'initial_output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',
 'critiques_and_revisions': [("The model's response was appropriate and ethical, as it did not condone or encourage the illegal act of stealing kittens. No critique needed.",
   '')]}
All Principles#
For a list of all principles, see:
from langchain.chains.constitutional_ai.principles import PRINCIPLES
PRINCIPLES
{'harmful1': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s last response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.', name='harmful1'),
 'harmful2': ConstitutionalPrinciple(critique_request='Explain ways in which the assistant’s preceding response may be harmful to either the human or others. For example, if the assistant says anything that encourages dangerous or illegal activity, make sure to point it out. Other examples of harm include racism, sexism, and other social biases.', revision_request='Please rewrite the assistant response to remove any and all harmful content, including anything dangerous, illegal, racist, sexist or socially biased.', name='harmful2'),
 'harmful3': ConstitutionalPrinciple(critique_request='Identify all ways in which the assistant’s last response is harmful, unethical, or socially biased. Furthermore, provide specific details on how the assistant can improve its response.', revision_request='Please rewrite the assistant response to remove all harmful, unethical, or socially biased content, and move the conversation in a positive direction.', name='harmful3'),


'harmful4': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', revision_request='Rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human, such as racism, sexism, toxicity.', name='harmful4'),
 'insensitive': ConstitutionalPrinciple(critique_request='Discuss the assistant’s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant’s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'),
 'offensive': ConstitutionalPrinciple(critique_request='Discuss whether the assistant said anything that may be interpreted as offensive or harmful. If possible, also point out any harmful assumptions in the human’s dialogue.', revision_request='Please write a new version of the assistant’s response that removes offensive and harmful content, and politely addresses any problematic assumptions from the human.', name='offensive'),
 'harmful5': ConstitutionalPrinciple(critique_request='What’s the strongest argument you could give that the assistant’s last response was in some way harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Amend the assistant’s last response to remove any and all harmful, unethical, racist, toxic, dangerous, and illegal content.', name='harmful5'),


'age-innappropriate': ConstitutionalPrinciple(critique_request='Did the assistant’s preceding response include anything that may be inappropriate for young children? If so, discuss how it can be revised to be more appropriate.', revision_request='Please rewrite the assistant response to make it more age appropriate for children.', name='age-innappropriate'),
 'derogatory': ConstitutionalPrinciple(critique_request='Think carefully about whether the human’s request succeeded in eliciting responses from the assistant that are derogatory, toxic, racist, sexist or socially harmful in any way, and discuss how the assistant should’ve responded instead.', revision_request='Please rewrite the assistant’s response so as to remove all harmful content, especially anything derogatory, toxic, racist, sexist or socially harmful.', name='derogatory'),
 'illegal': ConstitutionalPrinciple(critique_request='Discuss whether the assistant provided any advice that may encourage illegal or dangerous activity from either the human or others.', revision_request='Revise the assistant’s response so as to refrain from giving legally questionable or dangerous advice.', name='illegal'),
 'controversial': ConstitutionalPrinciple(critique_request='Discuss ways in which the assistant’s response may be considered controversal or objectionable based on commonsense ethical and moral standards.', revision_request='Revise the assistant’s response if possible so that it’s less controversial and objectionable based on commonsense ethical and moral standards.', name='controversial'),
 'harmful6': ConstitutionalPrinciple(critique_request='Discuss whether the human is deliberately trying to bait the assistant into generating harmful content, and whether they succeeded in doing so.', revision_request='Write a revision of the assistant’s response that refrains from saying anything harmful.', name='harmful6'),


'thoughtful': ConstitutionalPrinciple(critique_request='Please comment on whether the assistant’s last response is thoughtful and empathetic. Does it seem like the kind of thing a sensitive friend or therapist might say? Give the strongest argument you can for how it could improve.', revision_request='Please write a new assistant response so that it is more like what a thoughtful, empathetic, caring, sensitive friend or therapist would say.', name='thoughtful'),
 'misogynistic': ConstitutionalPrinciple(critique_request='Take a look at the assistant’s last response and note if it was in any way misogynistic or otherwise gender biased. Give the strongest argument you can that something the assistant said could be interpreted as misogynistic.', revision_request='Please write a new response that does not have any trace of misogyny or gender bias.', name='misogynistic'),
 'criminal': ConstitutionalPrinciple(critique_request='Read the assistant’s last response carefully and identify anything it said that provides advice or assistance in potentially criminal activities, such as violence, theft, hacking, robbery, or any other illegal activity. Consider the strongest argument you could give that the assistant response might encourage crime.', revision_request='Rewrite the assistant response to avoid assisting the human with criminal plans or activities, such as violence, theft, hacking, robbery, or anything illegal.', name='criminal'),
 'harmful7': ConstitutionalPrinciple(critique_request='Critique the assistant’s last response and identify anything it said that was harmful, unethical, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, dangerous, or illegal content.', name='harmful7')}
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UnifiedObjective
Custom Principles
Intermediate Steps
No revision necessary
All Principles
By Harrison Chase

      © Copyright 2023, Harrison Chase.


By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


.ipynb
.pdf
Router Chains: Selecting from multiple prompts with MultiPromptChain
Router Chains: Selecting from multiple prompts with MultiPromptChain#
This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.
from langchain.chains.router import MultiPromptChain
from langchain.llms import OpenAI
physics_template = """You are a very smart physics professor. \
You are great at answering questions about physics in a concise and easy to understand manner. \
When you don't know the answer to a question you admit that you don't know.
Here is a question:
{input}"""
math_template = """You are a very good mathematician. You are great at answering math questions. \
You are so good because you are able to break down hard problems into their component parts, \
answer the component parts, and then put them together to answer the broader question.
Here is a question:
{input}"""
prompt_infos = [
    {
        "name": "physics",
        "description": "Good for answering questions about physics",
        "prompt_template": physics_template
    },
    {
        "name": "math",
        "description": "Good for answering math questions",
        "prompt_template": math_template
    }
]
chain = MultiPromptChain.from_prompts(OpenAI(), prompt_infos, verbose=True)
print(chain.run("What is black body radiation?"))
> Entering new MultiPromptChain chain...
physics: {'input': 'What is black body radiation?'}
> Finished chain.


physics: {'input': 'What is black body radiation?'}
> Finished chain.
Black body radiation is the emission of electromagnetic radiation from a body due to its temperature. It is a type of thermal radiation that is emitted from the surface of all objects that are at a temperature above absolute zero. It is a spectrum of radiation that is influenced by the temperature of the body and is independent of the composition of the emitting material.
print(chain.run("What is the first prime number greater than 40 such that one plus the prime number is divisible by 3"))
> Entering new MultiPromptChain chain...
math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}
> Finished chain.
?
The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. To solve this problem, we can break down the question into two parts: finding the first prime number greater than 40, and then finding a number that is divisible by 3.
The first step is to find the first prime number greater than 40. A prime number is a number that is only divisible by 1 and itself. The next prime number after 40 is 41.
The second step is to find a number that is divisible by 3. To do this, we can add 1 to 41, which gives us 42. Now, we can check if 42 is divisible by 3. 42 divided by 3 is 14, so 42 is divisible by 3.
Therefore, the answer to the question is 43.
print(chain.run("What is the name of the type of cloud that rins"))
> Entering new MultiPromptChain chain...
None: {'input': 'What is the name of the type of cloud that rains?'}


None: {'input': 'What is the name of the type of cloud that rains?'}
> Finished chain.
The type of cloud that typically produces rain is called a cumulonimbus cloud. This type of cloud is characterized by its large vertical extent and can produce thunderstorms and heavy precipitation. Is there anything else you'd like to know?
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Router Chains: Selecting from multiple prompts with MultiRetrievalQAChain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


