id stringlengths 14 16 | text stringlengths 45 2.05k | source stringlengths 53 111 |
|---|---|---|
6512e81bd3f4-4 | "question": "What profession does Nicholas Ray and Elia Kazan have in common?",
"answer": "Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.\nAction 1: Search[Nicholas Ray]\nObservation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., A... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
6512e81bd3f4-5 | "question": "Which magazine was started first Arthur’s Magazine or First for Women?",
"answer": "Thought 1: I need to search Arthur’s Magazine and First for Women, and find which was started first.\nAction 1: Search[Arthur’s Magazine]\nObservation 1: Arthur’s Magazine (1844-1846) was an American literary periodical... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
6512e81bd3f4-6 | "answer": "Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.\nAction 1: Search[Pavel Urysohn]\nObservation 1: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
6512e81bd3f4-7 | 'Action 2: Search[Missouri orogeny]',
'Observation 2: The Missouri orogeny was a major tectonic event that occurred in the late Pennsylvanian and early Permian period (about 300 million years ago).',
'Thought 3: The Illinois orogeny is hypothesized and occurred in the Late Paleozoic and the Missouri orogeny was a maj... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
d1358bb189d0-0 | .ipynb
.pdf
Model Comparison
Model Comparison#
Constructing your language model application will likely involved choosing between many different options of prompts, models, and even chains to use. When doing so, you will want to compare these different options on different inputs in an easy, flexible, and intuitive way... | https://langchain.readthedocs.io/en/latest/use_cases/model_laboratory.html |
d1358bb189d0-1 | pink
prompt = PromptTemplate(template="What is the capital of {state}?", input_variables=["state"])
model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)
model_lab_with_prompt.compare("New York")
Input:
New York
OpenAI
Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p'... | https://langchain.readthedocs.io/en/latest/use_cases/model_laboratory.html |
d1358bb189d0-2 | names = [str(open_ai_llm), str(cohere_llm)]
model_lab = ModelLaboratory(chains, names=names)
model_lab.compare("What is the hometown of the reigning men's U.S. Open champion?")
Input:
What is the hometown of the reigning men's U.S. Open champion?
OpenAI
Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tok... | https://langchain.readthedocs.io/en/latest/use_cases/model_laboratory.html |
d1358bb189d0-3 | > Finished chain.
So the final answer is:
Carlos Alcaraz
previous
SQL Question Answering Benchmarking: Chinook
next
Installation
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/model_laboratory.html |
80e7d89b4c2e-0 | .md
.pdf
Question Answering
Contents
Document Question Answering
Adding in sources
Additional Related Resources
Question Answering#
Question answering in this context refers to question answering over your document data.
For question answering over other types of data, like SQL databases or APIs, please see here
For ... | https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html |
80e7d89b4c2e-1 | from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
chain.run(input_documents=docs, question=query)
The following resources exist:
Question Answering Notebook: A notebook walking through how to accomplish this task.
VectorDB Question Answering Notebook: A noteboo... | https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html |
80e7d89b4c2e-2 | CombineDocuments Chains: A conceptual overview of specific types of chains by which you can accomplish this task.
Data Augmented Generation: An overview of data augmented generation, which is the general concept of combining external data with LLMs (of which this is a subset).
previous
Data Augmented Generation
next
Su... | https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html |
04133414ca55-0 | .md
.pdf
Agents
Agents#
Agents are systems that use a language model to interact with other tools.
These can be used to do more grounded question/answering, interact with APIs, or even take actions.
These agents can be used to power the next generation of personal assistants -
systems that intelligently understand what... | https://langchain.readthedocs.io/en/latest/use_cases/agents.html |
1b3c40134265-0 | .md
.pdf
Chatbots
Chatbots#
Since language models are good at producing text, that makes them ideal for creating chatbots.
Aside from the base prompts/LLMs, an important concept to know for Chatbots is memory.
