add everyting for docs

This commit is contained in:
ishaan-jaff 2023-07-29 07:00:13 -07:00
parent de45a738ee
commit 0fe8799f94
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## Official release
To install LangChain run:
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import CodeBlock from "@theme/CodeBlock";
<Tabs>
<TabItem value="pip" label="Pip" default>
<CodeBlock language="bash">pip install langchain</CodeBlock>
</TabItem>
<TabItem value="conda" label="Conda">
<CodeBlock language="bash">conda install langchain -c conda-forge</CodeBlock>
</TabItem>
</Tabs>
This will install the bare minimum requirements of LangChain.
A lot of the value of LangChain comes when integrating it with various model providers, datastores, etc.
By default, the dependencies needed to do that are NOT installed.
However, there are two other ways to install LangChain that do bring in those dependencies.
To install modules needed for the common LLM providers, run:
```bash
pip install langchain[llms]
```
To install all modules needed for all integrations, run:
```bash
pip install langchain[all]
```
Note that if you are using `zsh`, you'll need to quote square brackets when passing them as an argument to a command, for example:
```bash
pip install 'langchain[all]'
```
## From source
If you want to install from source, you can do so by cloning the repo and running:
```bash
pip install -e .
```

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```python
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
llm.predict("hi!")
>>> "Hi"
chat_model.predict("hi!")
>>> "Hi"
```

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```python
from langchain.schema import HumanMessage
text = "What would be a good company name for a company that makes colorful socks?"
messages = [HumanMessage(content=text)]
llm.predict_messages(messages)
# >> Feetful of Fun
chat_model.predict_messages(messages)
# >> Socks O'Color
```

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```python
text = "What would be a good company name for a company that makes colorful socks?"
llm.predict(text)
# >> Feetful of Fun
chat_model.predict(text)
# >> Socks O'Color
```

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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import CodeBlock from "@theme/CodeBlock";
<Tabs>
<TabItem value="pip" label="Pip" default>
<CodeBlock language="bash">pip install langchain</CodeBlock>
</TabItem>
<TabItem value="conda" label="Conda">
<CodeBlock language="bash">conda install langchain -c conda-forge</CodeBlock>
</TabItem>
</Tabs>

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```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
template = """You are a helpful assistant who generates comma separated lists.
A user will pass in a category, and you should generated 5 objects in that category in a comma separated list.
ONLY return a comma separated list, and nothing more."""
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(
llm=ChatOpenAI(),
prompt=chat_prompt,
output_parser=CommaSeparatedListOutputParser()
)
chain.run("colors")
# >> ['red', 'blue', 'green', 'yellow', 'orange']
```

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First we'll need to install their Python package:
```bash
pip install openai
```
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:
```bash
export OPENAI_API_KEY="..."
```
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:
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="...")
```

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```python
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
CommaSeparatedListOutputParser().parse("hi, bye")
# >> ['hi', 'bye']
```

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```python
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.")
```
```pycon
[
SystemMessage(content="You are a helpful assistant that translates English to French.", additional_kwargs={}),
HumanMessage(content="I love programming.")
]
```

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```python
from langchain.prompts import PromptTemplate
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
prompt.format(product="colorful socks")
```
```pycon
What is a good name for a company that makes colorful socks?
```