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a2cb16c686c39c37b0cf934d40e435970efdf18f

Agent context: I’ll inspect the repo first to see what kind of agent stack it uses and where web retrieval would fit, then I’ll check current provider docs so the recommendation is grounded in actual integration and citation behavior. I’ll inspect the repo first to see what kind of agent stack it uses and where web retrieval would fit, then I’ll check current provider docs so the recommendation is grounded in actual integration and citation behavior.

Agent context: I’ll inspect the repo first to see what kind of agent stack it uses and where web retrieval would fit, then I’ll check current provider docs so the recommendation is grounded in actual integration and citation behavior.

Agent context: I’ll inspect the repo first to see what kind of agent stack it uses and where web retrieval would fit, then I’ll check current provider docs so the recommendation is grounded in actual integration and citation behavior. exit code 0 Output: /home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repo first to see what kind of agent stack it uses and where web retrieval would fit, then I’ll check current provider docs so the recommendation is grounded in actual integration and citation behavior.

Agent context: I’ll inspect the repo first to see what kind of agent stack it uses and where web retrieval would fit, then I’ll check current provider docs so the recommendation is grounded in actual integration and citation behavior. exit code 0

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean so far. `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean so far.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean so far.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean so far. exit code 0 Output: .env .git .gitignore README.md app.py data/alpha_society.pdf data/beta_society.pdf data/gamma_society.pdf embeddings/embeddings.py embeddings/openai_embeddings.py llm/llm.py llm/llm_factory.py populate_database.py requirements.txt retrieval/rag_retriever.py static/admin_settings.js static/demo_img/rag_demo.mp4 static/demo_img/screenshot_1.jpg static/demo_img/screenshot_2.jpg static/demo_img/screenshot_3.jpg static/demo_img/screenshot_4.jpg static/styles.css templates/admin.html templates/index.html test_rag.py

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean so far.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean so far. exit code 0 Output: ./README.md ./requirements.txt

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: A simple local Retrieval-Augmented Generation (RAG) chatbot that can answer to questions by acquiring information from personal pdf documents. (please, if you find this content useful please consider leaving a star ⭐) ## What is Retrieval-Augmented Generation (RAG)? <div style="text-align: center;"> <img src="https://miro.medium.com/v2/resize:fit:1400/1*J7vyY3EjY46AlduMvr9FbQ.png" alt="rag_pipeline" width="600" height="300"> </div> Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of information retrieval and natural language generation. In a RAG system, a retriever fetches relevant documents or text chunks from a database, and then a generator produces a response based on the retrieved context. 1. **Data Indexing** - Documents: This is the starting point where multiple documents are stored. - Vector DB: The documents are processed and indexed into a Vector Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: from flask import Flask, request, render_template, jsonify, redirect, url_for from llm.llm_factory import LLMFactory from retrieval.rag_retriever import RAGRetriever from dotenv import load_dotenv, set_key import os load_dotenv() VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-4.1', 'GPT-4o' or local LLM like 'llama3:8b', 'gemma2', 'mistral:7b' etc. LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt' or 'claude' EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') ENV_PATH = '.env' app = Flask(__name__) # Initialize the retriever and LLM retriever = None llm_model = None def get_vector_d Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: from langchain_chroma import Chroma from langchain_core.documents import Document from embeddings.embeddings import Embeddings class RAGRetriever: def __init__(self, vector_db_path: str, embedding_model_name: str, api_key: str): self.vector_db_path = vector_db_path embeddings = Embeddings(model_name=embedding_model_name, api_key=api_key) self.embedding_function = embeddings.get_embedding_function() self.db = Chroma(persist_directory=self.vector_db_path, embedding_function=self.embedding_function) def query(self, query_text: str, k: int = 4): # compute similarity between embeddings of query and of pdf text chunks results = self.db.similarity_search_with_score(query_text, k=k) return results def format_results(self, results: list[tuple[Document, float]]): enhanced_context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results]) sources = set(self.format_source(doc.metadata)

