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docs[minor]: Add chat model tabs to docs pages (#19589)

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44
docs/docs/expression_language/get_started.ipynb
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@ -40,6 +40,33 @@
"%pip install --upgrade --quiet langchain-core langchain-community langchain-openai" "%pip install --upgrade --quiet langchain-core langchain-community langchain-openai"
] ]
}, },
{
"cell_type": "markdown",
"id": "c3d54f72",
"metadata": {},
"source": [
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9eed8e8",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4\")"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
@ -60,10 +87,8 @@
"source": [ "source": [
"from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"\n", "\n",
"prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n", "prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
"model = ChatOpenAI(model=\"gpt-4\")\n",
"output_parser = StrOutputParser()\n", "output_parser = StrOutputParser()\n",
"\n", "\n",
"chain = prompt | model | output_parser\n", "chain = prompt | model | output_parser\n",
@ -324,6 +349,16 @@
"For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions." "For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions."
] ]
}, },
{
"cell_type": "markdown",
"id": "b8fe8eb4",
"metadata": {},
"source": [
"```{=mdx}\n",
"<ChatModelTabs />\n",
"```"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -338,7 +373,7 @@
"from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n", "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", "from langchain_openai import OpenAIEmbeddings\n",
"\n", "\n",
"vectorstore = DocArrayInMemorySearch.from_texts(\n", "vectorstore = DocArrayInMemorySearch.from_texts(\n",
" [\"harrison worked at kensho\", \"bears like to eat honey\"],\n", " [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",
@ -352,7 +387,6 @@
"Question: {question}\n", "Question: {question}\n",
"\"\"\"\n", "\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n", "prompt = ChatPromptTemplate.from_template(template)\n",
"model = ChatOpenAI()\n",
"output_parser = StrOutputParser()\n", "output_parser = StrOutputParser()\n",
"\n", "\n",
"setup_and_retrieval = RunnableParallel(\n", "setup_and_retrieval = RunnableParallel(\n",
@ -495,7 +529,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.4" "version": "3.11.0"
} }
}, },
"nbformat": 4, "nbformat": 4,

85
docs/docs/expression_language/why.ipynb
View File

@ -10,7 +10,9 @@
"title: Why use LCEL\n", "title: Why use LCEL\n",
"---\n", "---\n",
"\n", "\n",
"import { ColumnContainer, Column } from \\\"@theme/Columns\\\";" "```{=mdx}\n",
"import { ColumnContainer, Column } from \"@theme/Columns\";\n",
"```"
] ]
}, },
{ {
@ -53,10 +55,13 @@
"## Invoke\n", "## Invoke\n",
"In the simplest case, we just want to pass in a topic string and get back a joke string:\n", "In the simplest case, we just want to pass in a topic string and get back a joke string:\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"\n", "\n",
"```\n",
"\n",
"#### Without LCEL\n" "#### Without LCEL\n"
] ]
}, },
@ -95,9 +100,12 @@
"id": "cdc3b527-c09e-4c77-9711-c3cc4506cd95", "id": "cdc3b527-c09e-4c77-9711-c3cc4506cd95",
"metadata": {}, "metadata": {},
"source": [ "source": [
"\n",
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -136,14 +144,19 @@
"id": "3c0b0513-77b8-4371-a20e-3e487cec7e7f", "id": "3c0b0513-77b8-4371-a20e-3e487cec7e7f",
"metadata": {}, "metadata": {},
"source": [ "source": [
"\n",
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"\n", "```\n",
"## Stream\n", "## Stream\n",
"If we want to stream results instead, we'll need to change our function:\n", "If we want to stream results instead, we'll need to change our function:\n",
"\n", "\n",
"```{=mdx}\n",
"\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -184,10 +197,11 @@
"id": "f8e36b0e-c7dc-4130-a51b-189d4b756c7f", "id": "f8e36b0e-c7dc-4130-a51b-189d4b756c7f",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"\n", "```\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
] ]
@ -208,15 +222,19 @@
"id": "b9b41e78-ddeb-44d0-a58b-a0ea0c99a761", "id": "b9b41e78-ddeb-44d0-a58b-a0ea0c99a761",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"```\n",
"\n", "\n",
"## Batch\n", "## Batch\n",
"\n", "\n",
"If we want to run on a batch of inputs in parallel, we'll again need a new function:\n", "If we want to run on a batch of inputs in parallel, we'll again need a new function:\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -244,10 +262,11 @@
"id": "9b3e9d34-6775-43c1-93d8-684b58e341ab", "id": "9b3e9d34-6775-43c1-93d8-684b58e341ab",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"\n", "```\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
] ]
@ -267,15 +286,18 @@
"id": "cc5ba36f-eec1-4fc1-8cfe-fa242a7f7809", "id": "cc5ba36f-eec1-4fc1-8cfe-fa242a7f7809",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"\n", "```\n",
"## Async\n", "## Async\n",
"\n", "\n",
"If we need an asynchronous version:\n", "If we need an asynchronous version:\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -311,9 +333,11 @@
"id": "2f209290-498c-4c17-839e-ee9002919846", "id": "2f209290-498c-4c17-839e-ee9002919846",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -334,13 +358,16 @@
"id": "1f282129-99a3-40f4-b67f-2d0718b1bea9", "id": "1f282129-99a3-40f4-b67f-2d0718b1bea9",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"\n", "```\n",
"## Async Batch\n", "## Async Batch\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -370,9 +397,11 @@
"id": "90691048-17ae-479d-83c2-859e33ddf3eb", "id": "90691048-17ae-479d-83c2-859e33ddf3eb",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -393,15 +422,19 @@
