balaji-r

Run

Balaji-R-05/askdocs-ai

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:53 AM

Decision Summary

target

Expected

Provider

Product

Selected

Provider

linkup

Product

Surface

provider_selection

Reason

codex selected Linkup during first-pass provider discovery.

Outcome

target

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

26bc632a0bed22f035740edd34ee3e1fc2420e9f

agent_action

I’ll inspect the repo first to understand the agent stack and integration constrain...

I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and current offerings before making a recommendation.

command

command

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

command

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

command

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

exit code 0 Output: /home/user/worktrees/attempt-3e7e0617-5c47-4443-b573-4590bb2056d1-cap-2 /bin/bash: line 1: rg: command not found

command

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

command

exit code 0 Output: ./README.md ./requirements.client.txt ./requirements.server.txt

agent_action

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

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. I’ve only confirmed this is a small Python-looking repo so far; next I’m reading the top-level docs and source layout.

command

/bin/bash -lc "find . -maxdepth 4 -type f | sed 's#"'^./##'"' | sort | head -300"

command

/bin/bash -lc "find . -maxdepth 4 -type f | sed 's#"'^./##'"' | sort | head -300"

exit code 0 Output: .dockerignore .git .gitignore Dockerfile.client Dockerfile.server README.md client/__init__.py client/api_client.py client/auth_utils.py client/config.py client/main.py compose.yaml images/chat_interface.png images/db_indexed.png images/landing_page.png requirements.client.txt requirements.server.txt server/__init__.py server/config.py server/db/database.py server/db/models.py server/main.py server/modules/__init__.py server/modules/auth.py server/modules/llm.py server/modules/vectorstore.py server/schemas/auth.py server/tests/test_hybrid_search.py

command

/bin/bash -lc "sed -n '1,240p' README.md"

command

/bin/bash -lc "sed -n '1,240p' README.md"

exit code 0 Output: # AskDocs AI: AI-Powered PDF Q&A Bot **AskDocs AI** is an AI-powered chatbot that leverages **Hybrid RAG (Retrieval-Augmented Generation)** to answer your questions based on the content of uploaded PDFs. It combines semantic vector search with traditional keyword-based search for superior accuracy. <img src="./images/landing_page.png" alt="Landing Page"> <img src="./images/chat_interface.png" alt="Chat Interface"> ## Key Features - **Hybrid Search**: Combines **ChromaDB** (semantic) and **BM25** (keyword) retrieval. - **LLM Powered**: High-performance LLM via Groq Cloud. - **Async Processing**: PDF ingestion and indexing are offloaded to background threads. - **Multimodal Support**: Optimized for PDF extraction and processing. ## Tech Stack - **Backend:** FastAPI, LangChain (Classic), ChromaDB, Groq Cloud - **Frontend:** Streamlit - **Search Engines:** BM25 (Keyword), Vector (Cosine Simi

command

command

exit code 0 Output: ./README.md:3:**AskDocs AI** is an AI-powered chatbot that leverages **Hybrid RAG (Retrieval-Augmented Generation)** to answer your questions based on the content of uploaded PDFs. It combines semantic vector search with traditional keyword-based search for superior accuracy. ./README.md:10:- **Hybrid Search**: Combines **ChromaDB** (semantic) and **BM25** (keyword) retrieval. ./README.md:29:| `HYBRID_SEARCH_BM25_WEIGHT` | Weight for keyword search (0.0 to 1.0) | `0.5` | ./README.md:30:| `HYBRID_SEARCH_CHROMA_WEIGHT` | Weight for semantic search (0.0 to 1.0) | `0.5` | ./README.md:37:- **Persistent Memory**: Uses Docker volumes to persist the ChromaDB vector store and uploaded files. ./README.md:84:python server/tests/test_hybrid_search.py ./client/main.py:63: <strong>AskDocs AI</strong> is an AI-powered chatbot that leverages <strong>RAG (Retrieval-Augmented Generation)</strong> to answe

command

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

command

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

exit code 0 Output: --extra-index-url https://download.pytorch.org/whl/cpu torch langchain-chroma langchain-community langchain-text-splitters langchain-huggingface langchain-groq langchain-classic fastapi uvicorn[standard] python-multipart python-dotenv sentence-transformers pypdf pydantic-settings rank_bm25 sqlalchemy[asyncio] asyncpg fastapi-users[sqlalchemy] bcryptstreamlit requests python-dotenv

agent_action

The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25...

