id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
5640ee21701e-0 | .ipynb
.pdf
NGram Overlap ExampleSelector
NGram Overlap ExampleSelector#
The NGramOverlapExampleSelector selects and orders examples based on which examples are most similar to the input, according to an ngram overlap score. The ngram overlap score is a float between 0.0 and 1.0, inclusive.
The selector allows for a th... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
5640ee21701e-1 | {"input": "Spot can run.", "output": "Spot puede correr."},
]
example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}\nOutput: {output}",
)
example_selector = NGramOverlapExampleSelector(
# These are the examples it has available to choose from.
examples=examples, ... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
5640ee21701e-2 | Output: Ver correr a Spot.
Input: My dog barks.
Output: Mi perro ladra.
Input: Spot can run fast.
Output:
# You can add examples to NGramOverlapExampleSelector as well.
new_example = {"input": "Spot plays fetch.", "output": "Spot juega a buscar."}
example_selector.add_example(new_example)
print(dynamic_prompt.format(se... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
5640ee21701e-3 | Input: Spot plays fetch.
Output: Spot juega a buscar.
Input: Spot can play fetch.
Output:
# Setting threshold greater than 1.0
example_selector.threshold=1.0+1e-9
print(dynamic_prompt.format(sentence="Spot can play fetch."))
Give the Spanish translation of every input
Input: Spot can play fetch.
Output:
previous
Maxima... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
c8b3cbe7b475-0 | .md
.pdf
How to create a custom example selector
Contents
Implement custom example selector
Use custom example selector
How to create a custom example selector#
In this tutorial, we’ll create a custom example selector that selects every alternate example from a given list of examples.
An ExampleSelector must implemen... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
c8b3cbe7b475-1 | # Add new example to the set of examples
example_selector.add_example({"foo": "4"})
example_selector.examples
# -> [{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]
# Select examples
example_selector.select_examples({"foo": "foo"})
# -> array([{'foo': '1'}, {'foo': '4'}], dtype=object)
previous
Example Selectors... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
196aa3c678c0-0 | .ipynb
.pdf
Output Parsers
Output Parsers#
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an output parser must impl... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/getting_started.html |
196aa3c678c0-1 | punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator('setup')
def question_ends_with_question_mark(cls, field):
if field[-1] != '?':
raise ValueError("Badly formed question!")
return field
# S... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/getting_started.html |
3b71f38fc2a7-0 | .ipynb
.pdf
RetryOutputParser
RetryOutputParser#
While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can’t. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.
from langchain.prompts... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
3b71f38fc2a7-1 | 23 json_object = json.loads(json_str)
---> 24 return self.pydantic_object.parse_obj(json_object)
26 except (json.JSONDecodeError, ValidationError) as e:
File ~/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pydantic/main.py:527, in pydantic.main.BaseModel.parse_obj()
File ~/.pyenv/version... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
3b71f38fc2a7-2 | fix_parser.parse(bad_response)
Action(action='search', action_input='')
Instead, we can use the RetryOutputParser, which passes in the prompt (as well as the original output) to try again to get a better response.
from langchain.output_parsers import RetryWithErrorOutputParser
retry_parser = RetryWithErrorOutputParser.... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
e331ec1ac93f-0 | .ipynb
.pdf
OutputFixingParser
OutputFixingParser#
This output parser wraps another output parser and tries to fix any mistakes
The Pydantic guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the mi... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
e331ec1ac93f-1 | 24 return self.pydantic_object.parse_obj(json_object)
File ~/.pyenv/versions/3.9.1/lib/python3.9/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
343 if (cls is None and object_hook is None and
344 parse_int is None and parse_float is N... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
e331ec1ac93f-2 | Cell In[6], line 1
----> 1 parser.parse(misformatted)
File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)
27 name = self.pydantic_object.__name__
28 msg = f"Failed to parse {name} from completion {text}. Got: {e}"
---> 29 raise OutputParserException(ms... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
8c6fabfe839a-0 | .ipynb
.pdf
CommaSeparatedListOutputParser
CommaSeparatedListOutputParser#
Here’s another parser strictly less powerful than Pydantic/JSON parsing.
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langch... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/comma_separated.html |
925acb90f659-0 | .ipynb
.pdf
Structured Output Parser
Structured Output Parser#
While the Pydantic/JSON parser is more powerful, we initially experimented data structures having text fields only.