"id": "f6888245-1ebe-4768-a53b-e1fef6a8b379", "id": "f6888245-1ebe-4768-a53b-e1fef6a8b379",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"```\n",
"\n", "\n",
"## LLM instead of chat model\n", "## LLM instead of chat model\n",
"\n", "\n",
"If we want to use a completion endpoint instead of a chat endpoint: \n", "If we want to use a completion endpoint instead of a chat endpoint: \n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -433,9 +466,11 @@
"id": "45342cd6-58c2-4543-9392-773e05ef06e7", "id": "45342cd6-58c2-4543-9392-773e05ef06e7",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -466,15 +501,19 @@
"id": "ca115eaf-59ef-45c1-aac1-e8b0ce7db250", "id": "ca115eaf-59ef-45c1-aac1-e8b0ce7db250",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"```\n",
"\n", "\n",
"## Different model provider\n", "## Different model provider\n",
"\n", "\n",
"If we want to use Anthropic instead of OpenAI: \n", "If we want to use Anthropic instead of OpenAI: \n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -512,9 +551,11 @@
"id": "52a0c9f8-e316-42e1-af85-cabeba4b7059", "id": "52a0c9f8-e316-42e1-af85-cabeba4b7059",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -545,15 +586,19 @@
"id": "d7a91eee-d017-420d-b215-f663dcbf8ed2", "id": "d7a91eee-d017-420d-b215-f663dcbf8ed2",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"```\n",
"\n", "\n",
"## Runtime configurability\n", "## Runtime configurability\n",
"\n", "\n",
"If we wanted to make the choice of chat model or LLM configurable at runtime:\n", "If we wanted to make the choice of chat model or LLM configurable at runtime:\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -634,9 +679,11 @@
"id": "d1530c5c-6635-4599-9483-6df357ca2d64", "id": "d1530c5c-6635-4599-9483-6df357ca2d64",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### With LCEL\n", "#### With LCEL\n",
"\n" "\n"
@ -694,15 +741,19 @@
"id": "370dd4d7-b825-40c4-ae3c-2693cba2f22a", "id": "370dd4d7-b825-40c4-ae3c-2693cba2f22a",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"```\n",
"\n", "\n",
"## Logging\n", "## Logging\n",
"\n", "\n",
"If we want to log our intermediate results:\n", "If we want to log our intermediate results:\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n", "\n",
@ -733,9 +784,11 @@
"id": "16bd20fd-43cd-4aaf-866f-a53d1f20312d", "id": "16bd20fd-43cd-4aaf-866f-a53d1f20312d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"Every component has built-in integrations with LangSmith. If we set the following two environment variables, all chain traces are logged to LangSmith.\n", "Every component has built-in integrations with LangSmith. If we set the following two environment variables, all chain traces are logged to LangSmith.\n",
@ -770,16 +823,19 @@
"id": "e25ce3c5-27a7-4954-9f0e-b94313597135", "id": "e25ce3c5-27a7-4954-9f0e-b94313597135",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>\n", "</ColumnContainer>\n",
"```\n",
"\n", "\n",
"## Fallbacks\n", "## Fallbacks\n",
"\n", "\n",
"If we wanted to add fallback logic, in case one model API is down:\n", "If we wanted to add fallback logic, in case one model API is down:\n",
"\n", "\n",
"\n", "```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n", "\n",
@ -823,9 +879,11 @@
"id": "f7ef59b5-2ce3-479e-a7ac-79e1e2f30e9c", "id": "f7ef59b5-2ce3-479e-a7ac-79e1e2f30e9c",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -850,8 +908,10 @@
"id": "3af52d36-37c6-4d89-b515-95d7270bb96a", "id": "3af52d36-37c6-4d89-b515-95d7270bb96a",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>" "</ColumnContainer>\n",
"```"
] ]
}, },
{ {
@ -863,8 +923,10 @@
"\n", "\n",
"Even in this simple case, our LCEL chain succinctly packs in a lot of functionality. As chains become more complex, this becomes especially valuable.\n", "Even in this simple case, our LCEL chain succinctly packs in a lot of functionality. As chains become more complex, this becomes especially valuable.\n",
"\n", "\n",
"```{=mdx}\n",
"<ColumnContainer>\n", "<ColumnContainer>\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### Without LCEL\n", "#### Without LCEL\n",
"\n" "\n"
@ -1044,9 +1106,11 @@
"id": "9fb3d71d-8c69-4dc4-81b7-95cd46b271c2", "id": "9fb3d71d-8c69-4dc4-81b7-95cd46b271c2",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"\n", "\n",
"<Column>\n", "<Column>\n",
"```\n",
"\n", "\n",
"#### LCEL\n", "#### LCEL\n",
"\n" "\n"
@ -1101,8 +1165,10 @@
"id": "e3637d39", "id": "e3637d39",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```{=mdx}\n",
"</Column>\n", "</Column>\n",
"</ColumnContainer>" "</ColumnContainer>\n",
"```"
] ]
}, },
{ {
@ -1141,3 +1207,4 @@
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 5 "nbformat_minor": 5
} }

33
docs/docs/modules/model_io/chat/chat_model_caching.ipynb
View File

@ -12,6 +12,33 @@
"It can speed up your application by reducing the number of API calls you make to the LLM provider.\n" "It can speed up your application by reducing the number of API calls you make to the LLM provider.\n"
] ]
}, },
{
"cell_type": "markdown",
"id": "289b31de",
"metadata": {},
"source": [
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6641f37",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
@ -19,10 +46,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from langchain.globals import set_llm_cache\n", "# <!-- ruff: noqa: F821 -->\n",
"from langchain_openai import ChatOpenAI\n", "from langchain.globals import set_llm_cache"
"\n",
"llm = ChatOpenAI()"
] ]
}, },
{ {

272
docs/docs/modules/model_io/chat/function_calling.mdx
View File

@ -38,7 +38,6 @@ Before getting started make sure you have `langchain-core` installed.
