I want to build an agent that will get info from docs and websites

Thanks for reaching out, Yassine. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Provide a hight level architecture showing how to combine provider tools web search, function tools docs and rag etc ...the agent will answer and will assist the user to understand my platform also the website is private

Thanks — I’m responding about the high-level architecture you requested: combining provider tools for web search, function tools for document access, and RAG so the agent can answer questions and help users understand your platform. Below is a concise, actionable architecture and integration plan you can implement with LiveKit Agents. High-level goals • Answer user questions about your platform accurately and with up-to-date info. • Use RAG to ground answers in your internal docs. • Use provider web-search tools for fresh public info. • Use function tools to securely fetch private or dynamic data. • Present responses via chat and optional voice (TTS). Core components • Client (web or mobile): UI, file upload, audio capture, TTS playback, session controls. • LiveKit Agent runtime: orchestrates LLM, tools, RAG, and session hooks. • Provider Tools (Web Search): xAI/Gemini/GoogleSearch plugin for live public web retrieval. • Function Tools (Document Access): custom functions to fetch internal docs, DBs, or cloud storage. • Vector store + Embeddings: for RAG over internal docs (Milvus, Pinecone, Weaviate, or cloud vector DB). • Auth & Access Control: token-based auth for private docs and agent tools. • Optional: Crawler/Indexer for periodic site/document ingestion. Interaction flow (step-by-step) 1. User submits a question (text or speech). 2. STT (if speech) → transcribed user query. 3. on_user_turn_completed hook fires: ◦ Run semantic search against your vector store using the query. ◦ Retrieve top-k passages / metadata from internal docs. ◦ Optionally run quick heuristics to decide if a live web search is needed. 1. Inject retrieved RAG context into the chat context (as assistant/system messages). 2. LLM evaluates whether to: ◦ Answer directly using injected RAG context. ◦ Call a Provider Tool (WebSearch) for current public info. ◦ Call a Function Tool to fetch authenticated or large docs (PDFs, DB records). ◦ Trigger structured actions (open ticket, show snippet links). 1. LLM returns response; agent can: ◦ Send as chat text. ◦ Generate TTS for spoken reply. ◦ Show source citations and links (internal doc IDs, external URLs). 1. Session state updates (store conversational context, user preferences, last documents referenced). Where to use each capability • RAG (Vector search + injected context): primary source for static platform docs, API references, user guides, FAQ content. Use RAG for correctness and citations. • Function Tools: fetch entire documents or authenticated content on demand (full PDF text, private KB entries, recent account data). Also use for any action that requires backend access. • Provider Tools (WebSearch): use when query likely needs up-to-the-minute public info (news, third-party integrations, industry trends). Restrict or prompt the LLM to confirm web results before asserting. Implementation sketch (logical) 1. Ingest & index docs ◦ Parse PDFs, DOCX, HTML to text, chunk into passages with metadata (doc_id, section). ◦ Create embeddings and store in vector DB. 1. Build function tools ◦

get_document(doc_id)

— returns full doc or specific sections (auth-protected). ◦

list_releases()

— returns recent changelog entries (dynamic). 1. Register provider tools ◦ Add

WebSearch

provider plugin to agent so the LLM can call it when needed. 1. on_user_turn_completed ◦ Run vector search with embeddings; get top-k. ◦ If vector results confidence low or user asks for latest info, enable WebSearch. ◦ Inject passages into chat context as assistant/system message before response generation. 1. Response & citation ◦ Always attach sources: internal doc IDs with permalinks and external URLs from web search. ◦ If answer used live web results, indicate that the info came from a web search and show link(s). 1. UX considerations ◦ Show “sources” UI panel with clickable docs. ◦ Allow users to ask “show me the doc” which calls

get_document

. ◦ Provide follow-ups like “Would you like step-by-step setup?” to keep conversation actionable. Best practices and safeguards • Limit RAG injection size: pass only top relevant passages to avoid context bloat. • Rate-limit or gate web searches to control cost and spurious results. • Always surface citations and let users request original docs. • Use function tools for private data; never expose private content through provider tools. • Pre-warm frequently used docs in memory for low-latency responses. • Add a confidence note when the LLM relied primarily on web search rather than internal docs. Example conceptual code snippets • RAG injection in on_user_turn_completed:

async def on_user_turn_completed(self, turn_ctx, new_message):
    query = new_message.text_content()
    rag_passages = await vector_search(query, top_k=4)
    turn_ctx.add_message(role="assistant",
                         content=f"RAG_CONTEXT_START\n{rag_passages}\nRAG_CONTEXT_END")

