parasail-ai/search-grounded-ai-pipeline

parasail-ai/search-grounded-ai-pipeline

Description: You.com Search × Parasail — give open-weight LLMs live web access. Three tabs: grounded inference, research chat, and an agentic outreach pipeline.

License: MIT

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Forks: 0

Open issues: 1

Created: 2026-07-21T00:06:32Z

Pushed: 2026-07-21T00:16:17Z

Default branch: main

Fork: yes

Parent repository: pc1438/search-grounded-ai-pipeline

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README:

Search-Grounded AI Pipeline

Open-weight LLMs have a hard knowledge cutoff — typically 12 to 20 months behind today. This repo shows how to close that gap at inference time by giving the model live web search as a tool, and how to wire that pattern into a real business workflow.

The inference provider here is Parasail — a cloud GPU platform hosting open-weight models via an OpenAI-compatible API. The search layer is You.com Search API. Both are swappable.

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What's in the demo

Three tabs, each showing a different shape of the same pattern:

Grounded Inference — Ask any question. The LLM decides when to call You.com as a tool, retrieves live results, and reasons over them before answering. Toggle You.com Web Search off to see the model answer from training data alone — the difference is the point.

Research Chat — Multi-turn conversation with the same grounded loop. Prior turns are passed as context so the model builds on what it already found across messages.

Outreach Pipeline — A three-stage agentic pipeline: You.com fetches live company intel → one LLM distills it into sales signals → a second LLM drafts a personalized outreach email. End-to-end from a company name in under 30 seconds.

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How it works

When You.com Web Search is on:

User question
→ LLM receives question + web_search tool definition
→ LLM calls web_search(query="...")
→ You.com Search API returns live results
→ Results injected into LLM context
→ LLM reasons over results and answers
→ Repeat if LLM decides another search round is needed

When You.com Web Search is off:

User question
→ Direct POST to /v1/chat/completions
→ LLM answers from training data only

The LLM never sees the raw API — it just calls a tool named web_search. The tool schema, execution, and result formatting all live in search_tool.py.

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Prerequisites

• Python 3.11+
• A Parasail API key (inference)
• A You.com API key (search)

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Quickstart

git clone https://github.com/pc1438/search-grounded-ai-pipeline
cd search-grounded-ai-pipeline

pip install -r requirements.txt

cp .env.example .env
# Add your PARASAIL_API_KEY and YDC_API_KEY to .env

python3 server.py
# Open http://localhost:8091

`EXPOSE_SOURCE` — When set to true (the default), a "View Source" button in the UI lets viewers inspect the full server code and prompts in-browser. This is intentional for demos — it shows partners exactly how the pipeline works. Set it to false if you're running on a public URL and don't want to expose your server code.

EXPOSE_SOURCE=false # disable for shared/cloud deployments

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Swapping the inference provider

The agent layer uses the OpenAI-compatible chat.completions API. Any provider that speaks that format works — Together AI, Fireworks, Groq, Anyscale, or your own vLLM deployment.

Change two lines in agents/parasail_agent.py:

from openai import OpenAI

client = OpenAI(
api_key="your-key",
base_url="https://your-provider.com/v1", # ← change this
)

And update the model registry in models.py with your provider's model IDs and pricing.

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Project structure

| File | What it does | |------|-------------| | server.py | HTTP server — routes requests, streams SSE events to the UI | | index.html | Single-page frontend — all three tabs, no build step | | search_tool.py | You.com Search API wrapper — single source of truth for the tool schema, system prompt, and execute_search() | | base_agent.py | Tool-use loop — the OpenAICompatibleAgent class that drives the search → LLM → search cycle | | models.py | Model registry — display names, model IDs, and pricing for the Parasail-hosted models | | costs.py | Cost calculation — token + search call pricing, formatted for the UI | | agents/parasail_agent.py | Parasail-specific agent — client setup, model defaults, wires into OpenAICompatibleAgent | | agents/pipeline_agent.py | Three-stage pipeline — enrich() (You.com search), brief_stream() (signals LLM), email_stream() (pitch LLM) | | logger_setup.py | Structured logging configuration | | .env.example | Environment variable template |

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Key design decisions

`search_tool.py` as single source of truth — the tool schema, system prompt, search constants, and execute_search() all live here. Agents import from it rather than defining their own. Changing search behavior means changing one file.

Three-stage pipeline as separate endpoints — enrich, brief, and email are independent server endpoints. The UI controls sequencing, not the server. Each stage can be inspected, re-run, or replaced independently.

Non-streaming LLM calls in the pipeline — Parasail's reasoning models put output in model_extra['reasoning'], not delta.content, making streaming unreliable. The pipeline collects the full response and re-emits it in 8-character chunks to preserve the streaming UX without losing output.

Open-weight models via OpenAI-compatible API — no vendor lock-in at the model layer. The same tool-use loop works with any provider that speaks chat.completions. Swapping providers is a two-line change.

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Running on a shared URL

If you expose this demo on a public or shared URL:

• Set EXPOSE_SOURCE=false in .env — the default is true for local demos, but you probably don't want to expose server source code publicly.
• Bind to localhost and put a reverse proxy (nginx, Caddy) in front with TLS.
• Add HTTP basic auth or an allow-list at the proxy layer — the server itself has no authentication.
• The /api/source endpoint is disabled when EXPOSE_SOURCE=false.