mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
docs: ollama doc update (toolcalling, install, notebook examples) (#25549)
The new `langchain-ollama` package seems pretty well implemented, but I noticed the docs were still outdated so I decided to fix em up a bit. - Llama3.1 was release on 23rd of July; https://ai.meta.com/blog/meta-llama-3-1/ - Ollama supports tool calling since 25th of July; https://ollama.com/blog/tool-support - LangChain Ollama partner package was released 1st of august; https://pypi.org/project/langchain-ollama/ **Problem**: Docs note langchain-community instead of langchain-ollama **Solution**: Update docs to https://python.langchain.com/v0.2/docs/integrations/chat/ollama/ **Problem**: OllamaFunctions is deprecated, as noted on [Integrations](https://python.langchain.com/v0.2/docs/integrations/chat/ollama_functions/): This was an experimental wrapper that attempts to bolt-on tool calling support to models that do not natively support it. The [primary Ollama integration](https://python.langchain.com/v0.2/docs/integrations/chat/ollama/) now supports tool calling, and should be used instead. **Solution**: Delete old notebook from repo, update the existing one with @tool decorator + pydantic examples to the notebook **Problem**: Llama3.1 was released while llama3-groq-tool-call fine-tune Is noted in notebooks. **Solution**: update docs + notebooks to llama3.1 (which has improved tool calling support) **Problem**: Install instructions are incomplete, there is no information to download a model and/or run the Ollama server **Solution**: Add simple instructions to start the ollama service and pull model (for toolcalling) --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
Bob Merkus
GitHub
parent
12e490ea56
commit
8e3e532e7d
@ -110,7 +110,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 9,
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [],
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@ -118,7 +118,7 @@
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"from langchain_ollama import ChatOllama\n",
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"\n",
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"llm = ChatOllama(\n",
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" model=\"llama3\",\n",
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" model=\"llama3.1\",\n",
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" temperature=0,\n",
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" # other params...\n",
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")"
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@ -134,7 +134,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 10,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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@ -143,10 +143,10 @@
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Je adore le programmation.\\n\\n(Note: \"programmation\" is not commonly used in French, but I translated it as \"le programmation\" to maintain the same grammatical structure and meaning as the original English sentence.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-22T17:43:54.731273Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 11094839375, 'load_duration': 10121854667, 'prompt_eval_count': 36, 'prompt_eval_duration': 146569000, 'eval_count': 46, 'eval_duration': 816593000}, id='run-befccbdc-e1f9-42a9-85cf-e69b926d6b8b-0', usage_metadata={'input_tokens': 36, 'output_tokens': 46, 'total_tokens': 82})"
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"AIMessage(content='The translation of \"I love programming\" from English to French is:\\n\\n\"J\\'adore programmer.\"', response_metadata={'model': 'llama3.1', 'created_at': '2024-08-19T16:05:32.81965Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2167842917, 'load_duration': 54222584, 'prompt_eval_count': 35, 'prompt_eval_duration': 893007000, 'eval_count': 22, 'eval_duration': 1218962000}, id='run-0863daa2-43bf-4a43-86cc-611b23eae466-0', usage_metadata={'input_tokens': 35, 'output_tokens': 22, 'total_tokens': 57})"
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]
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},
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"execution_count": 4,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -167,7 +167,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 11,
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"id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
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"metadata": {},
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"outputs": [
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@ -175,9 +175,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Je adore le programmation.\n",
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"The translation of \"I love programming\" from English to French is:\n",
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"\n",
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"(Note: \"programmation\" is not commonly used in French, but I translated it as \"le programmation\" to maintain the same grammatical structure and meaning as the original English sentence.)\n"
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"\"J'adore programmer.\"\n"
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]
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}
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],
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@ -197,17 +197,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 12,
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"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Ich liebe Programmieren!\\n\\n(Note: \"Ich liebe\" means \"I love\", \"Programmieren\" is the verb for \"programming\")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T04:22:33.864132Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1310800083, 'load_duration': 1782000, 'prompt_eval_count': 16, 'prompt_eval_duration': 250199000, 'eval_count': 29, 'eval_duration': 1057192000}, id='run-cbadbe59-2de2-4ec0-a18a-b3220226c3d2-0')"
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"AIMessage(content='Das Programmieren ist mir ein Leidenschaft! (That\\'s \"Programming is my passion!\" in German.) Would you like me to translate anything else?', response_metadata={'model': 'llama3.1', 'created_at': '2024-08-19T16:05:34.893548Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2045997333, 'load_duration': 22584792, 'prompt_eval_count': 30, 'prompt_eval_duration': 213210000, 'eval_count': 32, 'eval_duration': 1808541000}, id='run-d18e1c6b-50e0-4b1d-b23a-973fa058edad-0', usage_metadata={'input_tokens': 30, 'output_tokens': 32, 'total_tokens': 62})"
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]
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},
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"execution_count": 9,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -242,33 +242,32 @@
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"source": [
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"## Tool calling\n",
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"\n",
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"We can use [tool calling](https://blog.langchain.dev/improving-core-tool-interfaces-and-docs-in-langchain/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/library/llama3-groq-tool-use): \n",
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"We can use [tool calling](https://blog.langchain.dev/improving-core-tool-interfaces-and-docs-in-langchain/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/library/llama3.1): \n",
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"\n",
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"```\n",
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"ollama pull llama3-groq-tool-use\n",
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"ollama pull llama3.1\n",
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"```\n",
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"\n",
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"We can just pass normal Python functions directly as tools."
