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docs: update rag.md example code to prevent errors (#1009)

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Michael Clifford 2025-02-10 09:25:30 -05:00 • committed by GitHub
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1 changed files with 13 additions and 10 deletions
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23
docs/source/building_applications/rag.md
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@ -36,13 +36,12 @@ chunks = [
"content": "Your document text here",
"mime_type": "text/plain",
},
...,
]
client.vector_io.insert(vector_db_id, chunks)
client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks)
# You can then query for these chunks
chunks_response = client.vector_io.query(
vector_db_id, query="What do you know about..."
vector_db_id=vector_db_id, query="What do you know about..."
)
```
@ -72,8 +71,8 @@ client.tool_runtime.rag_tool.insert(
# Query documents
results = client.tool_runtime.rag_tool.query(
vector_db_id=vector_db_id,
query="What do you know about...",
vector_db_ids=[vector_db_id],
content="What do you know about...",
)
```
@ -82,10 +81,14 @@ results = client.tool_runtime.rag_tool.query(
One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
```python
from llama_stack_client.types.agent_create_params import AgentConfig
from llama_stack_client.lib.agents.agent import Agent
# Configure agent with memory
agent_config = AgentConfig(
model="Llama3.2-3B-Instruct",
model="meta-llama/Llama-3.2-3B-Instruct",
instructions="You are a helpful assistant",
enable_session_persistence=False,
toolgroups=[
{
"name": "builtin::rag",
@ -105,10 +108,10 @@ response = agent.create_turn(
{"role": "user", "content": "I am providing some documents for reference."}
],
documents=[
dict(
content="https://raw.githubusercontent.com/example/doc.rst",
mime_type="text/plain",
)
{
"content": "https://raw.githubusercontent.com/example/doc.rst",
"mime_type": "text/plain",
}
],
session_id=session_id,
)