Most chat based applications rely on remembering what happened in previous interactions, which is memory is de... | https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html |
51c56b9b1fdb-0 | .md
.pdf
Data Augmented Generation
Contents
Overview
Related Literature
Fetching
Text Splitting
Relevant Documents
Augmenting
Use Cases
Data Augmented Generation#
Overview#
Language models are trained on large amounts of unstructured data, which makes them fantastic at general purpose text generation. However, there ... | https://langchain.readthedocs.io/en/latest/use_cases/combine_docs.html |
51c56b9b1fdb-1 | RAG: Retrieval Augmented Generation.
This paper introduces RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever.
REALM: Retrieval-Augmented Language Model Pre-Training.
To capture knowledge ... | https://langchain.readthedocs.io/en/latest/use_cases/combine_docs.html |
51c56b9b1fdb-2 | and task the language model with summarizing it.
Document Retrieval: One of the more common use cases involves fetching relevant documents or pieces of text from
a large corpus of data. A common example of this is question answering over a private collection of documents.
API Querying: Another common way to fetch data ... | https://langchain.readthedocs.io/en/latest/use_cases/combine_docs.html |
51c56b9b1fdb-3 | One concrete example of this is vector stores for document retrieval, often used for semantic search or question answering.
With this method, larger documents are split up into
smaller chunks and then each chunk of text is passed to an embedding function which creates an embedding for that piece of text.
Those are embe... | https://langchain.readthedocs.io/en/latest/use_cases/combine_docs.html |
51c56b9b1fdb-4 | Text Splitting
Relevant Documents
Augmenting
Use Cases
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/combine_docs.html |
c14da507fa0e-0 | .md
.pdf
Summarization
Summarization#
Summarization involves creating a smaller summary of multiple longer documents.
This can be useful for distilling long documents into the core pieces of information.
The recommended way to get started using a summarization chain is:
from langchain.chains.summarize import load_summa... | https://langchain.readthedocs.io/en/latest/use_cases/summarization.html |
0a060ca34199-0 | .md
.pdf
Querying Tabular Data
Contents
Document Loading
Querying
Chains
Agents
Querying Tabular Data#
Lots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables.
This page covers all resources available in LangChain for working with data in this format.
Document Loading#
... | https://langchain.readthedocs.io/en/latest/use_cases/tabular.html |
ca869ba4fc0d-0 | .ipynb
.pdf
Using Hugging Face Datasets
Contents
Setup
Examples
Predictions
Evaluation
Using Hugging Face Datasets#
This example shows how to use Hugging Face datasets to evaluate models. Specifically, we show how to load examples to evaluate models on from Hugging Face’s dataset package.
Setup#
For demonstration pur... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/huggingface_datasets.html |
ca869ba4fc0d-1 | 'You will not digest the watermelon seeds',
'The watermelon seeds will be excreted'],
'incorrect_answers': ['You grow watermelons in your stomach',
'You get sick',
'You have bad dreams',
'You die',
'You get indigestion',
'You fall unconscious',
'You digest the watermelon seeds'],
'source': 'https://wond... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/huggingface_datasets.html |
ca869ba4fc0d-2 | from langchain.evaluation.qa import QAEvalChain
llm = OpenAI(temperature=0)
eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(examples, predictions, question_key="question", answer_key="best_answer", prediction_key="text")
graded_outputs
[{'text': ' INCORRECT'},
{'text': ' INCORRECT'},
{'tex... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/huggingface_datasets.html |
ed403ac882a6-0 | .ipynb
.pdf
QA Generation
QA Generation#
This notebook shows how to use the QAGenerationChain to come up with question-answer pairs over a specific document.
This is important because often times you may not have data to evaluate your question-answer system over, so this is a cheap and lightweight way to generate it!
f... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_generation.html |
e5e48de863db-0 | .ipynb
.pdf
Question Answering Benchmarking: State of the Union Address
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Question Answering Benchmarking: State of the Union Address#
Here we go over how to benchmark performance on a question answering task over ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
e5e48de863db-1 | Dataset json downloaded and prepared to /Users/harrisonchase/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--question-answering-state-of-the-union-a7e5a3b2db4f440d/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.