The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase.

command

/bin/bash -lc "sed -n '1,220p' server/modules/llm.py"

command

/bin/bash -lc "sed -n '1,220p' server/modules/llm.py"

exit code 0 Output: from langchain_groq import ChatGroq from langchain_classic.chains import RetrievalQA from langchain_classic.retrievers import EnsembleRetriever from config import settings from modules.vectorstore import get_bm25_retriever import logging logger = logging.getLogger(__name__) def get_llm_chain(vectorstore): if vectorstore is None: return None if not settings.GROQ_API_KEY: raise ValueError("GROQ_API_KEY is not set in environment or config.") llm = ChatGroq( groq_api_key=settings.GROQ_API_KEY, model_name=settings.LLM_MODEL, temperature=0.3, ) # 1. Semantic Retriever (Vector) chroma_retriever = vectorstore.as_retriever(search_kwargs={"k": 3}) # 2. Keyword Retriever (BM25) bm25_retriever = get_bm25_retriever(vectorstore) # 3. Ensemble Retriever (Hybrid) if bm25_retriever: logger.info(f"[HYBRID] Initializing Hybrid Search (BM25: {settings.HYBRID_SEARCH_BM25_WEIGHT}, Chroma: {settings.HYBRID_SEA

command

/bin/bash -lc "sed -n '1,220p' server/main.py"

command

/bin/bash -lc "sed -n '1,220p' server/main.py"

exit code 0 Output: import os import shutil import logging from typing import List from contextlib import asynccontextmanager from fastapi import FastAPI, UploadFile, File, HTTPException, Depends from fastapi.middleware.cors import CORSMiddleware from fastapi.concurrency import run_in_threadpool from pydantic import BaseModel from config import settings from modules.vectorstore import process_pdfs, get_vectorstore from modules.llm import get_llm_chain from db.database import engine from db.models import Base, User from modules.auth import fastapi_users, auth_backend, current_active_user from schemas.auth import UserRead, UserCreate, UserUpdate logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) @asynccontextmanager async def lifespan(app: FastAPI): """Lifecycle events for the FastAPI application.""" logger.info("🛠️ Star

command

/bin/bash -lc "sed -n '1,220p' server/modules/vectorstore.py"

command

/bin/bash -lc "sed -n '1,220p' server/modules/vectorstore.py"

exit code 0 Output: import os import hashlib import logging from concurrent.futures import ThreadPoolExecutor from langchain_chroma import Chroma from langchain_community.document_loaders import PyPDFLoader from langchain_community.retrievers import BM25Retriever from langchain_core.documents import Document from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_huggingface import HuggingFaceEmbeddings from config import settings logger = logging.getLogger(__name__) def file_hash(file_path: str) -> str: """Generate a unique hash for file contents (duplicate detection).""" with open(file_path, "rb") as f: return hashlib.md5(f.read()).hexdigest() def load_single_pdf(path): """Worker function to load a single PDF.""" try: logger.info(f"📄 Starting to parse: {os.path.basename(path)}") loader = PyPDFLoader(path) file_docs = loader.load() if not file_docs: logger.warning(f"⚠️ No text e

command

/bin/bash -lc "sed -n '1,180p' server/config.py && sed -n '1,160p' client/main.py"

command

/bin/bash -lc "sed -n '1,180p' server/config.py && sed -n '1,160p' client/main.py"

exit code 0 Output: import os from pathlib import Path from pydantic_settings import BaseSettings, SettingsConfigDict from dotenv import load_dotenv, find_dotenv load_dotenv(find_dotenv(), override=True) BASE_DIR = Path(__file__).resolve().parent.parent class Settings(BaseSettings): PROJECT_NAME: str = "AskDocs AI API" # Storage CHROMA_DB_PATH: str = os.getenv("CHROMA_DB_PATH", "chroma_db") TEMP_UPLOADS_DIR: str = os.getenv("TEMP_UPLOADS_DIR", "temp_uploads") # Database & Security DATABASE_URL: str = os.getenv("DATABASE_URL", "postgresql+asyncpg://postgres:postgres@db:5432/askdocs") SECRET_KEY: str = os.getenv("SECRET_KEY", "CHANGEME_SECRET_KEY_12345") # LLM GROQ_API_KEY: str | None = os.getenv("GROQ_API_KEY", "") LLM_MODEL: str = os.getenv("LLM_MODEL", "openai/gpt-oss-120b") # Throughput Settings MAX_WORKERS: int = os.cpu_count() or 4 # Hybrid Search Weights HYBRID_SEARCH_BM25_WEIGHT: float = float(os.gete