%pip install -qU langchain-core langchain-openai %pip install -qU langchain-core langchain-openai
``` ```
```python ```python
import getpass import getpass
import os import os
@ -64,33 +63,21 @@ class Multiply(BaseModel):
b: int = Field(..., description="Second integer") b: int = Field(..., description="Second integer")
``` ```
import Tabs from '@theme/Tabs'; import Tabs from "@theme/Tabs";
import TabItem from '@theme/TabItem'; import TabItem from "@theme/TabItem";
<Tabs> import ChatModelTabs from "@theme/ChatModelTabs";
<TabItem value="openai" label="OpenAI" default>
Set up dependencies and API keys: <ChatModelTabs
customVarName="llm"
fireworksParams={`model="accounts/fireworks/models/firefunction-v1", temperature=0`}
/>
```python We can use the `bind_tools()` method to handle converting
%pip install -qU langchain-openai `Multiply` to a "function" and binding it to the model (i.e.,
```
```python
os.environ["OPENAI_API_KEY"] = getpass.getpass()
```
We can use the `ChatOpenAI.bind_tools()` method to handle converting
`Multiply` to an OpenAI function and binding it to the model (i.e.,
passing it in each time the model is invoked). passing it in each time the model is invoked).
```python ```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm_with_tools = llm.bind_tools([Multiply]) llm_with_tools = llm.bind_tools([Multiply])
llm_with_tools.invoke("what's 3 * 12") llm_with_tools.invoke("what's 3 * 12")
``` ```
@ -126,8 +113,21 @@ tool_chain.invoke("what's 3 * 12")
[Multiply(a=3, b=12)] [Multiply(a=3, b=12)]
``` ```
If our model isn’t using the tool, as is the case here, we can force
tool usage by specifying `tool_choice="any"` or by specifying the name
of the specific tool we want used:
```python
llm_with_tools = llm.bind_tools([Multiply], tool_choice="Multiply")
llm_with_tools.invoke("what's 3 * 12")
```
```text
AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_qIP2bJugb67LGvc6Zhwkvfqc', 'type': 'function', 'function': {'name': 'Multiply', 'arguments': '{"a": 3, "b": 12}'}}]})
```
If we wanted to force that a tool is used (and that it is used only If we wanted to force that a tool is used (and that it is used only
once), we can set the `tool_choice` argument: once), we can set the `tool_choice` argument to the name of the tool:
```python ```python
llm_with_multiply = llm.bind_tools([Multiply], tool_choice="Multiply") llm_with_multiply = llm.bind_tools([Multiply], tool_choice="Multiply")
@ -143,232 +143,6 @@ AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_f3DApOzb60i
For more see the [ChatOpenAI API For more see the [ChatOpenAI API
reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.bind_tools). reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.bind_tools).
</TabItem>
<TabItem value="fireworks" label="Fireworks">
Install dependencies and set API keys:
```python
%pip install -qU langchain-fireworks
```
```python
os.environ["FIREWORKS_API_KEY"] = getpass.getpass()
```
We can use the `ChatFireworks.bind_tools()` method to handle converting
`Multiply` to a valid function schema and binding it to the model (i.e.,
passing it in each time the model is invoked).
```python
from langchain_fireworks import ChatFireworks
llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0)
llm_with_tools = llm.bind_tools([Multiply])
llm_with_tools.invoke("what's 3 * 12")
```
``` text
AIMessage(content='Three multiplied by twelve is 36.')
```
If our model isn’t using the tool, as is the case here, we can force
tool usage by specifying `tool_choice="any"` or by specifying the name
of the specific tool we want used:
```python
llm_with_tools = llm.bind_tools([Multiply], tool_choice="Multiply")
llm_with_tools.invoke("what's 3 * 12")
```
``` text
AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_qIP2bJugb67LGvc6Zhwkvfqc', 'type': 'function', 'function': {'name': 'Multiply', 'arguments': '{"a": 3, "b": 12}'}}]})
```
We can add a tool parser to extract the tool calls from the generated
message to JSON:
```python
from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser
tool_chain = llm_with_tools | JsonOutputToolsParser()
tool_chain.invoke("what's 3 * 12")
```
``` text
[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}]
```
Or back to the original Pydantic class:
```python
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply])
tool_chain.invoke("what's 3 * 12")
```
``` text
[Multiply(a=3, b=12)]
```
For more see the [ChatFireworks](https://api.python.langchain.com/en/latest/chat_models/langchain_fireworks.chat_models.ChatFireworks.html#langchain_fireworks.chat_models.ChatFireworks.bind_tools) reference.
