Run

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a2cb16c686c39c37b0cf934d40e435970efdf18f

Agent context: I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation. I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation.

Agent context: I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation.

Agent context: I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation. exit code 0 Output: /home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation.

Agent context: I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation. exit code 0

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and direct file reads. The worktree is clean, so there are no existing edits to account for. `rg` is not installed in this workspace, so I’m falling back to `find` and direct file reads. The worktree is clean, so there are no existing edits to account for.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and direct file reads. The worktree is clean, so there are no existing edits to account for.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and direct file reads. The worktree is clean, so there are no existing edits to account for. exit code 0 Output: .git .env .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 direct file reads. The worktree is clean, so there are no existing edits to account for.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and direct file reads. The worktree is clean, so there are no existing edits to account for. exit code 0 Output: ./README.md ./requirements.txt

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. 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: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. 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: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. 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: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. 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: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. exit code 0 Output: from embeddings.openai_embeddings import OpenAIEmbeddings from langchain_community.embeddings.ollama import OllamaEmbeddings from langchain_community.embeddings.bedrock import BedrockEmbeddings class Embeddings: def __init__(self, model_name: str, api_key: str = None): self.model_name = model_name self.api_key = api_key def get_embedding_function(self): if self.model_name == "ollama": return OllamaEmbeddings(model="mxbai-embed-large") elif self.model_name == "openai": if not self.api_key: raise ValueError("OpenAI API key must be provided for OpenAI embeddings") return OpenAIEmbeddings(api_key=self.api_key) elif self.model_name == "bedrock": return BedrockEmbeddings(credentials_profile_name="default", region_name="us-east-1") else: raise ValueError(f"Unsupported embedding model: {self.model_name}") Provider mentions: openai

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. exit code 0 Output: import argparse import os import shutil from embeddings.embeddings import Embeddings from langchain_community.document_loaders import PyPDFDirectoryLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_core.documents import Document from langchain_chroma import Chroma from dotenv import load_dotenv load_dotenv() OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') DATA_PATH = os.getenv('DATA_PATH') VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') def main(): # check whether the database should be cleared or not (using the --clear flag) parser = argparse.ArgumentParser() parser.add_argument("--reset", nargs="?", const="both", choices=["ollama", "openai", "both"], help="Reset the database.") parser.add_argument("--embedding-model", type=str, default="openai", help="The embedding model to use (o Provider mentions: openai

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. 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: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. 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: Tavily API documentation search extract LangChain integration Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: Tavily API documentation search extract LangChain integration Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Expanded queries: Tavily API documentation search extract LangChain integration | Exa API documentation search contents LangChain integration | Brave Search API documentation Web Search API AI agents | SerpAPI documentation Google Search API organic results Python

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: LangChain Python TavilySearch tool docs Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: LangChain Python TavilySearch tool docs Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Expanded queries: LangChain Python TavilySearch tool docs | python.langchain.com TavilySearchResults

Query/prompt: https://docs.langchain.com/oss/python/integrations/tools/tavily_search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.langchain.com/oss/python/integrations/tools/tavily_search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: https://docs.exa.ai/ Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.exa.ai/ Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: Tavily pricing API credits free 1000 monthly search credits Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: Tavily pricing API credits free 1000 monthly search credits Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Expanded queries: Tavily pricing API credits free 1000 monthly search credits | Brave Search API pricing $5 per 1000 requests LLM context

Query/prompt: https://docs.tavily.com/documentation/api-credits Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: https://docs.tavily.com/documentation/api-credits Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Query/prompt: '$5 per 1,000' in https://brave.com/search/api/ Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Query: '$5 per 1,000' in https://brave.com/search/api/ Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Agent context: The repository’s current query path is deterministic: Flask receives `/query`, `RAGRetriever` returns local Chroma chunks, and `LLM.generate_response()` answers only from that context. For web grounding, the best fit is a web retriever that produces source URLs and text chunks in the same shape as the local PDF retriever, with an optional fallback/merge policy. The repository’s current query path is deterministic: Flask receives `/query`, `RAGRetriever` returns local Chroma chunks, and `LLM.generate_response()` answers only from that context. For web grounding, the best fit is a web retriever that produces source URLs and text chunks in the same shape as the local PDF retriever, with an optional fallback/merge policy.