Setting up a chain#
N... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
e5e48de863db-2 | Make many predictions#
Now we can make predictions
predictions = chain.apply(dataset)
Evaluate performance#
Now we can evaluate the predictions. The first thing we can do is look at them by eye.
predictions[0]
{'question': 'What is the purpose of the NATO Alliance?',
'answer': 'The purpose of the NATO Alliance is to s... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
e5e48de863db-3 | 'grade': ' INCORRECT'}
previous
Question Answering Benchmarking: Paul Graham Essay
next
QA Generation
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
63c27553568f-0 | .ipynb
.pdf
LLM Math
Contents
Setting up a chain
LLM Math#
Evaluating chains that know how to do math.
# Comment this out if you are NOT using tracing
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("llm-math")
Downloading and prepar... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/llm_math.html |
63c27553568f-1 | sum(correct) / len(correct)
1.0
for i, example in enumerate(dataset):
print("input: ", example["question"])
print("expected output :", example["answer"])
print("prediction: ", numeric_output[i])
input: 5
expected output : 5.0
prediction: 5.0
input: 5 + 3
expected output : 8.0
prediction: 8.0
input: 2^3... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/llm_math.html |
63c27553568f-2 | next
Question Answering Benchmarking: Paul Graham Essay
Contents
Setting up a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/llm_math.html |
24903b74c020-0 | .ipynb
.pdf
Agent VectorDB Question Answering Benchmarking
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Agent VectorDB Question Answering Benchmarking#
Here we go over how to benchmark performance on a question answering task using an agent to route between... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_vectordb_sota_pg.html |
24903b74c020-1 | dataset[-1]
{'question': 'What is the purpose of YC?',
'answer': 'The purpose of YC is to cause startups to be founded that would not otherwise have existed.',
'steps': [{'tool': 'Paul Graham QA System', 'tool_input': None},
{'tool': None, 'tool_input': 'What is the purpose of YC?'}]}
Setting up a chain#
Now we nee... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_vectordb_sota_pg.html |
24903b74c020-2 | We can now set up an agent to route between them.
from langchain.agents import initialize_agent, Tool
tools = [
Tool(
name = "State of Union QA System",
func=chain_sota.run,
description="useful for when you need to answer questions about the most recent state of the union address. Input shou... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_vectordb_sota_pg.html |
24903b74c020-3 | 'output': 'The purpose of the NATO Alliance is to promote peace and security in the North Atlantic region by providing a collective defense against potential threats.'}
Next, we can use a language model to score them programatically
from langchain.evaluation.qa import QAEvalChain
llm = OpenAI(temperature=0)
eval_chain ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_vectordb_sota_pg.html |
0a27933eb75f-0 | .ipynb
.pdf
Agent Benchmarking: Search + Calculator
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Agent Benchmarking: Search + Calculator#
Here we go over how to benchmark performance of an agent on tasks where it has access to a calculator and a search tool... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_benchmarking.html |
0a27933eb75f-1 | Make a prediction#
First, we can make predictions one datapoint at a time. Doing it at this level of granularity allows use to explore the outputs in detail, and also is a lot cheaper than running over multiple datapoints
agent.run(dataset[0]['question'])
'38,630,316 people live in Canada as of 2023.'
Make many predict... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_benchmarking.html |
0a27933eb75f-2 | eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(dataset, predictions, question_key="question", prediction_key="output")
We can add in the graded output to the predictions dict and then get a count of the grades.
for i, prediction in enumerate(predictions):
prediction['grade'] = graded_ou... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_benchmarking.html |
0a27933eb75f-3 | (AgentAction(tool='Search', tool_input='Isaac Carew age', log=' I need to find out Isaac\'s age\nAction: Search\nAction Input: "Isaac Carew age"'),
'36 years'),
(AgentAction(tool='Calculator', tool_input='36^.43', log=' I need to calculate 36 raised to the .43 power\nAction: Calculator\nAction Input: 36^.43'),
... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_benchmarking.html |
261f56248a7f-0 | .ipynb
.pdf
Question Answering
Contents
Setup
Examples
Predictions
Evaluation
Customize Prompt
Comparing to other evaluation metrics
Question Answering#
This notebook covers how to evaluate generic question answering problems. This is a situation where you have an example containing a question and its corresponding g... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/question_answering.html |
261f56248a7f-1 | predictions = chain.apply(examples)
predictions
[{'text': ' 11 tennis balls'},
{'text': ' No, this sentence is not plausible. Joao Moutinho is a professional soccer player, not an American football player, so it is not likely that he would be catching a screen pass in the NFC championship.'}]
Evaluation#
We can see th... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/question_answering.html |
261f56248a7f-2 | Real Answer: No
Predicted Answer: No, this sentence is not plausible. Joao Moutinho is a professional soccer player, not an American football player, so it is not likely that he would be catching a screen pass in the NFC championship.