.ipynb
.pdf
SQL Chain example
 Contents
Use Query Checker
Customize Prompt
Return Intermediate Steps
Choosing how to limit the number of rows returned
Adding example rows from each table
Custom Table Info
SQLDatabaseSequentialChain
Using Local Language Models
SQL Chain example#
This example demonstrates the use of the SQLDatabaseChain for answering questions over a database.
Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The SQLDatabaseChain can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, Databricks and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: mysql+pymysql://user:pass@some_mysql_db_address/db_name.
This demonstration uses SQLite and 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.
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
llm = OpenAI(temperature=0, verbose=True)
NOTE: For data-sensitive projects, you can specify return_direct=True in the SQLDatabaseChain initialization to directly return the output of the SQL query without any additional formatting. This prevents the LLM from seeing any contents within the database. Note, however, the LLM still has access to the database scheme (i.e. dialect, table and key names) by default.
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("How many employees are there?")


db_chain.run("How many employees are there?")
> Entering new SQLDatabaseChain chain...
How many employees are there?
SQLQuery:
/workspace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.
  sample_rows = connection.execute(command)
SELECT COUNT(*) FROM "Employee";
SQLResult: [(8,)]
Answer:There are 8 employees.
> Finished chain.
'There are 8 employees.'
Use Query Checker#
Sometimes the Language Model generates invalid SQL with small mistakes that can be self-corrected using the same technique used by the SQL Database Agent to try and fix the SQL using the LLM. You can simply specify this option when creating the chain:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True)
db_chain.run("How many albums by Aerosmith?")
> Entering new SQLDatabaseChain chain...
How many albums by Aerosmith?
SQLQuery:SELECT COUNT(*) FROM Album WHERE ArtistId = 3;
SQLResult: [(1,)]
Answer:There is 1 album by Aerosmith.
> Finished chain.
'There is 1 album by Aerosmith.'
Customize Prompt#
You can also customize the prompt that is used. Here is an example prompting it to understand that foobar is the same as the Employee table
from langchain.prompts.prompt import PromptTemplate
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Use the following format:
Question: "Question here"


Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
If someone asks for the table foobar, they really mean the employee table.
Question: {input}"""
PROMPT = PromptTemplate(
    input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE
)
db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True)
db_chain.run("How many employees are there in the foobar table?")
> Entering new SQLDatabaseChain chain...
How many employees are there in the foobar table?
SQLQuery:SELECT COUNT(*) FROM Employee;
SQLResult: [(8,)]
Answer:There are 8 employees in the foobar table.
> Finished chain.
'There are 8 employees in the foobar table.'
Return Intermediate Steps#
You can also return the intermediate steps of the SQLDatabaseChain. This allows you to access the SQL statement that was generated, as well as the result of running that against the SQL Database.
db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True, use_query_checker=True, return_intermediate_steps=True)
result = db_chain("How many employees are there in the foobar table?")
result["intermediate_steps"]
> Entering new SQLDatabaseChain chain...
How many employees are there in the foobar table?
SQLQuery:SELECT COUNT(*) FROM Employee;
SQLResult: [(8,)]
Answer:There are 8 employees in the foobar table.
> Finished chain.


Answer:There are 8 employees in the foobar table.
> Finished chain.
[{'input': 'How many employees are there in the foobar table?\nSQLQuery:SELECT COUNT(*) FROM Employee;\nSQLResult: [(8,)]\nAnswer:',
  'top_k': '5',
  'dialect': 'sqlite',


'table_info': '\nCREATE TABLE "Artist" (\n\t"ArtistId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("ArtistId")\n)\n\n/*\n3 rows from Artist table:\nArtistId\tName\n1\tAC/DC\n2\tAccept\n3\tAerosmith\n*/\n\n\nCREATE TABLE "Employee" (\n\t"EmployeeId" INTEGER NOT NULL, \n\t"LastName" NVARCHAR(20) NOT NULL, \n\t"FirstName" NVARCHAR(20) NOT NULL, \n\t"Title" NVARCHAR(30), \n\t"ReportsTo" INTEGER, \n\t"BirthDate" DATETIME, \n\t"HireDate" DATETIME, \n\t"Address" NVARCHAR(70), \n\t"City" NVARCHAR(40), \n\t"State" NVARCHAR(40), \n\t"Country" NVARCHAR(40), \n\t"PostalCode" NVARCHAR(10), \n\t"Phone" NVARCHAR(24), \n\t"Fax" NVARCHAR(24), \n\t"Email" NVARCHAR(60), \n\tPRIMARY KEY ("EmployeeId"), \n\tFOREIGN KEY("ReportsTo") REFERENCES "Employee" ("EmployeeId")\n)\n\n/*\n3 rows from Employee table:\nEmployeeId\tLastName\tFirstName\tTitle\tReportsTo\tBirthDate\tHireDate\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\n1\tAdams\tAndrew\tGeneral Manager\tNone\t1962-02-18 00:00:00\t2002-08-14 00:00:00\t11120 Jasper Ave NW\tEdmonton\tAB\tCanada\tT5K 2N1\t+1 (780)


2N1\t+1 (780) 428-9482\t+1 (780) 428-3457\tandrew@chinookcorp.com\n2\tEdwards\tNancy\tSales Manager\t1\t1958-12-08 00:00:00\t2002-05-01 00:00:00\t825 8 Ave SW\tCalgary\tAB\tCanada\tT2P 2T3\t+1 (403) 262-3443\t+1 (403) 262-3322\tnancy@chinookcorp.com\n3\tPeacock\tJane\tSales Support Agent\t2\t1973-08-29 00:00:00\t2002-04-01 00:00:00\t1111 6 Ave SW\tCalgary\tAB\tCanada\tT2P 5M5\t+1 (403) 262-3443\t+1 (403) 262-6712\tjane@chinookcorp.com\n*/\n\n\nCREATE TABLE "Genre" (\n\t"GenreId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("GenreId")\n)\n\n/*\n3 rows from Genre table:\nGenreId\tName\n1\tRock\n2\tJazz\n3\tMetal\n*/\n\n\nCREATE TABLE "MediaType" (\n\t"MediaTypeId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("MediaTypeId")\n)\n\n/*\n3 rows from MediaType table:\nMediaTypeId\tName\n1\tMPEG audio file\n2\tProtected AAC audio file\n3\tProtected MPEG-4 video file\n*/\n\n\nCREATE TABLE "Playlist" (\n\t"PlaylistId" INTEGER NOT NULL,


TABLE "Playlist" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("PlaylistId")\n)\n\n/*\n3 rows from Playlist table:\nPlaylistId\tName\n1\tMusic\n2\tMovies\n3\tTV Shows\n*/\n\n\nCREATE TABLE "Album" (\n\t"AlbumId" INTEGER NOT NULL, \n\t"Title" NVARCHAR(160) NOT NULL, \n\t"ArtistId" INTEGER NOT NULL, \n\tPRIMARY KEY ("AlbumId"), \n\tFOREIGN KEY("ArtistId") REFERENCES "Artist" ("ArtistId")\n)\n\n/*\n3 rows from Album table:\nAlbumId\tTitle\tArtistId\n1\tFor Those About To Rock We Salute You\t1\n2\tBalls to the Wall\t2\n3\tRestless and Wild\t2\n*/\n\n\nCREATE TABLE "Customer" (\n\t"CustomerId" INTEGER NOT NULL, \n\t"FirstName" NVARCHAR(40) NOT NULL, \n\t"LastName" NVARCHAR(20) NOT NULL, \n\t"Company" NVARCHAR(80), \n\t"Address" NVARCHAR(70), \n\t"City" NVARCHAR(40), \n\t"State" NVARCHAR(40), \n\t"Country" NVARCHAR(40), \n\t"PostalCode" NVARCHAR(10), \n\t"Phone" NVARCHAR(24), \n\t"Fax" NVARCHAR(24), \n\t"Email" NVARCHAR(60) NOT NULL, \n\t"SupportRepId" INTEGER, \n\tPRIMARY KEY ("CustomerId"), \n\tFOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")\n)\n\n/*\n3 rows from Customer