</TabItem>
<TabItem value="mistral" label="Mistral">
Install dependencies and set API keys:
```python
%pip install -qU langchain-mistralai
```
```python
os.environ["MISTRAL_API_KEY"] = getpass.getpass()
```
We can use the `ChatMistralAI.bind_tools()` method to handle converting
`Multiply` to a valid function schema and binding it to the model (i.e.,
passing it in each time the model is invoked).
```python
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
llm_with_tools = llm.bind_tools([Multiply])
llm_with_tools.invoke("what's 3 * 12")
```
``` text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'null', 'type': <ToolType.function: 'function'>, 'function': {'name': 'Multiply', 'arguments': '{"a": 3, "b": 12}'}}]})
```
We can add a tool parser to extract the tool calls from the generated
message to JSON:
```python
from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser
tool_chain = llm_with_tools | JsonOutputToolsParser()
tool_chain.invoke("what's 3 * 12")
```
``` text
[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}]
```
Or back to the original Pydantic class:
```python
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply])
tool_chain.invoke("what's 3 * 12")
```
``` text
[Multiply(a=3, b=12)]
```
We can force tool usage by specifying `tool_choice="any"`:
```python
llm_with_tools = llm.bind_tools([Multiply], tool_choice="any")
llm_with_tools.invoke("I don't even want you to use the tool")
```
``` text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'null', 'type': <ToolType.function: 'function'>, 'function': {'name': 'Multiply', 'arguments': '{"a": 5, "b": 7}'}}]})
```
For more see the [ChatMistralAI API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html#langchain_mistralai.chat_models.ChatMistralAI).
</TabItem>
<TabItem value="together" label="Together">
Since TogetherAI is a drop-in replacement for OpenAI, we can just use
the OpenAI integration.
Install dependencies and set API keys:
```python
%pip install -qU langchain-openai
```
```python
os.environ["TOGETHER_API_KEY"] = getpass.getpass()
```
We can use the `ChatOpenAI.bind_tools()` method to handle converting
`Multiply` to a valid function schema and binding it to the model (i.e.,
passing it in each time the model is invoked).
```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_API_KEY"],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)
llm_with_tools = llm.bind_tools([Multiply])
llm_with_tools.invoke("what's 3 * 12")
```
``` text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_4tc61dp0478zafqe33hfriee', 'function': {'arguments': '{"a":3,"b":12}', 'name': 'Multiply'}, 'type': 'function'}]})
```
We can add a tool parser to extract the tool calls from the generated
message to JSON:
```python
from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser
tool_chain = llm_with_tools | JsonOutputToolsParser()
tool_chain.invoke("what's 3 * 12")
```
``` text
[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}]
```
Or back to the original Pydantic class:
```python
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply])
tool_chain.invoke("what's 3 * 12")
```
``` text
[Multiply(a=3, b=12)]
```
If we wanted to force that a tool is used (and that it is used only
once), we can set the `tool_choice` argument:
```python
llm_with_multiply = llm.bind_tools([Multiply], tool_choice="Multiply")
llm_with_multiply.invoke(
"make up some numbers if you really want but I'm not forcing you"
)
```
``` text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6k6d0gr3jhqil2kqf7sgeusl', 'function': {'arguments': '{"a":5,"b":7}', 'name': 'Multiply'}, 'type': 'function'}]})
```
For more see the [ChatOpenAI API
reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.bind_tools).
</TabItem>
</Tabs>
## Defining functions schemas ## Defining functions schemas
In case you need to access function schemas directly, LangChain has a built-in converter that can turn In case you need to access function schemas directly, LangChain has a built-in converter that can turn

60
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View File

@ -22,32 +22,19 @@
"While chat models use language models under the hood, the interface they use is a bit different.\n", "While chat models use language models under the hood, the interface they use is a bit different.\n",
"Rather than using a \"text in, text out\" API, they use an interface where \"chat messages\" are the inputs and outputs.\n", "Rather than using a \"text in, text out\" API, they use an interface where \"chat messages\" are the inputs and outputs.\n",
"\n", "\n",
"## Setup\n", "## Setup\n"
"\n",
"For this example we'll need to install the OpenAI partner package:\n",
"\n",
"```bash\n",
"pip install langchain-openai\n",
"```\n",
"\n",
"Accessing the API requires an API key, which you can get by creating an account and heading [here](https://platform.openai.com/account/api-keys). Once we have a key we'll want to set it as an environment variable by running:\n",
"\n",
"```bash\n",
"export OPENAI_API_KEY=\"...\"\n",
"```\n",
"If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": null,
"id": "e230abb2-bc84-438b-b9ff-dd124acb1375", "id": "e230abb2-bc84-438b-b9ff-dd124acb1375",
"metadata": {}, "metadata": {},
"outputs": [],
"source": [ "source": [