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"Details on creating custom tools are available in [this guide](/docs/how_to/custom_tools/). Below, we demonstrate how to create a tool using the `@tool` decorator on a normal python function."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "5250bceb-1029-41ff-b447-983518704d88",
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"execution_count": 13,
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"id": "f767015f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'name': 'validate_user',\n",
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" 'args': {'addresses': ['123 Fake St, Boston MA',\n",
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" '234 Pretend Boulevard, Houston TX'],\n",
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" 'user_id': 123},\n",
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" 'id': 'fe2148d3-95fb-48e9-845a-4bfecc1f1f96',\n",
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" 'args': {'addresses': '[\"123 Fake St, Boston, MA\", \"234 Pretend Boulevard, Houston, TX\"]',\n",
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" 'user_id': '123'},\n",
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" 'id': '40fe3de0-500c-4b91-9616-5932a929e640',\n",
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" 'type': 'tool_call'}]"
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]
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},
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"execution_count": 10,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -276,22 +275,23 @@
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"source": [
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"from typing import List\n",
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"\n",
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"from langchain_core.tools import tool\n",
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"from langchain_ollama import ChatOllama\n",
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"from typing_extensions import TypedDict\n",
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"\n",
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"\n",
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"def validate_user(user_id: int, addresses: List) -> bool:\n",
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"@tool\n",
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"def validate_user(user_id: int, addresses: List[str]) -> bool:\n",
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" \"\"\"Validate user using historical addresses.\n",
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"\n",
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" Args:\n",
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" user_id: (int) the user ID.\n",
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" addresses: Previous addresses.\n",
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" user_id (int): the user ID.\n",
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" addresses (List[str]): Previous addresses as a list of strings.\n",
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" \"\"\"\n",
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" return True\n",
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"\n",
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"\n",
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"llm = ChatOllama(\n",
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" model=\"llama3-groq-tool-use\",\n",
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" model=\"llama3.1\",\n",
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" temperature=0,\n",
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").bind_tools([validate_user])\n",
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"\n",
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@ -303,18 +303,6 @@
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"result.tool_calls"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2bb034ff-218f-4865-afea-3f5e57d3bdee",
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"metadata": {},
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"source": [
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"We look at the LangSmith trace to see that the tool call was performed: \n",
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"\n",
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"https://smith.langchain.com/public/4169348a-d6be-45df-a7cf-032f6baa4697/r\n",
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"\n",
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"In particular, the trace shows how the tool schema was populated."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c5e0197",
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@ -331,7 +319,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 15,
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"id": "36c9b1c2",
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"metadata": {},
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"outputs": [
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@ -391,7 +379,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 16,
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"id": "32b3ba7b",
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"metadata": {},
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"outputs": [
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@ -467,7 +455,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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@ -1,322 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Ollama Functions\n",
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"sidebar_class_name: hidden\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# OllamaFunctions\n",
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"\n",
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":::warning\n",
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"\n",
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"This was an experimental wrapper that attempts to bolt-on tool calling support to models that do not natively support it. The [primary Ollama integration](/docs/integrations/chat/ollama/) now supports tool calling, and should be used instead.\n",
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"\n",
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":::\n",
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"This notebook shows how to use an experimental wrapper around Ollama that gives it [tool calling capabilities](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling).\n",
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"\n",
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"Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use llama3 and phi3 models.\n",
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"For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n",
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"\n",
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"## Overview\n",