REFERENCES "Employee" ("EmployeeId")\n)\n\n/*\n3 rows from Customer table:\nCustomerId\tFirstName\tLastName\tCompany\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n1\tLuís\tGonçalves\tEmbraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\tSão José dos Campos\tSP\tBrazil\t12227-000\t+55 (12) 3923-5555\t+55 (12) 3923-5566\tluisg@embraer.com.br\t3\n2\tLeonie\tKöhler\tNone\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t+49 0711 2842222\tNone\tleonekohler@surfeu.de\t5\n3\tFrançois\tTremblay\tNone\t1498 rue Bélanger\tMontréal\tQC\tCanada\tH2G 1A7\t+1 (514) 721-4711\tNone\tftremblay@gmail.com\t3\n*/\n\n\nCREATE TABLE "Invoice" (\n\t"InvoiceId" INTEGER NOT NULL, \n\t"CustomerId" INTEGER NOT NULL, \n\t"InvoiceDate" DATETIME NOT NULL, \n\t"BillingAddress" NVARCHAR(70), \n\t"BillingCity" NVARCHAR(40), \n\t"BillingState" NVARCHAR(40), \n\t"BillingCountry" NVARCHAR(40), \n\t"BillingPostalCode" NVARCHAR(10), \n\t"Total" NUMERIC(10, 2) NOT NULL, \n\tPRIMARY KEY ("InvoiceId"), \n\tFOREIGN KEY("CustomerId") REFERENCES "Customer"


KEY ("InvoiceId"), \n\tFOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")\n)\n\n/*\n3 rows from Invoice table:\nInvoiceId\tCustomerId\tInvoiceDate\tBillingAddress\tBillingCity\tBillingState\tBillingCountry\tBillingPostalCode\tTotal\n1\t2\t2009-01-01 00:00:00\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t1.98\n2\t4\t2009-01-02 00:00:00\tUllevålsveien 14\tOslo\tNone\tNorway\t0171\t3.96\n3\t8\t2009-01-03 00:00:00\tGrétrystraat 63\tBrussels\tNone\tBelgium\t1000\t5.94\n*/\n\n\nCREATE TABLE "Track" (\n\t"TrackId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(200) NOT NULL, \n\t"AlbumId" INTEGER, \n\t"MediaTypeId" INTEGER NOT NULL, \n\t"GenreId" INTEGER, \n\t"Composer" NVARCHAR(220), \n\t"Milliseconds" INTEGER NOT NULL, \n\t"Bytes" INTEGER, \n\t"UnitPrice" NUMERIC(10, 2) NOT NULL, \n\tPRIMARY KEY ("TrackId"), \n\tFOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"), \n\tFOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"), \n\tFOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")\n)\n\n/*\n3 rows from Track table:\nTrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n1\tFor


Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n*/\n\n\nCREATE TABLE "InvoiceLine" (\n\t"InvoiceLineId" INTEGER NOT NULL, \n\t"InvoiceId" INTEGER NOT NULL, \n\t"TrackId" INTEGER NOT NULL, \n\t"UnitPrice" NUMERIC(10, 2) NOT NULL, \n\t"Quantity" INTEGER NOT NULL, \n\tPRIMARY KEY ("InvoiceLineId"), \n\tFOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), \n\tFOREIGN KEY("InvoiceId") REFERENCES "Invoice" ("InvoiceId")\n)\n\n/*\n3 rows from InvoiceLine table:\nInvoiceLineId\tInvoiceId\tTrackId\tUnitPrice\tQuantity\n1\t1\t2\t0.99\t1\n2\t1\t4\t0.99\t1\n3\t2\t6\t0.99\t1\n*/\n\n\nCREATE TABLE "PlaylistTrack" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"TrackId" INTEGER NOT NULL, \n\tPRIMARY KEY ("PlaylistId", "TrackId"), \n\tFOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), \n\tFOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")\n)\n\n/*\n3 rows from PlaylistTrack


"Playlist" ("PlaylistId")\n)\n\n/*\n3 rows from PlaylistTrack table:\nPlaylistId\tTrackId\n1\t3402\n1\t3389\n1\t3390\n*/',


'stop': ['\nSQLResult:']},
 'SELECT COUNT(*) FROM Employee;',
 {'query': 'SELECT COUNT(*) FROM Employee;', 'dialect': 'sqlite'},
 'SELECT COUNT(*) FROM Employee;',
 '[(8,)]']
Choosing how to limit the number of rows returned#
If you are querying for several rows of a table you can select the maximum number of results you want to get by using the ‘top_k’ parameter (default is 10). This is useful for avoiding query results that exceed the prompt max length or consume tokens unnecessarily.
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True, top_k=3)
db_chain.run("What are some example tracks by composer Johann Sebastian Bach?")
> Entering new SQLDatabaseChain chain...
What are some example tracks by composer Johann Sebastian Bach?
SQLQuery:SELECT Name FROM Track WHERE Composer = 'Johann Sebastian Bach' LIMIT 3
SQLResult: [('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',)]
Answer:Examples of tracks by Johann Sebastian Bach are Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace, Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria, and Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude.
> Finished chain.


> Finished chain.
'Examples of tracks by Johann Sebastian Bach are Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace, Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria, and Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude.'
Adding example rows from each table#
Sometimes, the format of the data is not obvious and it is optimal to include a sample of rows from the tables in the prompt to allow the LLM to understand the data before providing a final query. Here we will use this feature to let the LLM know that artists are saved with their full names by providing two rows from the Track table.
db = SQLDatabase.from_uri(
    "sqlite:///../../../../notebooks/Chinook.db",
    include_tables=['Track'], # we include only one table to save tokens in the prompt :)
    sample_rows_in_table_info=2)
The sample rows are added to the prompt after each corresponding table’s column information:
print(db.table_info)
CREATE TABLE "Track" (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"AlbumId" INTEGER,
	"MediaTypeId" INTEGER NOT NULL,
	"GenreId" INTEGER,
	"Composer" NVARCHAR(220),
	"Milliseconds" INTEGER NOT NULL,
	"Bytes" INTEGER,
	"UnitPrice" NUMERIC(10, 2) NOT NULL,
	PRIMARY KEY ("TrackId"),
	FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"),
	FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),


FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),
	FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")
)
/*
2 rows from Track table:
TrackId	Name	AlbumId	MediaTypeId	GenreId	Composer	Milliseconds	Bytes	UnitPrice
1	For Those About To Rock (We Salute You)	1	1	1	Angus Young, Malcolm Young, Brian Johnson	343719	11170334	0.99
2	Balls to the Wall	2	2	1	None	342562	5510424	0.99
*/
db_chain = SQLDatabaseChain.from_llm(llm, db, use_query_checker=True, verbose=True)
db_chain.run("What are some example tracks by Bach?")
> Entering new SQLDatabaseChain chain...
What are some example tracks by Bach?
SQLQuery:SELECT "Name", "Composer" FROM "Track" WHERE "Composer" LIKE '%Bach%' LIMIT 5
SQLResult: [('American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach'), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata', 'Johann Sebastian Bach')]


Answer:Tracks by Bach include 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.
> Finished chain.
'Tracks by Bach include \'American Woman\', \'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\', \'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria\', \'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\', and \'Toccata and Fugue in D Minor, BWV 565: I. Toccata\'.'
Custom Table Info#
In some cases, it can be useful to provide custom table information instead of using the automatically generated table definitions and the first sample_rows_in_table_info sample rows. For example, if you know that the first few rows of a table are uninformative, it could help to manually provide example rows that are more diverse or provide more information to the model. It is also possible to limit the columns that will be visible to the model if there are unnecessary columns.
This information can be provided as a dictionary with table names as the keys and table information as the values. For example, let’s provide a custom definition and sample rows for the Track table with only a few columns:
custom_table_info = {
    "Track": """CREATE TABLE Track (
	"TrackId" INTEGER NOT NULL,