• Function tool skeleton:

@function_tool()
async def get_document(context, doc_id: str) -> str:
    # auth checks...
    return await fetch_full_document_text(doc_id)

• Registering WebSearch provider:

agent = Agent(llm=xai.realtime.RealtimeModel(),
              tools=[xai.realtime.WebSearch(), get_document])

Metrics to monitor • Answer accuracy (user feedback / thumbs up/down). • Latency (RAG lookup + response time). • Tool usage frequency (how often WebSearch vs internal docs were used). • Source click-through to ensure citations are useful. Next steps I can provide • A tailored sequence diagram for your specific platform (include components like SSO, S3, or private KB). • A sample repo-level starter that wires a LiveKit Agent with vector DB + WebSearch + document tools. • Guidance on chunking/embedding parameters for best RAG performance. Which of those next steps would you like me to produce? If you want the sample starter, please tell me your preferred LLM provider and which document types/storage you’ll use (S3, Google Drive, Notion, etc.). Sources: xAI Grok Voice Agent API plugin | LiveKit Documentation | External data and RAG | LiveKit Documentation | Pipeline nodes and hooks | LiveKit Documentation | GetEmailTask | LiveKit Documentation | Building multi-agent architectures with LiveKit agents | LiveKit

Yes provide me the full code maybe if we use groq llm it will be great

Thanks — I’ll build a full, practical starter you can run that wires together: • Groq LLM as the model, • a provider web-search tool for public searches, • function tools for secure access to your private docs, • RAG using embeddings + a vector store, • LiveKit Agent hooks to orchestrate everything. Before I produce the code: I need two quick clarifying details so the example matches your environment and stays secure. 1. Where will your private docs be stored? Choose one: ◦ S3-compatible bucket ◦ Google Drive ◦ A relational DB (Postgres) ◦ Local filesystem (for testing) 1. Which vector DB do you prefer (or can run locally)? Choose one: ◦ Pinecone ◦ Milvus ◦ Weaviate ◦ Local FAISS (file-based) If you prefer, I can pick sensible defaults (S3 + Pinecone) and show clear places to replace with other providers. Tell me which option you want, and I’ll produce the full codebase (server + minimal client snippets), configuration notes, and instructions to run it.

Will be inside s3 for the vector db use the simple one

Thanks — I’ll provide a full starter code example that wires together: • Groq LLM as the model, • a web-search provider tool for public searches, • function tools to securely fetch private documents from S3, • RAG using embeddings + a simple local FAISS vector store, • LiveKit Agent hooks to orchestrate the flow (RAG injection, tool calls, TTS optional). I’ll make sensible defaults and mark clearly where to plug your credentials and customize behavior. This example is a runnable reference; replace keys, endpoints, and small adaptions as needed. What you’ll get in this package • Server: an async Python agent using LiveKit Agents patterns (agent, function tools, provider tool registration, RAG flow). • Doc ingestion script: parse files from S3, chunk, embed, and index into local FAISS. • Simple vector search helper using FAISS + sentence-transformer embeddings. • Function tool to fetch full docs (authenticated) from S3. • WebSearch provider integration example (provider plugin call pattern). • Example on_user_turn_completed implementation that injects RAG context. • Minimal client snippets showing how to call the agent (text only). • Clear spots commented for credentials (Groq API key, S3 creds). Before I produce the full code, two quick confirmations: 1. Do you want the web-search provider to be enabled by default (agent can call it automatically), or should it be gated behind a user prompt/flag? (default = gated by a quick heuristic) 2. Do you want TTS included in the starter, or keep responses text-only? (default = text-only; TTS can be added later) If you’re fine with the defaults (gated web search, text-only), I’ll produce the complete code and instructions now. Which do you prefer?

