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f8204e91e394-10 | Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3
Observation:
SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity
FROM Artist
INNER JOIN Track ON Artist.ArtistId = Track.ArtistId
INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId
GROUP BY Artist.Name
ORDER BY TotalQuantity DESC
LIMIT 3;
Thought: I now know the final answer.
Action: query_sql_db
Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Album ON Artist.ArtistId = Album.ArtistId INNER JOIN Track ON Album.AlbumId = Track.AlbumId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3
Observation: [('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]
Thought: I now know the final answer.
Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica.
> Finished chain.
'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'
previous
Python Agent
next
Vectorstore Agent
Contents
Initialization
Example: describing a table
Example: describing a table, recovering from an error
Example: running queries
Recovering from an error
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html"
} |
beb6e2fc1df0-0 | .ipynb
.pdf
CSV Agent
CSV Agent#
This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.
NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.
from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)
agent.run("how many rows are there?")
> Entering new AgentExecutor chain...
Thought: I need to count the number of rows
Action: python_repl_ast
Action Input: len(df)
Observation: 891
Thought: I now know the final answer
Final Answer: There are 891 rows in the dataframe.
> Finished chain.
'There are 891 rows in the dataframe.'
agent.run("how many people have more than 3 sibligngs")
> Entering new AgentExecutor chain...
Thought: I need to count the number of people with more than 3 siblings
Action: python_repl_ast
Action Input: df[df['SibSp'] > 3].shape[0]
Observation: 30
Thought: I now know the final answer
Final Answer: 30 people have more than 3 siblings.
> Finished chain.
'30 people have more than 3 siblings.'
agent.run("whats the square root of the average age?")
> Entering new AgentExecutor chain...
Thought: I need to calculate the average age first
Action: python_repl_ast
Action Input: df['Age'].mean()
Observation: 29.69911764705882 | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/csv.html"
} |
beb6e2fc1df0-1 | Observation: 29.69911764705882
Thought: I can now calculate the square root
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: name 'math' is not defined
Thought: I need to import the math library
Action: python_repl_ast
Action Input: import math
Observation: None
Thought: I can now calculate the square root
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: 5.449689683556195
Thought: I now know the final answer
Final Answer: 5.449689683556195
> Finished chain.
'5.449689683556195'
previous
Toolkits
next
JSON Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/csv.html"
} |
605c00b1ec92-0 | .ipynb
.pdf
OpenAPI agents
Contents
OpenAPI agents
1st example: hierarchical planning agent
To start, let’s collect some OpenAPI specs.
How big is this spec?
Let’s see some examples!
Try another API.
2nd example: “json explorer” agent
OpenAPI agents#
We can construct agents to consume arbitrary APIs, here APIs conformant to the OpenAPI/Swagger specification.
1st example: hierarchical planning agent#
In this example, we’ll consider an approach called hierarchical planning, common in robotics and appearing in recent works for LLMs X robotics. We’ll see it’s a viable approach to start working with a massive API spec AND to assist with user queries that require multiple steps against the API.
The idea is simple: to get coherent agent behavior over long sequences behavior & to save on tokens, we’ll separate concerns: a “planner” will be responsible for what endpoints to call and a “controller” will be responsible for how to call them.
In the initial implementation, the planner is an LLM chain that has the name and a short description for each endpoint in context. The controller is an LLM agent that is instantiated with documentation for only the endpoints for a particular plan. There’s a lot left to get this working very robustly :)
To start, let’s collect some OpenAPI specs.#
import os, yaml
!wget https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml
!mv openapi.yaml openai_openapi.yaml
!wget https://www.klarna.com/us/shopping/public/openai/v0/api-docs
!mv api-docs klarna_openapi.yaml
!wget https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yaml
!mv openapi.yaml spotify_openapi.yaml | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-1 | !mv openapi.yaml spotify_openapi.yaml
--2023-03-31 15:45:56-- https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 122995 (120K) [text/plain]
Saving to: ‘openapi.yaml’
openapi.yaml 100%[===================>] 120.11K --.-KB/s in 0.01s
2023-03-31 15:45:56 (10.4 MB/s) - ‘openapi.yaml’ saved [122995/122995]
--2023-03-31 15:45:57-- https://www.klarna.com/us/shopping/public/openai/v0/api-docs
Resolving www.klarna.com (www.klarna.com)... 52.84.150.34, 52.84.150.46, 52.84.150.61, ...
Connecting to www.klarna.com (www.klarna.com)|52.84.150.34|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [application/json]
Saving to: ‘api-docs’
api-docs [ <=> ] 1.87K --.-KB/s in 0s
2023-03-31 15:45:57 (261 MB/s) - ‘api-docs’ saved [1916] | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-2 | --2023-03-31 15:45:57-- https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yaml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 286747 (280K) [text/plain]
Saving to: ‘openapi.yaml’
openapi.yaml 100%[===================>] 280.03K --.-KB/s in 0.02s
2023-03-31 15:45:58 (13.3 MB/s) - ‘openapi.yaml’ saved [286747/286747]
from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec
with open("openai_openapi.yaml") as f:
raw_openai_api_spec = yaml.load(f, Loader=yaml.Loader)
openai_api_spec = reduce_openapi_spec(raw_openai_api_spec)
with open("klarna_openapi.yaml") as f:
raw_klarna_api_spec = yaml.load(f, Loader=yaml.Loader)
klarna_api_spec = reduce_openapi_spec(raw_klarna_api_spec)
with open("spotify_openapi.yaml") as f:
raw_spotify_api_spec = yaml.load(f, Loader=yaml.Loader)
spotify_api_spec = reduce_openapi_spec(raw_spotify_api_spec)
We’ll work with the Spotify API as one of the examples of a somewhat complex API. There’s a bit of auth-related setup to do if you want to replicate this. | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-3 | You’ll have to set up an application in the Spotify developer console, documented here, to get credentials: CLIENT_ID, CLIENT_SECRET, and REDIRECT_URI.
To get an access tokens (and keep them fresh), you can implement the oauth flows, or you can use spotipy. If you’ve set your Spotify creedentials as environment variables SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET, and SPOTIPY_REDIRECT_URI, you can use the helper functions below:
import spotipy.util as util
from langchain.requests import RequestsWrapper
def construct_spotify_auth_headers(raw_spec: dict):
scopes = list(raw_spec['components']['securitySchemes']['oauth_2_0']['flows']['authorizationCode']['scopes'].keys())
access_token = util.prompt_for_user_token(scope=','.join(scopes))
return {
'Authorization': f'Bearer {access_token}'
}
# Get API credentials.
headers = construct_spotify_auth_headers(raw_spotify_api_spec)
requests_wrapper = RequestsWrapper(headers=headers)
How big is this spec?#
endpoints = [
(route, operation)
for route, operations in raw_spotify_api_spec["paths"].items()
for operation in operations
if operation in ["get", "post"]
]
len(endpoints)
63
import tiktoken
enc = tiktoken.encoding_for_model('text-davinci-003')
def count_tokens(s): return len(enc.encode(s))
count_tokens(yaml.dump(raw_spotify_api_spec))
80326
Let’s see some examples!#
Starting with GPT-4. (Some robustness iterations under way for GPT-3 family.)
from langchain.llms.openai import OpenAI
from langchain.agents.agent_toolkits.openapi import planner | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-4 | from langchain.agents.agent_toolkits.openapi import planner
llm = OpenAI(model_name="gpt-4", temperature=0.0)
/Users/jeremywelborn/src/langchain/langchain/llms/openai.py:169: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
warnings.warn(
/Users/jeremywelborn/src/langchain/langchain/llms/openai.py:608: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
warnings.warn(
spotify_agent = planner.create_openapi_agent(spotify_api_spec, requests_wrapper, llm)
user_query = "make me a playlist with the first song from kind of blue. call it machine blues."
spotify_agent.run(user_query)
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to create a playlist with the first song from Kind of Blue and name it Machine Blues
Observation: 1. GET /search to search for the album "Kind of Blue"
2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album
3. GET /me to get the current user's information
4. POST /users/{user_id}/playlists to create a new playlist named "Machine Blues" for the current user
5. POST /playlists/{playlist_id}/tracks to add the first song from "Kind of Blue" to the "Machine Blues" playlist
Thought:I have the plan, now I need to execute the API calls.