Predicted Grade: CORRECT
Customize Prompt#
You can also customize the prompt that i... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/question_answering.html |
261f56248a7f-3 | for i, eg in enumerate(examples):
eg['id'] = str(i)
eg['answers'] = {"text": [eg['answer']], "answer_start": [0]}
predictions[i]['id'] = str(i)
predictions[i]['prediction_text'] = predictions[i]['text']
for p in predictions:
del p['text']
new_examples = examples.copy()
for eg in new_examples:
de... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/question_answering.html |
08aa7d09d9c1-0 | .ipynb
.pdf
SQL Question Answering Benchmarking: Chinook
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
SQL Question Answering Benchmarking: Chinook#
Here we go over how to benchmark performance on a question answering task over a SQL database.
It is highly r... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
08aa7d09d9c1-1 | {'question': 'How many employees are there?', 'answer': '8'}
Setting up a chain#
This uses the example Chinook database.
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository.
Note that here we load a simple c... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
08aa7d09d9c1-2 | llm = OpenAI(temperature=0)
eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(predicted_dataset, predictions, question_key="question", prediction_key="result")
We can add in the graded output to the predictions dict and then get a count of the grades.
for i, prediction in enumerate(predictions... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
f84b8d1bb9c8-0 | .ipynb
.pdf
Benchmarking Template
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Benchmarking Template#
This is an example notebook that can be used to create a benchmarking notebook for a task of your choice. Evaluation is really hard, and so we greatly welc... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/benchmarking_template.html |
f84b8d1bb9c8-1 | # Othertimes you may want to write a for loop to catch errors
Evaluate performance#
Any guide to evaluating performance in a more systematic manner goes here.
previous
Agent VectorDB Question Answering Benchmarking
next
Data Augmented Question Answering
Contents
Loading the data
Setting up a chain
Make a prediction... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/benchmarking_template.html |
7303eb6fa45b-0 | .ipynb
.pdf
Question Answering Benchmarking: Paul Graham Essay
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Question Answering Benchmarking: Paul Graham Essay#
Here we go over how to benchmark performance on a question answering task over a Paul Graham essa... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
7303eb6fa45b-1 | Now we can create a question answering chain.
from langchain.chains import VectorDBQA
from langchain.llms import OpenAI
chain = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=vectorstore, input_key="question")
Make a prediction#
First, we can make predictions one datapoint at a time. Doing it ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
7303eb6fa45b-2 | from collections import Counter
Counter([pred['grade'] for pred in predictions])
Counter({' CORRECT': 12, ' INCORRECT': 10})
We can also filter the datapoints to the incorrect examples and look at them.
incorrect = [pred for pred in predictions if pred['grade'] == " INCORRECT"]
incorrect[0]
{'question': 'What did the a... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
6d63260414e0-0 | .ipynb
.pdf
Data Augmented Question Answering
Contents
Setup
Examples
Evaluate
Evaluate with Other Metrics
Data Augmented Question Answering#
This notebook uses some generic prompts/language models to evaluate an question answering system that uses other sources of data besides what is in the model. For example, this... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-1 | "answer": "Nothing"
}
]
# Generated examples
from langchain.evaluation.qa import QAGenerateChain
example_gen_chain = QAGenerateChain.from_llm(OpenAI())
new_examples = example_gen_chain.apply_and_parse([{"doc": t} for t in texts[:5]])
new_examples
[{'query': 'According to the document, what did Vladimir Putin miscal... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-2 | eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(examples, predictions)
for i, eg in enumerate(examples):
print(f"Example {i}:")
print("Question: " + predictions[i]['query'])
print("Real Answer: " + predictions[i]['answer'])
print("Predicted Answer: " + predictions[i]['result'... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-3 | Predicted Answer: I don't know.
