github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-06-28 17:38:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Brace Sproul 2024-03-29 14:23:55 -07:00 committed by GitHub
parent bd02b83acd
commit ce0a588ae6
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
9 changed files with 1504 additions and 1569 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
docs/docs/expression_language/get_started.ipynb
View File

@ -40,6 +40,33 @@
"%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",
"execution_count": 1,
@ -60,10 +87,8 @@
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
"model = ChatOpenAI(model=\"gpt-4\")\n",
"output_parser = StrOutputParser()\n",
"\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."
]
},
{
"cell_type": "markdown",
"id": "b8fe8eb4",
"metadata": {},
"source": [
"```{=mdx}\n",
"<ChatModelTabs />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -338,7 +373,7 @@
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"vectorstore = DocArrayInMemorySearch.from_texts(\n",
" [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",
@ -352,7 +387,6 @@
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"model = ChatOpenAI()\n",
"output_parser = StrOutputParser()\n",
"\n",
"setup_and_retrieval = RunnableParallel(\n",
@ -495,7 +529,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.11.0"
}
},
"nbformat": 4,

89
docs/docs/expression_language/why.ipynb
View File

@ -10,7 +10,9 @@
"title: Why use LCEL\n",
"---\n",
"\n",
"import { ColumnContainer, Column } from \\\"@theme/Columns\\\";"
"```{=mdx}\n",
"import { ColumnContainer, Column } from \"@theme/Columns\";\n",
"```"
]
},
{
@ -53,10 +55,13 @@
"## Invoke\n",
"In the simplest case, we just want to pass in a topic string and get back a joke string:\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"\n",
"<Column>\n",
"\n",
"```\n",
"\n",
"#### Without LCEL\n"
]
},
@ -95,9 +100,12 @@
"id": "cdc3b527-c09e-4c77-9711-c3cc4506cd95",
"metadata": {},
"source": [
"\n",
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
@ -136,14 +144,19 @@
"id": "3c0b0513-77b8-4371-a20e-3e487cec7e7f",
"metadata": {},
"source": [
"\n",
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"\n",
"```\n",
"## Stream\n",
"If we want to stream results instead, we'll need to change our function:\n",
"\n",
"```{=mdx}\n",
"\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -184,10 +197,11 @@
"id": "f8e36b0e-c7dc-4130-a51b-189d4b756c7f",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"\n",
"```\n",
"#### LCEL\n",
"\n"
]
@ -208,15 +222,19 @@
"id": "b9b41e78-ddeb-44d0-a58b-a0ea0c99a761",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"```\n",
"\n",
"## Batch\n",
"\n",
"If we want to run on a batch of inputs in parallel, we'll again need a new function:\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -244,10 +262,11 @@
"id": "9b3e9d34-6775-43c1-93d8-684b58e341ab",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"\n",
"```\n",
"#### LCEL\n",
"\n"
]
@ -267,15 +286,18 @@
"id": "cc5ba36f-eec1-4fc1-8cfe-fa242a7f7809",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"\n",
"```\n",
"## Async\n",
"\n",
"If we need an asynchronous version:\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -311,10 +333,12 @@
"id": "2f209290-498c-4c17-839e-ee9002919846",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
" \n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
]
@ -334,13 +358,16 @@
"id": "1f282129-99a3-40f4-b67f-2d0718b1bea9",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"\n",
"```\n",
"## Async Batch\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -370,9 +397,11 @@
"id": "90691048-17ae-479d-83c2-859e33ddf3eb",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
@ -393,15 +422,19 @@
"id": "f6888245-1ebe-4768-a53b-e1fef6a8b379",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"```\n",
"\n",
"## LLM instead of chat model\n",
"\n",
"If we want to use a completion endpoint instead of a chat endpoint: \n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -433,9 +466,11 @@
"id": "45342cd6-58c2-4543-9392-773e05ef06e7",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
@ -466,15 +501,19 @@
"id": "ca115eaf-59ef-45c1-aac1-e8b0ce7db250",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"```\n",
"\n",
"## Different model provider\n",
"\n",
"If we want to use Anthropic instead of OpenAI: \n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -512,9 +551,11 @@
