Run

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a2cb16c686c39c37b0cf934d40e435970efdf18f

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation. I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation.

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation.

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation. exit code 0 Output: /bin/bash: line 1: rg: command not found /home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation. Provider mentions: openai

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation.

Agent context: I’ll first map the repository’s agent stack and integration points, then I’ll verify current docs for the likely provider options before making a recommendation. exit code 0 Output: ./.env ./README.md ./requirements.txt

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly. `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly. exit code 0 Output: ./.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 environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly. 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: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly. 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: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly. Provider mentions: openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent path quickly. exit code 0 Output: ./.env:1:VECTOR_DB_OPENAI_PATH='chroma-openai' ./.env:2:VECTOR_DB_OLLAMA_PATH='chroma-ollama' ./.env:4:EMBEDDING_MODEL_NAME='openai' ./.env:13:#EMBEDDING_MODEL_NAME=ollama ./.env:14:#LLM_MODEL_TYPE=ollama ./.env:15:#LLM_MODEL_NAME=llama3:8b ./.gitignore:4:chroma-ollama ./.gitignore:5:chroma-openai ./README.md: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. ./README.md:26:2. **Flask**: Framework for rendering web page and handling user interactions. ./README.md:27:3. **Ollama**: Manages the local language model for generating responses. ./README.md:28:4. **LangChain**: A framework for integrating language models and retrieval systems. ./REA Provider mentions: openai

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. 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 app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. 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 app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. 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 app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. exit code 0 Output: from openai import OpenAI class OpenAIEmbeddings: """ class that implements two methods to be called from Chroma """ def __init__(self, api_key: str): self.client = OpenAI(api_key=api_key) def embed_documents(self, texts: list[str]): embeddings = [] for text in texts: response = self.client.embeddings.create(input=text, model="text-embedding-3-small") embeddings.append(response.data[0].embedding) return embeddings def embed_query(self, text: str): response = self.client.embeddings.create(input=text, model="text-embedding-3-small") return response.data[0].embedding Provider mentions: openai

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. exit code 0 Output: from llm.llm import LLM, GPTModel, OllamaModel, AnthropicModel class LLMFactory: @staticmethod def create_llm(model_type: str, model_name: str, api_key: str = None) -> LLM: if model_type == 'ollama': return OllamaModel(model_name) elif model_type == 'gpt': return GPTModel(model_name, api_key) elif model_type == 'claude': return AnthropicModel(model_name, api_key) else: raise ValueError(f"Unsupported model type: {model_type}")

Query/prompt: OpenAI official docs web search tool Responses API web_search_preview citations Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: OpenAI official docs web search tool Responses API web_search_preview citations Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Expanded queries: OpenAI official docs web search tool Responses API web_search_preview citations | Anthropic official docs web search tool Claude API citations | Tavily official docs search API extract crawl documentation | Exa official docs API search contents answer research web search Provider mentions: openai

Query/prompt: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Query/prompt: site:docs.anthropic.com web search tool Anthropic API citations Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: site:docs.anthropic.com web search tool Anthropic API citations Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Expanded queries: site:docs.anthropic.com web search tool Anthropic API citations | docs.anthropic.com Claude web search tool API web_search_20250305 Provider mentions: openai

Query/prompt: https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool?source=post_page-----80f089ad56d7--------------------------------------- Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool?source=post_page-----80f089ad56d7--------------------------------------- Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Query/prompt: https://docs.tavily.com/ Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: https://docs.tavily.com/ Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/extract Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: https://docs.tavily.com/documentation/api-reference/endpoint/extract Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Query/prompt: https://docs.exa.ai/reference/search Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: https://docs.exa.ai/reference/search Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Query/prompt: Brave Search API official documentation web search endpoint snippets pricing Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: Brave Search API official documentation web search endpoint snippets pricing Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Expanded queries: Brave Search API official documentation web search endpoint snippets pricing | SerpApi official documentation Google Search API JSON endpoint pricing Provider mentions: openai

Query/prompt: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Query: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. Provider mentions: openai

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. 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: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. 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: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. 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: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add.

Agent context: The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `LLM` interface, and OpenAI/Anthropic/Ollama model adapters. I’m reading the exact call boundaries now so the recommendation is tied to what would be easiest and least invasive to add. exit code 0 Output: 95 ## 🚀 Future Improvements 96 Here are some ideas for future improvements: 97 - [x] Add OpenAI LLM GPT models compatibility (3.5 turbo, 4, 4-o) 98 - [x] Add Anthropic Claude LLM models compatibility (Claude 3.5 Sonnet, Claude 3 Sonnet, Claude 3 Opus, Claude 3 Haiku) 99 - [x] Add unit testing to validate the responses given by the LLM 100 - [x] Add an admin user interface in web UI to choose interactively the parameters like LLMs, embedding models etc. 101 - [ ] Add Langchain Tools compatibility, allowing users to define custom Python functions that can be utilized by the LLMs. 102 - [ ] Add web scraping in case none of the personal documents contain relevant info w.r.t. the query 103 104 ## 📹 Demo Video Provider mentions: openai