Predicted Grade: INCORRECT
Example 4:
Question: How many countries were part of the coalition formed to confront Putin?
Real Answer: 27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzer... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-4 | Predicted Grade: CORRECT
Evaluate with Other Metrics#
In addition to predicting whether the answer is correct or incorrect using a language model, we can also use other metrics to get a more nuanced view on the quality of the answers. To do so, we can use the Critique library, which allows for simple calculation of va... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-5 | for k, v in metrics.items()
}
Finally, we can print out the results. We can see that overall the scores are higher when the output is semantically correct, and also when the output closely matches with the gold-standard answer.
for i, eg in enumerate(examples):
score_string = ", ".join([f"{k}={v['examples'][i]['val... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-6 | Example 2:
Question: According to the document, what did Vladimir Putin miscalculate?
Real Answer: He miscalculated that he could roll into Ukraine and the world would roll over.
Predicted Answer: Putin miscalculated that the world would roll over when he rolled into Ukraine.
Predicted Scores: rouge=0.5185, chrf=0.695... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
6d63260414e0-7 | Question: What action is the U.S. Department of Justice taking to target Russian oligarchs?
Real Answer: The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.
P... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
d8ae5e547eae-0 | .rst
.pdf
Indexes
Indexes#
Indexes refer to ways to structure documents so that LLMs can best interact with them.
This module contains utility functions for working with documents, different types of indexes, and then examples for using those indexes in chains.
LangChain provides common indices for working with data (m... | https://langchain.readthedocs.io/en/latest/modules/indexes.html |
2bf656e5b8e8-0 | .rst
.pdf
Prompt Templates
Prompt Templates#
Language models take text as input - that text is commonly referred to as a prompt.
Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
LangChain provides several classes and functions to make constructing an... | https://langchain.readthedocs.io/en/latest/modules/prompts.html |
c38aa659f0e5-0 | .rst
.pdf
Utils
Utils#
While LLMs are powerful on their own, they are more powerful when connected with other sources of knowledge or computation.
This section highlights those sources of knowledge or computation,
and goes over how to easily use them from within LangChain.
The following sections of documentation are pr... | https://langchain.readthedocs.io/en/latest/modules/utils.html |
3100ae2bf1fa-0 | .rst
.pdf
Chains
Chains#
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of use.
The following se... | https://langchain.readthedocs.io/en/latest/modules/chains.html |
fcc062094bff-0 | .rst
.pdf
Agents
Agents#
Some applications will require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user’s input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending on the user input, the agent can then decid... | https://langchain.readthedocs.io/en/latest/modules/agents.html |
effc734a8ec8-0 | .rst
.pdf
Memory
Memory#
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a short term but also at a... | https://langchain.readthedocs.io/en/latest/modules/memory.html |
d0ee91954b0e-0 | .rst
.pdf
Document Loaders
Document Loaders#
Combining language models with your own text data is a powerful way to differentiate them.
The first step in doing this is to load the data into “documents” - a fancy way of say some pieces of text.
This module is aimed at making this easy.
A primary driver of a lot of this ... | https://langchain.readthedocs.io/en/latest/modules/document_loaders.html |
370392b37602-0 | .rst
.pdf
LLMs
LLMs#
Large Language Models (LLMs) are a core component of LangChain.
LangChain is not a provider of LLMs, but rather provides a standard interface through which
you can interact with a variety of LLMs.
The following sections of documentation are provided:
Getting Started: An overview of all the function... | https://langchain.readthedocs.io/en/latest/modules/llms.html |
b7fa56adf03e-0 | .rst
.pdf
Chat
Chat#
Chat models are a variation on language models.
While chat models use language models under the hood, the interface they expose is a bit different.
Rather than expose a “text in, text out” API, they expose an interface where “chat messages” are the inputs and outputs.
Chat model APIs are fairly new... | https://langchain.readthedocs.io/en/latest/modules/chat.html |
7c49af275482-0 | .md
.pdf
Key Concepts
Contents
Memory
Conversational Memory
Entity Memory
Key Concepts#
Memory#
By default, Chains and Agents are stateless, meaning that they treat each incoming query independently.