Action: api_controller | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-5 | Thought:I have the plan, now I need to execute the API calls.
Action: api_controller
Action Input: 1. GET /search to search for the album "Kind of Blue"
2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album
3. GET /me to get the current user's information
4. POST /users/{user_id}/playlists to create a new playlist named "Machine Blues" for the current user
5. POST /playlists/{playlist_id}/tracks to add the first song from "Kind of Blue" to the "Machine Blues" playlist
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/search?q=Kind%20of%20Blue&type=album", "output_instructions": "Extract the id of the first album in the search results"}
Observation: 1weenld61qoidwYuZ1GESA
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/albums/1weenld61qoidwYuZ1GESA/tracks", "output_instructions": "Extract the id of the first track in the album"}
Observation: 7q3kkfAVpmcZ8g6JUThi3o
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/me", "output_instructions": "Extract the id of the current user"}
Observation: 22rhrz4m4kvpxlsb5hezokzwi
Thought:Action: requests_post | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-6 | Thought:Action: requests_post
Action Input: {"url": "https://api.spotify.com/v1/users/22rhrz4m4kvpxlsb5hezokzwi/playlists", "data": {"name": "Machine Blues"}, "output_instructions": "Extract the id of the created playlist"}
Observation: 7lzoEi44WOISnFYlrAIqyX
Thought:Action: requests_post
Action Input: {"url": "https://api.spotify.com/v1/playlists/7lzoEi44WOISnFYlrAIqyX/tracks", "data": {"uris": ["spotify:track:7q3kkfAVpmcZ8g6JUThi3o"]}, "output_instructions": "Confirm that the track was added to the playlist"}
Observation: The track was added to the playlist, confirmed by the snapshot_id: MiwxODMxNTMxZTFlNzg3ZWFlZmMxYTlmYWQyMDFiYzUwNDEwMTAwZmE1.
Thought:I am finished executing the plan.
Final Answer: The first song from the "Kind of Blue" album has been added to the "Machine Blues" playlist.
> Finished chain.
Observation: The first song from the "Kind of Blue" album has been added to the "Machine Blues" playlist.
Thought:I am finished executing the plan and have created the playlist with the first song from Kind of Blue.
Final Answer: I have created a playlist called "Machine Blues" with the first song from the "Kind of Blue" album.
> Finished chain.
'I have created a playlist called "Machine Blues" with the first song from the "Kind of Blue" album.'
user_query = "give me a song I'd like, make it blues-ey" | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-7 | user_query = "give me a song I'd like, make it blues-ey"
spotify_agent.run(user_query)
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to get a blues song recommendation for the user
Observation: 1. GET /me to get the current user's information
2. GET /recommendations/available-genre-seeds to retrieve a list of available genres
3. GET /recommendations with the seed_genre parameter set to "blues" to get a blues song recommendation for the user
Thought:I have the plan, now I need to execute the API calls.
Action: api_controller
Action Input: 1. GET /me to get the current user's information
2. GET /recommendations/available-genre-seeds to retrieve a list of available genres
3. GET /recommendations with the seed_genre parameter set to "blues" to get a blues song recommendation for the user
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/me", "output_instructions": "Extract the user's id and username"}
Observation: ID: 22rhrz4m4kvpxlsb5hezokzwi, Username: Jeremy Welborn
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/recommendations/available-genre-seeds", "output_instructions": "Extract the list of available genres"} | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-8 | Observation: acoustic, afrobeat, alt-rock, alternative, ambient, anime, black-metal, bluegrass, blues, bossanova, brazil, breakbeat, british, cantopop, chicago-house, children, chill, classical, club, comedy, country, dance, dancehall, death-metal, deep-house, detroit-techno, disco, disney, drum-and-bass, dub, dubstep, edm, electro, electronic, emo, folk, forro, french, funk, garage, german, gospel, goth, grindcore, groove, grunge, guitar, happy, hard-rock, hardcore, hardstyle, heavy-metal, hip-hop, holidays, honky-tonk, house, idm, indian, indie, indie-pop, industrial, iranian, j-dance, j-idol, j-pop, j-rock, jazz, k-pop, kids, latin, latino, malay, mandopop, metal, metal-misc, metalcore, minimal-techno, movies, mpb, new-age, new-release, opera, pagode, party, philippines-
Thought:
Retrying langchain.llms.openai.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: That model is currently overloaded with other requests. You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID 2167437a0072228238f3c0c5b3882764 in your message.).
Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/recommendations?seed_genres=blues", "output_instructions": "Extract the list of recommended tracks with their ids and names"}
Observation: [
{ | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-9 | Observation: [
{
id: '03lXHmokj9qsXspNsPoirR',
name: 'Get Away Jordan'
}
]
Thought:I am finished executing the plan.
Final Answer: The recommended blues song for user Jeremy Welborn (ID: 22rhrz4m4kvpxlsb5hezokzwi) is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR.
> Finished chain.
Observation: The recommended blues song for user Jeremy Welborn (ID: 22rhrz4m4kvpxlsb5hezokzwi) is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR.
Thought:I am finished executing the plan and have the information the user asked for.
Final Answer: The recommended blues song for you is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR.
> Finished chain.
'The recommended blues song for you is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR.'
Try another API.#
headers = {
"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"
}
openai_requests_wrapper=RequestsWrapper(headers=headers)
# Meta!
llm = OpenAI(model_name="gpt-4", temperature=0.25)
openai_agent = planner.create_openapi_agent(openai_api_spec, openai_requests_wrapper, llm)
user_query = "generate a short piece of advice"
openai_agent.run(user_query)
> Entering new AgentExecutor chain...
Action: api_planner | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-10 | > Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to generate a short piece of advice
Observation: 1. GET /engines to retrieve the list of available engines
2. POST /completions with the selected engine and a prompt for generating a short piece of advice
Thought:I have the plan, now I need to execute the API calls.
Action: api_controller
Action Input: 1. GET /engines to retrieve the list of available engines
2. POST /completions with the selected engine and a prompt for generating a short piece of advice
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.openai.com/v1/engines", "output_instructions": "Extract the ids of the engines"}
Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-001, ada, babbage-code-search-text, babbage-similarity, whisper-1, code-search-babbage-text-001, text-curie-001, code-search-babbage-code-001, text-ada-001, text-embedding-ada-002, text-similarity-ada-001, curie-instruct-beta, ada-code-search-code, ada-similarity, text-davinci-003, code-search-ada-text-001, text-search-ada-query-001, davinci-search-document, ada-code-search-text, text-search-ada-doc-001, davinci-instruct-beta, text-similarity-curie-001, code-search-ada-code-001
Thought:I will use the "davinci" engine to generate a short piece of advice.