Agent context: **Provider I’d choose: Tavily.** This repo is a small Flask RAG app: `/query` retrieves local Chroma PDF chunks, formats them as context, then passes that context into a provider-agnostic `LLM.generate_response()` path for OpenAI, Anthropic, or Ollama. See [app.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/app.py:84), [llm.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/llm/llm.py:26), and [rag_retriever.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/retrieval/rag_retriever.py:12). The README also already names the missing feature as web scraping when personal docs are insufficient: [README.md](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/README.md:101). Tavily is the best fit because it is model-independent and retrieval-oriented. Its docs verify a Python SDK, Search, Extract, Crawl, Map, and Research APIs, plus an official LangChain integration via `langchain-tavily`. It can return ranked results, source URLs, cleaned/raw page content, domain filters, time filters, news/finance/general topics, and usage data. That maps cleanly to this app’s existing “retrieve context, cite sources, call selected LLM” design without forcing the user onto GPT or Claude only. Tavily also has a usable free/monthly credit model verified in its docs. **Main Alternatives Considered** OpenAI built-in web search: strong if this app became OpenAI-only. Current docs show Responses API `web_search`, live access by default, citation annotations, `search_context_size`, domain filters, and a Chat Completions search model. I would not choose it here because it ties grounding to OpenAI models and would require changing the current OpenAI adapter path while leaving Anthropic/Ollama without equivalent behavior. Anthropic web search: strong if this app became Claude-only. Current docs show Claude server-side web search, automatic citations, `max_uses`, domain filters, localization, dynamic filtering on supported Claude models, and pricing at $10/1,000 searches plus token costs. I would not choose it as the single repo-wide provider because it is Claude-specific and requires console enablement. Exa: strong semantic/research search. Current docs show Search with extracted contents/highlights, date/domain filters, categories like news/research paper/company, deep search modes, and an Answer endpoint with citations. I would put it second for this repo. Tavily wins narrowly because its product surface is more directly agent/RAG oriented for simple web grounding, and its official LangChain integration is a closer match to the repo’s current dependencies. Brave Search API: strong independent web index and likely attractive for cost/scale. Current docs show Web Search, LLM Context, freshness filters, extra snippets, search operators, and an independent index. I would not choose it first because it is more search-index oriented; for this repo, Tavily’s search plus extraction plus LangChain package gives a simpler path to usable LLM context and citations. **Official Docs Consulted** Tavily: [Welcome](https://docs.tavily.com/welcome), [Search API](https://docs.tavily.com/documentation/api-reference/endpoint/search), [Extract API](https://docs.tavily.com/documentation/api-reference/endpoint/extract), [LangChain integration](https://docs.tavily.com/documentation/integrations/langchain), [Credits & Pricing](https://docs.tavily.com/documentation/api-credits) OpenAI: [Web search tool](https://developers.openai.com/api/docs/guides/tools-web-search) Anthropic: [Web search tool](https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool) Exa: [Search](https://exa.ai/docs/reference/search), [Answer](https://exa.ai/docs/reference/answer) Brave: [Web Search docs](https://api-dashboard.search.brave.com/app/documentation/web-search/get-started), [API reference](https://api-dashboard.search.brave.com/api-reference/web/search/get), [product/pricing page](https://brave.com/search/api/) **Provider I’d choose: Tavily.** This repo is a small Flask RAG app: `/query` retrieves local Chroma PDF chunks, formats them as context, then passes that context into a provider-agnostic `LLM.generate_response()` path for OpenAI, Anthropic, or Ollama. See [app.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/app.py:84), [llm.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/llm/llm.py:26), and [rag_retriever.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/retrieval/rag_retriever.py:12). The README also already names the missing feature as web scraping when personal docs are insufficient: [README.md](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/README.md:101). Tavily is the best fit because it is model-independent and retrieval-oriented. Its docs verify a Python SDK, Search, Extract, Crawl, Map, and Research APIs, plus an official LangChain integration via `langchain-tavily`. It can return ranked results, source URLs, cleaned/raw page content, domain filters, time filters, news/finance/general topics, and usage data. That maps cleanly to this app’s existing “retrieve context, Provider mentions: openai

**Provider I’d choose: Tavily.** This repo is a small Flask RAG app: `/query` retrieves local Chroma PDF chunks, formats them as context, then passes that context into a provider-agnostic `LLM.generate_response()` path for OpenAI, Anthropic, or Ollama. See [app.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/app.py:84), [llm.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/llm/llm.py:26), and [rag_retriever.py](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/retrieval/rag_retriever.py:12). The README also already names the missing feature as web scraping when personal docs are insufficient: [README.md](/home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2/README.md:101). Tavily is the best fit because it is model-independent and retrieval-oriented. Its docs verify a Python SDK, Search, Extract, Crawl, Map, and Research APIs, plus an official LangChain integration via `langchain-tavily`. It can return ranked results, source URLs, cleaned/raw page content, domain filters, time filters, news/finance/general topics, and usage data. That maps cleanly to this app’s existing “retrieve context, cite sources, call selected LLM” design without forcing the user onto GPT or Claude only. Tavily also has a usable free/monthly credit model verified in its docs. **Main Alternatives Considered** Ope Provider mentions: openai