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: from abc import ABC, abstractmethod from langchain_community.llms.ollama import Ollama from openai import OpenAI from langchain_core.prompts import ChatPromptTemplate import anthropic PROMPT_TEMPLATE = """ Basing only on the following context: {context} --- Answer the following question: {question} Avoid to start the answer saying that you are basing on the provided context and go straight with the response. """ class LLM(ABC): def __init__(self, model_name: str): self.model_name = model_name @abstractmethod def invoke(self, prompt: str) -> str: pass def generate_response(self, context: str, question: str) -> str: prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) prompt = prompt_template.format(context=context, question=question) response_text = self.invoke(prompt) return response_text class OllamaModel(LLM): def __init__(self, model_name: str): super().__init__(model_n Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: import os from dotenv import load_dotenv from llm.llm import GPTModel, OllamaModel from llm.llm_factory import LLMFactory from retrieval.rag_retriever import RAGRetriever load_dotenv() VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-3.5-turbo', 'GPT-4o' or local LLM like 'llama3:8b' LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt', 'claude' EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') EVAL_PROMPT = """ Expected Response: {expected_response} Actual Response: {actual_response} --- (Answer with 'true' or 'false') Does the actual response match the expected response? """ def get_v Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: pypdf langchain==1.1.3 langchain-core==1.2.5 langchain-community==0.4.1 langchain-chroma==1.0.0 langchain-text-splitters==1.0.0 numpy<2.0.0 chromadb # vector db pytest boto3 anthropic openai python-dotenv Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: VECTOR_DB_OPENAI_PATH='chroma-openai' VECTOR_DB_OLLAMA_PATH='chroma-ollama' DATA_PATH='data' EMBEDDING_MODEL_NAME='openai' LLM_MODEL_TYPE='gpt' LLM_MODEL_NAME='gpt-4.1' NUM_RELEVANT_DOCS='3' OPENAI_API_KEY='YOUR_OPENAI_KEY_HERE' CLAUDE_API_KEY='YOUR_CLAUDE_KEY_HERE' # UNCOMMENT FOR LOCAL SETUP: #EMBEDDING_MODEL_NAME=ollama #LLM_MODEL_TYPE=ollama #LLM_MODEL_NAME=llama3:8b Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>⚙️ Admin Settings</title> <link rel="stylesheet" href="../static/styles.css"> <script src="../static/admin_settings.js"></script> </head> <body> <h1>⚙️ Admin Settings</h1> <form action="{{ url_for('update_settings') }}" method="POST" onsubmit="handleFormSubmission(event)"> <div id="admin-container"> <label for="llm_model_type">LLM Model Type:</label> <select id="llm_model_type" name="llm_model_type" onchange="updateApiKeyField();"> <option value="gpt" {% if llm_model_type == 'gpt' %}selected{% endif %}>GPT</option> <option value="ollama" {% if llm_model_type == 'ollama' %}selected{% endif %}>Ollama</option> <option value="claude" {% if llm_model_type == 'claude' %}selected{% endif %}>Claude</option> </select><br> <label for="llm_model_name">LLM Mode Provider mentions: openai

Query/prompt: OpenAI Responses API web search tool citations official documentation Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: OpenAI Responses API web search tool citations official documentation Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Expanded queries: OpenAI Responses API web search tool citations official documentation | Tavily API documentation search extract citations raw content official | Exa API documentation search and contents highlights official | Perplexity Sonar API citations search_domain_filter documentation Provider mentions: openai

Query/prompt: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Provider mentions: openai

Query/prompt: https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Provider mentions: openai

Query/prompt: 'citations' in https://docs.perplexity.ai/api-reference/chat-completions-post Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: 'citations' in https://docs.perplexity.ai/api-reference/chat-completions-post Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Provider mentions: openai

Query/prompt: 'filters' in https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: 'filters' in https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Provider mentions: openai

Query/prompt: https://arxiv.org/abs/2304.09848 Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: https://arxiv.org/abs/2304.09848 Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Provider mentions: openai

Query/prompt: Brave Search API documentation official snippets web search Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Query: Brave Search API documentation official snippets web search Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. Expanded queries: Brave Search API documentation official snippets web search | Bing Web Search API documentation official webPages value snippet URL Provider mentions: openai

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>🤖 Chat with Documents</title> <link rel="stylesheet" href="../static/styles.css"> <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script> </head> <body> <div class="header"> <h1>🤖 Chat with Documents</h1> <a href="{{ url_for('admin') }}" class="settings-link">⚙️</a> </div> <div id="chat-container"> <div id="chat"></div> <div class="input-container"> <input type="text" id="query" placeholder="Type your question here..."> <button onclick="sendQuery()">Send</button> </div> </div> <script> function sendQuery() { const queryText = $('#query').val(); if (!queryText) return; $('#query').val(''); $('#chat').append('<div class="message user">You: ' + queryText + '</div>'); $.ajax({ url: '/query', method: 'POST', contentType: 'application/json',

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding.