In some applications (chatbots being a GREAT example) it is highly important to remember previous interactions,
both a... | https://langchain.readthedocs.io/en/latest/modules/memory/key_concepts.html |
c973bf4b39bc-0 | .rst
.pdf
How-To Guides
Contents
Types
Usage
How-To Guides#
Types#
The first set of examples all highlight different types of memory.
Buffer: How to use a type of memory that just keeps previous messages in a buffer.
Buffer Window: How to use a type of memory that keeps previous messages in a buffer but only uses the... | https://langchain.readthedocs.io/en/latest/modules/memory/how_to_guides.html |
fa88bc0d832e-0 | .ipynb
.pdf
Getting Started
Contents
ChatMessageHistory
ConversationBufferMemory
Using in a chain
Saving Message History
Getting Started#
This notebook walks through how LangChain thinks about memory.
Memory involves keeping a concept of state around throughout a user’s interactions with an language model. A user’s i... | https://langchain.readthedocs.io/en/latest/modules/memory/getting_started.html |
fa88bc0d832e-1 | history.messages
[HumanMessage(content='hi!', additional_kwargs={}),
AIMessage(content='whats up?', additional_kwargs={})]
ConversationBufferMemory#
We now show how to use this simple concept in a chain. We first showcase ConversationBufferMemory which is just a wrapper around ChatMessageHistory that extracts the mess... | https://langchain.readthedocs.io/en/latest/modules/memory/getting_started.html |
fa88bc0d832e-2 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://langchain.readthedocs.io/en/latest/modules/memory/getting_started.html |
fa88bc0d832e-3 | Human: Tell me about yourself.
AI:
> Finished chain.
" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
Saving ... | https://langchain.readthedocs.io/en/latest/modules/memory/getting_started.html |
2ebd857f9043-0 | .ipynb
.pdf
ConversationBufferWindowMemory
Contents
ConversationBufferWindowMemory
Using in a chain
ConversationBufferWindowMemory#
ConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of th... | https://langchain.readthedocs.io/en/latest/modules/memory/types/buffer_window.html |
2ebd857f9043-1 | memory=ConversationBufferWindowMemory(k=2),
verbose=True
)
conversation_with_summary.predict(input="Hi, what's up?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from ... | https://langchain.readthedocs.io/en/latest/modules/memory/types/buffer_window.html |
2ebd857f9043-2 | Current conversation:
Human: Hi, what's up?
AI: Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
Human: What's their issues?
AI: The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
Human: Is... | https://langchain.readthedocs.io/en/latest/modules/memory/types/buffer_window.html |
ac6462d5dfe9-0 | .ipynb
.pdf
Entity Memory
Contents
Using in a chain
Inspecting the memory store
Entity Memory#
This notebook shows how to work with a memory module that remembers things about specific entities. It extracts information on entities (using LLMs) and builds up its knowledge about that entity over time (also using LLMs).... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-1 | AIMessage(content=' That sounds like a great project! What kind of project are they working on?', additional_kwargs={})],
'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}
Using in a chain#
Let’s now use it in a chain!
from langchain.chains import ConversationChain
from langchain.memory import ... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-2 | Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.
Context:
{'Deven': '', 'Sam': ''... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-3 | Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.
Context:
{'Deven': 'Deven is wor... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-4 | You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the ... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-5 | > Entering new ConversationChain chain...
Prompt after formatting:
You are an assistant to a human, powered by a large language model trained by OpenAI.
You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-6 | AI: That sounds like a great project! What kind of project are they working on?
Human: They are trying to add more complex memory structures to Langchain
AI: That sounds like an interesting project! What kind of memory structures are they trying to add?
Human: They are adding in a key-value store for entities mention... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-7 | 'store for entities mentioned so far in the conversation.'}
conversation.predict(input="Sam is the founder of a company called Daimon.")
> Entering new ConversationChain chain...
Prompt after formatting:
You are an assistant to a human, powered by a large language model trained by OpenAI.
You are designed to be able to... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-8 | Human: They are adding in a key-value store for entities mentioned so far in the conversation.
AI: That sounds like a great idea! How will the key-value store work?
Human: What do you know about Deven & Sam?