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate, ChatPromptTemplate,... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/structured.html |
925acb90f659-1 | prompt = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template("answer the users question as best as possible.\n{format_instructions}\n{question}")
],
input_variables=["question"],
partial_variables={"format_instructions": format_instructions}
)
_input = prompt.format_prompt(... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/structured.html |
9c901d257219-0 | .ipynb
.pdf
Enum Output Parser
Enum Output Parser#
This notebook shows how to use an Enum output parser
from langchain.output_parsers.enum import EnumOutputParser
from enum import Enum
class Colors(Enum):
RED = "red"
GREEN = "green"
BLUE = "blue"
parser = EnumOutputParser(enum=Colors)
parser.parse("red")
<C... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/enum.html |
9c901d257219-1 | During handling of the above exception, another exception occurred:
OutputParserException Traceback (most recent call last)
Cell In[8], line 2
1 # And raises errors when appropriate
----> 2 parser.parse("yellow")
File ~/workplace/langchain/langchain/output_parsers/enum.py:27, in EnumOutputPars... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/enum.html |
52f0f40ddc9c-0 | .ipynb
.pdf
PydanticOutputParser
PydanticOutputParser#
This output parser allows users to specify an arbitrary JSON schema and query LLMs for JSON outputs that conform to that schema.
Keep in mind that large language models are leaky abstractions! You’ll have to use an LLM with sufficient capacity to generate well-form... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
52f0f40ddc9c-1 | prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
_input = prompt.format_prompt(query=joke_query)
output = model(_input.to_string())
parser.parse(output)
Joke(... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
b691ce7c7a15-0 | .ipynb
.pdf
Datetime
Datetime#
This OutputParser shows out to parse LLM output into datetime format.
from langchain.prompts import PromptTemplate
from langchain.output_parsers import DatetimeOutputParser
from langchain.chains import LLMChain
from langchain.llms import OpenAI
output_parser = DatetimeOutputParser()
templ... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/datetime.html |
634c1863de2c-0 | .rst
.pdf
How-To Guides
How-To Guides#
If you’re new to the library, you may want to start with the Quickstart.
The user guide here shows more advanced workflows and how to use the library in different ways.
Connecting to a Feature Store
How to create a custom prompt template
How to create a prompt template that uses f... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/how_to_guides.html |
4c791ccd8d07-0 | .md
.pdf
Getting Started
Contents
What is a prompt template?
Create a prompt template
Template formats
Validate template
Serialize prompt template
Pass few shot examples to a prompt template
Select examples for a prompt template
Getting Started#
In this tutorial, we will learn about:
what a prompt template is, and wh... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4c791ccd8d07-1 | no_input_prompt.format()
# -> "Tell me a joke."
# An example prompt with one input variable
one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
one_input_prompt.format(adjective="funny")
# -> "Tell me a funny joke."
# An example prompt with multiple input variables
m... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4c791ccd8d07-2 | # -> Tell me a funny joke about chickens.
Currently, PromptTemplate only supports jinja2 and f-string templating format. If there is any other templating format that you would like to use, feel free to open an issue in the Github page.
Validate template#
By default, PromptTemplate will validate the template string by c... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4c791ccd8d07-3 | To generate a prompt with few shot examples, you can use the FewShotPromptTemplate. This class takes in a PromptTemplate and a list of few shot examples. It then formats the prompt template with the few shot examples.
In this example, we’ll create a prompt to generate word antonyms.
from langchain import PromptTemplate... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4c791ccd8d07-4 | input_variables=["input"],
# The example_separator is the string we will use to join the prefix, examples, and suffix together with.
example_separator="\n",
)
# We can now generate a prompt using the `format` method.
print(few_shot_prompt.format(input="big"))
# -> Give the antonym of every input
# ->
# -> Word... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4c791ccd8d07-5 | {"word": "windy", "antonym": "calm"},
]
# We'll use the `LengthBasedExampleSelector` to select the examples.
example_selector = LengthBasedExampleSelector(
# These are the examples is has available to choose from.
examples=examples,
# This is the PromptTemplate being used to format the examples.
exampl... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4c791ccd8d07-6 | # -> Antonym: lethargic
# ->
# -> Word: sunny
# -> Antonym: gloomy
# ->
# -> Word: windy
# -> Antonym: calm
# ->
# -> Word: big
# -> Antonym:
In contrast, if we provide a very long input, the LengthBasedExampleSelector will select fewer examples to include in the prompt.
long_string = "big and huge and massive and larg... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
27dbae2411cd-0 | .ipynb
.pdf
How to serialize prompts
Contents
PromptTemplate
Loading from YAML
Loading from JSON
Loading Template from a File
FewShotPromptTemplate
Examples
Loading from YAML
Loading from JSON
Examples in the Config
Example Prompt from a File
PromptTempalte with OutputParser
How to serialize prompts#
It is often pref... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
27dbae2411cd-1 | prompt = load_prompt("simple_prompt.yaml")
print(prompt.format(adjective="funny", content="chickens"))
Tell me a funny joke about chickens.