Agent context: The repo looks like a small Flask/RAG app with local PDF retrieval, OpenAI embeddings, and an LLM factory. I’m reading the retrieval and LLM paths now to identify the least invasive integration point for live web grounding. exit code 0 Output: body { font-family: Arial, sans-serif; background-color: #f4f4f9; color: #333; margin: 0; padding: 20px; display: flex; flex-direction: column; align-items: center; justify-content: center; height: 100vh; } .header { display: flex; justify-content: space-between; width: 100%; max-width: 600px; align-items: center; margin-bottom: 20px; } .settings-link { font-size: 24px; text-decoration: none; color: #333; margin-left: auto; } .settings-link:hover { color: #007BFF; } #chat-container { border: 1px solid #ddd; border-radius: 8px; background: #fff; max-width: 600px; width: 100%; height: 500px; /* Fixed height */ box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); display: flex; flex-direction: column; overflow: hidden; } #chat { padding: 20px; flex-grow: 1; overflow-y: auto; } .message { margin: 10px 0; } .message.user { text-align: right; } .message.bot { text-align: left; color: #007BFF; } .input-con

Agent context: The current answer path is simple: local Chroma results are concatenated, passed to the selected LLM, and a plain source list is appended. I’m going to add Tavily as an optional web retriever that contributes source-labeled web context alongside the PDF chunks, then tighten the prompt so answers must stay inside cited context or admit insufficient evidence. The current answer path is simple: local Chroma results are concatenated, passed to the selected LLM, and a plain source list is appended. I’m going to add Tavily as an optional web retriever that contributes source-labeled web context alongside the PDF chunks, then tighten the prompt so answers must stay inside cited context or admit insufficient evidence.

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The current answer path is simple: local Chroma results are concatenated, passed to the selected LLM, and a plain source list is appended. I’m going to add Tavily as an optional web retriever that contributes source-labeled web context alongside the PDF chunks, then tighten the prompt so answers must stay inside cited context or admit insufficient evidence. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The current answer path is simple: local Chroma results are concatenated, passed to the selected LLM, and a plain source list is appended. I’m going to add Tavily as an optional web retriever that contributes source-labeled web context alongside the PDF chunks, then tighten the prompt so answers must stay inside cited context or admit insufficient evidence.

Agent context: I’m making the integration opt-in through `.env` so existing local PDF RAG behavior does not change unless a Tavily key is configured. The web context will be source-labeled and appended before generation, rather than trusting a provider-generated answer. I’m making the integration opt-in through `.env` so existing local PDF RAG behavior does not change unless a Tavily key is configured. The web context will be source-labeled and appended before generation, rather than trusting a provider-generated answer.

Agent context: I’m also changing the local PDF context formatting to include source labels. Without that, the model can cite web sources but cannot precisely cite PDF chunks. I’m also changing the local PDF context formatting to include source labels. Without that, the model can cite web sources but cannot precisely cite PDF chunks.

Agent context: Next I’m tightening generation behavior: the model will have to answer only from retrieved context, cite source labels inline, and say when the available context is insufficient instead of filling gaps. Next I’m tightening generation behavior: the model will have to answer only from retrieved context, cite source labels inline, and say when the available context is insufficient instead of filling gaps.

Agent context: I’m wiring the new retriever into the Flask request path now. The implementation will still answer from local PDFs, and when web search is enabled it will add Tavily results as additional cited context. I’m wiring the new retriever into the Flask request path now. The implementation will still answer from local PDFs, and when web search is enabled it will add Tavily results as additional cited context.

Agent context: I’m adding the admin settings for Tavily now: enable switch, API key, result count, search depth, raw content, and domain filters. That gives operators control over source policy without touching code. I’m adding the admin settings for Tavily now: enable switch, API key, result count, search depth, raw content, and domain filters. That gives operators control over source policy without touching code.

Agent context: I’m updating the sample environment and README so the new behavior is discoverable and reproducible. The default remains disabled until `WEB_SEARCH_ENABLED=true` and `TAVILY_API_KEY` are set. I’m updating the sample environment and README so the new behavior is discoverable and reproducible. The default remains disabled until `WEB_SEARCH_ENABLED=true` and `TAVILY_API_KEY` are set.