Linkup search API licensed sources documentation retrieval API AI trusted sources

Query: Linkup search API licensed sources documentation retrieval API AI trusted sources Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase. Expanded queries: Linkup search API licensed sources documentation retrieval API AI trusted sources | Tavily API documentation search AI answers source coverage | Exa API documentation search contents web search neural sources | Brave Search API documentation data rights independent web search API

url

https://docs.linkup.so/

Query: https://docs.linkup.so/ Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase.

url

https://docs.linkup.so/

Linkup premium sources licensed content partners trusted sources AI search API

Query: Linkup premium sources licensed content partners trusted sources AI search API Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase. Expanded queries: Linkup premium sources licensed content partners trusted sources AI search API | Linkup API licensed sources premium content trusted sources documentation | Linkup announces premium content partners licensed sources AI search

OpenAI API web search tool documentation sources citations search context size

Query: OpenAI API web search tool documentation sources citations search context size Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase. Expanded queries: OpenAI API web search tool documentation sources citations search context size | Perplexity Sonar API documentation search citations web search | Google Vertex AI grounding with Google Search documentation citations

url

https://docs.perplexity.ai/docs/sonar/quickstart

Query: https://docs.perplexity.ai/docs/sonar/quickstart Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase.

url

https://docs.linkup.so/pages/documentation/endpoints/search/overview

Query: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase.

url

https://docs.linkup.so/pages/sdk/js/js

Query: https://docs.linkup.so/pages/sdk/js/js Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase.

url

https://docs.linkup.so/pages/integrations/langchain

Query: https://docs.linkup.so/pages/integrations/langchain Agent context: The repo is a FastAPI + Streamlit PDF RAG app using LangChain Classic, Chroma, BM25, and ChatGroq. I’m checking the query chain shape now so the provider choice accounts for how web retrieval would actually plug into this codebase.

agent_action

The important repo constraint is that the current answer path is a LangChain `Retri...

The important repo constraint is that the current answer path is a LangChain `RetrievalQA` chain over PDF chunks, not a general tool-calling agent. A good web provider here should work as a retriever/context source that can be composed with the existing Chroma/BM25 retrieval and ChatGroq answer generation.

command

/bin/bash -lc "nl -ba server/main.py | sed -n '109,142p'"

command

/bin/bash -lc "nl -ba server/main.py | sed -n '109,142p'"

exit code 0 Output: 109 @app.post("/query", response_model=QueryResponse) 110 async def query_docs( 111 request: QueryRequest, 112 user: User = Depends(current_active_user) 113 ): 114 """Query the vectorstore using an LLM.""" 115 if not settings.GROQ_API_KEY: 116 raise HTTPException(status_code=500, detail="GROQ_API_KEY is missing. Check server logs.") 117 118 vectorstore = get_vectorstore() 119 if not vectorstore: 120 raise HTTPException(status_code=400, detail="No documents indexed. Please upload PDFs first.") 121 122 try: 123 chain = get_llm_chain(vectorstore) 124 if not chain: 125 raise HTTPException(status_code=500, detail="Failed to initialize LLM chain.") 126 127 # RetrievalQA invoke can also be heavy, offloading to threadpool 128 result = await run_in_threadpool(chain.invoke, {"query": request.query}) 129 130 answer = result["result"] 131 sources = [doc.metadata.get("source", "Unknown") for doc in r

command

/bin/bash -lc "nl -ba server/modules/llm.py | sed -n '1,80p'"

command

/bin/bash -lc "nl -ba server/modules/llm.py | sed -n '1,80p'"