Action: requests_post | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-11 | Action: requests_post
Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"engine": "davinci", "prompt": "Give me a short piece of advice on how to be more productive."}, "output_instructions": "Extract the text from the first choice"}
Observation: "you must provide a model parameter"
Thought:!! Could not _extract_tool_and_input from "I cannot finish executing the plan without knowing how to provide the model parameter correctly." in _get_next_action
I cannot finish executing the plan without knowing how to provide the model parameter correctly.
> Finished chain.
Observation: I need more information on how to provide the model parameter correctly in the POST request to generate a short piece of advice.
Thought:I need to adjust my plan to include the model parameter in the POST request.
Action: api_planner
Action Input: I need to find the right API calls to generate a short piece of advice, including the model parameter in the POST request
Observation: 1. GET /models to retrieve the list of available models
2. Choose a suitable model from the list
3. POST /completions with the chosen model as a parameter to generate a short piece of advice
Thought:I have an updated plan, now I need to execute the API calls.
Action: api_controller
Action Input: 1. GET /models to retrieve the list of available models
2. Choose a suitable model from the list
3. POST /completions with the chosen model as a parameter to generate a short piece of advice
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.openai.com/v1/models", "output_instructions": "Extract the ids of the available models"} | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-12 | Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-edit-001, ada
Thought:Action: requests_post
Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"model": "davinci", "prompt": "Give me a short piece of advice on how to improve communication skills."}, "output_instructions": "Extract the text from the first choice"}
Observation: "I'd like to broaden my horizon.\n\nI was trying to"
Thought:I cannot finish executing the plan without knowing some other information.
Final Answer: The generated text is not a piece of advice on improving communication skills. I would need to retry the API call with a different prompt or model to get a more relevant response.
> Finished chain.
Observation: The generated text is not a piece of advice on improving communication skills. I would need to retry the API call with a different prompt or model to get a more relevant response.
Thought:I need to adjust my plan to include a more specific prompt for generating a short piece of advice on improving communication skills.
Action: api_planner
Action Input: I need to find the right API calls to generate a short piece of advice on improving communication skills, including the model parameter in the POST request
Observation: 1. GET /models to retrieve the list of available models
2. Choose a suitable model for generating text (e.g., text-davinci-002)
3. POST /completions with the chosen model and a prompt related to improving communication skills to generate a short piece of advice
Thought:I have an updated plan, now I need to execute the API calls.
Action: api_controller | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-13 | Action: api_controller
Action Input: 1. GET /models to retrieve the list of available models
2. Choose a suitable model for generating text (e.g., text-davinci-002)
3. POST /completions with the chosen model and a prompt related to improving communication skills to generate a short piece of advice
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.openai.com/v1/models", "output_instructions": "Extract the names of the models"}
Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-edit-001, ada
Thought:Action: requests_post
Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"model": "text-davinci-002", "prompt": "Give a short piece of advice on how to improve communication skills"}, "output_instructions": "Extract the text from the first choice"}
Observation: "Some basic advice for improving communication skills would be to make sure to listen"
Thought:I am finished executing the plan.
Final Answer: Some basic advice for improving communication skills would be to make sure to listen.
> Finished chain.
Observation: Some basic advice for improving communication skills would be to make sure to listen.
Thought:I am finished executing the plan and have the information the user asked for.
Final Answer: A short piece of advice for improving communication skills is to make sure to listen.
> Finished chain.
'A short piece of advice for improving communication skills is to make sure to listen.'
Takes awhile to get there!
2nd example: “json explorer” agent# | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-14 | Takes awhile to get there!
2nd example: “json explorer” agent#
Here’s an agent that’s not particularly practical, but neat! The agent has access to 2 toolkits. One comprises tools to interact with json: one tool to list the keys of a json object and another tool to get the value for a given key. The other toolkit comprises requests wrappers to send GET and POST requests. This agent consumes a lot calls to the language model, but does a surprisingly decent job.
from langchain.agents import create_openapi_agent
from langchain.agents.agent_toolkits import OpenAPIToolkit
from langchain.llms.openai import OpenAI
from langchain.requests import TextRequestsWrapper
from langchain.tools.json.tool import JsonSpec
with open("openai_openapi.yaml") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
json_spec=JsonSpec(dict_=data, max_value_length=4000)
openapi_toolkit = OpenAPIToolkit.from_llm(OpenAI(temperature=0), json_spec, openai_requests_wrapper, verbose=True)
openapi_agent_executor = create_openapi_agent(
llm=OpenAI(temperature=0),
toolkit=openapi_toolkit,
verbose=True
)
openapi_agent_executor.run("Make a post request to openai /completions. The prompt should be 'tell me a joke.'")
> Entering new AgentExecutor chain...
Action: json_explorer
Action Input: What is the base url for the API?
> Entering new AgentExecutor chain...
Action: json_spec_list_keys
Action Input: data
Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']
Thought: I should look at the servers key to see what the base url is | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-15 | Thought: I should look at the servers key to see what the base url is
Action: json_spec_list_keys
Action Input: data["servers"][0]
Observation: ValueError('Value at path `data["servers"][0]` is not a dict, get the value directly.')
Thought: I should get the value of the servers key
Action: json_spec_get_value
Action Input: data["servers"][0]
Observation: {'url': 'https://api.openai.com/v1'}
Thought: I now know the base url for the API
Final Answer: The base url for the API is https://api.openai.com/v1
> Finished chain.
Observation: The base url for the API is https://api.openai.com/v1
Thought: I should find the path for the /completions endpoint.
Action: json_explorer
Action Input: What is the path for the /completions endpoint?
> Entering new AgentExecutor chain...
Action: json_spec_list_keys
Action Input: data
Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']
Thought: I should look at the paths key to see what endpoints exist
Action: json_spec_list_keys
Action Input: data["paths"] | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-16 | Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/chat/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/audio/transcriptions', '/audio/translations', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']
Thought: I now know the path for the /completions endpoint
Final Answer: The path for the /completions endpoint is data["paths"][2]
> Finished chain.
Observation: The path for the /completions endpoint is data["paths"][2]
Thought: I should find the required parameters for the POST request.
Action: json_explorer
Action Input: What are the required parameters for a POST request to the /completions endpoint?
> Entering new AgentExecutor chain...
Action: json_spec_list_keys
Action Input: data
Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']
Thought: I should look at the paths key to see what endpoints exist
Action: json_spec_list_keys
Action Input: data["paths"] | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-17 | Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/chat/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/audio/transcriptions', '/audio/translations', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']
Thought: I should look at the /completions endpoint to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]
Observation: ['post']
Thought: I should look at the post key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]
Observation: ['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']
Thought: I should look at the requestBody key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]
Observation: ['required', 'content']
Thought: I should look at the content key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]
Observation: ['application/json']
Thought: I should look at the application/json key to see what parameters are required
Action: json_spec_list_keys | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-18 | Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]
Observation: ['schema']
Thought: I should look at the schema key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]
Observation: ['$ref']
Thought: I should look at the $ref key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"]
Observation: ValueError('Value at path `data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"]` is not a dict, get the value directly.')