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"\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
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"|:-----------------------------------------------------------------------------------------------------------------------------------:|:-------:|:-----:|:------------:|:----------:|:-----------------:|:--------------:|\n",
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"| [OllamaFunctions](https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.ollama_function.OllamaFunctions.html) | [langchain-experimental](https://api.python.langchain.com/en/latest/openai_api_reference.html) | ✅ | ❌ | ❌ |  |  |\n",
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"\n",
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"### Model features\n",
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"\n",
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"| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
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"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
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"| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |\n",
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"\n",
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"## Setup\n",
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"\n",
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"To access `OllamaFunctions` you will need to install `langchain-experimental` integration package.\n",
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"Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance as well as download and serve [supported models](https://ollama.com/library).\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Credentials support is not present at this time.\n",
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"\n",
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"### Installation\n",
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"\n",
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"The `OllamaFunctions` class lives in the `langchain-experimental` package:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-experimental"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"`OllamaFunctions` takes the same init parameters as `ChatOllama`. \n",
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"\n",
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"In order to use tool calling, you must also specify `format=\"json\"`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-23T15:20:21.818089Z",
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"start_time": "2024-06-23T15:20:21.815759Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain_experimental.llms.ollama_functions import OllamaFunctions\n",
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"\n",
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"llm = OllamaFunctions(model=\"phi3\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Invocation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-23T15:20:46.794689Z",
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"start_time": "2024-06-23T15:20:44.982632Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"J'adore programmer.\", id='run-94815fcf-ae11-438a-ba3f-00819328b5cd-0')"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"I love programming.\"),\n",
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"]\n",
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"ai_msg = llm.invoke(messages)\n",
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"ai_msg"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\"J'adore programmer.\""
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ai_msg.content"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"We can [chain](https://python.langchain.com/v0.2/docs/how_to/sequence/) our model with a prompt template like so:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Programmieren ist sehr verrückt! Es freut mich, dass Sie auf Programmierung so positiv eingestellt sind.', id='run-ee99be5e-4d48-4ab6-b602-35415f0bdbde-0')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\n",
|
||||
" \"system\",\n",
|
||||
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
|
||||
" ),\n",
|
||||
" (\"human\", \"{input}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"chain.invoke(\n",
|
||||
" {\n",
|
||||
" \"input_language\": \"English\",\n",
|
||||
" \"output_language\": \"German\",\n",
|
||||
" \"input\": \"I love programming.\",\n",
|
||||
" }\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Tool Calling\n",
|
||||
"\n",
|
||||
"### OllamaFunctions.bind_tools()\n",
|
||||
"\n",
|
||||
"With `OllamaFunctions.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to a tool definition schemas, which looks like:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class GetWeather(BaseModel):\n",
|
||||
" \"\"\"Get the current weather in a given location\"\"\"\n",
|
||||
"\n",
|
||||
" location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"llm_with_tools = llm.bind_tools([GetWeather])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='', id='run-b9769435-ec6a-4cb8-8545-5a5035fc19bd-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_064c4e1cb27e4adb9e4e7ed60362ecc9'}])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ai_msg = llm_with_tools.invoke(\n",
|
||||
" \"what is the weather like in San Francisco\",\n",
|
||||
")\n",
|
||||
"ai_msg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### AIMessage.tool_calls\n",
|
||||
"\n",
|
||||
"Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized `ToolCall` format that is model-provider agnostic."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'GetWeather',\n",
|
||||
" 'args': {'location': 'San Francisco, CA'},\n",
|
||||
" 'id': 'call_064c4e1cb27e4adb9e4e7ed60362ecc9'}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ai_msg.tool_calls"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For more on binding tools and tool call outputs, head to the [tool calling](../../how_to/function_calling.ipynb) docs."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all ToolCallingLLM features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.ollama_functions.OllamaFunctions.html\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@ -68,7 +68,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 2,
|
||||
"id": "035dea0f",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
@ -77,10 +77,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'A great start!\\n\\nLangChain is a type of AI model that uses language processing techniques to generate human-like text based on input prompts or chains of reasoning. In other words, it can have a conversation with humans, understanding the context and responding accordingly.\\n\\nHere\\'s a possible breakdown:\\n\\n* \"Lang\" likely refers to its focus on natural language processing (NLP) and linguistic analysis.\\n* \"Chain\" suggests that LangChain is designed to generate text in response to a series of connected ideas or prompts, rather than simply generating random text.\\n\\nSo, what do you think LangChain\\'s capabilities might be?'"