Dox will be by default no prompt need , for the tts yes

Thanks — I’ll produce a full, runnable starter that matches your choices: • Groq LLM • Web-search provider (enabled automatically) • Function tools to fetch private docs from S3 (no prompt required) • RAG with local FAISS + sentence-transformer embeddings • LiveKit Agent orchestration with on_user_turn_completed • Text-to-speech (TTS) enabled by default Below is a complete, practical Python example and supporting files. Replace credentials and endpoints where indicated. Project layout • agent/ ◦ app.py # main agent server (FastAPI + asyncio) ◦ agent_runtime.py # Agent class, tools registration, lifecycle hooks ◦ tools.py # function tools (S3 doc fetch), web-search wrapper ◦ rag.py # ingestion, FAISS index helpers, embeddings ◦ tts.py # TTS helper (simple example) ◦ config.py # credentials & config (fill in) • ingest/ ◦ ingest_s3.py # script to download from S3, chunk, embed, index into FAISS • client/ ◦ simple_client.py # example client to send queries and receive responses Prerequisites • Python 3.10+ • Install: pip install fastapi uvicorn aiohttp boto3 faiss-cpu sentence-transformers numpy scikit-learn groq-client # adjust to your Groq client package name (Add any LiveKit Agents / plugin packages if available; the example uses function definitions that follow LiveKit agent patterns.) Important: Replace all placeholder API keys, S3 bucket names, and endpoints in config.py. 1) config.py

# config.py
GROQ_API_KEY = "YOUR_GROQ_API_KEY"
GROQ_ENDPOINT = "<https://api.groq.ai/v1>"  # adjust if needed

AWS_ACCESS_KEY_ID = "YOUR_AWS_KEY"
AWS_SECRET_ACCESS_KEY = "YOUR_AWS_SECRET"
AWS_REGION = "us-east-1"
S3_BUCKET = "your-private-doc-bucket"

VECTOR_INDEX_PATH = "faiss_index.pkl"
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"

WEBSEARCH_ENABLED = True
TTS_ENABLED = True

# Groq model name
GROQ_MODEL = "groq-model-name"  # replace with actual model id

2) rag.py — chunking, embeddings, FAISS index helpers

# rag.py
import os
import pickle
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import faiss

from config import EMBEDDING_MODEL, VECTOR_INDEX_PATH

EMB_MODEL = SentenceTransformer(EMBEDDING_MODEL)

def chunk_text(text, max_tokens=500, overlap=50):
    # simple character-based chunker tuned for moderate sizes
    words = text.split()
    chunks = []
    i = 0
    while i < len(words):
        chunk = words[i:i+max_tokens]
        chunks.append(" ".join(chunk))
        i += max_tokens - overlap
    return chunks

def embed_texts(texts):
    return EMB_MODEL.encode(texts, show_progress_bar=False)

def build_faiss_index(docs):
    # docs: list of dict {doc_id, section_id, text}
    texts = [d["text"] for d in docs]
    embeddings = embed_texts(texts).astype("float32")
    dim = embeddings.shape[1]
    index = faiss.IndexFlatIP(dim)
    faiss.normalize_L2(embeddings)
    index.add(embeddings)
    meta = {"docs": docs}
    with open(VECTOR_INDEX_PATH, "wb") as f:
        pickle.dump((index, meta), f)
    return index, meta

def load_faiss_index():
    if not os.path.exists(VECTOR_INDEX_PATH):
        return None, None
    with open(VECTOR_INDEX_PATH, "rb") as f:
        index, meta = pickle.load(f)
    return index, meta

def vector_search(query, top_k=4):
    index, meta = load_faiss_index()
    if index is None:
        return []
    q_emb = embed_texts([query]).astype("float32")
    faiss.normalize_L2(q_emb)
    D, I = index.search(q_emb, top_k)
    results = []
    for idx in I[0]:
        if idx < 0: continue
        results.append(meta["docs"][idx])
    return results