Thought: I should look at the $ref key to get the value directly
Action: json_spec_get_value
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]["schema"]["$ref"]
Observation: #/components/schemas/CreateCompletionRequest
Thought: I should look at the CreateCompletionRequest schema to see what parameters are required
Action: json_spec_list_keys
Action Input: data["components"]["schemas"]["CreateCompletionRequest"]
Observation: ['type', 'properties', 'required']
Thought: I should look at the required key to see what parameters are required
Action: json_spec_get_value
Action Input: data["components"]["schemas"]["CreateCompletionRequest"]["required"]
Observation: ['model']
Thought: I now know the final answer
Final Answer: The required parameters for a POST request to the /completions endpoint are 'model'.
> Finished chain. | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-19 | > Finished chain.
Observation: The required parameters for a POST request to the /completions endpoint are 'model'.
Thought: I now know the parameters needed to make the request.
Action: requests_post
Action Input: { "url": "https://api.openai.com/v1/completions", "data": { "model": "davinci", "prompt": "tell me a joke" } }
Observation: {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\n\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":4,"completion_tokens":16,"total_tokens":20}}
Thought: I now know the final answer.
Final Answer: The response of the POST request is {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\n\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":4,"completion_tokens":16,"total_tokens":20}}
> Finished chain. | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
605c00b1ec92-20 | > Finished chain.
'The response of the POST request is {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\\n\\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":4,"completion_tokens":16,"total_tokens":20}}'
previous
JSON Agent
next
Pandas Dataframe Agent
Contents
OpenAPI agents
1st example: hierarchical planning agent
To start, let’s collect some OpenAPI specs.
How big is this spec?
Let’s see some examples!
Try another API.
2nd example: “json explorer” agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html"
} |
0736e283dcc9-0 | .ipynb
.pdf
Custom MultiAction Agent
Custom MultiAction Agent#
This notebook goes through how to create your own custom agent.
An agent consists of three parts:
- Tools: The tools the agent has available to use.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a custom agent that predicts/takes multiple steps at a time.
from langchain.agents import Tool, AgentExecutor, BaseMultiActionAgent
from langchain import OpenAI, SerpAPIWrapper
def random_word(query: str) -> str:
print("\nNow I'm doing this!")
return "foo"
search = SerpAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
),
Tool(
name = "RandomWord",
func=random_word,
description="call this to get a random word."
)
]
from typing import List, Tuple, Any, Union
from langchain.schema import AgentAction, AgentFinish
class FakeAgent(BaseMultiActionAgent):
"""Fake Custom Agent."""
@property
def input_keys(self):
return ["input"]
def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
if len(intermediate_steps) == 0:
return [ | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_multi_action_agent.html"
} |
0736e283dcc9-1 | """
if len(intermediate_steps) == 0:
return [
AgentAction(tool="Search", tool_input="foo", log=""),
AgentAction(tool="RandomWord", tool_input="foo", log=""),
]
else:
return AgentFinish(return_values={"output": "bar"}, log="")
async def aplan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
if len(intermediate_steps) == 0:
return [
AgentAction(tool="Search", tool_input="foo", log=""),
AgentAction(tool="RandomWord", tool_input="foo", log=""),
]
else:
return AgentFinish(return_values={"output": "bar"}, log="")
agent = FakeAgent()
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Foo Fighters is an American rock band formed in Seattle in 1994. Foo Fighters was initially formed as a one-man project by former Nirvana drummer Dave Grohl. Following the success of the 1995 eponymous debut album, Grohl recruited a band consisting of Nate Mendel, William Goldsmith, and Pat Smear.
Now I'm doing this!
foo
> Finished chain.
'bar'
previous
Custom MRKL Agent
next | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_multi_action_agent.html"
} |
0736e283dcc9-2 | foo
> Finished chain.
'bar'
previous
Custom MRKL Agent
next
Custom Agent with Tool Retrieval
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_multi_action_agent.html"
} |
053b8851e5ab-0 | .md
.pdf
Agent Types
Contents
zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
Agent Types#
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
Here are the agents available in LangChain.
zero-shot-react-description#
This agent uses the ReAct framework to determine which tool to use
based solely on the tool’s description. Any number of tools can be provided.
This agent requires that a description is provided for each tool.
react-docstore#
This agent uses the ReAct framework to interact with a docstore. Two tools must
be provided: a Search tool and a Lookup tool (they must be named exactly as so).
The Search tool should search for a document, while the Lookup tool should lookup
a term in the most recently found document.
This agent is equivalent to the
original ReAct paper, specifically the Wikipedia example.
self-ask-with-search#
This agent utilizes a single tool that should be named Intermediate Answer.
This tool should be able to lookup factual answers to questions. This agent
is equivalent to the original self ask with search paper,
where a Google search API was provided as the tool.
conversational-react-description#
This agent is designed to be used in conversational settings.
The prompt is designed to make the agent helpful and conversational.
It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
previous
Agents
next
Custom Agent
Contents
zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
By Harrison Chase
© Copyright 2023, Harrison Chase. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/agent_types.html"
} |
053b8851e5ab-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/agent_types.html"
} |
6319746682d4-0 | .ipynb
.pdf
Custom Agent with Tool Retrieval
Contents
Set up environment
Set up tools
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
Custom Agent with Tool Retrieval#
This notebook builds off of this notebook and assumes familiarity with how agents work.
The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. This is useful when you have many many tools to select from. You cannot put the description of all the tools in the prompt (because of context length issues) so instead you dynamically select the N tools you do want to consider using at run time.
In this notebook we will create a somewhat contrieved example. We will have one legitimate tool (search) and then 99 fake tools which are just nonsense. We will then add a step in the prompt template that takes the user input and retrieves tool relevant to the query.
Set up environment#
Do necessary imports, etc.
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
Set up tools#
We will create one legitimate tool (search) and then 99 fake tools
# Define which tools the agent can use to answer user queries
search = SerpAPIWrapper()
search_tool = Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
)
def fake_func(inp: str) -> str:
return "foo"
fake_tools = [
Tool( | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-1 | return "foo"
fake_tools = [
Tool(
name=f"foo-{i}",
func=fake_func,
description=f"a silly function that you can use to get more information about the number {i}"
)
for i in range(99)
]
ALL_TOOLS = [search_tool] + fake_tools
Tool Retriever#
We will use a vectorstore to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools.
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import Document
docs = [Document(page_content=t.description, metadata={"index": i}) for i, t in enumerate(ALL_TOOLS)]
vector_store = FAISS.from_documents(docs, OpenAIEmbeddings())
retriever = vector_store.as_retriever()
def get_tools(query):
docs = retriever.get_relevant_documents(query)
return [ALL_TOOLS[d.metadata["index"]] for d in docs]
We can now test this retriever to see if it seems to work.
get_tools("whats the weather?") | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-2 | get_tools("whats the weather?")
[Tool(name='Search', description='useful for when you need to answer questions about current events', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='c657176b327b17e79b55306ab968d164ee2369a7c7fa5b3f8a5f7889903de882', aiosession=None)>, coroutine=None),
Tool(name='foo-95', description='a silly function that you can use to get more information about the number 95', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-12', description='a silly function that you can use to get more information about the number 12', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-15', description='a silly function that you can use to get more information about the number 15', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None)] | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-3 | get_tools("whats the number 13?")
[Tool(name='foo-13', description='a silly function that you can use to get more information about the number 13', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-12', description='a silly function that you can use to get more information about the number 12', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-14', description='a silly function that you can use to get more information about the number 14', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-11', description='a silly function that you can use to get more information about the number 11', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None)]
Prompt Template#
The prompt template is pretty standard, because we’re not actually changing that much logic in the actual prompt template, but rather we are just changing how retrieval is done.