"id": "52a0c9f8-e316-42e1-af85-cabeba4b7059",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
@ -545,15 +586,19 @@
"id": "d7a91eee-d017-420d-b215-f663dcbf8ed2",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"```\n",
"\n",
"## Runtime configurability\n",
"\n",
"If we wanted to make the choice of chat model or LLM configurable at runtime:\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -634,9 +679,11 @@
"id": "d1530c5c-6635-4599-9483-6df357ca2d64",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### With LCEL\n",
"\n"
@ -694,15 +741,19 @@
"id": "370dd4d7-b825-40c4-ae3c-2693cba2f22a",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"```\n",
"\n",
"## Logging\n",
"\n",
"If we want to log our intermediate results:\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n",
@ -733,9 +784,11 @@
"id": "16bd20fd-43cd-4aaf-866f-a53d1f20312d",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\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",
@ -770,16 +823,19 @@
"id": "e25ce3c5-27a7-4954-9f0e-b94313597135",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>\n",
"```\n",
"\n",
"## Fallbacks\n",
"\n",
"If we wanted to add fallback logic, in case one model API is down:\n",
"\n",
"\n",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n",
@ -823,9 +879,11 @@
"id": "f7ef59b5-2ce3-479e-a7ac-79e1e2f30e9c",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
@ -850,8 +908,10 @@
"id": "3af52d36-37c6-4d89-b515-95d7270bb96a",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>"
"</ColumnContainer>\n",
"```"
]
},
{
@ -863,8 +923,10 @@
"\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",
"```{=mdx}\n",
"<ColumnContainer>\n",
"<Column>\n",
"```\n",
"\n",
"#### Without LCEL\n",
"\n"
@ -1044,9 +1106,11 @@
"id": "9fb3d71d-8c69-4dc4-81b7-95cd46b271c2",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"\n",
"<Column>\n",
"```\n",
"\n",
"#### LCEL\n",
"\n"
@ -1101,8 +1165,10 @@
"id": "e3637d39",
"metadata": {},
"source": [
"```{=mdx}\n",
"</Column>\n",
"</ColumnContainer>"
"</ColumnContainer>\n",
"```"
]
},
{
@ -1140,4 +1206,5 @@
},
"nbformat": 4,
"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"
]
},
{
"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",
"execution_count": 1,
@ -19,10 +46,8 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain.globals import set_llm_cache\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
"# <!-- ruff: noqa: F821 -->\n",
"from langchain.globals import set_llm_cache"
]
},
{

264
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
```
```python
import getpass
import os
@ -64,38 +63,26 @@ class Multiply(BaseModel):
b: int = Field(..., description="Second integer")
```
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
<Tabs>
<TabItem value="openai" label="OpenAI" default>
import ChatModelTabs from "@theme/ChatModelTabs";
Set up dependencies and API keys:
<ChatModelTabs
customVarName="llm"
fireworksParams={`model="accounts/fireworks/models/firefunction-v1", temperature=0`}
/>
```python
%pip install -qU langchain-openai
```
```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.,
We can use the `bind_tools()` method to handle converting
`Multiply` to a "function" 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(model="gpt-3.5-turbo-0125", temperature=0)
llm_with_tools = llm.bind_tools([Multiply])
llm_with_tools.invoke("what's 3 * 12")
```
``` text
```text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Q8ZQ97Qrj5zalugSkYMGV1Uo', 'function': {'arguments': '{"a":3,"b":12}', 'name': 'Multiply'}, 'type': 'function'}]})
```
@ -109,7 +96,7 @@ tool_chain = llm_with_tools | JsonOutputToolsParser()
tool_chain.invoke("what's 3 * 12")
```
``` text
```text
[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}]
```
@ -122,57 +109,10 @@ tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply])
tool_chain.invoke("what's 3 * 12")
```
``` text
```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_f3DApOzb60iYjTfOhVFhDRMI', 'function': {'arguments': '{"a":5,"b":10}', '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>
<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:
@ -182,175 +122,12 @@ llm_with_tools = llm.bind_tools([Multiply], tool_choice="Multiply")
llm_with_tools.invoke("what's 3 * 12")
```
``` text
```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:
once), we can set the `tool_choice` argument to the name of the tool:
```python
llm_with_multiply = llm.bind_tools([Multiply], tool_choice="Multiply")