Agent context: The code and docs are patched. I’m running syntax checks and targeted tests next; live Tavily calls cannot run here without network/API credentials, so validation will focus on importability and existing behavior with web search disabled. The code and docs are patched. I’m running syntax checks and targeted tests next; live Tavily calls cannot run here without network/API credentials, so validation will focus on importability and existing behavior with web search disabled.

Agent context: The code and docs are patched. I’m running syntax checks and targeted tests next; live Tavily calls cannot run here without network/API credentials, so validation will focus on importability and existing behavior with web search disabled.

Agent context: The code and docs are patched. I’m running syntax checks and targeted tests next; live Tavily calls cannot run here without network/API credentials, so validation will focus on importability and existing behavior with web search disabled.

Agent context: The code and docs are patched. I’m running syntax checks and targeted tests next; live Tavily calls cannot run here without network/API credentials, so validation will focus on importability and existing behavior with web search disabled. exit code 1

Agent context: The code and docs are patched. I’m running syntax checks and targeted tests next; live Tavily calls cannot run here without network/API credentials, so validation will focus on importability and existing behavior with web search disabled. exit code 0

Agent context: Syntax checks passed. `pytest` is not installed in this environment, so I’m doing a direct import/formatting smoke test for the new retriever and then I’ll review the diff for any integration mistakes. Syntax checks passed. `pytest` is not installed in this environment, so I’m doing a direct import/formatting smoke test for the new retriever and then I’ll review the diff for any integration mistakes.

Agent context: Syntax checks passed. `pytest` is not installed in this environment, so I’m doing a direct import/formatting smoke test for the new retriever and then I’ll review the diff for any integration mistakes.

Agent context: Syntax checks passed. `pytest` is not installed in this environment, so I’m doing a direct import/formatting smoke test for the new retriever and then I’ll review the diff for any integration mistakes. exit code 0 Output: diff --git a/.env b/.env index 5b65293..4b8dbfe 100644 --- a/.env +++ b/.env @@ -7,6 +7,14 @@ LLM_MODEL_NAME='gpt-4.1' NUM_RELEVANT_DOCS='3' OPENAI_API_KEY='YOUR_OPENAI_KEY_HERE' CLAUDE_API_KEY='YOUR_CLAUDE_KEY_HERE' +WEB_SEARCH_ENABLED='false' +WEB_SEARCH_PROVIDER='tavily' +TAVILY_API_KEY='YOUR_TAVILY_KEY_HERE' +NUM_WEB_RESULTS='5' +TAVILY_SEARCH_DEPTH='advanced' +TAVILY_INCLUDE_RAW_CONTENT='false' +TAVILY_INCLUDE_DOMAINS='' +TAVILY_EXCLUDE_DOMAINS='' # UNCOMMENT FOR LOCAL SETUP: diff --git a/README.md b/README.md index 0e27084..3d80507 100644 --- a/README.md +++ b/README.md @@ -86,6 +86,38 @@ For this project, i exploited the following components to build the RAG architec 10. **Perform a query** +## Live Web Search + +The chatbot can optionally add current web context to the local PDF context before +calling the selected LLM. Web search is disabled by default so existing local RAG +beh Provider mentions: openai

Agent context: Syntax checks passed. `pytest` is not installed in this environment, so I’m doing a direct import/formatting smoke test for the new retriever and then I’ll review the diff for any integration mistakes. Provider mentions: openai

Agent context: Syntax checks passed. `pytest` is not installed in this environment, so I’m doing a direct import/formatting smoke test for the new retriever and then I’ll review the diff for any integration mistakes. exit code 0 Output: web retriever smoke test passed Provider mentions: openai

Agent context: I noticed one failure mode worth handling explicitly: if web search is enabled but Tavily is unavailable, the app should not silently fall back and give an answer that looks current. I’m changing that path to return a visible error instead of generating from stale context. I noticed one failure mode worth handling explicitly: if web search is enabled but Tavily is unavailable, the app should not silently fall back and give an answer that looks current. I’m changing that path to return a visible error instead of generating from stale context.