# Set up the base template
template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
{tools}
Use the following format: | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-4 | {tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Question: {input}
{agent_scratchpad}"""
The custom prompt template now has the concept of a tools_getter, which we call on the input to select the tools to use
from typing import Callable
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
############## NEW ######################
# The list of tools available
tools_getter: Callable
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
############## NEW ######################
tools = self.tools_getter(kwargs["input"])
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in tools]) | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-5 | # Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools_getter=get_tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
Output Parser#
The output parser is unchanged from the previous notebook, since we are not changing anything about the output format.
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output) | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-6 | output_parser = CustomOutputParser()
Set up LLM, stop sequence, and the agent#
Also the same as the previous notebook
llm = OpenAI(temperature=0)
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
Use the Agent#
Now we can use it!
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("What's the weather in SF?")
> Entering new AgentExecutor chain...
Thought: I need to find out what the weather is in SF
Action: Search
Action Input: Weather in SF
Observation:Mostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shifting to W at 10 to 15 mph. Humidity71%. UV Index6 of 10. I now know the final answer
Final Answer: 'Arg, 'tis mostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shiftin' to W at 10 to 15 mph. Humidity71%. UV Index6 of 10.
> Finished chain.
"'Arg, 'tis mostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shiftin' to W at 10 to 15 mph. Humidity71%. UV Index6 of 10."
previous
Custom MultiAction Agent
next
Conversation Agent (for Chat Models)
Contents
Set up environment | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
6319746682d4-7 | next
Conversation Agent (for Chat Models)
Contents
Set up environment
Set up tools
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html"
} |
35f9eede8045-0 | .ipynb
.pdf
Custom MRKL Agent
Contents
Custom LLMChain
Multiple inputs
Custom MRKL Agent#
This notebook goes through how to create your own custom MRKL agent.
A MRKL agent consists of three parts:
- Tools: The tools the agent has available to use.
- LLMChain: The LLMChain that produces the text that is parsed in a certain way to determine which action to take.
- The agent class itself: this parses the output of the LLMChain to determine which action to take.
In this notebook we walk through how to create a custom MRKL agent by creating a custom LLMChain.
Custom LLMChain#
The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly reccomended that you work with the ZeroShotAgent, as at the moment that is by far the most generalizable one.
Most of the work in creating the custom LLMChain comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. Additionally, we currently require an agent_scratchpad input variable to put notes on previous actions and observations. This should almost always be the final part of the prompt. However, besides those instructions, you can customize the prompt as you wish.
To ensure that the prompt contains the appropriate instructions, we will utilize a helper method on that class. The helper method for the ZeroShotAgent takes the following arguments:
tools: List of tools the agent will have access to, used to format the prompt.
prefix: String to put before the list of tools.
suffix: String to put after the list of tools.
input_variables: List of input variables the final prompt will expect. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html"
} |
35f9eede8045-1 | input_variables: List of input variables the final prompt will expect.
For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate.
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLMChain
search = SerpAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
)
]
prefix = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:"""
suffix = """Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "agent_scratchpad"]
)
In case we are curious, we can now take a look at the final prompt template to see what it looks like when its all put together.
print(prompt.template)
Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
Search: useful for when you need to answer questions about current events
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [Search]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html"
} |
35f9eede8045-2 | Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
Question: {input}
{agent_scratchpad}
Note that we are able to feed agents a self-defined prompt template, i.e. not restricted to the prompt generated by the create_prompt function, assuming it meets the agent’s requirements.
For example, for ZeroShotAgent, we will need to ensure that it meets the following requirements. There should a string starting with “Action:” and a following string starting with “Action Input:”, and both should be separated by a newline.
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada 2023
Observation: The current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.
Thought: I now know the final answer
Final Answer: Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!
> Finished chain. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html"
} |
35f9eede8045-3 | > Finished chain.
"Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!"
Multiple inputs#
Agents can also work with prompts that require multiple inputs.
prefix = """Answer the following questions as best you can. You have access to the following tools:"""
suffix = """When answering, you MUST speak in the following language: {language}.
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "language", "agent_scratchpad"]
)
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run(input="How many people live in canada as of 2023?", language="italian")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada in 2023.
Action: Search
Action Input: Population of Canada in 2023
Observation: The current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.
Thought: I now know the final answer.
Final Answer: La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.
> Finished chain. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html"
} |
35f9eede8045-4 | > Finished chain.
'La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.'
previous
Custom LLM Agent (with a ChatModel)
next
Custom MultiAction Agent
Contents
Custom LLMChain
Multiple inputs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html"
} |
5c95fecc9ccc-0 | .ipynb
.pdf
Custom Agent
Custom Agent#
This notebook goes through how to create your own custom agent.
An agent consists of three parts:
- Tools: The tools the agent has available to use.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a custom agent.
from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent
from langchain import OpenAI, SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events",
return_direct=True
)
]
from typing import List, Tuple, Any, Union
from langchain.schema import AgentAction, AgentFinish
class FakeAgent(BaseSingleActionAgent):
"""Fake Custom Agent."""
@property
def input_keys(self):
return ["input"]
def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
return AgentAction(tool="Search", tool_input="foo", log="")
async def aplan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date, | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent.html"
} |
5c95fecc9ccc-1 | Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
return AgentAction(tool="Search", tool_input="foo", log="")
agent = FakeAgent()
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Foo Fighters is an American rock band formed in Seattle in 1994. Foo Fighters was initially formed as a one-man project by former Nirvana drummer Dave Grohl. Following the success of the 1995 eponymous debut album, Grohl recruited a band consisting of Nate Mendel, William Goldsmith, and Pat Smear.
> Finished chain.
'Foo Fighters is an American rock band formed in Seattle in 1994. Foo Fighters was initially formed as a one-man project by former Nirvana drummer Dave Grohl. Following the success of the 1995 eponymous debut album, Grohl recruited a band consisting of Nate Mendel, William Goldsmith, and Pat Smear.'
previous
Agent Types
next
Custom LLM Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_agent.html"
} |
fb1abbbafd44-0 | .ipynb
.pdf
Custom LLM Agent
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Custom LLM Agent#
This notebook goes through how to create your own custom LLM agent.
An LLM agent consists of three parts:
PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
LLM: This is the language model that powers the agent
stop sequence: Instructs the LLM to stop generating as soon as this string is found
OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that:
Passes user input and any previous steps to the Agent (in this case, the LLMAgent)
If the Agent returns an AgentFinish, then return that directly to the user
If the Agent returns an AgentAction, then use that to call a tool and get an Observation
Repeat, passing the AgentAction and Observation back to the Agent until an AgentFinish is emitted.
AgentAction is a response that consists of action and action_input. action refers to which tool to use, and action_input refers to the input to that tool. log can also be provided as more context (that can be used for logging, tracing, etc).
AgentFinish is a response that contains the final message to be sent back to the user. This should be used to end an agent run.
In this notebook we walk through how to create a custom LLM agent.
Set up environment#
Do necessary imports, etc.
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html"
} |
fb1abbbafd44-1 | from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
Set up tool#
Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent knows to use these tools).
# Define which tools the agent can use to answer user queries
search = SerpAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
)
]
Prompt Template#
This instructs the agent on what to do. Generally, the template should incorporate:
tools: which tools the agent has access and how and when to call them.
intermediate_steps: These are tuples of previous (AgentAction, Observation) pairs. These are generally not passed directly to the model, but the prompt template formats them in a specific way.
input: generic user input
# Set up the base template
template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Question: {input} | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html"
} |
fb1abbbafd44-2 | Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
Output Parser#
The output parser is responsible for parsing the LLM output into AgentAction and AgentFinish. This usually depends heavily on the prompt used.
