AI assistant over your documents (RAG): product recipe
For an assistant that answers questions from your files (PDF, Office docs, pages) and the web, and cites its sources. Every service below is from AgentGid's catalog, with the price and free tier from its official page; the first pick in each part ranks best on free tier, official MCP server, real use and price.
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What it costs
To start
$0 / month
every first pick has a free tier or is open source (limits apply)
If you outgrow the free tiers
about $89 / month
LlamaCloud (LlamaParse) $50, LangSmith $39; billed by usage: Elasticsearch (Elastic Cloud), Exa
Model, per 1,000 questions
about $0.85
Claude Haiku 5.5; $6.38 with Gemini 3.8 Flash; AgentGid estimate at ~6,000 input + 500 output tokens each
How it fits together
- Parse each file into text and chunks; keep the source and page for citations.
- Embed the chunks and store them in the vector database with their metadata.
- For a question, search the vector database (and the web search API when the answer may be newer than your files), then send the best chunks to the model.
- Answer with citations to the chunks used; say so when nothing relevant was found.
- Trace every call and test with real questions before launch.
Parse documents (PDF, Office, scans)
| LlamaCloud (LlamaParse) first pick | Parses and extracts data from documents using AI agents.
Free: 10K credits/month · paid from $50/mo · $1.25 per 1,000 credits · in 49 public agent repos
pip install llama-cloud agent.md |
|---|---|
| Azure AI Document Intelligence | Extract text, layout, tables and fields from documents with prebuilt or custom models.
Free: 0 - 500 pages free per month · Per 1,000 pages: Read $1.50 (0-1M pages), $0.60 (1M+); Layout/prebuilt $10; custom extraction $30; classifier… · in 22 public agent repos
npm install @azure-rest/ai-document-intelligence pip install azure-ai-documentintelligence agent.md |
Also fits: Adobe PDF Services API, Reducto, Contextual AI, Eden AI · all document parsing and ocr
Embeddings
| BGE (FlagEmbedding) first pick | Open-source BGE embedding and reranker models to self-host for retrieval and RAG.
Open source · in 28 public agent repos
pip install FlagEmbedding agent.md |
|---|---|
| Voyage AI | API for text, code, multimodal and contextualized embeddings plus rerankers for retrieval.
Free: First 200 million tokens free per account (voyage-4 family, voyage-code-3, voyage-context-4) · $0.02/1M tokens (voyage-4-lite); $0.06/1M (voyage-4); $0.12/1M (voyage-4-large); rerank-3 $0.05/1M · in 21 public agent repos
pip install voyageai agent.md |
Also fits: OpenAI Embeddings, Mixedbread, Nomic · all embeddings and rerank
Vector database
| Elasticsearch (Elastic Cloud) first pick MCP |
Managed Elasticsearch for full-text, vector and hybrid search over indexed documents.
Open source · Serverless: Ingest as low as $0.14 per VCU per hour; Search as low as $0.09 per VCU per hour; Storage as low … · in 91 public agent repos
npm install @elastic/elasticsearch pip install elasticsearch agent.md |
|---|---|
| Zilliz Cloud MCP |
Fully managed Milvus vector database with serverless and dedicated clusters.
Free: 5 GB storage, 2.5M vCUs per month, up to 5 collections · Dedicated: from $63 per million vectors/month (performance-optimized), from $16 (capacity-optimized), from $5… · in 108 public agent repos
npm install @zilliz/milvus2-sdk-node pip install pymilvus agent.md |
| Qdrant Cloud MCP |
Managed clusters of the open-source Qdrant vector search engine.
Free: Free forever single-node cluster: 0.5 vCPU, 1 GB RAM, 4 GB disk · in 480 public agent repos
npm install @qdrant/js-client-rest pip install qdrant-client agent.md |
Also fits: Chroma Cloud, LanceDB, Weaviate Cloud, Azure AI Search · all vector databases
Web search
| Exa first pick MCP |
Neural web search API with page contents, answers and deep-research agent runs for LLMs.
Free: $10 credits every month plus a $10 onboarding bonus, no payment method · Search from $4 per 1,000 requests (Lite $4, Auto $7, Deep $12); Contents $1 per 1,000 pages; Monitors $15 per… · in 100 public agent repos
npm install exa-js pip install exa-py agent.md |
|---|---|
| Tavily MCP server page |
Web search, extract, crawl and research API that returns LLM-ready results for agents.
Free: 1,000 API credits/month, no credit card · paid from $30/mo · $0.008 per credit (pay as you go); monthly plans $0.0075-$0.005 per credit · in 377 public agent repos
npm install @tavily/core pip install tavily-python agent.md |
| SerpApi MCP |
Scrapes Google and other search engines and returns structured JSON results.
