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Update readme for v1.8

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Yu An 2025-03-28 18:48:47 +00:00
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docs/source/building_applications/tools.md
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@ -206,16 +206,20 @@ response = agent.create_turn(
session_id=session_id,
)
```
## Simple Example2: Using an Agent with the websearch Tool
## Simple Example 2: Using an Agent with the Web Search Tool
1. Start by registering a Tavily API key at [Tavily](https://tavily.com/).
2. When starting the Llama Stack server, ensure the API key is provided as an environment variable:
```
--env TAVILY_SEARCH_API_KEY=${TAVILY_SEARCH_API_KEY}
```
3. run the following script.
```python
from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.types.agent_create_params import AgentConfig
from llama_stack_client.lib.agents.event_logger import EventLogger
from llama_stack_client import LlamaStackClient
client = LlamaStackClient(
base_url=f"http://localhost:8321",
provider_data = {"tavily_search_api_key": "your_TAVILY_SEARCH_API_KEY"})
client = LlamaStackClient(base_url=f"http://localhost:8321")
agent = Agent(
client,
@ -229,7 +233,7 @@ agent = Agent(
session_id = agent.create_session("websearch-session")
response = agent.create_turn(
messages=[{"role": "user", "content": "How US performed in the latest olympics?"}],
messages=[{"role": "user", "content": "How did the USA perform in the last Olympics?"}],
session_id=session_id,
)
for log in EventLogger().log(response):
@ -237,30 +241,17 @@ for log in EventLogger().log(response):
```
## Simple Example3: Using an Agent with the WolframAlpha Tool
```python
from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.types.agent_create_params import AgentConfig
from llama_stack_client.lib.agents.event_logger import EventLogger
from llama_stack_client import LlamaStackClient
client = LlamaStackClient(
base_url=f"http://localhost:8321",
provider_data = {"wolfram_alpha_api_key": "your_wolfram_api_key"}
1. Start by registering for a WolframAlpha API key at [WolframAlpha Developer Portal](https://developer.wolframalpha.com/access).
2. When starting the Llama Stack server, ensure the API key is provided as an environment variable:
```bash
--env WOLFRAM_ALPHA_API_KEY=${WOLFRAM_ALPHA_API_KEY}
```
3. Configure the tools in the Agent by setting `tools=["builtin::wolfram_alpha"]`.
4. Example user query:
```python
response = agent.create_turn(
messages=[{"role": "user", "content": "Solve x^2 + 2x + 1 = 0 using WolframAlpha"}],
session_id=session_id,
)
agent = Agent(
client,
model="meta-llama/Llama-3.2-3B-Instruct",
instructions="You are a helpful wolfram_alpha assistant, must use wolfram_alpha tool as external source validation.",
tools=["builtin::wolfram_alpha"],
)
session_id = agent.create_session("wolframa-alpha-session")
response = agent.create_turn(
messages=[{"role": "user", "content": "solve x^2 + 2x + 1 = 0"}],
session_id=session_id,
)
for log in EventLogger().log(response):
log.print()
```
```