.

Google Search API: Google offers a robust API that allows developers to directly query their search engine from their applications.

Hugging Face Transformers: A library for working with pre-trained LLM models that can be integrated with a search API for RAG functionalities.

ed23x commented 1 day ago

id recommend duckduckgo instead of google. and maybe add a switch to enable/disable web access, or that globe button that would glow if the feature is enabled