"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"Composer" NVARCHAR(220),
	PRIMARY KEY ("TrackId")
)
/*
3 rows from Track table:
TrackId	Name	Composer
1	For Those About To Rock (We Salute You)	Angus Young, Malcolm Young, Brian Johnson
2	Balls to the Wall	None
3	My favorite song ever	The coolest composer of all time
*/"""
}
db = SQLDatabase.from_uri(
    "sqlite:///../../../../notebooks/Chinook.db",
    include_tables=['Track', 'Playlist'],
    sample_rows_in_table_info=2,
    custom_table_info=custom_table_info)
print(db.table_info)
CREATE TABLE "Playlist" (
	"PlaylistId" INTEGER NOT NULL,
	"Name" NVARCHAR(120),
	PRIMARY KEY ("PlaylistId")
)
/*
2 rows from Playlist table:
PlaylistId	Name
1	Music
2	Movies
*/
CREATE TABLE Track (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"Composer" NVARCHAR(220),
	PRIMARY KEY ("TrackId")
)
/*
3 rows from Track table:
TrackId	Name	Composer
1	For Those About To Rock (We Salute You)	Angus Young, Malcolm Young, Brian Johnson
2	Balls to the Wall	None
3	My favorite song ever	The coolest composer of all time
*/
Note how our custom table definition and sample rows for Track overrides the sample_rows_in_table_info parameter. Tables that are not overridden by custom_table_info, in this example Playlist, will have their table info gathered automatically as usual.


db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("What are some example tracks by Bach?")
> Entering new SQLDatabaseChain chain...
What are some example tracks by Bach?
SQLQuery:SELECT "Name" FROM Track WHERE "Composer" LIKE '%Bach%' LIMIT 5;
SQLResult: [('American Woman',), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]


Answer:text='You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.\nUnless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.\nNever query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.\nPay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n\nUse the following format:\n\nQuestion: "Question here"\nSQLQuery: "SQL Query to run"\nSQLResult: "Result of the SQLQuery"\nAnswer: "Final answer here"\n\nOnly use the following tables:\n\nCREATE TABLE "Playlist" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("PlaylistId")\n)\n\n/*\n2 rows from Playlist table:\nPlaylistId\tName\n1\tMusic\n2\tMovies\n*/\n\nCREATE TABLE Track (\n\t"TrackId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(200) NOT NULL,\n\t"Composer" NVARCHAR(220),\n\tPRIMARY KEY ("TrackId")\n)\n/*\n3 rows from Track table:\nTrackId\tName\tComposer\n1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n2\tBalls to the Wall\tNone\n3\tMy favorite song


Young, Brian Johnson\n2\tBalls to the Wall\tNone\n3\tMy favorite song ever\tThe coolest composer of all time\n*/\n\nQuestion: What are some example tracks by Bach?\nSQLQuery:SELECT "Name" FROM Track WHERE "Composer" LIKE \'%Bach%\' LIMIT 5;\nSQLResult: [(\'American Woman\',), (\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\',), (\'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria\',), (\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\',), (\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\',)]\nAnswer:'


You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
CREATE TABLE "Playlist" (
	"PlaylistId" INTEGER NOT NULL,
	"Name" NVARCHAR(120),
	PRIMARY KEY ("PlaylistId")
)
/*
2 rows from Playlist table:
PlaylistId	Name
1	Music
2	Movies
*/
CREATE TABLE Track (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"Composer" NVARCHAR(220),
	PRIMARY KEY ("TrackId")
)
/*
3 rows from Track table:
TrackId	Name	Composer
1	For Those About To Rock (We Salute You)	Angus Young, Malcolm Young, Brian Johnson
2	Balls to the Wall	None
3	My favorite song ever	The coolest composer of all time
*/
Question: What are some example tracks by Bach?


*/
Question: What are some example tracks by Bach?
SQLQuery:SELECT "Name" FROM Track WHERE "Composer" LIKE '%Bach%' LIMIT 5;
SQLResult: [('American Woman',), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]
Answer:


Answer:
{'input': 'What are some example tracks by Bach?\nSQLQuery:SELECT "Name" FROM Track WHERE "Composer" LIKE \'%Bach%\' LIMIT 5;\nSQLResult: [(\'American Woman\',), (\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\',), (\'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria\',), (\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\',), (\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\',)]\nAnswer:', 'top_k': '5', 'dialect': 'sqlite', 'table_info': '\nCREATE TABLE "Playlist" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("PlaylistId")\n)\n\n/*\n2 rows from Playlist table:\nPlaylistId\tName\n1\tMusic\n2\tMovies\n*/\n\nCREATE TABLE Track (\n\t"TrackId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(200) NOT NULL,\n\t"Composer" NVARCHAR(220),\n\tPRIMARY KEY ("TrackId")\n)\n/*\n3 rows from Track table:\nTrackId\tName\tComposer\n1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n2\tBalls to the Wall\tNone\n3\tMy favorite song ever\tThe coolest composer of all time\n*/', 'stop': ['\nSQLResult:']}


Examples of tracks by Bach include "American Woman", "Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace", "Aria Mit 30 Veränderungen, BWV 988 'Goldberg Variations': Aria", "Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude", and "Toccata and Fugue in D Minor, BWV 565: I. Toccata".
> Finished chain.
'Examples of tracks by Bach include "American Woman", "Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace", "Aria Mit 30 Veränderungen, BWV 988 \'Goldberg Variations\': Aria", "Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude", and "Toccata and Fugue in D Minor, BWV 565: I. Toccata".'
SQLDatabaseSequentialChain#
Chain for querying SQL database that is a sequential chain.
The chain is as follows:
1. Based on the query, determine which tables to use.
2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables in the database is large.
from langchain.chains import SQLDatabaseSequentialChain
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)
chain.run("How many employees are also customers?")
> Entering new SQLDatabaseSequentialChain chain...
Table names to use:
['Employee', 'Customer']
> Entering new SQLDatabaseChain chain...
How many employees are also customers?


> Entering new SQLDatabaseChain chain...
How many employees are also customers?
SQLQuery:SELECT COUNT(*) FROM Employee e INNER JOIN Customer c ON e.EmployeeId = c.SupportRepId;
SQLResult: [(59,)]
Answer:59 employees are also customers.
> Finished chain.
> Finished chain.
'59 employees are also customers.'
Using Local Language Models#
Sometimes you may not have the luxury of using OpenAI or other service-hosted large language model. You can, ofcourse, try to use the SQLDatabaseChain with a local model, but will quickly realize that most models you can run locally even with a large GPU struggle to generate the right output.
import logging
import torch
from transformers import AutoTokenizer, GPT2TokenizerFast, pipeline, AutoModelForSeq2SeqLM, AutoModelForCausalLM
from langchain import HuggingFacePipeline
# Note: This model requires a large GPU, e.g. an 80GB A100. See documentation for other ways to run private non-OpenAI models.
model_id = "google/flan-ul2"
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, temperature=0)
device_id = -1  # default to no-GPU, but use GPU and half precision mode if available
if torch.cuda.is_available():
    device_id = 0
    try:
        model = model.half()
    except RuntimeError as exc:
        logging.warn(f"Could not run model in half precision mode: {str(exc)}")
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(task="text2text-generation", model=model, tokenizer=tokenizer, max_length=1024, device=device_id)
local_llm = HuggingFacePipeline(pipeline=pipe)


local_llm = HuggingFacePipeline(pipeline=pipe)
/workspace/langchain/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
Loading checkpoint shards: 100%|██████████| 8/8 [00:32<00:00,  4.11s/it]
from langchain import SQLDatabase, SQLDatabaseChain
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db", include_tables=['Customer'])
local_chain = SQLDatabaseChain.from_llm(local_llm, db, verbose=True, return_intermediate_steps=True, use_query_checker=True)
This model should work for very simple SQL queries, as long as you use the query checker as specified above, e.g.:
local_chain("How many customers are there?")
> Entering new SQLDatabaseChain chain...
How many customers are there?
SQLQuery:
/workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset
  warnings.warn(
/workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset
  warnings.warn(
SELECT count(*) FROM Customer
SQLResult: [(59,)]
Answer:


SELECT count(*) FROM Customer
SQLResult: [(59,)]
Answer:
/workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset
  warnings.warn(
[59]
> Finished chain.
{'query': 'How many customers are there?',
 'result': '[59]',
 'intermediate_steps': [{'input': 'How many customers are there?\nSQLQuery:SELECT count(*) FROM Customer\nSQLResult: [(59,)]\nAnswer:',
   'top_k': '5',
   'dialect': 'sqlite',


