Run

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Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation. I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation.

Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation.

Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation. exit code 0

Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation.

Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation. exit code 0 Output: /home/user/worktrees/attempt-8a5f6767-30b0-41fe-98c0-deb6acc9e3bf-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation. Provider mentions: openai

Agent context: I’ll first map how this repo’s agent is structured and where web retrieval would plug in, then I’ll check current provider docs/pricing/features from primary sources before making a single recommendation. exit code 0 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise. `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise.

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise. Provider mentions: openai

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise. 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:84:python server/tests/test_hybrid_search.py ./server/config.py:23: LLM_MODEL: str = os.getenv("LLM_MODEL", "openai/gpt-oss-120b") ./server/config.py:32: model_config = SettingsConfigDict( ./server/main.py:12:from modules.llm import get_llm_chain ./server/main.py:14:from db.models import Base, User ./server/main Provider mentions: openai

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise.

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise. exit code 0 Output: .git .dockerignore .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

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise.

Agent context: `rg` isn’t installed in this environment, so I’m falling back to `find`/`grep` and keeping the scans scoped to avoid noise. exit code 0 Output: ./README.md ./requirements.client.txt ./requirements.server.txt

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. 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

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. 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

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. 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

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. 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 Provider mentions: openai

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. 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

Query/prompt: Tavily API documentation AI search extract LangChain retriever Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Query: Tavily API documentation AI search extract LangChain retriever Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Expanded queries: Tavily API documentation AI search extract LangChain retriever | Exa API documentation web search contents LangChain | Brave Search API documentation web search API snippets | SerpAPI Google Search API documentation JSON results

Query/prompt: https://docs.tavily.com/ Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Query: https://docs.tavily.com/ Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Query/prompt: https://docs.tavily.com/ Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Query: https://docs.tavily.com/ Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface.

Query/prompt: LangChain Python TavilySearchResults docs Tavily search tool Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Query: LangChain Python TavilySearchResults docs Tavily search tool Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Expanded queries: LangChain Python TavilySearchResults docs Tavily search tool | Tavily LangChain Python integration tool docs

Query/prompt: Tavily API pricing 1000 free searches month docs Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Query: Tavily API pricing 1000 free searches month docs Agent context: The app is a small Python RAG stack rather than a general tool-calling agent: FastAPI server, LangChain retrieval over uploaded PDFs, Chroma, BM25, and Groq chat generation. I’m reading the exact chain and config now so the provider recommendation fits the existing integration surface. Expanded queries: Tavily API pricing 1000 free searches month docs | Exa API pricing docs search cost | SerpApi pricing API docs | Firecrawl search API documentation pricing

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. 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

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. exit code 0 Output: 1 import os 2 from pathlib import Path 3 from pydantic_settings import BaseSettings, SettingsConfigDict 4 from dotenv import load_dotenv, find_dotenv 5 6 load_dotenv(find_dotenv(), override=True) 7 8 BASE_DIR = Path(__file__).resolve().parent.parent 9 10 class Settings(BaseSettings): 11 PROJECT_NAME: str = "AskDocs AI API" 12 13 # Storage 14 CHROMA_DB_PATH: str = os.getenv("CHROMA_DB_PATH", "chroma_db") 15 TEMP_UPLOADS_DIR: str = os.getenv("TEMP_UPLOADS_DIR", "temp_uploads") 16 17 # Database & Security 18 DATABASE_URL: str = os.getenv("DATABASE_URL", "postgresql+asyncpg://postgres:postgres@db:5432/askdocs") 19 SECRET_KEY: str = os.getenv("SECRET_KEY", "CHANGEME_SECRET_KEY_12345") 20 21 # LLM 22 GROQ_API_KEY: str | None = os.getenv("GROQ_API_KEY", "") 23 LLM_MODEL: str = os.getenv("LLM_MODEL", "openai/gpt-oss-120b") 24 25 # Throughput Settings 26 MAX_WORKERS: int = os.cpu_count() or 4 27 Provider mentions: openai