This is where you can change the parsing to do retries, handle whitespace, etc
class CustomOutputParser(AgentOutputParser): | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html"
} |
fb1abbbafd44-3 | class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
output_parser = CustomOutputParser()
Set up LLM#
Choose the LLM you want to use!
llm = OpenAI(temperature=0)
Define the stop sequence#
This is important because it tells the LLM when to stop generation.
This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an Observation (otherwise, the LLM may hallucinate an observation for you).
Set up the Agent#
We can now combine everything to set up our agent
# LLM chain consisting of the LLM and a prompt | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html"
} |
fb1abbbafd44-4 | # LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
Use the Agent#
Now we can use it!
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Action: Search
Action Input: Population of Canada in 2023
Observation:38,648,380 That's a lot of people!
Final Answer: Arrr, there be 38,648,380 people livin' in Canada come 2023!
> Finished chain.
"Arrr, there be 38,648,380 people livin' in Canada come 2023!"
previous
Custom Agent
next
Custom LLM Agent (with a ChatModel)
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html"
} |
b8212dd91fe4-0 | .ipynb
.pdf
Custom LLM Agent (with a ChatModel)
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Custom LLM Agent (with a ChatModel)#
This notebook goes through how to create your own custom agent based on a chat model.
An LLM chat agent consists of three parts:
PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
ChatModel: This is the language model that powers the agent
stop sequence: Instructs the LLM to stop generating as soon as this string is found
OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that:
Passes user input and any previous steps to the Agent (in this case, the LLMAgent)
If the Agent returns an AgentFinish, then return that directly to the user
If the Agent returns an AgentAction, then use that to call a tool and get an Observation
Repeat, passing the AgentAction and Observation back to the Agent until an AgentFinish is emitted.
AgentAction is a response that consists of action and action_input. action refers to which tool to use, and action_input refers to the input to that tool. log can also be provided as more context (that can be used for logging, tracing, etc).
AgentFinish is a response that contains the final message to be sent back to the user. This should be used to end an agent run.
In this notebook we walk through how to create a custom LLM agent.
Set up environment#
Do necessary imports, etc. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html"
} |
b8212dd91fe4-1 | Set up environment#
Do necessary imports, etc.
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import BaseChatPromptTemplate
from langchain import SerpAPIWrapper, LLMChain
from langchain.chat_models import ChatOpenAI
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish, HumanMessage
import re
Set up tool#
Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent knows to use these tools).
# Define which tools the agent can use to answer user queries
search = SerpAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
)
]
Prompt Template#
This instructs the agent on what to do. Generally, the template should incorporate:
tools: which tools the agent has access and how and when to call them.
intermediate_steps: These are tuples of previous (AgentAction, Observation) pairs. These are generally not passed directly to the model, but the prompt template formats them in a specific way.
input: generic user input
# Set up the base template
template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times) | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html"
} |
b8212dd91fe4-2 | ... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(BaseChatPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format_messages(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
formatted = self.template.format(**kwargs)
return [HumanMessage(content=formatted)]
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
) | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html"
} |
b8212dd91fe4-3 | input_variables=["input", "intermediate_steps"]
)
Output Parser#
The output parser is responsible for parsing the LLM output into AgentAction and AgentFinish. This usually depends heavily on the prompt used.
This is where you can change the parsing to do retries, handle whitespace, etc
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
output_parser = CustomOutputParser()
Set up LLM#
Choose the LLM you want to use!
llm = ChatOpenAI(temperature=0)
Define the stop sequence#
This is important because it tells the LLM when to stop generation. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html"
} |
b8212dd91fe4-4 | Define the stop sequence#
This is important because it tells the LLM when to stop generation.
This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an Observation (otherwise, the LLM may hallucinate an observation for you).
Set up the Agent#
We can now combine everything to set up our agent
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
Use the Agent#
Now we can use it!
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Thought: Wot year be it now? That be important to know the answer.
Action: Search
Action Input: "current population canada 2023"
Observation:38,649,283Ahoy! That be the correct year, but the answer be in regular numbers. 'Tis time to translate to pirate speak.
Action: Search
Action Input: "38,649,283 in pirate speak"
Observation:Brush up on your “Pirate Talk” with these helpful pirate phrases. Aaaarrrrgggghhhh! Pirate catch phrase of grumbling or disgust. Ahoy! Hello! Ahoy, Matey, Hello ...That be not helpful, I'll just do the translation meself. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html"
} |
b8212dd91fe4-5 | Final Answer: Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.
> Finished chain.
'Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.'
previous
Custom LLM Agent
next
Custom MRKL Agent
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html"
} |
00d6168a4a26-0 | .ipynb
.pdf
MRKL Chat
MRKL Chat#
This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models.
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.
from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm1 = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm1, verbose=True)
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events. You should ask targeted questions"
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
),
Tool(
name="FooBar DB",
func=db_chain.run,
description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context"
)
]
mrkl = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html"
} |
00d6168a4a26-1 | mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain...
Thought: The first question requires a search, while the second question requires a calculator.
Action:
```
{
"action": "Search",
"action_input": "Who is Leo DiCaprio's girlfriend?"
}
```
Observation: Camila Morrone
Thought:For the second question, I need to use the calculator tool to raise her current age to the 0.43 power.
Action:
```
{
"action": "Calculator",
"action_input": "22.0^(0.43)"
}
```
> Entering new LLMMathChain chain...
22.0^(0.43)
```python
import math
print(math.pow(22.0, 0.43))
```
Answer: 3.777824273683966
> Finished chain.
Observation: Answer: 3.777824273683966
Thought:I now know the final answer.
Final Answer: Camila Morrone, 3.777824273683966.
> Finished chain.
'Camila Morrone, 3.777824273683966.'
mrkl.run("What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?")
> Entering new AgentExecutor chain...
Question: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database? | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html"
} |
00d6168a4a26-2 | Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.
Action:
```
{
"action": "Search",
"action_input": "Who recently released an album called 'The Storm Before the Calm'"
}
```
Observation: Alanis Morissette
Thought:Now that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.
Action:
```
{
"action": "FooBar DB",
"action_input": "What albums does Alanis Morissette have in the database?"
}
```
> Entering new SQLDatabaseChain chain...
What albums does Alanis Morissette have in the database?
SQLQuery:
/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: 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 Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;
SQLResult: [('Jagged Little Pill',)]
Answer: Alanis Morissette has the album 'Jagged Little Pill' in the database.
> Finished chain.
Observation: Alanis Morissette has the album 'Jagged Little Pill' in the database.
Thought:I have found the answer to both parts of the question. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html"
} |
00d6168a4a26-3 | Thought:I have found the answer to both parts of the question.
Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.
> Finished chain.
"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database."
previous
MRKL
next
ReAct
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html"
} |
e5e97ed704cd-0 | .ipynb
.pdf
Conversation Agent
Conversation Agent#
This notebook walks through using an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.