Loading from JSON#
This shows an example of loading a PromptTemplate from JSON.
!cat simple_prompt.json
{
"_type": "prompt",
"input_variables": ["adjective", "content"],
... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
27dbae2411cd-2 | output: sad
- input: tall
output: short
Loading from YAML#
This shows an example of loading a few shot example from YAML.
!cat few_shot_prompt.yaml
_type: few_shot
input_variables:
["adjective"]
prefix:
Write antonyms for the following words.
example_prompt:
_type: prompt
input_variables:
["i... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
27dbae2411cd-3 | !cat few_shot_prompt.json
{
"_type": "few_shot",
"input_variables": ["adjective"],
"prefix": "Write antonyms for the following words.",
"example_prompt": {
"_type": "prompt",
"input_variables": ["input", "output"],
"template": "Input: {input}\nOutput: {output}"
},
"exampl... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
27dbae2411cd-4 | Output: short
Input: funny
Output:
Example Prompt from a File#
This shows an example of loading the PromptTemplate that is used to format the examples from a separate file. Note that the key changes from example_prompt to example_prompt_path.
!cat example_prompt.json
{
"_type": "prompt",
"input_variables": ["in... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
27dbae2411cd-5 | "_type": "regex_parser"
},
"partial_variables": {},
"template": "Given the following question and student answer, provide a correct answer and score the student answer.\nQuestion: {question}\nStudent Answer: {student_answer}\nCorrect Answer:",
"template_format": "f-string",
"validate_template": true... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
5dcfc9951c97-0 | .ipynb
.pdf
How to work with partial Prompt Templates
Contents
Partial With Strings
Partial With Functions
How to work with partial Prompt Templates#
A prompt template is a class with a .format method which takes in a key-value map and returns a string (a prompt) to pass to the language model. Like other methods, it ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
5dcfc9951c97-1 | print(prompt.format(bar="baz"))
foobaz
Partial With Functions#
The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to ha... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
5dcfc9951c97-2 | Contents
Partial With Strings
Partial With Functions
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
d2d2356563ce-0 | .ipynb
.pdf
How to create a prompt template that uses few shot examples
Contents
Use Case
Using an example set
Create the example set
Create a formatter for the few shot examples
Feed examples and formatter to FewShotPromptTemplate
Using an example selector
Feed examples into ExampleSelector
Feed example selector int... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
d2d2356563ce-1 | "answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
"""
},
... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
d2d2356563ce-2 | print(example_prompt.format(**examples[0]))
Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
d2d2356563ce-3 | Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
d2d2356563ce-4 | # This is the list of examples available to select from.
examples,
# This is the embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# This is the VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
d2d2356563ce-5 | suffix="Question: {input}",
input_variables=["input"]
)
print(prompt.format(input="Who was the father of Mary Ball Washington?"))
Question: Who was the maternal grandfather of George Washington?
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The m... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
efd7e1b9e28a-0 | .ipynb
.pdf
Connecting to a Feature Store
Contents
Feast
Load Feast Store
Prompts
Use in a chain
Tecton
Prerequisites
Define and Load Features
Prompts
Use in a chain
Featureform
Initialize Featureform
Prompts
Use in a chain
Connecting to a Feature Store#
Feature stores are a concept from traditional machine learning ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
efd7e1b9e28a-1 | Note that the input to this prompt template is just driver_id, since that is the only user defined piece (all other variables are looked up inside the prompt template).
from langchain.prompts import PromptTemplate, StringPromptTemplate
template = """Given the driver's up to date stats, write them note relaying those st... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
efd7e1b9e28a-2 | Here are the drivers stats:
Conversation rate: 0.4745151400566101
Acceptance rate: 0.055561766028404236
Average Daily Trips: 936
Your response:
Use in a chain#
We can now use this in a chain, successfully creating a chain that achieves personalization backed by a feature store
from langchain.chat_models import ChatOpen... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
efd7e1b9e28a-3 | user_transaction_metrics = FeatureService(
name = "user_transaction_metrics",
features = [user_transaction_counts]
)
The above Feature Service is expected to be applied to a live workspace. For this example, we will be using the “prod” workspace.