3) ingest/ingest_s3.py — download S3 docs, parse, chunk, build index

# ingest/ingest_s3.py
import boto3
import os
import io
import mimetypes
from botocore import UNSIGNED
from botocore.client import Config
from rag import chunk_text, build_faiss_index
from config import AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, S3_BUCKET

s3 = boto3.client(
    "s3",
    aws_access_key_id=AWS_ACCESS_KEY_ID,
    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
    region_name=AWS_REGION,
)

def list_bucket_objects(prefix=""):
    resp = s3.list_objects_v2(Bucket=S3_BUCKET, Prefix=prefix)
    for item in resp.get("Contents", []):
        yield item["Key"]

def download_text(key):
    obj = s3.get_object(Bucket=S3_BUCKET, Key=key)
    content_type = obj.get("ContentType", "")
    raw = obj["Body"].read()
    # naive handling:
    if content_type.startswith("text/") or key.endswith(".txt"):
        return raw.decode("utf-8")
    elif key.endswith(".pdf"):
        # minimal PDF handling using PyPDF2 if installed
        try:
            from PyPDF2 import PdfReader
            reader = PdfReader(io.BytesIO(raw))
            text = "\n".join(p.extract_text() or "" for p in reader.pages)
            return text
        except Exception:
            return ""
    elif key.endswith(".docx"):
        try:
            import docx
            doc = docx.Document(io.BytesIO(raw))
            return "\n".join(p.text for p in doc.paragraphs)
        except Exception:
            return ""
    else:
        return raw.decode("utf-8", errors="ignore")

def ingest_all():
    docs = []
    for key in list_bucket_objects():
        print("Downloading", key)
        text = download_text(key)
        if not text:
            continue
        chunks = chunk_text(text, max_tokens=300, overlap=50)
        for i, c in enumerate(chunks):
            docs.append({"doc_id": key, "section_id": f"{key}#chunk{i}", "text": c})
    build_faiss_index(docs)

if __name__ == "__main__":
    ingest_all()

4) tools.py — S3 fetch function tool, web-search wrapper

# tools.py
import boto3
from config import AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, S3_BUCKET, WEBSEARCH_ENABLED
import aiohttp
import asyncio

s3 = boto3.client(
    "s3",
    aws_access_key_id=AWS_ACCESS_KEY_ID,
    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
    region_name=AWS_REGION,
)

async def get_document_from_s3(doc_id: str) -> str:
    # doc_id is S3 key
    loop = asyncio.get_running_loop()
    resp = await loop.run_in_executor(None, s3.get_object, S3_BUCKET, doc_id)
    raw = resp["Body"].read()
    try:
        return raw.decode("utf-8")
    except Exception:
        return raw.decode("utf-8", errors="ignore")

# Simple web search wrapper that returns top snippet(s)
async def web_search(query: str, max_results: int = 3) -> list:
    if not WEBSEARCH_ENABLED:
        return []
    # Placeholder: replace with provider integration; here we call a generic websearch API or a simple SERP API.
    # Example uses an imaginary endpoint — replace with a real provider or plugin call.
    SEARCH_API = "<https://api.example-search.com/search>"
    async with aiohttp.ClientSession() as sess:
        async with sess.get(SEARCH_API, params={"q": query, "num": max_results}) as r:
            if r.status != 200:
                return []
            data = await r.json()
            return data.get("results", [])

Note: Replace web_search with actual provider plugin code or appropriate provider library (xAI/Gemini) that the agent can call directly. The example shows where that integration belongs. 5) tts.py — simple TTS using an offline TTS or external API

# tts.py
# Simple placeholder: use any TTS provider; here's an example using pyttsx3 (offline).
import pyttsx3
from config import TTS_ENABLED

_engine = None
def speak(text):
    if not TTS_ENABLED:
        return
    global _engine
    if _engine is None:
        _engine = pyttsx3.init()
    _engine.say(text)
    _engine.runAndWait()

6) agent_runtime.py — main Agent logic with on_user_turn_completed

# agent_runtime.py
import asyncio
from rag import vector_search
from tools import get_document_from_s3, web_search
from tts import speak
from config import GROQ_API_KEY, GROQ_MODEL, GROQ_ENDPOINT
import aiohttp
import json

#

# agent_runtime.py import asyncio from rag import vector_search from tools import get_document_from_s3, web_search from tts import speak from config import GROQ_API_KEY, GROQ_MODEL, GROQ_ENDPOINT import aiohttp import json #