enricoll

Run

enricollen/rag-conversational-agent

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:53 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

codex selected tavily during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

a2cb16c686c39c37b0cf934d40e435970efdf18f

agent_action

I’ll first map the repository’s agent stack and integration points, then I’ll verif...

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.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

exit code 0 Output: /bin/bash: line 1: rg: command not found /home/user/worktrees/attempt-6ebc30d5-1dc5-456b-b308-ccd372c1c500-cap-2

command

command

exit code 127 Output: /bin/bash: line 1: rg: command not found

command

/bin/bash -lc "find . -maxdepth 2 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '.env*' -o -name 'go.mod' -o -name 'Cargo.toml' \\) -print"

command

exit code 0 Output: ./.env ./README.md ./requirements.txt

agent_action

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. Th...

`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.

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -type f -print | sort | head -300"

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -type f -print | sort | head -300"

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

command

/bin/bash -lc "sed -n '1,220p' requirements.txt"

command

/bin/bash -lc "sed -n '1,220p' requirements.txt"

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

command

/bin/bash -lc "sed -n '1,220p' README.md"

command

/bin/bash -lc "sed -n '1,220p' README.md"

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

command

command

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

agent_action

The app is a compact Flask RAG chatbot: Chroma for local PDF retrieval, a simple `L...

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.

command

/bin/bash -lc "sed -n '1,180p' app.py"

command

/bin/bash -lc "sed -n '1,180p' app.py"

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

command

/bin/bash -lc "sed -n '1,180p' llm/llm.py"

command

/bin/bash -lc "sed -n '1,180p' llm/llm.py"

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

command

/bin/bash -lc "sed -n '1,120p' retrieval/rag_retriever.py"

command

/bin/bash -lc "sed -n '1,120p' retrieval/rag_retriever.py"

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)

command

/bin/bash -lc "sed -n '1,80p' embeddings/openai_embeddings.py"

command

/bin/bash -lc "sed -n '1,80p' embeddings/openai_embeddings.py"

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

command

/bin/bash -lc "sed -n '1,120p' llm/llm_factory.py"

command

/bin/bash -lc "sed -n '1,120p' llm/llm_factory.py"

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}")

OpenAI official docs web search tool Responses API web_search_preview citations

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

url

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

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.

site:docs.anthropic.com web search tool Anthropic API citations

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

url

https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool?source=post_page-----80f089ad56d7---------------------------------------

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.

url

https://docs.tavily.com/

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.

url

https://docs.tavily.com/documentation/api-reference/endpoint/extract

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.

url

https://docs.exa.ai/reference/search

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.

Brave Search API official documentation web search endpoint snippets pricing

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

url

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

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.

command

/bin/bash -lc "nl -ba llm/llm.py | sed -n '1,110p'"

command

/bin/bash -lc "nl -ba llm/llm.py | sed -n '1,110p'"

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

command

/bin/bash -lc "nl -ba app.py | sed -n '1,120p'"

command

/bin/bash -lc "nl -ba app.py | sed -n '1,120p'"

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

command

/bin/bash -lc "nl -ba retrieval/rag_retriever.py | sed -n '1,80p'"

command

/bin/bash -lc "nl -ba retrieval/rag_retriever.py | sed -n '1,80p'"

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

command

/bin/bash -lc "nl -ba README.md | sed -n '95,104p'"

command

/bin/bash -lc "nl -ba README.md | sed -n '95,104p'"

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

agent_action

**Provider I’d choose: Tavily.** This repo is a small Flask RAG app: `/query` retri...

**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,

result

Selected openai

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

exit 0

exit 127

exit 0

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -type f -print | sort | head -300"

exit 0

/bin/bash -lc "sed -n '1,220p' requirements.txt"

exit 0

/bin/bash -lc "sed -n '1,220p' README.md"

exit 0

/bin/bash -lc "sed -n '1,180p' app.py"

exit 0

/bin/bash -lc "sed -n '1,180p' llm/llm.py"

exit 0

/bin/bash -lc "sed -n '1,120p' retrieval/rag_retriever.py"

exit 0

/bin/bash -lc "sed -n '1,80p' embeddings/openai_embeddings.py"

exit 0

/bin/bash -lc "sed -n '1,120p' llm/llm_factory.py"

exit 0

/bin/bash -lc "nl -ba llm/llm.py | sed -n '1,110p'"

exit 0

/bin/bash -lc "nl -ba app.py | sed -n '1,120p'"

exit 0

/bin/bash -lc "nl -ba retrieval/rag_retriever.py | sed -n '1,80p'"

exit 0

/bin/bash -lc "nl -ba README.md | sed -n '95,104p'"

exit 0

URLs Fetched

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

status not materialized

https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool?source=post_page-----80f089ad56d7---------------------------------------

status not materialized

https://docs.tavily.com/

status not materialized

https://docs.tavily.com/documentation/api-reference/endpoint/extract

status not materialized

https://docs.exa.ai/reference/search

status not materialized

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files