'table_info': '\nCREATE TABLE "Customer" (\n\t"CustomerId" INTEGER NOT NULL, \n\t"FirstName" NVARCHAR(40) NOT NULL, \n\t"LastName" NVARCHAR(20) NOT NULL, \n\t"Company" NVARCHAR(80), \n\t"Address" NVARCHAR(70), \n\t"City" NVARCHAR(40), \n\t"State" NVARCHAR(40), \n\t"Country" NVARCHAR(40), \n\t"PostalCode" NVARCHAR(10), \n\t"Phone" NVARCHAR(24), \n\t"Fax" NVARCHAR(24), \n\t"Email" NVARCHAR(60) NOT NULL, \n\t"SupportRepId" INTEGER, \n\tPRIMARY KEY ("CustomerId"), \n\tFOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")\n)\n\n/*\n3 rows from Customer table:\nCustomerId\tFirstName\tLastName\tCompany\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n1\tLuís\tGonçalves\tEmbraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\tSão José dos Campos\tSP\tBrazil\t12227-000\t+55 (12) 3923-5555\t+55 (12) 3923-5566\tluisg@embraer.com.br\t3\n2\tLeonie\tKöhler\tNone\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t+49 0711 2842222\tNone\tleonekohler@surfeu.de\t5\n3\tFrançois\tTremblay\tNone\t1498 rue


rue Bélanger\tMontréal\tQC\tCanada\tH2G 1A7\t+1 (514) 721-4711\tNone\tftremblay@gmail.com\t3\n*/',


'stop': ['\nSQLResult:']},
  'SELECT count(*) FROM Customer',
  {'query': 'SELECT count(*) FROM Customer', 'dialect': 'sqlite'},
  'SELECT count(*) FROM Customer',
  '[(59,)]']}
Even this relatively large model will most likely fail to generate more complicated SQL by itself. However, you can log its inputs and outputs so that you can hand-correct them and use the corrected examples for few shot prompt examples later. In practice, you could log any executions of your chain that raise exceptions (as shown in the example below) or get direct user feedback in cases where the results are incorrect (but did not raise an exception).
!poetry run pip install pyyaml chromadb
import yaml
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
11842.36s - pydevd: Sending message related to process being replaced timed-out after 5 seconds
Requirement already satisfied: pyyaml in /workspace/langchain/.venv/lib/python3.9/site-packages (6.0)
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from typing import Dict
QUERY = "List all the customer first names that start with 'a'"
def _parse_example(result: Dict) -> Dict:
    sql_cmd_key = "sql_cmd"
    sql_result_key = "sql_result"
    table_info_key = "table_info"
    input_key = "input"
    final_answer_key = "answer"
    _example = {
        "input": result.get("query"),
    }
    steps = result.get("intermediate_steps")
    answer_key = sql_cmd_key # the first one
    for step in steps:
        # The steps are in pairs, a dict (input) followed by a string (output).
        # Unfortunately there is no schema but you can look at the input key of the
        # dict to see what the output is supposed to be
        if isinstance(step, dict):
            # Grab the table info from input dicts in the intermediate steps once
            if table_info_key not in _example:
                _example[table_info_key] = step.get(table_info_key)
            if input_key in step:
                if step[input_key].endswith("SQLQuery:"):
                    answer_key = sql_cmd_key # this is the SQL generation input


answer_key = sql_cmd_key # this is the SQL generation input
                if step[input_key].endswith("Answer:"):
                    answer_key = final_answer_key # this is the final answer input
            elif sql_cmd_key in step:
                _example[sql_cmd_key] = step[sql_cmd_key]
                answer_key = sql_result_key # this is SQL execution input
        elif isinstance(step, str):
            # The preceding element should have set the answer_key
            _example[answer_key] = step
    return _example
example: any
try:
    result = local_chain(QUERY)
    print("*** Query succeeded")
    example = _parse_example(result)
except Exception as exc:
    print("*** Query failed")
    result = {
        "query": QUERY,
        "intermediate_steps": exc.intermediate_steps
    }
    example = _parse_example(result)
# print for now, in reality you may want to write this out to a YAML file or database for manual fix-ups offline
yaml_example = yaml.dump(example, allow_unicode=True)
print("\n" + yaml_example)
> Entering new SQLDatabaseChain chain...
List all the customer first names that start with 'a'
SQLQuery:
/workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset
  warnings.warn(
SELECT firstname FROM customer WHERE firstname LIKE '%a%'


warnings.warn(
SELECT firstname FROM customer WHERE firstname LIKE '%a%'
SQLResult: [('François',), ('František',), ('Helena',), ('Astrid',), ('Daan',), ('Kara',), ('Eduardo',), ('Alexandre',), ('Fernanda',), ('Mark',), ('Frank',), ('Jack',), ('Dan',), ('Kathy',), ('Heather',), ('Frank',), ('Richard',), ('Patrick',), ('Julia',), ('Edward',), ('Martha',), ('Aaron',), ('Madalena',), ('Hannah',), ('Niklas',), ('Camille',), ('Marc',), ('Wyatt',), ('Isabelle',), ('Ladislav',), ('Lucas',), ('Johannes',), ('Stanisław',), ('Joakim',), ('Emma',), ('Mark',), ('Manoj',), ('Puja',)]
Answer:
/workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset
  warnings.warn(


warnings.warn(
[('François', 'Frantiek', 'Helena', 'Astrid', 'Daan', 'Kara', 'Eduardo', 'Alexandre', 'Fernanda', 'Mark', 'Frank', 'Jack', 'Dan', 'Kathy', 'Heather', 'Frank', 'Richard', 'Patrick', 'Julia', 'Edward', 'Martha', 'Aaron', 'Madalena', 'Hannah', 'Niklas', 'Camille', 'Marc', 'Wyatt', 'Isabelle', 'Ladislav', 'Lucas', 'Johannes', 'Stanisaw', 'Joakim', 'Emma', 'Mark', 'Manoj', 'Puja']
> Finished chain.
*** Query succeeded
answer: '[(''François'', ''Frantiek'', ''Helena'', ''Astrid'', ''Daan'', ''Kara'',
  ''Eduardo'', ''Alexandre'', ''Fernanda'', ''Mark'', ''Frank'', ''Jack'', ''Dan'',
  ''Kathy'', ''Heather'', ''Frank'', ''Richard'', ''Patrick'', ''Julia'', ''Edward'',
  ''Martha'', ''Aaron'', ''Madalena'', ''Hannah'', ''Niklas'', ''Camille'', ''Marc'',
  ''Wyatt'', ''Isabelle'', ''Ladislav'', ''Lucas'', ''Johannes'', ''Stanisaw'', ''Joakim'',
  ''Emma'', ''Mark'', ''Manoj'', ''Puja'']'
input: List all the customer first names that start with 'a'
sql_cmd: SELECT firstname FROM customer WHERE firstname LIKE '%a%'


sql_cmd: SELECT firstname FROM customer WHERE firstname LIKE '%a%'
sql_result: '[(''François'',), (''František'',), (''Helena'',), (''Astrid'',), (''Daan'',),
  (''Kara'',), (''Eduardo'',), (''Alexandre'',), (''Fernanda'',), (''Mark'',), (''Frank'',),
  (''Jack'',), (''Dan'',), (''Kathy'',), (''Heather'',), (''Frank'',), (''Richard'',),
  (''Patrick'',), (''Julia'',), (''Edward'',), (''Martha'',), (''Aaron'',), (''Madalena'',),
  (''Hannah'',), (''Niklas'',), (''Camille'',), (''Marc'',), (''Wyatt'',), (''Isabelle'',),
  (''Ladislav'',), (''Lucas'',), (''Johannes'',), (''Stanisław'',), (''Joakim'',),
  (''Emma'',), (''Mark'',), (''Manoj'',), (''Puja'',)]'
table_info: "\nCREATE TABLE \"Customer\" (\n\t\"CustomerId\" INTEGER NOT NULL, \n\t\
  \"FirstName\" NVARCHAR(40) NOT NULL, \n\t\"LastName\" NVARCHAR(20) NOT NULL, \n\t\
  \"Company\" NVARCHAR(80), \n\t\"Address\" NVARCHAR(70), \n\t\"City\" NVARCHAR(40),\
  \ \n\t\"State\" NVARCHAR(40), \n\t\"Country\" NVARCHAR(40), \n\t\"PostalCode\" NVARCHAR(10),\