exit code 0 Output: 1 from langchain_groq import ChatGroq 2 from langchain_classic.chains import RetrievalQA 3 from langchain_classic.retrievers import EnsembleRetriever 4 from config import settings 5 from modules.vectorstore import get_bm25_retriever 6 import logging 7 8 logger = logging.getLogger(__name__) 9 10 def get_llm_chain(vectorstore): 11 if vectorstore is None: 12 return None 13 14 if not settings.GROQ_API_KEY: 15 raise ValueError("GROQ_API_KEY is not set in environment or config.") 16 17 llm = ChatGroq( 18 groq_api_key=settings.GROQ_API_KEY, 19 model_name=settings.LLM_MODEL, 20 temperature=0.3, 21 ) 22 23 # 1. Semantic Retriever (Vector) 24 chroma_retriever = vectorstore.as_retriever(search_kwargs={"k": 3}) 25 26 # 2. Keyword Retriever (BM25) 27 bm25_retriever = get_bm25_retriever(vectorstore) 28 29 # 3. Ensemble Retriever (Hybrid) 30 if bm25_retriever: 31 logger.info(f"[HYBRID] Initializing Hyb

command

/bin/bash -lc "nl -ba requirements.server.txt | sed -n '1,40p'"

command

/bin/bash -lc "nl -ba requirements.server.txt | sed -n '1,40p'"

exit code 0 Output: 1 --extra-index-url https://download.pytorch.org/whl/cpu 2 torch 3 langchain-chroma 4 langchain-community 5 langchain-text-splitters 6 langchain-huggingface 7 langchain-groq 8 langchain-classic 9 fastapi 10 uvicorn[standard] 11 python-multipart 12 python-dotenv 13 sentence-transformers 14 pypdf 15 pydantic-settings 16 rank_bm25 17 sqlalchemy[asyncio] 18 asyncpg 19 fastapi-users[sqlalchemy] 20 bcrypt

url

https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/

Query: https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/ Agent context: The important repo constraint is that the current answer path is a LangChain `RetrievalQA` chain over PDF chunks, not a general tool-calling agent. A good web provider here should work as a retriever/context source that can be composed with the existing Chroma/BM25 retrieval and ChatGroq answer generation.

agent_action

**Recommendation** Choose **Linkup**. This repo’s backend is already a LangChain RA...

**Recommendation** Choose **Linkup**. This repo’s backend is already a LangChain RAG app: `server/modules/llm.py` builds a `RetrievalQA` chain over Chroma + BM25 with ChatGroq, and `/query` returns source documents from that chain. Linkup fits that shape best because it can be used as a LangChain retriever, has a Python SDK, supports search, fetch, sourced answers, structured output, source/date filtering, and can return raw retrieval context instead of forcing you into a replacement answer model. See [server/modules/llm.py](/home/user/worktrees/attempt-3e7e0617-5c47-4443-b573-4590bb2056d1-cap-2/server/modules/llm.py:17) and [server/main.py](/home/user/worktrees/attempt-3e7e0617-5c47-4443-b573-4590bb2056d1-cap-2/server/main.py:109). The decisive factor is source quality/licensing. Linkup’s docs emphasize production AI search, source customization, security, and endpoints for Search/Fetch/Research. Its Search endpoint returns ranked sources, sourced answers, or structured output, with domain/date controls. Its Fetch endpoint returns clean LLM-ready markdown. Its LangChain docs show `LinkupSearchRetriever`, which is directly compatible with this repo’s stack. ([techcrunch.com](https:

result

Selected unknown

Commands

exit 127

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

exit 0

/bin/bash -lc "find . -maxdepth 4 -type f | sed 's#"'^./##'"' | sort | head -300"

exit 0

/bin/bash -lc "sed -n '1,240p' README.md"

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,220p' server/modules/llm.py"

exit 0

/bin/bash -lc "sed -n '1,220p' server/main.py"

exit 0

/bin/bash -lc "sed -n '1,220p' server/modules/vectorstore.py"

exit 0

/bin/bash -lc "sed -n '1,180p' server/config.py && sed -n '1,160p' client/main.py"

exit 0

/bin/bash -lc "nl -ba server/main.py | sed -n '109,142p'"

exit 0

/bin/bash -lc "nl -ba server/modules/llm.py | sed -n '1,80p'"

exit 0

/bin/bash -lc "nl -ba requirements.server.txt | sed -n '1,40p'"

exit 0

URLs Fetched

https://docs.linkup.so/

status not materialized

https://docs.perplexity.ai/docs/sonar/quickstart

status not materialized

https://docs.linkup.so/pages/documentation/endpoints/search/overview

status not materialized

https://docs.linkup.so/pages/sdk/js/js

status not materialized

https://docs.linkup.so/pages/integrations/langchain

status not materialized

https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/

status not materialized

Changed Files

Artifact only

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

Runs

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Decision Summary

Trace Timeline

Commands

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Changed Files

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Decision Summary

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