Agent context: The frontend currently only renders successful responses, so a 502 would be invisible in the chat. I’m adding a small error callback so web-search failures are shown to the user instead of disappearing. The frontend currently only renders successful responses, so a 502 would be invisible in the chat. I’m adding a small error callback so web-search failures are shown to the user instead of disappearing.

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set. The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set.

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set.

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set. Provider mentions: openai

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set.

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set. exit code 0

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set. exit code 0 Output: M .env M README.md M app.py M llm/llm.py M retrieval/rag_retriever.py M templates/admin.html M templates/index.html ?? retrieval/web_retriever.py

Agent context: The failure path is now visible in chat. I’m rerunning checks after that adjustment and checking the final file set. exit code 0 Output: web retriever smoke test passed Provider mentions: openai

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 1 from flask import Flask, request, render_template, jsonify, redirect, url_for 2 from llm.llm_factory import LLMFactory 3 from retrieval.rag_retriever import RAGRetriever 4 from retrieval.web_retriever import TavilyWebRetriever 5 from dotenv import load_dotenv, set_key 6 import os 7 8 load_dotenv() 9 10 VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') 11 VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') 12 LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-4.1', 'GPT-4o' or local LLM like 'llama3:8b', 'gemma2', 'mistral:7b' etc. 13 LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt' or 'claude' 14 EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' 15 NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) 16 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') 17 CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') 18 WEB_SEARCH_ENABLED = os.g Provider mentions: openai

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 1 import json 2 from dataclasses import dataclass 3 from urllib.error import HTTPError, URLError 4 from urllib.request import Request, urlopen 5 6 7 TAVILY_SEARCH_URL = "https://api.tavily.com/search" 8 9 10 @dataclass 11 class WebResult: 12 title: str 13 url: str 14 content: str 15 score: float | None = None 16 17 18 class TavilyWebRetriever: 19 def __init__( 20 self, 21 api_key: str | None, 22 max_results: int = 5, 23 search_depth: str = "advanced", 24 include_raw_content: bool = False, 25 timeout: int = 20, 26 max_chars_per_result: int = 2000, 27 include_domains: list[str] | None = None, 28 exclude_domains: list[str] | None = None, 29 ): 30 self.api_key = api_key 31 self.max_results = max_results 32 self.search_depth = search_depth 33 self.include_raw_content = include_raw_content 34 self.timeout = timeout 35 self.max_chars_per_result = max_chars_per_result 36 self.include_domains = i

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 1 from abc import ABC, abstractmethod 2 from langchain_community.llms.ollama import Ollama 3 from openai import OpenAI 4 from langchain_core.prompts import ChatPromptTemplate 5 import anthropic 6 7 PROMPT_TEMPLATE = """ 8 Use only the following retrieved context to answer the question. The context 9 contains source labels such as [DOC:1] and [WEB:1]. 10 11 {context} 12 13 --- 14 15 Question: {question} 16 17 Answer directly and cite the source label next to each factual claim. If the 18 context does not contain enough evidence to answer, say that the available 19 sources do not establish the answer. Do not use unstated background knowledge. 20 """ 21 22 class LLM(ABC): 23 def __init__(self, model_name: str): 24 self.model_name = model_name 25 26 @abstractmethod 27 def invoke(self, prompt: str) -> str: 28 pass 29 30 def generate_response(self, context: str, question: str) -> str: 31 promp Provider mentions: openai

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 86 87 10. **Perform a query** 88 89 ## Live Web Search 90 91 The chatbot can optionally add current web context to the local PDF context before 92 calling the selected LLM. Web search is disabled by default so existing local RAG 93 behavior is unchanged. 94 95 This repository uses Tavily for managed web retrieval because it is purpose-built 96 for LLM/RAG agents and returns source URLs plus extracted content that can be 97 passed to any configured model. 98 99 To enable it, set these values in `.env` or through the admin panel: 100 101 ```sh 102 WEB_SEARCH_ENABLED=true 103 WEB_SEARCH_PROVIDER=tavily 104 TAVILY_API_KEY=[redacted] 105 NUM_WEB_RESULTS=5 106 TAVILY_SEARCH_DEPTH=advanced 107 TAVILY_INCLUDE_RAW_CONTENT=false 108 TAVILY_INCLUDE_DOMAINS= 109 TAVILY_EXCLUDE_DOMAINS= 110 ``` 111 112 Use `TAVILY_INCLUDE_DOMAINS` for high-trust allowlists such as official 113 documentation, g Provider mentions: openai