This is accomplished with a specific type of agent (conversational-react-description) which expects to be used with a memory component.
from langchain.agents import Tool
from langchain.agents import AgentType
from langchain.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.utilities import GoogleSearchAPIWrapper
from langchain.agents import initialize_agent
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name = "Current Search",
func=search.run,
description="useful for when you need to answer questions about current events or the current state of the world"
),
]
memory = ConversationBufferMemory(memory_key="chat_history")
llm=OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain.run(input="hi, i am bob")
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? No
AI: Hi Bob, nice to meet you! How can I help you today?
> Finished chain.
'Hi Bob, nice to meet you! How can I help you today?'
agent_chain.run(input="what's my name?")
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? No
AI: Your name is Bob!
> Finished chain.
'Your name is Bob!' | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/conversational_agent.html"
} |
e5e97ed704cd-1 | AI: Your name is Bob!
> Finished chain.
'Your name is Bob!'
agent_chain.run("what are some good dinners to make this week, if i like thai food?")
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? No
AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!
> Finished chain.
'If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!'
agent_chain.run(input="tell me the last letter in my name, and also tell me who won the world cup in 1978?")
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Current Search
Action Input: Who won the World Cup in 1978 | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/conversational_agent.html"
} |
e5e97ed704cd-2 | Observation: The Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ... | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/conversational_agent.html"
} |
e5e97ed704cd-3 | Thought: Do I need to use a tool? No
AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.
> Finished chain.
"The last letter in your name is 'b'. Argentina won the World Cup in 1978."
agent_chain.run(input="whats the current temperature in pomfret?")
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Current Search
Action Input: Current temperature in Pomfret | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/conversational_agent.html"
} |
e5e97ed704cd-4 | Action: Current Search
Action Input: Current temperature in Pomfret
Observation: A mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.
Thought: Do I need to use a tool? No | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/conversational_agent.html"
} |
e5e97ed704cd-5 | Thought: Do I need to use a tool? No
AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.
> Finished chain.
'The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.'
previous
Conversation Agent (for Chat Models)
next
MRKL
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/conversational_agent.html"
} |
cd159c71a3bd-0 | .ipynb
.pdf
MRKL
MRKL#
This notebook showcases using an agent to replicate the MRKL 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.
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events. You should ask targeted questions"
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
),
Tool(
name="FooBar DB",
func=db_chain.run,
description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context"
)
]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain... | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html"
} |
cd159c71a3bd-1 | > Entering new AgentExecutor chain...
I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.
Action: Search
Action Input: "Who is Leo DiCaprio's girlfriend?"
Observation: Camila Morrone
Thought: I need to find out Camila Morrone's age
Action: Search
Action Input: "How old is Camila Morrone?"
Observation: 25 years
Thought: I need to calculate 25 raised to the 0.43 power
Action: Calculator
Action Input: 25^0.43
> Entering new LLMMathChain chain...
25^0.43
```python
import math
print(math.pow(25, 0.43))
```
Answer: 3.991298452658078
> Finished chain.
Observation: Answer: 3.991298452658078
Thought: I now know the final answer
Final Answer: Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.
> Finished chain.
'Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.'
mrkl.run("What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?")
> Entering new AgentExecutor chain...
I need to find out the artist's full name and then search the FooBar database for their albums.
Action: Search
Action Input: "The Storm Before the Calm" artist | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html"
} |
cd159c71a3bd-2 | Action: Search
Action Input: "The Storm Before the Calm" artist
Observation: The Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis ...
Thought: I now need to search the FooBar database for Alanis Morissette's albums
Action: FooBar DB
Action Input: What albums by Alanis Morissette are in the FooBar database?
> Entering new SQLDatabaseChain chain...
What albums by Alanis Morissette are in the FooBar database?
SQLQuery: SELECT Title FROM Album INNER JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alanis Morissette' LIMIT 5;
SQLResult: [('Jagged Little Pill',)]
Answer: The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.
> Finished chain.
Observation: The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.
Thought: I now know the final answer
Final Answer: The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.
> Finished chain.
'The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.'
previous
Conversation Agent
next
MRKL Chat
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html"
} |
ed7d1a113502-0 | .ipynb
.pdf
Self Ask With Search
Self Ask With Search#
This notebook showcases the Self Ask With Search chain.
from langchain import OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
tools = [
Tool(
name="Intermediate Answer",
func=search.run,
description="useful for when you need to ask with search"
)
]
self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
> Entering new AgentExecutor chain...
Yes.
Follow up: Who is the reigning men's U.S. Open champion?
Intermediate answer: Carlos Alcaraz won the 2022 Men's single title while Poland's Iga Swiatek won the Women's single title defeating Tunisian's Ons Jabeur.
Follow up: Where is Carlos Alcaraz from?
Intermediate answer: El Palmar, Spain
So the final answer is: El Palmar, Spain
> Finished AgentExecutor chain.
'El Palmar, Spain'
previous
ReAct
next
Toolkits
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/self_ask_with_search.html"
} |
f345fbae2da5-0 | .ipynb
.pdf
Conversation Agent (for Chat Models)
Conversation Agent (for Chat Models)#
This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.
This is accomplished with a specific type of agent (chat-conversational-react-description) which expects to be used with a memory component.
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
from langchain.agents import Tool
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.utilities import SerpAPIWrapper
from langchain.agents import initialize_agent
from langchain.agents import AgentType
search = SerpAPIWrapper()
tools = [
Tool(
name = "Current Search",
func=search.run,
description="useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term."
),
]
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm=ChatOpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain.run(input="hi, i am bob")
> Entering new AgentExecutor chain...
{
"action": "Final Answer",
"action_input": "Hello Bob! How can I assist you today?"
}
> Finished chain.
'Hello Bob! How can I assist you today?'
agent_chain.run(input="what's my name?")
> Entering new AgentExecutor chain...
{ | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/chat_conversation_agent.html"
} |
f345fbae2da5-1 | > Entering new AgentExecutor chain...
{
"action": "Final Answer",
"action_input": "Your name is Bob."
}
> Finished chain.
'Your name is Bob.'
agent_chain.run("what are some good dinners to make this week, if i like thai food?")
> Entering new AgentExecutor chain...
{
"action": "Current Search",
"action_input": "Thai food dinner recipes"
}
Observation: 59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's ...
Thought:{
"action": "Final Answer",
"action_input": "Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website."
}
> Finished chain.
"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website."
agent_chain.run(input="tell me the last letter in my name, and also tell me who won the world cup in 1978?")
> Entering new AgentExecutor chain...
```json
{
"action": "Current Search",
"action_input": "who won the world cup in 1978"
}
``` | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/chat_conversation_agent.html"
} |
f345fbae2da5-2 | "action_input": "who won the world cup in 1978"
}
```
Observation: The Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.
Thought:```json
{
"action": "Final Answer",
"action_input": "The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978."
}
```
> Finished chain.
"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978."
agent_chain.run(input="whats the weather like in pomfret?")
> Entering new AgentExecutor chain...
{
"action": "Current Search",
"action_input": "weather in pomfret"
}
Observation: Mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers possible. High near 40F. Winds NNW at 20 to 30 mph.
Thought:{
"action": "Final Answer",
"action_input": "The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph."
}
> Finished chain.