@ -359,16 +136,13 @@ llm_with_multiply.invoke(
)
```
``` text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6k6d0gr3jhqil2kqf7sgeusl', 'function': {'arguments': '{"a":5,"b":7}', 'name': 'Multiply'}, 'type': 'function'}]})
```text
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_f3DApOzb60iYjTfOhVFhDRMI', 'function': {'arguments': '{"a":5,"b":10}', '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
In case you need to access function schemas directly, LangChain has a built-in converter that can turn
@ -395,7 +169,7 @@ def multiply(a: int, b: int) -> int:
print(json.dumps(convert_to_openai_tool(multiply), indent=2))
```
``` text
```text
{
"type": "function",
"function": {
@ -438,7 +212,7 @@ class multiply(BaseModel):
print(json.dumps(convert_to_openai_tool(multiply), indent=2))
```
``` text
```text
{
"type": "function",
"function": {
@ -493,7 +267,7 @@ class Multiply(BaseTool):
print(json.dumps(convert_to_openai_tool(Multiply()), indent=2))
```
``` text
```text
{
"type": "function",
"function": {

60
docs/docs/modules/model_io/chat/quick_start.ipynb
View File

@ -22,32 +22,19 @@
"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",
"\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"
"## Setup\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"id": "e230abb2-bc84-438b-b9ff-dd124acb1375",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"chat = ChatOpenAI(openai_api_key=\"...\")"
"<ChatModelTabs customVarName=\"chat\" />\n",
"```"
]
},
{
@ -55,19 +42,25 @@
"id": "609bbd5c-e5a1-4166-89e1-d6c52054860d",
"metadata": {},
"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",
"execution_count": 1,
"cell_type": "markdown",
"id": "3d9dbf70-2397-4d6b-87ec-3e6d4699f3df",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"\n",
"chat = ChatOpenAI()"
"```{=mdx}\n",
"<ChatModelTabs\n",
" 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",
"execution_count": 11,

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

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

92
docs/docs/use_cases/question_answering/quickstart.mdx
View File

@ -24,7 +24,7 @@ introduction](../../../docs/use_cases/question_answering/), which has
two main components:
**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
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
```
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:
```python
@ -125,10 +125,13 @@ from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
```
import ChatModelTabs from "@theme/ChatModelTabs";
<ChatModelTabs customVarName="llm" />
```python
# 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.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
@ -164,12 +165,11 @@ rag_chain = (
)
```
```python
rag_chain.invoke("What is Task Decomposition?")
```
``` text
```text
'Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It can be done through prompting techniques like Chain of Thought or Tree of Thoughts, or by using task-specific instructions or human inputs. Task decomposition helps agents plan ahead and manage complicated tasks more effectively.'
```
@ -219,12 +219,11 @@ loader = WebBaseLoader(
docs = loader.load()
```
```python
len(docs[0].page_content)
```
``` text
```text
42824
```
@ -232,7 +231,7 @@ len(docs[0].page_content)
print(docs[0].page_content[:500])
```
``` text
```text
LLM Powered Autonomous Agents
@ -249,12 +248,13 @@ In
`DocumentLoader`: Object that loads data from a source as list of
`Documents`.
- [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 to choose from.
integrations to choose from.
- [Interface](https://api.python.langchain.com/en/latest/document_loaders/langchain_core.document_loaders.base.BaseLoader.html):
API reference  for the base interface.
API reference  for the base interface.
## 2. Indexing: Split {#indexing-split}
@ -289,12 +289,11 @@ text_splitter = RecursiveCharacterTextSplitter(
all_splits = text_splitter.split_documents(docs)
```
```python
len(all_splits)
```
``` text
```text
66
```
@ -302,7 +301,7 @@ len(all_splits)
len(all_splits[0].page_content)
```
``` text
```text
969
```
@ -310,7 +309,7 @@ len(all_splits[0].page_content)
all_splits[10].metadata
```
``` text
```text
{'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/',
'start_index': 7056}
```
@ -319,15 +318,17 @@ all_splits[10].metadata
`TextSplitter`: Object that splits a list of `Document`s into smaller
chunks. Subclass of `DocumentTransformer`s.