Free: 250 searches/month · paid from $25/mo · Starter $25/month for 1,000 searches; Developer $75 for 5,000; Production $150 for 15,000; Big Data $275 for … · in 48 public agent repos
npm install serpapi pip install serpapi agent.md |
Also fits: Parallel, Linkup, Perplexity API (Sonar / Search API), Brave Search API · all web search apis
Traces and evals
| LangSmith first pick MCP |
Tracing, evaluation and monitoring of LLM apps and agents, including prompt and dataset management.
Free: Developer plan: 1 seat, up to 5k base traces / mo, then pay-as-you-go · paid from $39/mo · Plus: $39 / seat per month incl. up to 10k base traces / mo; trace overage is pay-as-you-go (rate not listed … · in 678 public agent repos
npm install langsmith pip install langsmith agent.md |
|---|---|
| LangWatch MCP |
LLM observability, evaluation and agent-simulation platform, open source and also hosted.
Free: Developer plan: 50k events/month, 14-day data access, 2 users, no credit card · Growth: EUR 29 per core-seat/month incl. 200k events, then EUR 5 per 100k events (prices shown in EUR only) · in 27 public agent repos
npm install langwatch pip install langwatch agent.md |
Also fits: W&B Weave, Ragas, Pydantic Logfire, AgentOps · all llm observability and evals
Model
| Default: fast and cheap | Claude Haiku 5.5 · $0.1 input / $0.5 output per 1M tokens · or GPT-6 Luna, MiMo V2.6 Flash |
|---|---|
| Harder questions | Gemini 3.8 Flash · $0.75 input / $3.75 output per 1M tokens · or Kimi K2.7 Code, DeepSeek V4 Pro |
| Hardest work | Kimi K3 · $3 input / $15 output per 1M tokens · or Claude Opus 5.5, Claude Fable 5.1 |
The cheapest model of each price tier that can call tools, from model prices. Try the default first and move up only for the questions it gets wrong.
Libraries
| LlamaIndex | Framework to build RAG applications connecting LLMs to your custom data and documents.pip install llama-index · MIT · Gid Score 63 |
|---|---|
| AI SDK | Vercel's unified SDK for building AI apps with OpenAI, Anthropic, Google and more.npm install ai · Apache-2.0 · Gid Score 80 |
| LangGraph | Framework for building stateful, multi-actor applications with LLMs.pip install langgraph · MIT · Gid Score 78 |
| Qdrant Client | Python client library for Qdrant vector database for semantic search and embeddings.pip install qdrant-client · Apache-2.0 · Gid Score 57 |
| PyPDF | Pure-Python library for reading, merging, splitting and transforming PDF files.pip install pypdf · BSD-3-Clause · Gid Score 76 |
| Unstructured | Parse and prepare raw documents for machine learning and data processing.pip install unstructured · Apache-2.0 · Gid Score 59 |
| Pydantic AI | AI agent framework built with Pydantic for structured, typed agent development.pip install pydantic-ai · MIT · Gid Score 62 |
Agent Skills
| agent-platform-rag-engine-management google/skills | Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus,…npx skills add google/skills --skill agent-platform-rag-engine-management |
|---|---|
| qdrant-vector-search Orchestra-Research/AI-Research-SKILLs | High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid sea…npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search |
| rag-architect Jeffallan/claude-skills | Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying r…npx skills add Jeffallan/claude-skills --skill rag-architect |
| embedding-strategies foryourhealth111-pixel/Vibe-Skills | Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedd…npx skills add foryourhealth111-pixel/Vibe-Skills --skill embedding-strategies |
| pgvector-semantic-search timescale/pg-aiguide | Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store o…npx skills add timescale/pg-aiguide --skill pgvector-semantic-search |
| rag-eval agentscope-ai/OpenJudge | Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when… has scriptsnpx skills add agentscope-ai/OpenJudge --skill rag-eval |
Read a skill before you install it: skills can run scripts.
Coding agent to build it
Also on AgentGid
Before you ship
- Keep API keys on the server, never in the browser or app; set spending limits where the provider offers them.
- Read each service's terms for your use: data retention, commercial use, rate limits.
- Log model calls (traces) and test with your own real inputs before launch.