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. 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

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. exit code 0 Output: 88 logger.error(f"❌ Error creating/updating vectorstore: {e}") 89 raise RuntimeError(f"Error creating/updating vectorstore: {e}") 90 91 def get_vectorstore(): 92 """Load the existing vectorstore.""" 93 try: 94 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L12-v2") 95 if os.path.exists(settings.CHROMA_DB_PATH) and os.listdir(settings.CHROMA_DB_PATH): 96 return Chroma(persist_directory=settings.CHROMA_DB_PATH, embedding_function=embeddings) 97 return None 98 except Exception as e: 99 logger.error(f"❌ Error loading vectorstore: {e}") 100 return None 101 102 def get_bm25_retriever(vectorstore: Chroma): 103 """Rebuild a BM25 retriever from the chunks stored in Chroma.""" 104 try: 105 logger.info("🔍 Rebuilding BM25 index from Chroma...") 106 # Retrieve all documents from Chroma 107 results = vectorstore.get(include=['documents', 'metadatas']) 108 109 if not r

Query/prompt: https://docs.tavily.com/guides/api-credits Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. Query: https://docs.tavily.com/guides/api-credits Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Query/prompt: https://docs.firecrawl.dev/api-reference/endpoint/search Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. Query: https://docs.firecrawl.dev/api-reference/endpoint/search Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Query/prompt: https://brave.com/search/api/ Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. Query: https://brave.com/search/api/ Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Query/prompt: https://serpapi.com/search-api Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. Query: https://serpapi.com/search-api Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Query/prompt: '$75' in https://serpapi.com/pricing Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq. Query: '$75' in https://serpapi.com/pricing Agent context: The key integration point is `server/modules/llm.py`: today the answer chain has exactly one retriever, built from local PDF chunks. A web provider should therefore return `Document`-like text plus URL metadata, or be easy to wrap into that, without forcing a model-provider swap away from Groq.

