> ## Documentation Index > Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt > Use this file to discover all available pages before exploring further. # Build a knowledge retrieval agent > Build an AI agent that uses Pinecone to retrieve knowledge and answer questions accurately, with Pinecone as a tool inside the agent. Build an AI agent that uses Pinecone to retrieve knowledge and answer questions accurately. This demo shows Pinecone as a tool inside an agent, which is the same pattern you'd use in production. This guide requires an [Anthropic](https://console.anthropic.com/) or [OpenAI](https://platform.openai.com/api-keys) API key alongside your Pinecone API key. ```bash Anthropic (Claude) theme={null} pip install pinecone anthropic ``` ```bash OpenAI theme={null} pip install pinecone openai ``` Create a Pinecone index with [integrated embedding](/guides/index-data/indexing-overview#integrated-embedding) and load a small knowledge base. These are facts your LLM doesn't know on its own, so retrieval is the only way to answer accurately. ```python Anthropic (Claude) theme={null} import anthropic from pinecone import Pinecone pc = Pinecone(api_key="{{YOUR_API_KEY}}") llm = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_API_KEY") # Create an index with integrated embedding if not pc.has_index("knowledge"): pc.create_index_for_model( name="knowledge", cloud="aws", region="us-east-1", embed={ "model": "llama-text-embed-v2", "field_map": {"text": "content"} } ) index = pc.Index("knowledge") # Load your knowledge base index.upsert_records( namespace="docs", records=[ {"_id": "policy-1", "content": "Refund requests must be submitted within 30 days of purchase. After 30 days, only store credit is available.", "category": "policies"}, {"_id": "policy-2", "content": "Enterprise customers get dedicated support with a 4-hour response time SLA. Standard support responds within 24 hours.", "category": "policies"}, {"_id": "spec-1", "content": "The WonderVector 5000 supports up to 100,000 vectors per namespace with a maximum dimensionality of 4096.", "category": "specs"}, {"_id": "spec-2", "content": "API rate limits: Free tier is 100 requests/minute, Pro tier is 1000 requests/minute, Enterprise is unlimited with fair use.", "category": "specs"}, {"_id": "spec-3", "content": "Data is encrypted at rest using AES-256 and in transit using TLS 1.3. SOC2 Type II compliance is maintained.", "category": "security"}, {"_id": "hr-1", "content": "New employees receive 15 days PTO in their first year, increasing to 20 days after 2 years and 25 days after 5 years.", "category": "hr"}, {"_id": "hr-2", "content": "The company matches 401k contributions up to 4% of salary. Vesting is immediate for all employees.", "category": "hr"}, {"_id": "proc-1", "content": "To request a new software license, submit a ticket in the IT portal. Approvals take 2-3 business days for standard software.", "category": "procedures"}, {"_id": "proc-2", "content": "Production deployments require approval from the team lead and a passing CI/CD pipeline. Hotfixes can bypass the lead approval.", "category": "procedures"}, {"_id": "proc-3", "content": "Vendor invoices over $10,000 require VP approval. Under $10,000 requires manager approval only.", "category": "procedures"}, ] ) ``` ```python OpenAI theme={null} from openai import OpenAI from pinecone import Pinecone pc = Pinecone(api_key="{{YOUR_API_KEY}}") llm = OpenAI(api_key="YOUR_OPENAI_API_KEY") # Create an index with integrated embedding if not pc.has_index("knowledge"): pc.create_index_for_model( name="knowledge", cloud="aws", region="us-east-1", embed={ "model": "llama-text-embed-v2", "field_map": {"text": "content"} } ) index = pc.Index("knowledge") # Load your knowledge base index.upsert_records( namespace="docs", records=[ {"_id": "policy-1", "content": "Refund requests must be submitted within 30 days of purchase. After 30 days, only store credit is available.", "category": "policies"}, {"_id": "policy-2", "content": "Enterprise customers get dedicated support with a 4-hour response time SLA. Standard support responds within 24 hours.", "category": "policies"}, {"_id": "spec-1", "content": "The WonderVector 5000 supports up to 100,000 vectors per namespace with a maximum dimensionality of 4096.", "category": "specs"}, {"_id": "spec-2", "content": "API rate limits: Free tier is 100 requests/minute, Pro tier is 1000 requests/minute, Enterprise is unlimited with fair use.", "category": "specs"}, {"_id": "spec-3", "content": "Data is encrypted at rest using AES-256 and in transit using TLS 1.3. SOC2 Type II compliance is maintained.", "category": "security"}, {"_id": "hr-1", "content": "New employees receive 15 days PTO in their first year, increasing to 20 days after 2 years and 25 days after 5 years.", "category": "hr"}, {"_id": "hr-2", "content": "The company matches 401k contributions up to 4% of salary. Vesting is immediate for all employees.", "category": "hr"}, {"_id": "proc-1", "content": "To request a new software license, submit a ticket in the IT portal. Approvals take 2-3 business days for standard software.", "category": "procedures"}, {"_id": "proc-2", "content": "Production deployments require approval from the team lead and a passing CI/CD pipeline. Hotfixes can bypass the lead approval.", "category": "procedures"}, {"_id": "proc-3", "content": "Vendor invoices over $10,000 require VP approval. Under $10,000 requires manager approval only.", "category": "procedures"}, ] ) ``` Pinecone is eventually consistent. New records may take a few seconds to become searchable. Wrap Pinecone search in a function your agent can call. Drop this into any agent codebase to add knowledge retrieval. Run all snippets in the same Python session so `index` and `llm` stay in scope.