The brief
# Product recipe: AI assistant over your documents (RAG) (AgentGid) For an assistant that answers questions from your files (PDF, Office docs, pages) and the web, and cites its sources. Parts were picked from AgentGid's catalog by rules: free tier, official MCP server, use in public projects built with AI agents, downloads, price. Prices are list prices from the official pages (see each card for the check date); confirm before you commit. ## What it costs - Start: $0 a month on the free tiers of the first picks (their limits apply). - If you outgrow the free tiers, the lowest paid plans of the first picks are LlamaCloud (LlamaParse) $50, LangSmith $39 (together about $89/month); usage beyond a plan's credits is billed on top. - Billed by usage (grows with traffic): Elasticsearch (Elastic Cloud), Exa. - Model, default: fast and cheap: about $0.85 per 1,000 questions with Claude Haiku 5.5 (AgentGid estimate at ~6,000 input + 500 output tokens each). - Model, harder questions: about $6.38 per 1,000 questions with Gemini 3.8 Flash (AgentGid estimate at ~6,000 input + 500 output tokens each). - Prices of the picks below were checked on the official pages between 2026-10-10 and 2026-10-20. ## How it fits together 1. Parse each file into text and chunks; keep the source and page for citations. 2. Embed the chunks and store them in the vector database with their metadata. 3. For a question, search the vector database (and the web search API when the answer may be newer than your files), then send the best chunks to the model. 4. Answer with citations to the chunks used; say so when nothing relevant was found. 5. Trace every call and test with real questions before launch. ## Parse documents (PDF, Office, scans) - LlamaCloud (LlamaParse): free: 10K credits/month · paid from $50/mo · usage: $1.25 per 1,000 credits · pip llama-cloud · card https://agentgid.com/apis/llamacloud/agent.md - Azure AI Document Intelligence: free: 0 - 500 pages free per month · usage: Per 1,000 pages: Read $1.50 (0-1M pages), $0.60 (1M+); Layout/prebuilt $10; custom extraction $30; classifier $3 (East US) · npm @azure-rest/ai-document-intelligence · pip azure-ai-documentintelligence · card https://agentgid.com/apis/azure-document-intelligence/agent.md - Also fits: Adobe PDF Services API, Reducto, Contextual AI, Eden AI (https://agentgid.com/apis/documents/) ## Embeddings - BGE (FlagEmbedding): open source · pip FlagEmbedding · card https://agentgid.com/apis/bge-embeddings/agent.md - Voyage AI: free: First 200 million tokens free per account (voyage-4 family, voyage-code-3, voyage-context-4) · usage: $0.02/1M tokens (voyage-4-lite); $0.06/1M (voyage-4); $0.12/1M (voyage-4-large); rerank-3 $0.05/1M · pip voyageai · card https://agentgid.com/apis/voyage-ai/agent.md - Also fits: OpenAI Embeddings, Mixedbread, Nomic (https://agentgid.com/apis/embeddings/) ## Vector database - Elasticsearch (Elastic Cloud): open source · usage: Serverless: Ingest as low as $0.14 per VCU per hour; Search as low as $0.09 per VCU per hour; Storage as low as $0.047 per GB retained per month · official MCP https://github.com/elastic/mcp-server-elasticsearch · npm @elastic/elasticsearch · pip elasticsearch · card https://agentgid.com/apis/elastic-cloud/agent.md - Zilliz Cloud: free: 5 GB storage, 2.5M vCUs per month, up to 5 collections · usage: Dedicated: from $63 per million vectors/month (performance-optimized), from $16 (capacity-optimized), from $5 (tiered-storage); Standard Dedicated from $126/GB/month · official MCP https://github.com/zilliztech/mcp-server-milvus · npm @zilliz/milvus2-sdk-node · pip pymilvus · card https://agentgid.com/apis/zilliz-cloud/agent.md - Qdrant Cloud: free: Free forever single-node cluster: 0.5 vCPU, 1 GB RAM, 4 GB disk · official MCP mcp-server-qdrant · npm @qdrant/js-client-rest · pip qdrant-client · card https://agentgid.com/apis/qdrant-cloud/agent.md - Also fits: Chroma Cloud, LanceDB, Weaviate Cloud, Azure AI Search (https://agentgid.com/apis/vector-db/) ## Web search - Exa: free: $10 credits every month plus a $10 onboarding bonus, no payment method · usage: Search from $4 per 1,000 requests (Lite $4, Auto $7, Deep $12); Contents $1 per 1,000 pages; Monitors $15 per 1,000 requests; Agent $0.012–$1.00 per run · official MCP exa-mcp-server · npm exa-js · pip exa-py · card https://agentgid.com/apis/exa/agent.md - Tavily: free: 1,000 API credits/month, no