Query/prompt: site:platform.openai.com/docs web search tool Responses API OpenAI Agent context: The repository’s current query path is deterministic: Flask receives `/query`, `RAGRetriever` returns local Chroma chunks, and `LLM.generate_response()` answers only from that context. For web grounding, the best fit is a web retriever that produces source URLs and text chunks in the same shape as the local PDF retriever, with an optional fallback/merge policy. Query: site:platform.openai.com/docs web search tool Responses API OpenAI Agent context: The repository’s current query path is deterministic: Flask receives `/query`, `RAGRetriever` returns local Chroma chunks, and `LLM.generate_response()` answers only from that context. For web grounding, the best fit is a web retriever that produces source URLs and text chunks in the same shape as the local PDF retriever, with an optional fallback/merge policy. Expanded queries: site:platform.openai.com/docs web search tool Responses API OpenAI Provider mentions: openai

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list. I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list.

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list.

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list. 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 dotenv import load_dotenv, set_key 5 import os 6 7 load_dotenv() 8 9 VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') 10 VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') 11 LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-4.1', 'GPT-4o' or local LLM like 'llama3:8b', 'gemma2', 'mistral:7b' etc. 12 LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt' or 'claude' 13 EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' 14 NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) 15 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') 16 CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') 17 ENV_PATH = '.env' 18 19 app = Flask(__name__) 20 21 # Initialize the retriever and Provider mentions: openai

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list.

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list. exit code 0 Output: 1 from langchain_chroma import Chroma 2 from langchain_core.documents import Document 3 from embeddings.embeddings import Embeddings 4 5 class RAGRetriever: 6 def __init__(self, vector_db_path: str, embedding_model_name: str, api_key: str): 7 self.vector_db_path = vector_db_path 8 embeddings = Embeddings(model_name=embedding_model_name, api_key=api_key) 9 self.embedding_function = embeddings.get_embedding_function() 10 self.db = Chroma(persist_directory=self.vector_db_path, embedding_function=self.embedding_function) 11 12 def query(self, query_text: str, k: int = 4): 13 # compute similarity between embeddings of query and of pdf text chunks 14 results = self.db.similarity_search_with_score(query_text, k=k) 15 return results 16 17 def format_results(self, results: list[tuple[Document, float]]): 18 enhanced_context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results]) 1

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list.

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list. exit code 0 Output: 1 A simple local Retrieval-Augmented Generation (RAG) chatbot that can answer to questions by acquiring information from personal pdf documents. 2 3 (please, if you find this content useful please consider leaving a star ⭐) 4 5 ## What is Retrieval-Augmented Generation (RAG)? 6 <div style="text-align: center;"> 7 <img src="https://miro.medium.com/v2/resize:fit:1400/1*J7vyY3EjY46AlduMvr9FbQ.png" alt="rag_pipeline" width="600" height="300"> 8 </div> 9 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. 10 11 1. **Data Indexing** 12 - Documents: This is the starting point where multiple documents are stored. 13 - Vector DB: The documents are proces Provider mentions: openai

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list.

Agent context: I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list. 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 Basing only on the following context: 9 10 {context} 11 12 --- 13 14 Answer the following question: {question} 15 Avoid to start the answer saying that you are basing on the provided context and go straight with the response. 16 """ 17 18 class LLM(ABC): 19 def __init__(self, model_name: str): 20 self.model_name = model_name 21 22 @abstractmethod 23 def invoke(self, prompt: str) -> str: 24 pass 25 26 def generate_response(self, context: str, question: str) -> str: 27 prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) 28 prompt = prompt_template.format(context=context, question=question) 29 response_text = self.invoke(prompt) 30 return response_text 31 3 Provider mentions: openai