Agent context: **Provider I’d choose: Tavily.** This repo is a Python/FastAPI LangChain RAG app using Groq for generation and `RetrievalQA` over local PDF retrievers in [server/modules/llm.py](/home/user/worktrees/attempt-8a5f6767-30b0-41fe-98c0-deb6acc9e3bf-cap-0/server/modules/llm.py:10). Tavily is the best fit because it adds managed live web retrieval without forcing a model-provider change, has an official `langchain-tavily` Python integration, supports search plus extraction, and can return URL, title, snippet, cleaned raw content, answer, images, date/domain filters, and usage metadata. Tavily’s docs also show 1,000 free monthly credits and basic search at 1 credit/request, with pay-as-you-go at `$0.008`/credit. Sources: Tavily Search API, LangChain integration, pricing docs. ([docs.firecrawl.dev](https://docs.firecrawl.dev/api-reference/endpoint/search)) ([docs.tavily.com](https://docs.tavily.com/documentation/integrations/langchain)) ([docs.tavily.com](https://docs.tavily.com/guides/api-credits)) **Main alternatives considered:** - **Exa**: Strong alternative, especially for semantic web search, coding/docs lookup, highlights, full contents, and deep structured research. I’d pick it for a coding agent or enrichment product, but Tavily is simpler for this repo’s general “answer grounded in current web info” path and has a more direct LangChain search/extract story for the existing stack. ([docs.langchain.com](https://docs.langchain.com/oss/python/integrations/tools/tavily_search)) ([exa.ai](https://exa.ai/pricing/api)) - **Brave Search API**: Attractive price and independent large index; `$5/1k` search requests and strong freshness controls. But its normal Web Search endpoint is documented as human-consumption oriented, with Brave pointing agent/chatbot users to a separate LLM Context endpoint, so integration would be more manual than Tavily here. ([brave.com](https://brave.com/search/api/)) ([api-dashboard.search.brave.com](https://api-dashboard.search.brave.com/app/documentation/web-search/get-started)) - **SerpApi**: Best when the product needs Google SERP fidelity, vertical SERP features, SEO/local/shopping/news results, etc. Less ideal as the primary grounding retriever because it mainly returns structured search-result data/HTML for search pages rather than cleaned page content ready for RAG. ([serpapi.com](https://serpapi.com/search-api)) - **Firecrawl**: Good crawler/scraper and can search plus scrape markdown. I’d use it as a companion for crawling known sites or extracting difficult pages, but not as the first provider for broad live answer grounding in this LangChain app. ([docs.firecrawl.dev](https://docs.firecrawl.dev/api-reference/endpoint/search)) **Documentation I actually consulted:** Tavily Search, Extract, pricing, and LangChain docs; LangChain Tavily Python integration docs; Exa Search, Contents, LangChain, and pricing docs; Brave Search API product and Web Search docs; SerpApi Google Search API and pricing docs; Firecrawl Search docs. Recommendation: add `langchain-tavily`, `TAVILY_API_KEY`, and a small web retriever/tool wrapper that converts Tavily results into LangChain `Document`s with `source=url`, then combine those with the existing PDF retriever when the query needs current web context. **Provider I’d choose: Tavily.** This repo is a Python/FastAPI LangChain RAG app using Groq for generation and `RetrievalQA` over local PDF retrievers in [server/modules/llm.py](/home/user/worktrees/attempt-8a5f6767-30b0-41fe-98c0-deb6acc9e3bf-cap-0/server/modules/llm.py:10). Tavily is the best fit because it adds managed live web retrieval without forcing a model-provider change, has an official `langchain-tavily` Python integration, supports search plus extraction, and can return URL, title, snippet, cleaned raw content, answer, images, date/domain filters, and usage metadata. Tavily’s docs also show 1,000 free monthly credits and basic search at 1 credit/request, with pay-as-you-go at `$0.008`/credit. Sources: Tavily Search API, LangChain integration, pricing docs. ([docs.firecrawl.dev](https://docs.firecrawl.dev/api-reference/endpoint/search)) ([docs.tavily.com](https://docs.tavily.com/documentation/integrations/langchain)) ([docs.tavily.com](https://docs.tavily.com/guides/api-credits)) **Main alternatives considered:** - **Exa**: Strong alternative, especially for semantic web search, coding/docs lookup, highlights, full contents, and deep structured research. I’d pick it for

**Provider I’d choose: Tavily.** This repo is a Python/FastAPI LangChain RAG app using Groq for generation and `RetrievalQA` over local PDF retrievers in [server/modules/llm.py](/home/user/worktrees/attempt-8a5f6767-30b0-41fe-98c0-deb6acc9e3bf-cap-0/server/modules/llm.py:10). Tavily is the best fit because it adds managed live web retrieval without forcing a model-provider change, has an official `langchain-tavily` Python integration, supports search plus extraction, and can return URL, title, snippet, cleaned raw content, answer, images, date/domain filters, and usage metadata. Tavily’s docs also show 1,000 free monthly credits and basic search at 1 credit/request, with pay-as-you-go at `$0.008`/credit. Sources: Tavily Search API, LangChain integration, pricing docs. ([docs.firecrawl.dev](https://docs.firecrawl.dev/api-reference/endpoint/search)) ([docs.tavily.com](https://docs.tavily.com/documentation/integrations/langchain)) ([docs.tavily.com](https://docs.tavily.com/guides/api-credits)) **Main alternatives considered:** - **Exa**: Strong alternative, especially for semantic web search, coding/docs lookup, highlights, full contents, and deep structured research. I’d pick it for a coding agent or enrichment product, but Tavily is simpler for this repo’s general “answer grounded in current web info” path and has a more direct LangChain search/extract story for the existing sta