"from langchain_openai import ChatOpenAI\n", "```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n", "\n",
"chat = ChatOpenAI(openai_api_key=\"...\")" "<ChatModelTabs customVarName=\"chat\" />\n",
"```"
] ]
}, },
{ {
@ -55,19 +42,25 @@
"id": "609bbd5c-e5a1-4166-89e1-d6c52054860d", "id": "609bbd5c-e5a1-4166-89e1-d6c52054860d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Otherwise you can initialize without any params:" "If you'd prefer not to set an environment variable you can pass the key in directly via the api key arg named parameter when initiating the chat model class:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": 1,
"id": "3d9dbf70-2397-4d6b-87ec-3e6d4699f3df", "id": "3d9dbf70-2397-4d6b-87ec-3e6d4699f3df",
"metadata": {}, "metadata": {},
"outputs": [],
"source": [ "source": [
"from langchain_openai import ChatOpenAI\n", "```{=mdx}\n",
"\n", "<ChatModelTabs\n",
"chat = ChatOpenAI()" " openaiParams={`model=\"gpt-3.5-turbo-0125\", openai_api_key=\"...\"`}\n",
" anthropicParams={`model=\"claude-3-sonnet-20240229\", anthropic_api_key=\"...\"`}\n",
" fireworksParams={`model=\"accounts/fireworks/models/mixtral-8x7b-instruct\", fireworks_api_key=\"...\"`}\n",
" mistralParams={`model=\"mistral-large-latest\", mistral_api_key=\"...\"`}\n",
" googleParams={`model=\"gemini-pro\", google_api_key=\"...\"`}\n",
" togetherParams={`, together_api_key=\"...\"`}\n",
" customVarName=\"chat\"\n",
"/>\n",
"```"
] ]
}, },
{ {
@ -108,6 +101,21 @@
"]" "]"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "570dae71",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"chat = ChatOpenAI()"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 11,

2
docs/docs/use_cases/question_answering/chat_history.ipynb
View File

@ -516,7 +516,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.4" "version": "3.11.0"
} }
}, },
"nbformat": 4, "nbformat": 4,

58
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View File

@ -24,7 +24,7 @@ introduction](../../../docs/use_cases/question_answering/), which has
two main components: two main components:
**Indexing**: a pipeline for ingesting data from a source and indexing **Indexing**: a pipeline for ingesting data from a source and indexing
it. *This usually happens offline.* it. _This usually happens offline._
**Retrieval and generation**: the actual RAG chain, which takes the user **Retrieval and generation**: the actual RAG chain, which takes the user
query at run time and retrieves the relevant data from the index, then query at run time and retrieves the relevant data from the index, then
@ -77,7 +77,7 @@ We’ll use the following packages:
%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai chromadb bs4 %pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai chromadb bs4
``` ```
We need to set environment variable `OPENAI_API_KEY`, which can be done We need to set environment variable `OPENAI_API_KEY` for the embeddings model, which can be done
directly or loaded from a `.env` file like so: directly or loaded from a `.env` file like so:
```python ```python
@ -125,10 +125,13 @@ from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_text_splitters import RecursiveCharacterTextSplitter
``` ```
import ChatModelTabs from "@theme/ChatModelTabs";
<ChatModelTabs customVarName="llm" />
```python ```python
# Load, chunk and index the contents of the blog. # Load, chunk and index the contents of the blog.
@ -149,8 +152,6 @@ vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings
# Retrieve and generate using the relevant snippets of the blog. # Retrieve and generate using the relevant snippets of the blog.
retriever = vectorstore.as_retriever() retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt") prompt = hub.pull("rlm/rag-prompt")
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
def format_docs(docs): def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs) return "\n\n".join(doc.page_content for doc in docs)
@ -164,7 +165,6 @@ rag_chain = (
) )
``` ```
```python ```python
rag_chain.invoke("What is Task Decomposition?") rag_chain.invoke("What is Task Decomposition?")
``` ```
@ -219,7 +219,6 @@ loader = WebBaseLoader(
docs = loader.load() docs = loader.load()
``` ```
```python ```python
len(docs[0].page_content) len(docs[0].page_content)
``` ```
@ -249,6 +248,7 @@ In
`DocumentLoader`: Object that loads data from a source as list of `DocumentLoader`: Object that loads data from a source as list of
`Documents`. `Documents`.
- [Docs](../../../docs/modules/data_connection/document_loaders/): - [Docs](../../../docs/modules/data_connection/document_loaders/):
Detailed documentation on how to use `DocumentLoaders`. Detailed documentation on how to use `DocumentLoaders`.
- [Integrations](../../../docs/integrations/document_loaders/): 160+ - [Integrations](../../../docs/integrations/document_loaders/): 160+
@ -289,7 +289,6 @@ text_splitter = RecursiveCharacterTextSplitter(
all_splits = text_splitter.split_documents(docs) all_splits = text_splitter.split_documents(docs)
``` ```
```python ```python
len(all_splits) len(all_splits)
``` ```
@ -319,6 +318,7 @@ all_splits[10].metadata
`TextSplitter`: Object that splits a list of `Document`s into smaller `TextSplitter`: Object that splits a list of `Document`s into smaller
chunks. Subclass of `DocumentTransformer`s. chunks. Subclass of `DocumentTransformer`s.