Agent context: **Provider Pick** I’d choose **Tavily**, specifically `langchain-tavily` or `tavily-python`, as the managed web-search/retrieval provider. This repo is a small Flask + LangChain RAG app. The current path is: `/query` retrieves Chroma chunks, formats context/sources, then sends that context into the LLM prompt: [app.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/app.py:84), [rag_retriever.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/retrieval/rag_retriever.py:12), [llm.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/llm/llm.py:7). Tavily fits that shape cleanly because it can return search results, cleaned page content, source URLs, and LangChain-compatible tooling without forcing the app to abandon Ollama or Claude support. **Why Tavily** Tavily is built for AI agent/RAG search, has a current official Python SDK, and supports Search plus Extract/Crawl/Map/Research APIs. Its Search API can include cleaned parsed HTML content from results, and Extract can pull page content from specific URLs. The Python SDK is a simple package install and supports both search and extraction. LangChain’s current Python integration is `langchain-tavily`, with fields this app needs: title, URL, content snippet, raw content, answer, images, async support, domain filters, and time filters. Sources: Tavily Search docs, Extract docs, Python SDK docs, and LangChain Tavily docs. https://docs.tavily.com/documentation/api-reference/endpoint/search https://docs.tavily.com/documentation/api-reference/endpoint/extract https://docs.tavily.com/sdk/python/quick-start https://docs.langchain.com/oss/python/integrations/tools/tavily_search I’d implement it as a separate `WebRetriever` that returns LangChain `Document` objects with URL/title metadata, then merge or fallback from the existing local PDF retriever. That preserves the repo’s current model-provider flexibility. **Main Alternatives Considered** **Brave Search API / LLM Context** was the strongest runner-up. Its LLM Context endpoint is explicitly built for agents and RAG, returns extracted snippets plus source metadata, supports freshness filters and token-budget controls, and Brave has its own large index. I’d choose Brave if the priority were independent index quality, cost at scale, or strict token-budget control. I would not pick it first here because the repo is already LangChain-oriented and Brave would require a custom request/response adapter. https://api-dashboard.search.brave.com/documentation/services/llm-context https://brave.com/search/api/ **Exa** is also strong, especially for semantic search, highlights, company/people/research-style retrieval, and LangChain RAG. I would consider it for deeper research workflows. I’d still pick Tavily here because Tavily’s docs and integrations map more directly to simple “search current web, extract usable content, cite URLs” behavior for this app. https://exa.ai/docs/reference/search-api-guide https://exa.ai/docs/reference/langchain **SerpAPI** is mature for Google SERP data, but it primarily returns search result metadata/snippets and rich SERP structures. For this repo, that means adding another crawler/extractor layer before the LLM gets enough grounded context. https://serpapi.com/search-api https://serpapi.com/organic-results **OpenAI Web Search** is attractive if the app were OpenAI-only. It supports live web search, citations, domain filters, and retrieved sources through the Responses API. But this repo intentionally supports Ollama, OpenAI, and Anthropic; using OpenAI web search as the retrieval layer would couple web grounding to one LLM provider and require a larger LLM wrapper refactor. https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses **Documentation Actually Consulted** Tavily Search, Extract, Python SDK, and LangChain integration docs; Brave Search API and LLM Context docs; Exa Search and LangChain docs; SerpAPI Search/Organic Results docs; OpenAI Web Search docs. **Provider Pick** I’d choose **Tavily**, specifically `langchain-tavily` or `tavily-python`, as the managed web-search/retrieval provider. This repo is a small Flask + LangChain RAG app. The current path is: `/query` retrieves Chroma chunks, formats context/sources, then sends that context into the LLM prompt: [app.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/app.py:84), [rag_retriever.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/retrieval/rag_retriever.py:12), [llm.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/llm/llm.py:7). Tavily fits that shape cleanly because it can return search results, cleaned page content, source URLs, and LangChain-compatible tooling without forcing the app to abandon Ollama or Claude support. **Why Tavily** Tavily is built for AI agent/RAG search, has a current official Python SDK, and supports Search plus Extract/Crawl/Map/Research APIs. Its Search API can include cleaned parsed HTML content from results, and Extract can pull page content from specific URLs. The Python SDK is a simple package install and supports both search and extraction. LangChain’s current Py Provider mentions: openai

**Provider Pick** I’d choose **Tavily**, specifically `langchain-tavily` or `tavily-python`, as the managed web-search/retrieval provider. This repo is a small Flask + LangChain RAG app. The current path is: `/query` retrieves Chroma chunks, formats context/sources, then sends that context into the LLM prompt: [app.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/app.py:84), [rag_retriever.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/retrieval/rag_retriever.py:12), [llm.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/llm/llm.py:7). Tavily fits that shape cleanly because it can return search results, cleaned page content, source URLs, and LangChain-compatible tooling without forcing the app to abandon Ollama or Claude support. **Why Tavily** Tavily is built for AI agent/RAG search, has a current official Python SDK, and supports Search plus Extract/Crawl/Map/Research APIs. Its Search API can include cleaned parsed HTML content from results, and Extract can pull page content from specific URLs. The Python SDK is a simple package install and supports both search and extraction. LangChain’s current Python integration is `langchain-tavily`, with fields this app needs: title, URL, content snippet, raw content, answer, images, async support, domain filters, and time filters. Sources: Tavily Search do Provider mentions: openai