import tecton
workspace = tecton.get_workspace("prod")
feature_se... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
efd7e1b9e28a-4 | kwargs["transaction_count_30d"] = feature_vector["user_transaction_counts.transaction_count_30d_1d"]
return prompt.format(**kwargs)
prompt_template = TectonPromptTemplate(input_variables=["user_id"])
print(prompt_template.format(user_id="user_469998441571"))
Given the vendor's up to date transaction stats, writ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
efd7e1b9e28a-5 | client = ff.Client(host="demo.featureform.com")
Prompts#
Here we will set up a custom FeatureformPromptTemplate. This prompt template will take in the average amount a user pays per transactions.
Note that the input to this prompt template is just avg_transaction, since that is the only user defined piece (all other va... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
efd7e1b9e28a-6 | Define and Load Features
Prompts
Use in a chain
Featureform
Initialize Featureform
Prompts
Use in a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
f0d68bcccf3a-0 | .ipynb
.pdf
How to create a custom prompt template
Contents
Why are custom prompt templates needed?
Creating a Custom Prompt Template
Use the custom prompt template
How to create a custom prompt template#
Let’s suppose we want the LLM to generate English language explanations of a function given its name. To achieve ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
f0d68bcccf3a-1 | import inspect
def get_source_code(function_name):
# Get the source code of the function
return inspect.getsource(function_name)
Next, we’ll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.
from langchain.prompt... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
f0d68bcccf3a-2 | prompt = fn_explainer.format(function_name=get_source_code)
print(prompt)
Given the function name and source code, generate an English language explanation of the function.
Function Name: get_source_code
Source Code:
def get_source_code(function_name):
# Get the source code of the fu... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
dc2648f618db-0 | .rst
.pdf
How-To Guides
How-To Guides#
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either prompts, models, arbitrary functions, or other chains.
The examples here are broken up into three sections:
Generic Functionality
Covers both generic chains (that are useful in a ... | https://python.langchain.com/en/latest/modules/chains/how_to_guides.html |
b885ab11db62-0 | .ipynb
.pdf
Getting Started
Contents
Why do we need chains?
Quick start: Using LLMChain
Different ways of calling chains
Add memory to chains
Debug Chain
Combine chains with the SequentialChain
Create a custom chain with the Chain class
Getting Started#
In this tutorial, we will learn about creating simple chains in ... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
b885ab11db62-1 | print(chain.run("colorful socks"))
Colorful Toes Co.
If there are multiple variables, you can input them all at once using a dictionary.
prompt = PromptTemplate(
input_variables=["company", "product"],
template="What is a good name for {company} that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
b885ab11db62-2 | llm_chain(inputs={"adjective":"corny"})
{'adjective': 'corny',
'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}
By default, __call__ returns both the input and output key values. You can configure it to only return output key values by setting return_only_outputs to True.
llm_chain("corny", r... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
b885ab11db62-3 | from langchain.memory import ConversationBufferMemory
conversation = ConversationChain(
llm=chat,
memory=ConversationBufferMemory()
)
conversation.run("Answer briefly. What are the first 3 colors of a rainbow?")
# -> The first three colors of a rainbow are red, orange, and yellow.
conversation.run("And the next... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
b885ab11db62-4 | Current conversation:
Human: What is ChatGPT?
AI:
> Finished chain.
'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
b885ab11db62-5 | catchphrase = overall_chain.run("colorful socks")
print(catchphrase)
> Entering new SimpleSequentialChain chain...
Rainbow Socks Co.
"Put a little rainbow in your step!"
> Finished chain.
"Put a little rainbow in your step!"
Create a custom chain with the Chain class#
LangChain provides many chains out of the box, but ... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
b885ab11db62-6 | prompt_1 = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain_1 = LLMChain(llm=llm, prompt=prompt_1)
prompt_2 = PromptTemplate(
input_variables=["product"],
template="What is a good slogan for a company that makes {product}?",
)
chain... | https://python.langchain.com/en/latest/modules/chains/getting_started.html |
8d54cd1936a6-0 | .ipynb
.pdf
Graph QA
Contents
Create the graph
Querying the graph
Save the graph
Graph QA#
This notebook goes over how to do question answering over a graph data structure.