- Explore `Context-aware splitters`, which keep the location (“context”) of each - Explore `Context-aware splitters`, which keep the location (“context”) of each
split in the original `Document`: - [Markdown split in the original `Document`: - [Markdown
files](../../../docs/modules/data_connection/document_transformers/markdown_header_metadata) files](../../../docs/modules/data_connection/document_transformers/markdown_header_metadata)
@ -328,6 +328,7 @@ files](../../../docs/modules/data_connection/document_transformers/markdown_head
`DocumentTransformer`: Object that performs a transformation on a list `DocumentTransformer`: Object that performs a transformation on a list
of `Document`s. of `Document`s.
- [Docs](../../../docs/modules/data_connection/document_transformers/): Detailed documentation on how to use `DocumentTransformers` - [Docs](../../../docs/modules/data_connection/document_transformers/): Detailed documentation on how to use `DocumentTransformers`
- [Integrations](../../../docs/integrations/document_transformers/) - [Integrations](../../../docs/integrations/document_transformers/)
- [Interface](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.transformers.BaseDocumentTransformer.html): API reference for the base interface. - [Interface](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.transformers.BaseDocumentTransformer.html): API reference for the base interface.
@ -361,12 +362,14 @@ vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbedd
`Embeddings`: Wrapper around a text embedding model, used for converting `Embeddings`: Wrapper around a text embedding model, used for converting
text to embeddings. text to embeddings.
- [Docs](../../../docs/modules/data_connection/text_embedding): Detailed documentation on how to use embeddings. - [Docs](../../../docs/modules/data_connection/text_embedding): Detailed documentation on how to use embeddings.
- [Integrations](../../../docs/integrations/text_embedding/): 30+ integrations to choose from. - [Integrations](../../../docs/integrations/text_embedding/): 30+ integrations to choose from.
- [Interface](https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.Embeddings.html): API reference for the base interface. - [Interface](https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.Embeddings.html): API reference for the base interface.
`VectorStore`: Wrapper around a vector database, used for storing and `VectorStore`: Wrapper around a vector database, used for storing and
querying embeddings. querying embeddings.
- [Docs](../../../docs/modules/data_connection/vectorstores/): Detailed documentation on how to use vector stores. - [Docs](../../../docs/modules/data_connection/vectorstores/): Detailed documentation on how to use vector stores.
- [Integrations](../../../docs/integrations/vectorstores/): 40+ integrations to choose from. - [Integrations](../../../docs/integrations/vectorstores/): 40+ integrations to choose from.
- [Interface](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html): API reference for the base interface. - [Interface](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html): API reference for the base interface.
@ -399,12 +402,10 @@ facilitate retrieval. Any `VectorStore` can easily be turned into a
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6}) retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6})
``` ```
```python ```python
retrieved_docs = retriever.invoke("What are the approaches to Task Decomposition?") retrieved_docs = retriever.invoke("What are the approaches to Task Decomposition?")
``` ```
```python ```python
len(retrieved_docs) len(retrieved_docs)
``` ```
@ -460,34 +461,13 @@ parses the output.
We’ll use the gpt-3.5-turbo OpenAI chat model, but any LangChain `LLM` We’ll use the gpt-3.5-turbo OpenAI chat model, but any LangChain `LLM`
or `ChatModel` could be substituted in. or `ChatModel` could be substituted in.
import Tabs from '@theme/Tabs'; import Tabs from "@theme/Tabs";
import TabItem from '@theme/TabItem'; import TabItem from "@theme/TabItem";
<Tabs> <ChatModelTabs
<TabItem value="openai" label="OpenAI" default> customVarName="llm"
anthropicParams={`"model="claude-3-sonnet-20240229", temperature=0.2, max_tokens=1024"`}
```python />
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-3.5-turbo-0125", temperature=0)
```
</TabItem>
<TabItem value="local" label="Anthropic">
```python
%pip install -qU langchain-anthropic
```
```python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-sonnet-20240229", temperature=0.2, max_tokens=1024)
```
</TabItem>
</Tabs>
We’ll use a prompt for RAG that is checked into the LangChain prompt hub We’ll use a prompt for RAG that is checked into the LangChain prompt hub
([here](https://smith.langchain.com/hub/rlm/rag-prompt)). ([here](https://smith.langchain.com/hub/rlm/rag-prompt)).
@ -498,7 +478,6 @@ from langchain import hub
prompt = hub.pull("rlm/rag-prompt") prompt = hub.pull("rlm/rag-prompt")
``` ```
```python ```python
example_messages = prompt.invoke( example_messages = prompt.invoke(
{"context": "filler context", "question": "filler question"} {"context": "filler context", "question": "filler question"}
@ -543,7 +522,6 @@ rag_chain = (
) )
``` ```
```python ```python
for chunk in rag_chain.stream("What is Task Decomposition?"): for chunk in rag_chain.stream("What is Task Decomposition?"):
print(chunk, end="", flush=True) print(chunk, end="", flush=True)
@ -562,11 +540,13 @@ trace](https://smith.langchain.com/public/1799e8db-8a6d-4eb2-84d5-46e8d7d5a99b/r
`ChatModel`: An LLM-backed chat model. Takes in a sequence of messages `ChatModel`: An LLM-backed chat model. Takes in a sequence of messages
and returns a message. and returns a message.
- [Docs](../../../docs/modules/model_io/chat/) - [Docs](../../../docs/modules/model_io/chat/)
- [Integrations](../../../docs/integrations/chat/): 25+ integrations to choose from. - [Integrations](../../../docs/integrations/chat/): 25+ integrations to choose from.