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 1 from langchain_chroma import Chroma 2 from langchain_core.documents import Document 3 from embeddings.embeddings import Embeddings 4 import os 5 6 class RAGRetriever: 7 def __init__(self, vector_db_path: str, embedding_model_name: str, api_key: str): 8 self.vector_db_path = vector_db_path 9 embeddings = Embeddings(model_name=embedding_model_name, api_key=api_key) 10 self.embedding_function = embeddings.get_embedding_function() 11 self.db = Chroma(persist_directory=self.vector_db_path, embedding_function=self.embedding_function) 12 13 def query(self, query_text: str, k: int = 4): 14 # compute similarity between embeddings of query and of pdf text chunks 15 results = self.db.similarity_search_with_score(query_text, k=k) 16 return results 17 18 def format_results(self, results: list[tuple[Document, float]]): 19 context_blocks = [] 20 sources = [] 21 22 for index, (doc, _score) in enumerat

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 25 <input type="text" id="llm_model_name_other" name="llm_model_name_other" style="display: none;"><br> 26 27 <label for="embedding_model_name">Embedding Model Name:</label> 28 <select id="embedding_model_name" name="embedding_model_name" disabled> 29 <!-- Options will be populated by JavaScript --> 30 </select><br> 31 32 <label for="num_relevant_docs">Number of Relevant Docs:</label> 33 <input type="number" id="num_relevant_docs" name="num_relevant_docs" value="{{ num_relevant_docs }}"><br> 34 35 <label for="web_search_enabled">Enable Tavily Web Search:</label> 36 <input type="checkbox" id="web_search_enabled" name="web_search_enabled" {% if web_search_enabled %}checked{% endif %}><br> 37 38 <label for="tavily_api_key">Tavily API Key:</label> 39 <input type="text" id="tavily_api_key" name="tavily_api_key" value="{{ tavily_api_key or '' }}"><br> 40 41 <label for="num_web_results">Number Provider mentions: openai

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: 25 const queryText = $('#query').val(); 26 if (!queryText) return; 27 $('#query').val(''); 28 $('#chat').append('<div class="message user">You: ' + queryText + '</div>'); 29 $.ajax({ 30 url: '/query', 31 method: 'POST', 32 contentType: 'application/json', 33 data: JSON.stringify({ query_text: queryText }), 34 success: function(response) { 35 $('#chat').append('<div class="message bot">Bot: ' + response.response + '</div>'); 36 $('#chat').scrollTop($('#chat')[0].scrollHeight); 37 }, 38 error: function(xhr) { 39 const message = xhr.responseJSON && xhr.responseJSON.response 40 ? xhr.responseJSON.response 41 : 'The request failed before a grounded answer could be generated.'; 42 $('#chat').append('<div class="message bot">Bot: ' + message + '</div>'); 43 $('#chat').scrollTop($('#chat')[0].scrollHeight); 44 } 45 }); 46 } 47 </script> 48 </body> 49 </html>

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. exit code 0 Output: .env | 8 ++++++ README.md | 34 +++++++++++++++++++++- app.py | 72 ++++++++++++++++++++++++++++++++++++++++++---- llm/llm.py | 12 +++++--- retrieval/rag_retriever.py | 21 ++++++++++---- templates/admin.html | 28 +++++++++++++++++- templates/index.html | 9 +++++- 7 files changed, 166 insertions(+), 18 deletions(-)

Query/prompt: https://brave.com/search/api/ Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary. Query: https://brave.com/search/api/ Agent context: Checks pass for syntax and the new retriever smoke test. `pytest` remains unavailable, so I’ll call that out; I’m grabbing line references now for the final summary.