**Agent tool: `search_knowledge_base`** ```python Python theme={null} def search_knowledge_base(query: str) -> str: """Search the knowledge base for relevant information.""" results = index.search( namespace="docs", # To scope by metadata, add "filter": {"category": {"$eq": "policies"}} to the query dict query={"top_k": 3, "inputs": {"text": query}}, rerank={ "model": "bge-reranker-v2-m3", "top_n": 3, "rank_fields": ["content"] } ) return "\n\n".join( hit.fields["content"] for hit in results["result"]["hits"] ) ```
Give your LLM the ability to call the search function when it needs information. ```python Anthropic (Claude) theme={null} tools = [{ "name": "search_knowledge_base", "description": "Search the company knowledge base for policies, specs, HR info, and procedures.", "input_schema": { "type": "object", "properties": { "query": {"type": "string", "description": "The search query"} }, "required": ["query"] } }] def ask(question: str) -> str: messages = [{"role": "user", "content": question}] # disable_parallel_tool_use keeps this loop simple: with parallel calls, # every tool_use block would need a matching tool_result in the next message response = llm.messages.create( model="claude-sonnet-5", max_tokens=1024, tools=tools, tool_choice={"type": "auto", "disable_parallel_tool_use": True}, messages=messages ) # If the model wants to use a tool, call it and return the result while response.stop_reason == "tool_use": tool_block = next(b for b in response.content if b.type == "tool_use") tool_result = search_knowledge_base(tool_block.input["query"]) messages += [ {"role": "assistant", "content": response.content}, {"role": "user", "content": [{ "type": "tool_result", "tool_use_id": tool_block.id, "content": tool_result }]} ] response = llm.messages.create( model="claude-sonnet-5", max_tokens=1024, tools=tools, tool_choice={"type": "auto", "disable_parallel_tool_use": True}, messages=messages ) return next(b.text for b in response.content if hasattr(b, "text")) ``` ```python OpenAI theme={null} import json tools = [{ "type": "function", "function": { "name": "search_knowledge_base", "description": "Search the company knowledge base for policies, specs, HR info, and procedures.", "parameters": { "type": "object", "properties": { "query": {"type": "string", "description": "The search query"} }, "required": ["query"] } } }] def ask(question: str) -> str: messages = [{"role": "user", "content": question}] # parallel_tool_calls=False keeps this loop simple: with parallel calls, # every tool call would need a matching tool message in the next turn response = llm.chat.completions.create( model="gpt-4o", tools=tools, parallel_tool_calls=False, messages=messages ) # If the model wants to use a tool, call it and return the result while response.choices[0].finish_reason == "tool_calls": tool_call = response.choices[0].message.tool_calls[0] args = json.loads(tool_call.function.arguments) tool_result = search_knowledge_base(args["query"]) messages += [ response.choices[0].message, {"role": "tool", "tool_call_id": tool_call.id, "content": tool_result} ] response = llm.chat.completions.create( model="gpt-4o", tools=tools, parallel_tool_calls=False, messages=messages ) return response.choices[0].message.content ``` ```python theme={null} print(ask("What's the refund policy?")) ``` If the agent says it can't find the information, wait a few seconds and retry. Pinecone is eventually consistent, so freshly upserted records take a moment to become searchable. Your agent searches Pinecone, retrieves the relevant policy, and answers: ```console Output theme={null} Refund requests must be submitted within 30 days of purchase. After that 30-day window, you can still receive store credit but not a direct refund. ``` Try a few more questions: ```python theme={null} print(ask("How much PTO do new employees get?")) print(ask("What approval do I need for a $15,000 vendor invoice?")) ``` **What just happened:** Your LLM received a question, decided it needed more information, and called the `search_knowledge_base` tool. Pinecone returned the most relevant records with reranking, and the LLM synthesized an accurate answer from the retrieved context. Production RAG agents use this same pattern, and the `search_knowledge_base` function works in any agent framework. ### Next steps Give agents Pinecone access over the Model Context Protocol Explore semantic, hybrid, and full-text search Model your data for efficient retrieval