- Explore `Context-aware splitters`, which keep the location (“context”) of each
split in the original `Document`: - [Markdown
files](../../../docs/modules/data_connection/document_transformers/markdown_header_metadata)
split in the original `Document`: - [Markdown
files](../../../docs/modules/data_connection/document_transformers/markdown_header_metadata)
- [Code (py or js)](../../../docs/integrations/document_loaders/source_code)
- [Scientific papers](../../../docs/integrations/document_loaders/grobid)
- [Interface](https://api.python.langchain.com/en/latest/base/langchain_text_splitters.base.TextSplitter.html): API reference for the base interface.
`DocumentTransformer`: Object that performs a transformation on a list
of `Document`s.
- [Docs](../../../docs/modules/data_connection/document_transformers/): Detailed documentation on how to use `DocumentTransformers`
- [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.
@ -361,12 +362,14 @@ vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbedd
`Embeddings`: Wrapper around a text embedding model, used for converting
text to 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.
- [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
querying embeddings.
- [Docs](../../../docs/modules/data_connection/vectorstores/): Detailed documentation on how to use vector stores.
- [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.
@ -399,17 +402,15 @@ facilitate retrieval. Any `VectorStore` can easily be turned into a
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6})
```
```python
retrieved_docs = retriever.invoke("What are the approaches to Task Decomposition?")
```
```python
len(retrieved_docs)
```
``` text
```text
6
```
@ -417,7 +418,7 @@ len(retrieved_docs)
print(retrieved_docs[0].page_content)
```
``` text
```text
Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.
Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.
```
@ -460,34 +461,13 @@ parses the output.
We’ll use the gpt-3.5-turbo OpenAI chat model, but any LangChain `LLM`
or `ChatModel` could be substituted in.
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
<Tabs>
<TabItem value="openai" label="OpenAI" default>
```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>
<ChatModelTabs
customVarName="llm"
anthropicParams={`"model="claude-3-sonnet-20240229", temperature=0.2, max_tokens=1024"`}
/>
We’ll use a prompt for RAG that is checked into the LangChain prompt hub
([here](https://smith.langchain.com/hub/rlm/rag-prompt)).
@ -498,7 +478,6 @@ from langchain import hub
prompt = hub.pull("rlm/rag-prompt")
```
```python
example_messages = prompt.invoke(
{"context": "filler context", "question": "filler question"}
@ -506,7 +485,7 @@ example_messages = prompt.invoke(
example_messages
```
``` text
```text
[HumanMessage(content="You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: filler question \nContext: filler context \nAnswer:")]
```
@ -514,7 +493,7 @@ example_messages
print(example_messages[0].content)
```
``` text
```text
You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: filler question
Context: filler context
@ -543,13 +522,12 @@ rag_chain = (
)
```
```python
for chunk in rag_chain.stream("What is Task Decomposition?"):
print(chunk, end="", flush=True)
```
``` text
```text
Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It involves transforming big tasks into multiple manageable tasks, allowing for easier interpretation and execution by autonomous agents or models. Task decomposition can be done through various methods, such as using prompting techniques, task-specific instructions, or human inputs.
```
@ -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
and returns a message.
- [Docs](../../../docs/modules/model_io/chat/)
- [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.
`LLM`: A text-in-text-out LLM. Takes in a string and returns a string.
- [Docs](../../../docs/modules/model_io/llms)
- [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.
@ -605,7 +585,7 @@ rag_chain = (
rag_chain.invoke("What is Task Decomposition?")
```
``` text
```text
'Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It involves transforming big tasks into multiple manageable tasks, allowing for a more systematic and organized approach to problem-solving. Thanks for asking!'