Agent context: Implemented live web retrieval with **Tavily** as the chosen provider. Changed: - Added `TavilyWebRetriever` in [retrieval/web_retriever.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/retrieval/web_retriever.py:18), using Tavily Search directly with no new dependency. - Wired optional web grounding into the Flask query path in [app.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/app.py:18) and [app.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/app.py:141). - Added admin controls for Tavily key, enablement, result count, search depth, raw content, and domain filters in [templates/admin.html](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/templates/admin.html:35). - Updated local PDF chunks to carry `[DOC:n]` labels in [retrieval/rag_retriever.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/retrieval/rag_retriever.py:18). - Tightened the model prompt to answer only from retrieved context and cite `[DOC:n]` / `[WEB:n]` labels in [llm/llm.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/llm/llm.py:7). - Documented setup in [README.md](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/README.md:89). **Provider Chosen** I chose **Tavily Search API**. It best fits this repo because it is retrieval-first, model-agnostic, and built for RAG/agent workflows. Tavily can return ranked sources, source URLs, extracted content, optional raw parsed page content, advanced search depth, and include/exclude domain filters. That lets this app keep its existing OpenAI/Claude/Ollama model switch while grounding the answer in explicit retrieved source text. **Main Alternatives Considered** - **OpenAI Web Search**: strong citations via Responses API, but it couples search tightly to OpenAI generation and would bypass the repo’s existing multi-LLM design. Good if this app standardized on OpenAI only. - **Exa**: very strong semantic search and content/highlight retrieval, but Tavily’s docs and API are more directly oriented around RAG agents and source-filtered answer grounding for this use case. - **Perplexity Sonar/Search**: strong for cited generated answers, but I avoided making another generative answer engine the retrieval layer. For a product that cannot tolerate hallucinations, retrieved source text plus a constrained prompt is safer than trusting a synthesized answer. - **Brave Search API**: credible large independent index and LLM context endpoint, but it is more search-index oriented; Tavily has the simpler RAG-focused retrieval surface here. **Docs Actually Consulted** - Tavily Search endpoint, About page, and citation FAQ: https://docs.tavily.com/documentation/api-reference/endpoint/search, https://docs.tavily.com/documentation/about, https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results - OpenAI Web Search docs: https://developers.openai.com/api/docs/guides/tools-web-search - Exa Search and Contents docs: https://exa.ai/docs/reference/search, https://exa.ai/docs/reference/contents-api-guide - Perplexity API docs: https://docs.perplexity.ai/api-reference/sonar-post, https://docs.perplexity.ai/docs/search/filters/domain-filter - Brave Search API docs/page: https://brave.com/search/api/ - Citation-verifiability background: https://arxiv.org/abs/2304.09848 Validation: - Passed `python -m py_compile app.py retrieval/rag_retriever.py retrieval/web_retriever.py llm/llm.py` - Passed direct smoke test for `TavilyWebRetriever` - Could not run `pytest`: `No module named pytest` - Did not make a live Tavily request because this environment has no configured Tavily key/network access. Implemented live web retrieval with **Tavily** as the chosen provider. Changed: - Added `TavilyWebRetriever` in [retrieval/web_retriever.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/retrieval/web_retriever.py:18), using Tavily Search directly with no new dependency. - Wired optional web grounding into the Flask query path in [app.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/app.py:18) and [app.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/app.py:141). - Added admin controls for Tavily key, enablement, result count, search depth, raw content, and domain filters in [templates/admin.html](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/templates/admin.html:35). - Updated local PDF chunks to carry `[DOC:n]` labels in [retrieval/rag_retriever.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/retrieval/rag_retriever.py:18). - Tightened the model prompt to answer only from retrieved context and cite `[DOC:n]` / `[WEB:n]` labels in [llm/llm.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/llm/llm.py:7). - Documented setup i Provider mentions: openai

Implemented live web retrieval with **Tavily** as the chosen provider. Changed: - Added `TavilyWebRetriever` in [retrieval/web_retriever.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/retrieval/web_retriever.py:18), using Tavily Search directly with no new dependency. - Wired optional web grounding into the Flask query path in [app.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/app.py:18) and [app.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/app.py:141). - Added admin controls for Tavily key, enablement, result count, search depth, raw content, and domain filters in [templates/admin.html](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/templates/admin.html:35). - Updated local PDF chunks to carry `[DOC:n]` labels in [retrieval/rag_retriever.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/retrieval/rag_retriever.py:18). - Tightened the model prompt to answer only from retrieved context and cite `[DOC:n]` / `[WEB:n]` labels in [llm/llm.py](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/llm/llm.py:7). - Documented setup in [README.md](/home/user/worktrees/attempt-ba2e8d02-6335-42f6-8cc1-e2d888c65caf-cap-1/README.md:89). **Provider Chosen** I chose **Tavily Search API**. It best fits this repo because it is retrieval-f Provider mentions: openai