Create the graph#
In this section, we construct an example graph. At the moment, this works best for small pieces of text.
from langchain.indexes... | https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
8d54cd1936a6-1 | 'is the ground on which')]
Querying the graph#
We can now use the graph QA chain to ask question of the graph
from langchain.chains import GraphQAChain
chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)
chain.run("what is Intel going to build?")
> Entering new GraphQAChain chain...
Entities... | https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
8d54cd1936a6-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
b8d8ac6c27e0-0 | .ipynb
.pdf
Retrieval Question Answering with Sources
Contents
Chain Type
Retrieval Question Answering with Sources#
This notebook goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index.
from langch... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
b8d8ac6c27e0-1 | 'sources': '31-pl'}
Chain Type#
You can easily specify different chain types to load and use in the RetrievalQAWithSourcesChain chain. For a more detailed walkthrough of these types, please see this notebook.
There are two ways to load different chain types. First, you can specify the chain type argument in the from_ch... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
b8d8ac6c27e0-2 | {'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
'sources': '31-pl'}
previous
Retrieval Question/Answering
next
Vector DB Text Generation
Contents
Chain Type
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, ... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
abbc23b05ba7-0 | .ipynb
.pdf
Chat Over Documents with Chat History
Contents
Pass in chat history
Return Source Documents
ConversationalRetrievalChain with search_distance
ConversationalRetrievalChain with map_reduce
ConversationalRetrievalChain with Question Answering with sources
ConversationalRetrievalChain with streaming to stdout... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-1 | Using embedded DuckDB without persistence: data will be transient
We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
We now in... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-2 | result = qa({"question": query, "chat_history": chat_history})
result["answer"]
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-3 | result['source_documents'][0]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-4 | from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce")
chain = Convers... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-5 | combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-6 | chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
abbc23b05ba7-7 | result = qa({"question": query, "chat_history": chat_history})
result['answer']
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
46ff9fe12096-0 | .ipynb
.pdf
Question Answering with Sources
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering with Sources#
This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four differe... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-1 | from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import OpenAI
Quickstart#
If you just want to get started as quickly as possible, this is the recommended way to do it:
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the presiden... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-2 | PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"])
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'out... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-3 | ' None',
' None',
' None'],
'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
question_prompt_template = """Use the following portion of a long document to see if an... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-4 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema d... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-5 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': "\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked him for his service and praised his c... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-6 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ['\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service.... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-7 | '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-8 | '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-9 | 'output_text': '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal publi... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-10 | "answer the question (in Italian)"
"If you do update it, please update the sources as well. "
"If the context isn't useful, return the original answer."
)
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=refine_template,
)
question_template = (
... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-11 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-12 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-13 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-14 | 'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sotto... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-15 | 'score': '100'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'}]
Custom Prompts
You can also use your own prompts with this chain. In this example... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
46ff9fe12096-16 | result
{'source': 30,
'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',
'score': '100'},
{'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
'score': '100'},
{'answer': ' Non so.', '... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
7743725c1ab3-0 | .ipynb
.pdf
Analyze Document
Contents
Summarize
Question Answering
Analyze Document#
The AnalyzeDocumentChain is more of an end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain. This can be used as more of an end-to-end chain.
with open("../../state_of_th... | https://python.langchain.com/en/latest/modules/chains/index_examples/analyze_document.html |
7743725c1ab3-1 | qa_chain = load_qa_chain(llm, chain_type="map_reduce")
qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)
qa_document_chain.run(input_document=state_of_the_union, question="what did the president say about justice breyer?")
' The president thanked Justice Breyer for his service.'
previous
Transformat... | https://python.langchain.com/en/latest/modules/chains/index_examples/analyze_document.html |
a14e5c2ac01f-0 | .ipynb
.pdf
Question Answering
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering#
This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: stuff, map_reduce, ... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
a14e5c2ac01f-1 | from langchain.llms import OpenAI
Quickstart#
If you just want to get started as quickly as possible, this is the recommended way to do it:
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain.run(input_documents=docs, question=query)
' The pre... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
a14e5c2ac01f-2 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}
The map_reduce Chain#
This sections shows results of using the map_reduce Chain to do ques... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
a14e5c2ac01f-3 | ' None',
' None'],
'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Ital... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
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