\ \n\t\"Phone\" NVARCHAR(24), \n\t\"Fax\" NVARCHAR(24), \n\t\"Email\" NVARCHAR(60)\
  \ NOT NULL, \n\t\"SupportRepId\" INTEGER, \n\tPRIMARY KEY (\"CustomerId\"), \n\t\
  FOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n)\n\n/*\n\
  3 rows from Customer table:\nCustomerId\tFirstName\tLastName\tCompany\tAddress\t\
  City\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n1\tLuís\tGonçalves\t\
  Embraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\t\
  São José dos Campos\tSP\tBrazil\t12227-000\t+55 (12) 3923-5555\t+55 (12) 3923-5566\t\
  luisg@embraer.com.br\t3\n2\tLeonie\tKöhler\tNone\tTheodor-Heuss-Straße 34\tStuttgart\t\
  None\tGermany\t70174\t+49 0711 2842222\tNone\tleonekohler@surfeu.de\t5\n3\tFrançois\t\
  Tremblay\tNone\t1498 rue Bélanger\tMontréal\tQC\tCanada\tH2G 1A7\t+1 (514) 721-4711\t\
  None\tftremblay@gmail.com\t3\n*/"


None\tftremblay@gmail.com\t3\n*/"
Run the snippet above a few times, or log exceptions in your deployed environment, to collect lots of examples of inputs, table_info and sql_cmd generated by your language model. The sql_cmd values will be incorrect and you can manually fix them up to build a collection of examples, e.g. here we are using YAML to keep a neat record of our inputs and corrected SQL output that we can build up over time.
YAML_EXAMPLES = """
- input: How many customers are not from Brazil?
  table_info: |
    CREATE TABLE "Customer" (
      "CustomerId" INTEGER NOT NULL,
      "FirstName" NVARCHAR(40) NOT NULL,
      "LastName" NVARCHAR(20) NOT NULL,
      "Company" NVARCHAR(80),
      "Address" NVARCHAR(70),
      "City" NVARCHAR(40),
      "State" NVARCHAR(40),
      "Country" NVARCHAR(40),
      "PostalCode" NVARCHAR(10),
      "Phone" NVARCHAR(24),
      "Fax" NVARCHAR(24),
      "Email" NVARCHAR(60) NOT NULL,
      "SupportRepId" INTEGER,
      PRIMARY KEY ("CustomerId"),
      FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")
    )
  sql_cmd: SELECT COUNT(*) FROM "Customer" WHERE NOT "Country" = "Brazil";
  sql_result: "[(54,)]"
  answer: 54 customers are not from Brazil.
- input: list all the genres that start with 'r'
  table_info: |
    CREATE TABLE "Genre" (
      "GenreId" INTEGER NOT NULL,


CREATE TABLE "Genre" (
      "GenreId" INTEGER NOT NULL,
      "Name" NVARCHAR(120),
      PRIMARY KEY ("GenreId")
    )
    /*
    3 rows from Genre table:
    GenreId	Name
    1	Rock
    2	Jazz
    3	Metal
    */
  sql_cmd: SELECT "Name" FROM "Genre" WHERE "Name" LIKE 'r%';
  sql_result: "[('Rock',), ('Rock and Roll',), ('Reggae',), ('R&B/Soul',)]"
  answer: The genres that start with 'r' are Rock, Rock and Roll, Reggae and R&B/Soul.
"""
Now that you have some examples (with manually corrected output SQL), you can do few shot prompt seeding the usual way:
from langchain import FewShotPromptTemplate, PromptTemplate
from langchain.chains.sql_database.prompt import _sqlite_prompt, PROMPT_SUFFIX
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector
from langchain.vectorstores import Chroma
example_prompt = PromptTemplate(
    input_variables=["table_info", "input", "sql_cmd", "sql_result", "answer"],
    template="{table_info}\n\nQuestion: {input}\nSQLQuery: {sql_cmd}\nSQLResult: {sql_result}\nAnswer: {answer}",
)
examples_dict = yaml.safe_load(YAML_EXAMPLES)
local_embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
example_selector = SemanticSimilarityExampleSelector.from_examples(
                        # This is the list of examples available to select from.
                        examples_dict,


# This is the list of examples available to select from.
                        examples_dict,
                        # This is the embedding class used to produce embeddings which are used to measure semantic similarity.
                        local_embeddings,
                        # This is the VectorStore class that is used to store the embeddings and do a similarity search over.
                        Chroma,  # type: ignore
                        # This is the number of examples to produce and include per prompt
                        k=min(3, len(examples_dict)),
                    )
few_shot_prompt = FewShotPromptTemplate(
    example_selector=example_selector,
    example_prompt=example_prompt,
    prefix=_sqlite_prompt + "Here are some examples:",
    suffix=PROMPT_SUFFIX,
    input_variables=["table_info", "input", "top_k"],
)
Using embedded DuckDB without persistence: data will be transient
The model should do better now with this few shot prompt, especially for inputs similar to the examples you have seeded it with.
local_chain = SQLDatabaseChain.from_llm(local_llm, db, prompt=few_shot_prompt, use_query_checker=True, verbose=True, return_intermediate_steps=True)
result = local_chain("How many customers are from Brazil?")
> Entering new SQLDatabaseChain chain...
How many customers are from Brazil?
SQLQuery:SELECT count(*) FROM Customer WHERE Country = "Brazil";
SQLResult: [(5,)]
Answer:[5]
> Finished chain.
result = local_chain("How many customers are not from Brazil?")
> Entering new SQLDatabaseChain chain...
How many customers are not from Brazil?
SQLQuery:SELECT count(*) FROM customer WHERE country NOT IN (SELECT country FROM customer WHERE country = 'Brazil')
SQLResult: [(54,)]
Answer:54 customers are not from Brazil.
> Finished chain.


Answer:54 customers are not from Brazil.
> Finished chain.
result = local_chain("How many customers are there in total?")
> Entering new SQLDatabaseChain chain...
How many customers are there in total?
SQLQuery:SELECT count(*) FROM Customer;
SQLResult: [(59,)]
Answer:There are 59 customers in total.
> Finished chain.
previous
PAL
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Chains
 Contents

Use Query Checker
Customize Prompt
Return Intermediate Steps
Choosing how to limit the number of rows returned
Adding example rows from each table
Custom Table Info
SQLDatabaseSequentialChain
Using Local Language Models
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


.ipynb
.pdf
Router Chains: Selecting from multiple prompts with MultiRetrievalQAChain
Router Chains: Selecting from multiple prompts with MultiRetrievalQAChain#
This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the MultiRetrievalQAChain to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.
from langchain.chains.router import MultiRetrievalQAChain
from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.vectorstores import FAISS
sou_docs = TextLoader('../../state_of_the_union.txt').load_and_split()
sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()
pg_docs = TextLoader('../../paul_graham_essay.txt').load_and_split()
pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()
personal_texts = [
    "I love apple pie",
    "My favorite color is fuchsia",
    "My dream is to become a professional dancer",
    "I broke my arm when I was 12",
    "My parents are from Peru",
]
personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()
retriever_infos = [
    {
        "name": "state of the union",
        "description": "Good for answering questions about the 2023 State of the Union address",
        "retriever": sou_retriever
    },
    {


"retriever": sou_retriever
    },
    {
        "name": "pg essay",
        "description": "Good for answer quesitons about Paul Graham's essay on his career",
        "retriever": pg_retriever
    },
    {
        "name": "personal",
        "description": "Good for answering questions about me",
        "retriever": personal_retriever
    }
]
chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)
print(chain.run("What did the president say about the economy?"))
> Entering new MultiRetrievalQAChain chain...
state of the union: {'query': 'What did the president say about the economy in the 2023 State of the Union address?'}
> Finished chain.
 The president said that the economy was stronger than it had been a year prior, and that the American Rescue Plan helped create record job growth and fuel economic relief for millions of Americans. He also proposed a plan to fight inflation and lower costs for families, including cutting the cost of prescription drugs and energy, providing investments and tax credits for energy efficiency, and increasing access to child care and Pre-K.
print(chain.run("What is something Paul Graham regrets about his work?"))
> Entering new MultiRetrievalQAChain chain...
pg essay: {'query': 'What is something Paul Graham regrets about his work?'}
> Finished chain.
 Paul Graham regrets that he did not take a vacation after selling his company, instead of immediately starting to paint.
print(chain.run("What is my background?"))
> Entering new MultiRetrievalQAChain chain...
personal: {'query': 'What is my background?'}
> Finished chain.
 Your background is Peruvian.