credit card · paid from $30/mo · usage: $0.008 per credit (pay as you go); monthly plans $0.0075-$0.005 per credit · official MCP tavily-mcp · npm @tavily/core · pip tavily-python · card https://agentgid.com/apis/tavily/agent.md · MCP server card https://agentgid.com/mcp/servers/io-github-tavily-ai-tavily-mcp/agent.md - SerpApi: free: 250 searches/month · paid from $25/mo · usage: Starter $25/month for 1,000 searches; Developer $75 for 5,000; Production $150 for 15,000; Big Data $275 for 30,000 · official MCP https://mcp.serpapi.com/mcp · npm serpapi · pip serpapi · card https://agentgid.com/apis/serpapi/agent.md - Also fits: Parallel, Linkup, Perplexity API (Sonar / Search API), Brave Search API (https://agentgid.com/apis/web-search/) ## Traces and evals - LangSmith: free: Developer plan: 1 seat, up to 5k base traces / mo, then pay-as-you-go · paid from $39/mo · usage: Plus: $39 / seat per month incl. up to 10k base traces / mo; trace overage is pay-as-you-go (rate not listed on pricing page); 1 LSU = $1 (Deployment/Fleet/Sandbox usage) · official MCP https://docs.langchain.com/langsmith/langsmith-mcp-server · npm langsmith · pip langsmith · card https://agentgid.com/apis/langsmith/agent.md - LangWatch: free: Developer plan: 50k events/month, 14-day data access, 2 users, no credit card · usage: Growth: EUR 29 per core-seat/month incl. 200k events, then EUR 5 per 100k events (prices shown in EUR only) · official MCP @langwatch/mcp-server · npm langwatch · pip langwatch · card https://agentgid.com/apis/langwatch/agent.md - Also fits: W&B Weave, Ragas, Pydantic Logfire, AgentOps (https://agentgid.com/apis/observability/) ## Model (all of these can call tools; prices per 1M tokens) - Default: fast and cheap: Claude Haiku 5.5 ($0.1 in / $0.5 out) · https://agentgid.com/models/claude-haiku-5-5/agent.md · or GPT-6 Luna, MiMo V2.6 Flash - Harder questions: Gemini 3.8 Flash ($0.75 in / $3.75 out) · https://agentgid.com/models/gemini-3-8-flash/agent.md · or Kimi K2.7 Code, DeepSeek V4 Pro - Hardest work: Kimi K3 ($3 in / $15 out) · https://agentgid.com/models/kimi-k3/agent.md · or Claude Opus 5.5, Claude Fable 5.1 ## Libraries - `pip install llama-index`: LlamaIndex (MIT) · https://agentgid.com/tools/llama-index/agent.md - `npm install ai`: AI SDK (Apache-2.0) · https://agentgid.com/tools/ai/agent.md - `pip install langgraph`: LangGraph (MIT) · https://agentgid.com/tools/langgraph/agent.md - `pip install qdrant-client`: Qdrant Client (Apache-2.0) · https://agentgid.com/tools/qdrant-client/agent.md - `pip install pypdf`: PyPDF (BSD-3-Clause) · https://agentgid.com/tools/pypdf/agent.md - `pip install unstructured`: Unstructured (Apache-2.0) · https://agentgid.com/tools/unstructured/agent.md - `pip install pydantic-ai`: Pydantic AI (MIT) · https://agentgid.com/tools/pydantic-ai/agent.md ## Agent Skills (read a skill before you install it: skills can run scripts) - `npx skills add google/skills --skill agent-platform-rag-engine-management` - `npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search` - `npx skills add Jeffallan/claude-skills --skill rag-architect` - `npx skills add foryourhealth111-pixel/Vibe-Skills --skill embedding-strategies` - `npx skills add timescale/pg-aiguide --skill pgvector-semantic-search` - `npx skills add agentscope-ai/OpenJudge --skill rag-eval` ## Coding agent to build it - OpenAI Codex: free tier, from $8/mo · https://agentgid.com/agents/openai-codex/agent.md - Claude Code: from $20/mo · https://agentgid.com/agents/claude-code/agent.md - OpenCode: free tier, from $10/mo · https://agentgid.com/agents/opencode/agent.md ## Also on AgentGid - PDF tool kit: https://agentgid.com/build/kits/pdf-tool/ - MCP server for your own service: https://agentgid.com/build/kits/mcp-server/ ## Before you ship - Keep API keys on the server, never in the client; set spending limits where the provider offers them. - Read each service's terms for your use (data retention, commercial use, rate limits). - Log model calls (traces) and test with your own real inputs before launch. Recipe page: https://agentgid.com/build/recipes/rag-assistant/ · all product recipes: https://agentgid.com/build/recipes/agent.md
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Picked by rules from AgentGid's catalog, not paid placement. Prices are list prices from the official pages, checked between 2026-10-10 and 2026-10-20; check them before you commit.