```

81
docs/src/theme/ChatModelTabs.js
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 Tabs from "@theme/Tabs";
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) {
const {
@ -29,24 +46,36 @@ export default function ChatModelTabs(props) {
fireworksParams,
mistralParams,
googleParams,
togetherParams,
hideOpenai,
hideAnthropic,
hideFireworks,
hideMistral,
hideGoogle,
hideTogether,
customVarName,
} = props;
const openAIParamsOrDefault = openaiParams ?? `model="gpt-3.5-turbo-0125"`
const anthropicParamsOrDefault = anthropicParams ?? `model="claude-3-sonnet-20240229"`
const fireworksParamsOrDefault = fireworksParams ?? `model="accounts/fireworks/models/mixtral-8x7b-instruct"`
const mistralParamsOrDefault = mistralParams ?? `model="mistral-large-latest"`
const googleParamsOrDefault = googleParams ?? `model="gemini-pro"`
const openAIParamsOrDefault = openaiParams ?? `model="gpt-3.5-turbo-0125"`;
const anthropicParamsOrDefault =
anthropicParams ?? `model="claude-3-sonnet-20240229"`;
const fireworksParamsOrDefault =
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 = [
{
value: "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",
packageName: "langchain-openai",
default: true,
@ -55,7 +84,7 @@ export default function ChatModelTabs(props) {
{
value: "Anthropic",
label: "Anthropic",
text: `from langchain_anthropic import ChatAnthropic\n\nmodel = ChatAnthropic(${anthropicParamsOrDefault})`,
text: `from langchain_anthropic import ChatAnthropic\n\n${llmVarName} = ChatAnthropic(${anthropicParamsOrDefault})`,
apiKeyName: "ANTHROPIC_API_KEY",
packageName: "langchain-anthropic",
default: false,
@ -64,7 +93,7 @@ export default function ChatModelTabs(props) {
{
value: "FireworksAI",
label: "FireworksAI",
text: `from langchain_fireworks import ChatFireworks\n\nmodel = ChatFireworks(${fireworksParamsOrDefault})`,
text: `from langchain_fireworks import ChatFireworks\n\n${llmVarName} = ChatFireworks(${fireworksParamsOrDefault})`,
apiKeyName: "FIREWORKS_API_KEY",
packageName: "langchain-fireworks",
default: false,
@ -73,7 +102,7 @@ export default function ChatModelTabs(props) {
{
value: "MistralAI",
label: "MistralAI",
text: `from langchain_mistralai import ChatMistralAI\n\nmodel = ChatMistralAI(${mistralParamsOrDefault})`,
text: `from langchain_mistralai import ChatMistralAI\n\n${llmVarName} = ChatMistralAI(${mistralParamsOrDefault})`,
apiKeyName: "MISTRAL_API_KEY",
packageName: "langchain-mistralai",
default: false,
@ -82,19 +111,37 @@ export default function ChatModelTabs(props) {
{
value: "Google",
label: "Google",
text: `from langchain_google_genai import ChatGoogleGenerativeAI\n\nmodel = ChatGoogleGenerativeAI(${googleParamsOrDefault})`,
text: `from langchain_google_genai import ChatGoogleGenerativeAI\n\n${llmVarName} = ChatGoogleGenerativeAI(${googleParamsOrDefault})`,
apiKeyName: "GOOGLE_API_KEY",
packageName: "langchain-google-genai",
default: false,
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 (
<Tabs groupId="modelTabs">
{tabItems.filter((tabItem) => !tabItem.shouldHide).map((tabItem) => (
<TabItem value={tabItem.value} label={tabItem.label} default={tabItem.default}>
<Setup apiKeyName={tabItem.apiKeyName} packageName={tabItem.packageName} />
{tabItems
.filter((tabItem) => !tabItem.shouldHide)
.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>
</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
# install quarto
wget -q https://github.com/quarto-dev/quarto-cli/releases/download/v1.3.450/quarto-1.3.450-linux-amd64.tar.gz
tar -xzf quarto-1.3.450-linux-amd64.tar.gz
export PATH=$PATH:$(pwd)/quarto-1.3.450/bin/
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-1.4.552-linux-amd64.tar.gz
export PATH=$PATH:$(pwd)/quarto-1.4.552/bin/
# setup python env