How to Use Claude for Search and Retrieval Augmentation

Claude for search and retrieval augmentation enables large language models to invoke external search tools, retrieve real-time data from Wikipedia or vector databases, and synthesize those results into grounded, accurate responses.

The anthropics/prompt-eng-interactive-tutorial repository demonstrates how to build retrieval-augmented generation (RAG) pipelines by combining Claude's tool-use capabilities with external data sources. According to the source code in Anthropic 1P/10.3_Appendix_Search & Retrieval.ipynb, this approach follows a three-stage architecture where Claude decides when to search, executes the query against your chosen datastore, and then generates answers using the retrieved context.

The Three-Stage Architecture for Claude Search and Retrieval

The notebook Anthropic 1P/10.3_Appendix_Search & Retrieval.ipynb explains that Claude can "search through Wikipedia for you" and "search over your own docs, whether stored as plain text or embedded in a vector datastore". This workflow divides into three distinct phases:

Stage 1: Tool-Enabled Request

You define a searchable tool using a JSON schema that describes the data source. The client sends a prompt to Claude together with this tool definition, instructing the model that external search capabilities are available.

Stage 2: Dynamic Search and Retrieval

When the user query requires external data, Claude generates a tool invocation request containing the search parameters. Your application executes the actual search against Wikipedia, Pinecone, or a private API, then returns the raw results (titles, snippets, or embedded vectors) to Claude.

Stage 3: Context Synthesis

Claude consumes the retrieved material as additional context and generates a final answer, optionally summarizing, comparing, or weaving the information into new content.

Implementing Wikipedia Search with Claude

The repository provides concrete implementation patterns in Anthropic 1P/10.3_Appendix_Search & Retrieval.ipynb, which also references the Anthropic RAG cookbook for production-ready examples. The hints.py files in both the Anthropic 1P and AmazonBedrock/utils directories contain reusable snippets encouraging users to "ask Claude to search" and to "wrap examples in XML tags" to improve parsing.

Here is a self-contained Python implementation using Claude's tool-use API to search Wikipedia:

import anthropic

client = anthropic.Anthropic()

# Define the Wikipedia search tool

search_tool = {
    "name": "search_wikipedia",
    "description": "Search Wikipedia for the given query and return top results.",
    "input_schema": {
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "Search term"},
            "top_k": {"type": "integer", "default": 3, "description": "How many results to return"}
        },
        "required": ["query"]
    }
}

# Prompt that asks Claude to use the tool

user_prompt = """
Please find the latest research on \"quantum error correction\" using the Wikipedia search tool,
then give me a concise summary of the top three articles.
"""

response = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    temperature=0,
    system="You have access to a Wikipedia search tool.",
    tools=[search_tool],
    messages=[{"role": "user", "content": user_prompt}]
)

print(response.content[0].text)   # Claude’s synthesized answer

When executed, Claude reads the user request, calls search_wikipedia with the appropriate query, receives a JSON list of article titles and snippets, and then crafts a concise synthesis.

Building Vector Store Retrieval Pipelines

For enterprise applications, Claude can query private vector databases such as Pinecone. The architecture remains identical: define a retrieval tool, allow Claude to invoke it with natural language queries, and return the matching documents as context.

import anthropic, pinecone

# Initialise Pinecone (replace with your own API key & index name)

pinecone.init(api_key="YOUR_PINECONE_KEY", environment="us-west2-gcp")
index = pinecone.Index("my-doc-index")

def retrieve(query: str, top_k: int = 4):
    # Convert query to embeddings using Claude's embeddings endpoint

    embed = anthropic.Anthropic().embeddings.create(
        model="claude-3-5-sonnet-20240620",
        input=query
    )
    # Perform similarity search

    results = index.query(vector=embed["embedding"], top_k=top_k, include_metadata=True)
    return [{"text": r["metadata"]["text"], "score": r["score"]} for r in results["matches"]]

# Tool definition for vector retrieval

vector_tool = {
    "name": "retrieve_documents",
    "description": "Fetch relevant documents from the vector store.",
    "input_schema": {
        "type": "object",
        "properties": {"query": {"type": "string"}, "top_k": {"type": "integer"}},
        "required": ["query"]
    }
}

def tool_handler(name, arguments):
    if name == "retrieve_documents":
        return retrieve(arguments["query"], arguments.get("top_k", 4))

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    temperature=0,
    system="You may call retrieve_documents to get relevant passages.",
    tools=[vector_tool],
    messages=[{"role": "user", "content": "Summarize the key privacy‑policy points for our new app."}]
)

print(response.content[0].text)

In this workflow, Claude decides to call retrieve_documents with a natural-language query, receives the most relevant passages from the vector store, and then produces a polished summary grounded in your proprietary data.

Summary

• Claude for search and retrieval augmentation follows a three-stage pipeline: tool definition, dynamic retrieval, and context synthesis.
• The Anthropic 1P/10.3_Appendix_Search & Retrieval.ipynb notebook provides the conceptual foundation and links to the Anthropic RAG cookbook.
• Both Anthropic 1P/hints.py and AmazonBedrock/utils/hints.py contain best-practice prompting tips, including XML wrapping for improved parsing.
• You can implement Wikipedia search or vector-store retrieval using Claude's tool-use API with JSON schema definitions.
• This approach works with any data source exposed via HTTP endpoints or SDK calls, enabling flexible enterprise RAG pipelines.

Frequently Asked Questions

What is retrieval-augmented generation (RAG) with Claude?

Retrieval-augmented generation with Claude is an architecture where the model invokes external search tools to fetch real-time or proprietary data before generating responses. According to the anthropics/prompt-eng-interactive-tutorial repository, this allows Claude to "search through Wikipedia for you" or query private vector datastores, then synthesize those results into accurate, up-to-date answers rather than relying solely on training data.

How does Claude decide when to call a search tool?

Claude evaluates the user prompt against the available tool definitions provided in the API request. When the query requires information not present in the model's training data—such as recent Wikipedia articles or specific document contents—Claude generates a tool use request containing the search query parameters. Your application executes the search and returns results, which Claude then incorporates into its response.

Can Claude search private enterprise documents?

Yes. As documented in Anthropic 1P/10.3_Appendix_Search & Retrieval.ipynb, Claude can search "over your own docs, whether stored as plain text or embedded in a vector datastore." By defining a custom tool that queries your Pinecone index, Elasticsearch cluster, or internal API, you enable Claude to retrieve and synthesize information from proprietary knowledge bases while maintaining data security.

What are the best practices for structuring search prompts?

The hints.py files in both the Anthropic and Amazon Bedrock sections of the repository recommend wrapping examples in XML tags to improve parsing and explicitly instructing Claude to use the available tools. For optimal results, provide clear system prompts stating "You have access to a search tool," define precise JSON schemas for tool inputs, and include explicit user instructions such as "use the Wikipedia search tool to find recent developments."

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}