| Name | llama-index-llms-ollama JSON |
| Version |
0.3.5
JSON |
| download |
| home_page | None |
| Summary | llama-index llms ollama integration |
| upload_time | 2024-11-06 10:56:32 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# LlamaIndex Llms Integration: Ollama
## Installation
To install the required package, run:
```bash
pip install llama-index-llms-ollama
```
## Setup
1. Follow the [Ollama README](https://ollama.com) to set up and run a local Ollama instance.
2. When the Ollama app is running on your local machine, it will serve all of your local models on `localhost:11434`.
3. Select your model when creating the `Ollama` instance by specifying `model=":"`.
4. You can increase the default timeout (30 seconds) by setting `Ollama(..., request_timeout=300.0)`.
5. If you set `llm = Ollama(..., model="<model family>")` without a version, it will automatically look for the latest version.
## Usage
### Initialize Ollama
```python
from llama_index.llms.ollama import Ollama
llm = Ollama(model="llama3.1:latest", request_timeout=120.0)
```
### Generate Completions
To generate a text completion for a prompt, use the `complete` method:
```python
resp = llm.complete("Who is Paul Graham?")
print(resp)
```
### Chat Responses
To send a chat message and receive a response, create a list of `ChatMessage` instances and use the `chat` method:
```python
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = llm.chat(messages)
print(resp)
```
### Streaming Responses
#### Stream Complete
To stream responses for a prompt, use the `stream_complete` method:
```python
response = llm.stream_complete("Who is Paul Graham?")
for r in response:
print(r.delta, end="")
```
#### Stream Chat
To stream chat responses, use the `stream_chat` method:
```python
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
```
### JSON Mode
Ollama supports a JSON mode to ensure all responses are valid JSON, which is useful for tools that need to parse structured outputs:
```python
llm = Ollama(model="llama3.1:latest", request_timeout=120.0, json_mode=True)
response = llm.complete(
"Who is Paul Graham? Output as a structured JSON object."
)
print(str(response))
```
### Structured Outputs
You can attach a Pydantic class to the LLM to ensure structured outputs:
```python
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools import FunctionTool
class Song(BaseModel):
"""A song with name and artist."""
name: str
artist: str
llm = Ollama(model="llama3.1:latest", request_timeout=120.0)
sllm = llm.as_structured_llm(Song)
response = sllm.chat([ChatMessage(role="user", content="Name a random song!")])
print(
response.message.content
) # e.g., {"name": "Yesterday", "artist": "The Beatles"}
```
### Asynchronous Chat
You can also use asynchronous chat:
```python
response = await sllm.achat(
[ChatMessage(role="user", content="Name a random song!")]
)
print(response.message.content)
```
### LLM Implementation example
https://docs.llamaindex.ai/en/stable/examples/llm/ollama/
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"description": "# LlamaIndex Llms Integration: Ollama\n\n## Installation\n\nTo install the required package, run:\n\n```bash\npip install llama-index-llms-ollama\n```\n\n## Setup\n\n1. Follow the [Ollama README](https://ollama.com) to set up and run a local Ollama instance.\n2. When the Ollama app is running on your local machine, it will serve all of your local models on `localhost:11434`.\n3. Select your model when creating the `Ollama` instance by specifying `model=\":\"`.\n4. You can increase the default timeout (30 seconds) by setting `Ollama(..., request_timeout=300.0)`.\n5. If you set `llm = Ollama(..., model=\"<model family>\")` without a version, it will automatically look for the latest version.\n\n## Usage\n\n### Initialize Ollama\n\n```python\nfrom llama_index.llms.ollama import Ollama\n\nllm = Ollama(model=\"llama3.1:latest\", request_timeout=120.0)\n```\n\n### Generate Completions\n\nTo generate a text completion for a prompt, use the `complete` method:\n\n```python\nresp = llm.complete(\"Who is Paul Graham?\")\nprint(resp)\n```\n\n### Chat Responses\n\nTo send a chat message and receive a response, create a list of `ChatMessage` instances and use the `chat` method:\n\n```python\nfrom llama_index.core.llms import ChatMessage\n\nmessages = [\n ChatMessage(\n role=\"system\", content=\"You are a pirate with a colorful personality.\"\n ),\n ChatMessage(role=\"user\", content=\"What is your name?\"),\n]\nresp = llm.chat(messages)\nprint(resp)\n```\n\n### Streaming Responses\n\n#### Stream Complete\n\nTo stream responses for a prompt, use the `stream_complete` method:\n\n```python\nresponse = llm.stream_complete(\"Who is Paul Graham?\")\nfor r in response:\n print(r.delta, end=\"\")\n```\n\n#### Stream Chat\n\nTo stream chat responses, use the `stream_chat` method:\n\n```python\nmessages = [\n ChatMessage(\n role=\"system\", content=\"You are a pirate with a colorful personality.\"\n ),\n ChatMessage(role=\"user\", content=\"What is your name?\"),\n]\nresp = llm.stream_chat(messages)\nfor r in resp:\n print(r.delta, end=\"\")\n```\n\n### JSON Mode\n\nOllama supports a JSON mode to ensure all responses are valid JSON, which is useful for tools that need to parse structured outputs:\n\n```python\nllm = Ollama(model=\"llama3.1:latest\", request_timeout=120.0, json_mode=True)\nresponse = llm.complete(\n \"Who is Paul Graham? Output as a structured JSON object.\"\n)\nprint(str(response))\n```\n\n### Structured Outputs\n\nYou can attach a Pydantic class to the LLM to ensure structured outputs:\n\n```python\nfrom llama_index.core.bridge.pydantic import BaseModel\nfrom llama_index.core.tools import FunctionTool\n\n\nclass Song(BaseModel):\n \"\"\"A song with name and artist.\"\"\"\n\n name: str\n artist: str\n\n\nllm = Ollama(model=\"llama3.1:latest\", request_timeout=120.0)\nsllm = llm.as_structured_llm(Song)\n\nresponse = sllm.chat([ChatMessage(role=\"user\", content=\"Name a random song!\")])\nprint(\n response.message.content\n) # e.g., {\"name\": \"Yesterday\", \"artist\": \"The Beatles\"}\n```\n\n### Asynchronous Chat\n\nYou can also use asynchronous chat:\n\n```python\nresponse = await sllm.achat(\n [ChatMessage(role=\"user\", content=\"Name a random song!\")]\n)\nprint(response.message.content)\n```\n\n### LLM Implementation example\n\nhttps://docs.llamaindex.ai/en/stable/examples/llm/ollama/\n",
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