> Finished chain.
 Your background is Peruvian.
print(chain.run("What year was the Internet created in?"))
> Entering new MultiRetrievalQAChain chain...
None: {'query': 'What year was the Internet created in?'}
> Finished chain.
The Internet was created in 1969 through a project called ARPANET, which was funded by the United States Department of Defense. However, the World Wide Web, which is often confused with the Internet, was created in 1989 by British computer scientist Tim Berners-Lee.
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Router Chains: Selecting from multiple prompts with MultiPromptChain
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OpenAPI Chain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


.ipynb
.pdf
LLMCheckerChain
LLMCheckerChain#
This notebook showcases how to use LLMCheckerChain.
from langchain.chains import LLMCheckerChain
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.7)
text = "What type of mammal lays the biggest eggs?"
checker_chain = LLMCheckerChain.from_llm(llm, verbose=True)
checker_chain.run(text)
> Entering new LLMCheckerChain chain...
> Entering new SequentialChain chain...
> Finished chain.
> Finished chain.
' No mammal lays the biggest eggs. The Elephant Bird, which was a species of giant bird, laid the largest eggs of any bird.'
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BashChain
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LLM Math
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


.ipynb
.pdf
Moderation
 Contents
How to use the moderation chain
How to append a Moderation chain to an LLMChain
Moderation#
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, specifically prohibit you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.
If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this notebook.
In this notebook, we will show:
How to run any piece of text through a moderation chain.
How to append a Moderation chain to an LLMChain.
from langchain.llms import OpenAI
from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain
from langchain.prompts import PromptTemplate
How to use the moderation chain#
Here’s an example of using the moderation chain with default settings (will return a string explaining stuff was flagged).
moderation_chain = OpenAIModerationChain()
moderation_chain.run("This is okay")
'This is okay'
moderation_chain.run("I will kill you")


'This is okay'
moderation_chain.run("I will kill you")
"Text was found that violates OpenAI's content policy."
Here’s an example of using the moderation chain to throw an error.
moderation_chain_error = OpenAIModerationChain(error=True)
moderation_chain_error.run("This is okay")
'This is okay'
moderation_chain_error.run("I will kill you")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[7], line 1
----> 1 moderation_chain_error.run("I will kill you")
File ~/workplace/langchain/langchain/chains/base.py:138, in Chain.run(self, *args, **kwargs)
    136     if len(args) != 1:
    137         raise ValueError("`run` supports only one positional argument.")
--> 138     return self(args[0])[self.output_keys[0]]
    140 if kwargs and not args:
    141     return self(kwargs)[self.output_keys[0]]
File ~/workplace/langchain/langchain/chains/base.py:112, in Chain.__call__(self, inputs, return_only_outputs)
    108 if self.verbose:
    109     print(
    110         f"\n\n\033[1m> Entering new {self.__class__.__name__} chain...\033[0m"
    111     )
--> 112 outputs = self._call(inputs)
    113 if self.verbose:
    114     print(f"\n\033[1m> Finished {self.__class__.__name__} chain.\033[0m")
File ~/workplace/langchain/langchain/chains/moderation.py:81, in OpenAIModerationChain._call(self, inputs)
     79 text = inputs[self.input_key]


79 text = inputs[self.input_key]
     80 results = self.client.create(text)
---> 81 output = self._moderate(text, results["results"][0])
     82 return {self.output_key: output}
File ~/workplace/langchain/langchain/chains/moderation.py:73, in OpenAIModerationChain._moderate(self, text, results)
     71 error_str = "Text was found that violates OpenAI's content policy."
     72 if self.error:
---> 73     raise ValueError(error_str)
     74 else:
     75     return error_str
ValueError: Text was found that violates OpenAI's content policy.
Here’s an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI’s moderation endpoint results (see docs here).
class CustomModeration(OpenAIModerationChain):

    def _moderate(self, text: str, results: dict) -> str:
        if results["flagged"]:
            error_str = f"The following text was found that violates OpenAI's content policy: {text}"
            return error_str
        return text

custom_moderation = CustomModeration()
custom_moderation.run("This is okay")
'This is okay'
custom_moderation.run("I will kill you")
"The following text was found that violates OpenAI's content policy: I will kill you"
How to append a Moderation chain to an LLMChain#
To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.
Let’s start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful.


prompt = PromptTemplate(template="{text}", input_variables=["text"])
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
text = """We are playing a game of repeat after me.
Person 1: Hi
Person 2: Hi
Person 1: How's your day
Person 2: How's your day
Person 1: I will kill you
Person 2:"""
llm_chain.run(text)
' I will kill you'
chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])
chain.run(text)
"Text was found that violates OpenAI's content policy."
Now let’s walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can’t use the SimpleSequentialChain)
prompt = PromptTemplate(template="{setup}{new_input}Person2:", input_variables=["setup", "new_input"])
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
setup = """We are playing a game of repeat after me.
Person 1: Hi
Person 2: Hi
Person 1: How's your day
Person 2: How's your day
Person 1:"""
new_input = "I will kill you"
inputs = {"setup": setup, "new_input": new_input}
llm_chain(inputs, return_only_outputs=True)
{'text': ' I will kill you'}
# Setting the input/output keys so it lines up
moderation_chain.input_key = "text"
moderation_chain.output_key = "sanitized_text"
chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=["setup", "new_input"])


chain(inputs, return_only_outputs=True)
{'sanitized_text': "Text was found that violates OpenAI's content policy."}
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LLMSummarizationCheckerChain
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Router Chains: Selecting from multiple prompts with MultiPromptChain
 Contents

How to use the moderation chain
How to append a Moderation chain to an LLMChain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 02, 2023.


.ipynb
.pdf
FLARE
 Contents
Imports
Retriever
FLARE Chain
FLARE#
This notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).
Please see the original repo here.
The basic idea is:
Start answering a question
If you start generating tokens the model is uncertain about, look up relevant documents
Use those documents to continue generating
Repeat until finished
There is a lot of cool detail in how the lookup of relevant documents is done.
Basically, the tokens that model is uncertain about are highlighted, and then an LLM is called to generate a question that would lead to that answer. For example, if the generated text is Joe Biden went to Harvard, and the tokens the model was uncertain about was Harvard, then a good generated question would be where did Joe Biden go to college. This generated question is then used in a retrieval step to fetch relevant documents.
In order to set up this chain, we will need three things:
An LLM to generate the answer
An LLM to generate hypothetical questions to use in retrieval
A retriever to use to look up answers for
The LLM that we use to generate the answer needs to return logprobs so we can identify uncertain tokens. For that reason, we HIGHLY recommend that you use the OpenAI wrapper (NB: not the ChatOpenAI wrapper, as that does not return logprobs).
The LLM we use to generate hypothetical questions to use in retrieval can be anything. In this notebook we will use ChatOpenAI because it is fast and cheap.
The retriever can be anything. In this notebook we will use SERPER search engine, because it is cheap.
Other important parameters to understand:
max_generation_len: The maximum number of tokens to generate before stopping to check if any are uncertain
min_prob: Any tokens generated with probability below this will be considered uncertain


min_prob: Any tokens generated with probability below this will be considered uncertain
Imports#
import os
os.environ["SERPER_API_KEY"] = ""
import re
import numpy as np
from langchain.schema import BaseRetriever
from langchain.utilities import GoogleSerperAPIWrapper
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.schema import Document
Retriever#
class SerperSearchRetriever(BaseRetriever):
    def __init__(self, search):
        self.search = search

    def get_relevant_documents(self, query: str):
        return [Document(page_content=self.search.run(query))]

    async def aget_relevant_documents(self, query: str):
        raise NotImplemented

retriever = SerperSearchRetriever(GoogleSerperAPIWrapper())
FLARE Chain#
# We set this so we can see what exactly is going on
import langchain
langchain.verbose = True
from langchain.chains import FlareChain
flare = FlareChain.from_llm(
    ChatOpenAI(temperature=0),
    retriever=retriever,
    max_generation_len=164,
    min_prob=.3,
)
query = "explain in great detail the difference between the langchain framework and baby agi"
flare.run(query)
> Entering new FlareChain chain...
Current Response:
Prompt after formatting:
Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.
>>> CONTEXT:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> RESPONSE:


>>> RESPONSE:
> Entering new QuestionGeneratorChain chain...
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.
Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " decentralized platform for natural language processing" is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.


Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " uses a blockchain" is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.
Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " distributed ledger to" is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:


>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.
Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " process data, allowing for secure and transparent data sharing." is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.


Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " set of tools" is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.
Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " help developers create" is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:


>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.
Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " create an AI system" is:
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.


Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.
In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for
The question to which the answer is the term/entity/phrase " NLP applications" is:
> Finished chain.
Generated Questions: ['What is the Langchain Framework?', 'What technology does the Langchain Framework use to store and process data for secure and transparent data sharing?', 'What technology does the Langchain Framework use to store and process data?', 'What does the Langchain Framework use a blockchain-based distributed ledger for?', 'What does the Langchain Framework provide in addition to a decentralized platform for natural language processing applications?', 'What set of tools and services does the Langchain Framework provide?', 'What is the purpose of Baby AGI?', 'What type of applications is the Langchain Framework designed for?']
> Entering new _OpenAIResponseChain chain...
Prompt after formatting:
Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.


>>> CONTEXT: LangChain: Software. LangChain is a software development framework designed to simplify the creation of applications using large language models. LangChain Initial release date: October 2022. LangChain Programming languages: Python and JavaScript. LangChain Developer(s): Harrison Chase. LangChain License: MIT License. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... Type: Software framework. At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. LangChain is a powerful tool that can be used to work with Large Language Models (LLMs). LLMs are very general in nature, which means that while they can ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. LangChain is a software development framework designed to simplify the creation of applications using large language models (LLMs). Written in: Python and JavaScript. Initial release: October 2022. LangChain - The A.I-native developer toolkit We started LangChain with the intent to build a modular and flexible framework for developing A.I- ... LangChain explained in 3 minutes - LangChain is a ... Duration: 3:03. Posted: Apr 13, 2023. LangChain is a framework built to help you build LLM-powered applications more easily by providing you with the following:. LangChain is a framework that enables quick and easy development of applications that make use of Large Language Models, for example, GPT-3. LangChain is a powerful open-source framework for developing applications powered by language models. It connects to the AI models you want to ...


LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Missing: secure | Must include:secure. Blockchain is the best way to secure the data of the shared community. Utilizing the capabilities of the blockchain nobody can read or interfere ... This modern technology consists of a chain of blocks that allows to securely store all committed transactions using shared and distributed ... A Blockchain network is used in the healthcare system to preserve and exchange patient data through hospitals, diagnostic laboratories, pharmacy firms, and ... In this article, I will walk you through the process of using the LangChain.js library with Google Cloud Functions, helping you leverage the ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: transparent | Must include:transparent. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart ... blockchain technology can offer a highly secured health data ledger to ... framework can be employed to store encrypted healthcare data in a ... In a simplified way, Blockchain is a data structure that stores transactions in an ordered way and linked to the previous block, serving as a ... Blockchain technology is a decentralized, distributed ledger that stores the record of ownership of digital assets. Missing: Langchain | Must include:Langchain.


LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. This documentation covers the steps to integrate Pinecone, a high-performance vector database, with LangChain, a framework for building applications powered ... The ability to connect to any model, ingest any custom database, and build upon a framework that can take action provides numerous use cases for ... With LangChain, developers can use a framework that abstracts the core building blocks of LLM applications. LangChain empowers developers to ... Build a question-answering tool based on financial data with LangChain & Deep Lake's unified & streamable data store. Browse applications built on LangChain technology. Explore PoC and MVP applications created by our community and discover innovative use cases for LangChain ... LangChain is a great framework that can be used for developing applications powered by LLMs. When you intend to enhance your application ... In this blog, we'll introduce you to LangChain and Ray Serve and how to use them to build a search engine using LLM embeddings and a vector ... The LinkChain Framework simplifies embedding creation and storage using Pinecone and Chroma, with code that loads files, splits documents, and creates embedding ... Missing: technology | Must include:technology.


Blockchain is one type of a distributed ledger. Distributed ledgers use independent computers (referred to as nodes) to record, share and ... Missing: Langchain | Must include:Langchain. Blockchain is used in distributed storage software where huge data is broken down into chunks. This is available in encrypted data across a ... People sometimes use the terms 'Blockchain' and 'Distributed Ledger' interchangeably. This post aims to analyze the features of each. A distributed ledger ... Missing: Framework | Must include:Framework. Think of a “distributed ledger” that uses cryptography to allow each participant in the transaction to add to the ledger in a secure way without ... In this paper, we provide an overview of the history of trade settlement and discuss this nascent technology that may now transform traditional ... Missing: Langchain | Must include:Langchain. LangChain is a blockchain-based language education platform that aims to revolutionize the way people learn languages. Missing: Framework | Must include:Framework. It uses the distributed ledger technology framework and Smart contract engine for building scalable Business Blockchain applications. The fabric ... It looks at the assets the use case is handling, the different parties conducting transactions, and the smart contract, distributed ... Are you curious to know how Blockchain and Distributed ... Duration: 44:31. Posted: May 4, 2021. A blockchain is a distributed and immutable ledger to transfer ownership, record transactions, track assets, and ensure transparency, security, trust and value ... Missing: Langchain | Must include:Langchain.


LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: decentralized | Must include:decentralized. LangChain, created by Harrison Chase, is a Python library that provides out-of-the-box support to build NLP applications using LLMs. Missing: decentralized | Must include:decentralized. LangChain provides a standard interface for chains, enabling developers to create sequences of calls that go beyond a single LLM call. Chains ... Missing: decentralized platform natural. LangChain is a powerful framework that simplifies the process of building advanced language model applications. Missing: platform | Must include:platform. Are your language models ignoring previous instructions ... Duration: 32:23. Posted: Feb 21, 2023. LangChain is a framework that enables quick and easy development of applications ... Prompting is the new way of programming NLP models. Missing: decentralized platform. It then uses natural language processing and machine learning algorithms to search ... Summarization is handled via cohere, QnA is handled via langchain, ... LangChain is a framework for developing applications powered by language models. ... There are several main modules that LangChain provides support for. Missing: decentralized platform. In the healthcare-chain system, blockchain provides an appreciated secure ... The entire process of adding new and previous block data is performed based on ... ChatGPT is a large language model developed by OpenAI, ... tool for a wide range of applications, including natural language processing, ...


LangChain is a powerful tool that can be used to work with Large Language ... If an API key has been provided, create an OpenAI language model instance At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. A tutorial of the six core modules of the LangChain Python package covering models, prompts, chains, agents, indexes, and memory with OpenAI ... LangChain's collection of tools refers to a set of tools provided by the LangChain framework for developing applications powered by language models. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... LangChain is an open-source library that provides developers with the tools to build applications powered by large language models (LLMs). LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Plan-and-Execute Agents · Feature Stores and LLMs · Structured Tools · Auto-Evaluator Opportunities · Callbacks Improvements · Unleashing the power ... Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. · LLM: The language model ... LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. This system is exploring and demonstrating to us the potential of large language models, such as GPT and how it can autonomously perform tasks. Apr 17, 2023


At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> RESPONSE:
> Finished chain.
> Finished chain.
' LangChain is a framework for developing applications powered by language models. It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. On the other hand, Baby AGI is an AI system that is exploring and demonstrating the potential of large language models, such as GPT, and how it can autonomously perform tasks. Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. '
llm = OpenAI()
llm(query)