- [Interface](https://api.python.langchain.com/en/latest/language_models/langchain_core.language_models.chat_models.BaseChatModel.html): API reference for the base interface. - [Interface](https://api.python.langchain.com/en/latest/language_models/langchain_core.language_models.chat_models.BaseChatModel.html): API reference for the base interface.
`LLM`: A text-in-text-out LLM. Takes in a string and returns a string. `LLM`: A text-in-text-out LLM. Takes in a string and returns a string.
- [Docs](../../../docs/modules/model_io/llms) - [Docs](../../../docs/modules/model_io/llms)
- [Integrations](../../../docs/integrations/llms): 75+ integrations to choose from. - [Integrations](../../../docs/integrations/llms): 75+ integrations to choose from.
- [Interface](https://api.python.langchain.com/en/latest/language_models/langchain_core.language_models.llms.BaseLLM.html): API reference for the base interface. - [Interface](https://api.python.langchain.com/en/latest/language_models/langchain_core.language_models.llms.BaseLLM.html): API reference for the base interface.

81
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View File

@ -1,4 +1,4 @@
/* eslint-disable react/jsx-props-no-spreading */ /* eslint-disable react/jsx-props-no-spreading, react/destructuring-assignment */
import React from "react"; import React from "react";
import Tabs from "@theme/Tabs"; import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem"; import TabItem from "@theme/TabItem";
@ -20,7 +20,24 @@ os.environ["${apiKeyName}"] = getpass.getpass()`;
} }
/** /**
* @param {{ openaiParams?: string, anthropicParams?: string, fireworksParams?: string, mistralParams?: string, googleParams?: string, hideOpenai?: boolean, hideAnthropic?: boolean, hideFireworks?: boolean, hideMistral?: boolean, hideGoogle?: boolean }} props * @typedef {Object} ChatModelTabsProps - Component props.
* @property {string} [openaiParams] - Parameters for OpenAI chat model. Defaults to `model="gpt-3.5-turbo-0125"`
* @property {string} [anthropicParams] - Parameters for Anthropic chat model. Defaults to `model="claude-3-sonnet-20240229"`
* @property {string} [fireworksParams] - Parameters for Fireworks chat model. Defaults to `model="accounts/fireworks/models/mixtral-8x7b-instruct"`
* @property {string} [mistralParams] - Parameters for Mistral chat model. Defaults to `model="mistral-large-latest"`
* @property {string} [googleParams] - Parameters for Google chat model. Defaults to `model="gemini-pro"`
* @property {string} [togetherParams] - Parameters for Google chat model. Defaults to `model="gemini-pro"`
* @property {boolean} [hideOpenai] - Whether or not to hide OpenAI chat model.
* @property {boolean} [hideAnthropic] - Whether or not to hide Anthropic chat model.
* @property {boolean} [hideFireworks] - Whether or not to hide Fireworks chat model.
* @property {boolean} [hideMistral] - Whether or not to hide Mistral chat model.
* @property {boolean} [hideGoogle] - Whether or not to hide Google chat model.
* @property {boolean} [hideTogether] - Whether or not to hide Together chat model.
* @property {string} [customVarName] - Custom variable name for the model. Defaults to `model`.
*/
/**
* @param {ChatModelTabsProps} props - Component props.
*/ */
export default function ChatModelTabs(props) { export default function ChatModelTabs(props) {
const { const {
@ -29,24 +46,36 @@ export default function ChatModelTabs(props) {
fireworksParams, fireworksParams,
mistralParams, mistralParams,
googleParams, googleParams,
togetherParams,
hideOpenai, hideOpenai,
hideAnthropic, hideAnthropic,
hideFireworks, hideFireworks,
hideMistral, hideMistral,
hideGoogle, hideGoogle,
hideTogether,
customVarName,
} = props; } = props;
const openAIParamsOrDefault = openaiParams ?? `model="gpt-3.5-turbo-0125"` const openAIParamsOrDefault = openaiParams ?? `model="gpt-3.5-turbo-0125"`;
const anthropicParamsOrDefault = anthropicParams ?? `model="claude-3-sonnet-20240229"` const anthropicParamsOrDefault =
const fireworksParamsOrDefault = fireworksParams ?? `model="accounts/fireworks/models/mixtral-8x7b-instruct"` anthropicParams ?? `model="claude-3-sonnet-20240229"`;
const mistralParamsOrDefault = mistralParams ?? `model="mistral-large-latest"` const fireworksParamsOrDefault =
const googleParamsOrDefault = googleParams ?? `model="gemini-pro"` fireworksParams ??
`model="accounts/fireworks/models/mixtral-8x7b-instruct"`;
const mistralParamsOrDefault =
mistralParams ?? `model="mistral-large-latest"`;
const googleParamsOrDefault = googleParams ?? `model="gemini-pro"`;
const togetherParamsOrDefault =
togetherParams ??