|
||||
"\"Sounds like a plan!\\n\\nTo answer what LangChain is, let's break it down step by step.\\n\\n**Step 1: Understand the Context**\\nLangChain seems to be related to language or programming, possibly in an AI context. This makes me wonder if it's a framework, library, or tool for building models or interacting with them.\\n\\n**Step 2: Research Possible Definitions**\\nAfter some quick searching, I found that LangChain is actually a Python library for building and composing conversational AI models. It seems to provide a way to create modular and reusable components for chatbots, voice assistants, and other conversational interfaces.\\n\\n**Step 3: Explore Key Features and Use Cases**\\nLangChain likely offers features such as:\\n\\n* Easy composition of conversational flows\\n* Support for various input/output formats (e.g., text, audio)\\n* Integration with popular AI frameworks and libraries\\n\\nUse cases might include building chatbots for customer service, creating voice assistants for smart homes, or developing interactive stories.\\n\\n**Step 4: Confirm the Definition**\\nAfter this step-by-step analysis, I'm fairly confident that LangChain is a Python library for building conversational AI models. If you'd like to verify or provide more context, feel free to do so!\""
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -95,7 +95,7 @@
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_template(template)\n",
|
||||
"\n",
|
||||
"model = OllamaLLM(model=\"llama3\")\n",
|
||||
"model = OllamaLLM(model=\"llama3.1\")\n",
|
||||
"\n",
|
||||
"chain = prompt | model\n",
|
||||
"\n",
|
||||
@ -177,7 +177,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 3,
|
||||
"id": "79aaf863",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -218,7 +218,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.3"
|
||||
"version": "3.12.4"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# Ollama
|
||||
|
||||
>[Ollama](https://ollama.com/) allows you to run open-source large language models,
|
||||
> such as LLaMA2, locally.
|
||||
> such as [Llama3.1](https://ai.meta.com/blog/meta-llama-3-1/), locally.
|
||||
>
|
||||
>`Ollama` bundles model weights, configuration, and data into a single package, defined by a Modelfile.
|
||||
>It optimizes setup and configuration details, including GPU usage.
|
||||
@ -11,14 +11,36 @@ See [this guide](/docs/how_to/local_llms) for more details
|
||||
on how to use `Ollama` with LangChain.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
Follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama)
|
||||
### Ollama installation
|
||||
Follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama)
|
||||
to set up and run a local Ollama instance.
|
||||
|
||||
Ollama will start as a background service automatically, if this is disabled, run:
|
||||
|
||||
```bash
|
||||
# export OLLAMA_HOST=127.0.0.1 # environment variable to set ollama host
|
||||
# export OLLAMA_PORT=11434 # environment variable to set the ollama port
|
||||
ollama serve
|
||||
```
|
||||
|
||||
After starting ollama, run `ollama pull <model_checkpoint>` to download a model
|
||||
from the [Ollama model library](https://ollama.ai/library).
|
||||
|
||||
```bash
|
||||
ollama pull llama3.1
|
||||
```
|
||||
|
||||
We're now ready to install the `langchain-ollama` partner package and run a model.
|
||||
|
||||
### Ollama LangChain partner package install
|
||||
Install the integration package with:
|
||||
```bash
|
||||
pip install langchain-ollama
|
||||
```
|
||||
## LLM
|
||||
|
||||
```python
|
||||
from langchain_community.llms import Ollama
|
||||
from langchain_ollama.llms import OllamaLLM
|
||||
```
|
||||
|
||||
See the notebook example [here](/docs/integrations/llms/ollama).
|
||||
@ -28,18 +50,17 @@ See the notebook example [here](/docs/integrations/llms/ollama).
|
||||
### Chat Ollama
|
||||
|
||||
```python
|
||||
from langchain_community.chat_models import ChatOllama
|
||||
from langchain_ollama.chat_models import ChatOllama
|
||||
```
|
||||
|
||||
See the notebook example [here](/docs/integrations/chat/ollama).
|
||||
|
||||
### Ollama functions
|
||||
|
||||
```python
|
||||
from langchain_experimental.llms.ollama_functions import OllamaFunctions
|
||||
```
|
||||
|
||||
See the notebook example [here](/docs/integrations/chat/ollama_functions).
|
||||
### Ollama tool calling
|
||||
[Ollama tool calling](https://ollama.com/blog/tool-support) uses the
|
||||
OpenAI compatible web server specification, and can be used with
|
||||
the default `BaseChatModel.bind_tools()` methods
|
||||
as described [here](/docs/how_to/tool_calling/).
|
||||
Make sure to select an ollama model that supports [tool calling](https://ollama.com/search?&c=tools).
|
||||
|
||||
## Embedding models
|
||||
|
||||
|
||||
@ -101,6 +101,10 @@
|
||||
{
|
||||
"source": "/v0.2/docs/integrations/toolkits/xorbits/",
|
||||
"destination": "/v0.2/docs/integrations/tools#search"
|
||||
},
|
||||
{
|
||||
"source": "/v0.2/docs/integrations/chat/ollama_functions/",
|
||||
"destination": "https://python.langchain.com/v0.1/docs/integrations/chat/ollama_functions/"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user