AI: Deven and Sam are working on a hackathon project to add more complex memory structures to Langchain, inclu... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-9 | 'memory structures to Langchain, including a key-value store for '
'entities mentioned so far in the conversation. He is also the founder '
'of a company called Daimon.'}
conversation.predict(input="What do you know about Sam?")
> Entering new ConversationChain chain...
Prompt after formatting:
You are ... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
ac6462d5dfe9-10 | Current conversation:
Human: They are adding in a key-value store for entities mentioned so far in the conversation.
AI: That sounds like a great idea! How will the key-value store work?
Human: What do you know about Deven & Sam?
AI: Deven and Sam are working on a hackathon project to add more complex memory structur... | https://langchain.readthedocs.io/en/latest/modules/memory/types/entity_summary_memory.html |
86b3c5ec5b98-0 | .ipynb
.pdf
ConversationTokenBufferMemory
Contents
ConversationTokenBufferMemory
Using in a chain
ConversationTokenBufferMemory#
ConversationTokenBufferMemory keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.
Let’s first ... | https://langchain.readthedocs.io/en/latest/modules/memory/types/token_buffer.html |
86b3c5ec5b98-1 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
... | https://langchain.readthedocs.io/en/latest/modules/memory/types/token_buffer.html |
86b3c5ec5b98-2 | AI: Sounds like a productive day! What kind of documentation are you writing?
Human: For LangChain! Have you heard of it?
AI:
> Finished chain.
" Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're wr... | https://langchain.readthedocs.io/en/latest/modules/memory/types/token_buffer.html |
e3bd77bc6fca-0 | .ipynb
.pdf
ConversationSummaryBufferMemory
Contents
ConversationSummaryBufferMemory
Using in a chain
ConversationSummaryBufferMemory#
ConversationSummaryBufferMemory combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles ... | https://langchain.readthedocs.io/en/latest/modules/memory/types/summary_buffer.html |
e3bd77bc6fca-1 | from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
llm=llm,
# We set a very low max_token_limit for the purposes of testing.
memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),
verbose=True
)
conversation_with_summary.predict(input="Hi, w... | https://langchain.readthedocs.io/en/latest/modules/memory/types/summary_buffer.html |
e3bd77bc6fca-2 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
... | https://langchain.readthedocs.io/en/latest/modules/memory/types/summary_buffer.html |
e3bd77bc6fca-3 | AI:
> Finished chain.
' Oh, okay. What is LangChain?'
previous
ConversationSummaryMemory
next
ConversationTokenBufferMemory
Contents
ConversationSummaryBufferMemory
Using in a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/memory/types/summary_buffer.html |
9d553791143a-0 | .ipynb
.pdf
Conversation Knowledge Graph Memory
Contents
Conversation Knowledge Graph Memory
Using in a chain
Conversation Knowledge Graph Memory#
This type of memory uses a knowledge graph to recreate memory.
Let’s first walk through how to use the utilities
from langchain.memory import ConversationKGMemory
from lan... | https://langchain.readthedocs.io/en/latest/modules/memory/types/kg.html |
9d553791143a-1 | Using in a chain#
Let’s now use this in a chain!
llm = OpenAI(temperature=0)
from langchain.prompts.prompt import PromptTemplate
from langchain.chains import ConversationChain
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from... | https://langchain.readthedocs.io/en/latest/modules/memory/types/kg.html |
9d553791143a-2 | conversation_with_kg.predict(input="My name is James and I'm helping Will. He's an engineer.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.
If the AI doe... | https://langchain.readthedocs.io/en/latest/modules/memory/types/kg.html |
a6e3bd7602ef-0 | .ipynb
.pdf
ConversationBufferMemory
Contents
ConversationBufferMemory
Using in a chain
ConversationBufferMemory#
This notebook shows how to use ConversationBufferMemory. This memory allows for storing of messages and then extracts the messages in a variable.
We can first extract it as a string.
from langchain.memory... | https://langchain.readthedocs.io/en/latest/modules/memory/types/buffer.html |
a6e3bd7602ef-1 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://langchain.readthedocs.io/en/latest/modules/memory/types/buffer.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.