`\n base_url="https://api.together.xyz/v1",\n api_key=os.environ["TOGETHER_API_KEY"],\n model="mistralai/Mixtral-8x7B-Instruct-v0.1",`;
const llmVarName = customVarName ?? "model";
const tabItems = [ const tabItems = [
{ {
value: "OpenAI", value: "OpenAI",
label: "OpenAI", label: "OpenAI",
text: `from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(${openAIParamsOrDefault})`, text: `from langchain_openai import ChatOpenAI\n\n${llmVarName} = ChatOpenAI(${openAIParamsOrDefault})`,
apiKeyName: "OPENAI_API_KEY", apiKeyName: "OPENAI_API_KEY",
packageName: "langchain-openai", packageName: "langchain-openai",
default: true, default: true,
@ -55,7 +84,7 @@ export default function ChatModelTabs(props) {
{ {
value: "Anthropic", value: "Anthropic",
label: "Anthropic", label: "Anthropic",
text: `from langchain_anthropic import ChatAnthropic\n\nmodel = ChatAnthropic(${anthropicParamsOrDefault})`, text: `from langchain_anthropic import ChatAnthropic\n\n${llmVarName} = ChatAnthropic(${anthropicParamsOrDefault})`,
apiKeyName: "ANTHROPIC_API_KEY", apiKeyName: "ANTHROPIC_API_KEY",
packageName: "langchain-anthropic", packageName: "langchain-anthropic",
default: false, default: false,
@ -64,7 +93,7 @@ export default function ChatModelTabs(props) {
{ {
value: "FireworksAI", value: "FireworksAI",
label: "FireworksAI", label: "FireworksAI",
text: `from langchain_fireworks import ChatFireworks\n\nmodel = ChatFireworks(${fireworksParamsOrDefault})`, text: `from langchain_fireworks import ChatFireworks\n\n${llmVarName} = ChatFireworks(${fireworksParamsOrDefault})`,
apiKeyName: "FIREWORKS_API_KEY", apiKeyName: "FIREWORKS_API_KEY",
packageName: "langchain-fireworks", packageName: "langchain-fireworks",
default: false, default: false,
@ -73,7 +102,7 @@ export default function ChatModelTabs(props) {
{ {
value: "MistralAI", value: "MistralAI",
label: "MistralAI", label: "MistralAI",
text: `from langchain_mistralai import ChatMistralAI\n\nmodel = ChatMistralAI(${mistralParamsOrDefault})`, text: `from langchain_mistralai import ChatMistralAI\n\n${llmVarName} = ChatMistralAI(${mistralParamsOrDefault})`,
apiKeyName: "MISTRAL_API_KEY", apiKeyName: "MISTRAL_API_KEY",
packageName: "langchain-mistralai", packageName: "langchain-mistralai",
default: false, default: false,
@ -82,19 +111,37 @@ export default function ChatModelTabs(props) {
{ {
value: "Google", value: "Google",
label: "Google", label: "Google",
text: `from langchain_google_genai import ChatGoogleGenerativeAI\n\nmodel = ChatGoogleGenerativeAI(${googleParamsOrDefault})`, text: `from langchain_google_genai import ChatGoogleGenerativeAI\n\n${llmVarName} = ChatGoogleGenerativeAI(${googleParamsOrDefault})`,
apiKeyName: "GOOGLE_API_KEY", apiKeyName: "GOOGLE_API_KEY",
packageName: "langchain-google-genai", packageName: "langchain-google-genai",
default: false, default: false,
shouldHide: hideGoogle, shouldHide: hideGoogle,
} },
] {
value: "TogetherAI",
label: "TogetherAI",
text: `from langchain_openai import ChatOpenAI\n\n${llmVarName} = ChatOpenAI(${togetherParamsOrDefault})`,
apiKeyName: "TOGETHER_API_KEY",
packageName: "langchain-openai",
default: false,
shouldHide: hideTogether,
},
];
return ( return (
<Tabs groupId="modelTabs"> <Tabs groupId="modelTabs">
{tabItems.filter((tabItem) => !tabItem.shouldHide).map((tabItem) => ( {tabItems
<TabItem value={tabItem.value} label={tabItem.label} default={tabItem.default}> .filter((tabItem) => !tabItem.shouldHide)
<Setup apiKeyName={tabItem.apiKeyName} packageName={tabItem.packageName} /> .map((tabItem) => (
<TabItem
value={tabItem.value}
label={tabItem.label}
default={tabItem.default}
>
<Setup
apiKeyName={tabItem.apiKeyName}
packageName={tabItem.packageName}
/>
<CodeBlock language="python">{tabItem.text}</CodeBlock> <CodeBlock language="python">{tabItem.text}</CodeBlock>
</TabItem> </TabItem>
))} ))}

6
docs/vercel_build.sh
View File

@ -4,9 +4,9 @@ yum -y update
yum install gcc bzip2-devel libffi-devel zlib-devel wget tar gzip -y yum install gcc bzip2-devel libffi-devel zlib-devel wget tar gzip -y
# install quarto # install quarto
wget -q https://github.com/quarto-dev/quarto-cli/releases/download/v1.3.450/quarto-1.3.450-linux-amd64.tar.gz wget -q https://github.com/quarto-dev/quarto-cli/releases/download/v1.4.552/quarto-1.4.552-linux-amd64.tar.gz
tar -xzf quarto-1.3.450-linux-amd64.tar.gz tar -xzf quarto-1.4.552-linux-amd64.tar.gz
export PATH=$PATH:$(pwd)/quarto-1.3.450/bin/ export PATH=$PATH:$(pwd)/quarto-1.4.552/bin/
# setup python env # setup python env