'The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.'
previous
Custom Agent with Tool Retrieval
next
Conversation Agent
By Harrison Chase | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/chat_conversation_agent.html"
} |
f345fbae2da5-3 | previous
Custom Agent with Tool Retrieval
next
Conversation Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/chat_conversation_agent.html"
} |
536796d81cf5-0 | .ipynb
.pdf
ReAct
ReAct#
This notebook showcases using an agent to implement the ReAct logic.
from langchain import OpenAI, Wikipedia
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
docstore=DocstoreExplorer(Wikipedia())
tools = [
Tool(
name="Search",
func=docstore.search,
description="useful for when you need to ask with search"
),
Tool(
name="Lookup",
func=docstore.lookup,
description="useful for when you need to ask with lookup"
)
]
llm = OpenAI(temperature=0, model_name="text-davinci-002")
react = initialize_agent(tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True)
question = "Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?"
react.run(question)
> Entering new AgentExecutor chain...
Thought: I need to search David Chanoff and find the U.S. Navy admiral he collaborated with. Then I need to find which President the admiral served under.
Action: Search[David Chanoff] | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/react.html"
} |
536796d81cf5-1 | Action: Search[David Chanoff]
Observation: David Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.
Thought: The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe. I need to find which President he served under.
Action: Search[William J. Crowe]
Observation: William James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.
Thought: William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton, so the answer is Bill Clinton.
Action: Finish[Bill Clinton]
> Finished chain.
'Bill Clinton'
previous
MRKL Chat
next
Self Ask With Search
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/agents/agents/examples/react.html"
} |
640bcbfa0174-0 | .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 interactions with a language model are captured in the concept of ChatMessages, so this boils down to ingesting, capturing, transforming and extracting knowledge from a sequence of chat messages. There are many different ways to do this, each of which exists as its own memory type.
In general, for each type of memory there are two ways to understanding using memory. These are the standalone functions which extract information from a sequence of messages, and then there is the way you can use this type of memory in a chain.
Memory can return multiple pieces of information (for example, the most recent N messages and a summary of all previous messages). The returned information can either be a string or a list of messages.
In this notebook, we will walk through the simplest form of memory: “buffer” memory, which just involves keeping a buffer of all prior messages. We will show how to use the modular utility functions here, then show how it can be used in a chain (both returning a string as well as a list of messages).
ChatMessageHistory#
One of the core utility classes underpinning most (if not all) memory modules is the ChatMessageHistory class. This is a super lightweight wrapper which exposes convienence methods for saving Human messages, AI messages, and then fetching them all.
You may want to use this class directly if you are managing memory outside of a chain.
from langchain.memory import ChatMessageHistory
history = ChatMessageHistory()
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
[HumanMessage(content='hi!', additional_kwargs={}), | {
"url": "https://python.langchain.com/en/latest/modules/memory/getting_started.html"
} |
640bcbfa0174-1 | 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 messages in a variable.
We can first extract it as a string.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
memory.load_memory_variables({})
{'history': 'Human: hi!\nAI: whats up?'}
We can also get the history as a list of messages
memory = ConversationBufferMemory(return_messages=True)
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
memory.load_memory_variables({})
{'history': [HumanMessage(content='hi!', additional_kwargs={}),
AIMessage(content='whats up?', additional_kwargs={})]}
Using in a chain#
Finally, let’s take a look at using this in a chain (setting verbose=True so we can see the prompt).
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
llm = OpenAI(temperature=0)
conversation = ConversationChain(
llm=llm,
verbose=True,
memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")
> 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:
Human: Hi there!
AI: | {
"url": "https://python.langchain.com/en/latest/modules/memory/getting_started.html"
} |
640bcbfa0174-2 | 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 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:
Human: Hi there!
AI: Hi there! It's nice to meet you. How can I help you today?
Human: I'm doing well! Just having a conversation with an AI.
AI:
> Finished chain.
" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?"
conversation.predict(input="Tell me about yourself.")
> 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:
Human: Hi there!
AI: Hi there! It's nice to meet you. How can I help you today?
Human: I'm doing well! Just having a conversation with an AI.
AI: That's great! It's always nice to have a conversation with someone new. What would you like to talk about?
Human: Tell me about yourself.
AI:
> Finished chain. | {
"url": "https://python.langchain.com/en/latest/modules/memory/getting_started.html"
} |
640bcbfa0174-3 | 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 Message History#
You may often to save messages, and then load them to use again. This can be done easily by first converting the messages to normal python dictionaries, saving those (as json or something) and then loading those. Here is an example of doing that.
import json
from langchain.memory import ChatMessageHistory
from langchain.schema import messages_from_dict, messages_to_dict
history = ChatMessageHistory()
history.add_user_message("hi!")
history.add_ai_message("whats up?")
dicts = messages_to_dict(history.messages)
dicts
[{'type': 'human', 'data': {'content': 'hi!', 'additional_kwargs': {}}},
{'type': 'ai', 'data': {'content': 'whats up?', 'additional_kwargs': {}}}]
new_messages = messages_from_dict(dicts)
new_messages
[HumanMessage(content='hi!', additional_kwargs={}),
AIMessage(content='whats up?', additional_kwargs={})]
And that’s it for the getting started! There are plenty of different types of memory, check out our examples to see them all
previous
Memory
next
How-To Guides
Contents
ChatMessageHistory
ConversationBufferMemory
Using in a chain
Saving Message History
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/memory/getting_started.html"
} |
527e0c55f6fd-0 | .rst
.pdf
How-To Guides
Contents
Types
Usage
How-To Guides#
Types#
The first set of examples all highlight different types of memory.
ConversationBufferMemory
ConversationBufferWindowMemory
Entity Memory
Conversation Knowledge Graph Memory
ConversationSummaryMemory
ConversationSummaryBufferMemory
ConversationTokenBufferMemory
Usage#
The examples here all highlight how to use memory in different ways.
How to add Memory to an LLMChain
How to add memory to a Multi-Input Chain
How to add Memory to an Agent
Adding Message Memory backed by a database to an Agent
How to customize conversational memory
How to create a custom Memory class
How to use multiple memory classes in the same chain
Redis Chat Message History
previous
Getting Started
next
ConversationBufferMemory
Contents
Types
Usage
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/memory/how_to_guides.html"
} |
bdb967aa4f6a-0 | .ipynb
.pdf
Redis Chat Message History
Redis Chat Message History#
This notebook goes over how to use Redis to store chat message history.
from langchain.memory import RedisChatMessageHistory
history = RedisChatMessageHistory("foo")
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
[AIMessage(content='whats up?', additional_kwargs={}),
HumanMessage(content='hi!', additional_kwargs={})]
previous
How to use multiple memory classes in the same chain
next
Chains
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/redis_chat_message_history.html"
} |
8948bd65c7f0-0 | .ipynb
.pdf
How to add Memory to an Agent
How to add Memory to an Agent#
This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:
Adding memory to an LLM Chain
Custom Agents
In order to add a memory to an agent we are going to the the following steps:
We are going to create an LLMChain with memory.
We are going to use that LLMChain to create a custom Agent.
For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the ConversationBufferMemory class.
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain.memory import ConversationBufferMemory
from langchain import OpenAI, LLMChain
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
)
]
Notice the usage of the chat_history variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory.
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"]
)
memory = ConversationBufferMemory(memory_key="chat_history") | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-1 | )
memory = ConversationBufferMemory(memory_key="chat_history")
We can now construct the LLMChain, with the Memory object, and then create the agent.
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
agent_chain.run(input="How many people live in canada?")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-2 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real- ... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its ... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the ... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations ... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. • Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada ... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population ... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.
Thought: I now know the final answer
Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.
> Finished AgentExecutor chain. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-3 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly.
agent_chain.run(input="what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I need to find out what the national anthem of Canada is called.
Action: Search
Action Input: National Anthem of Canada | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-4 | Action: Search
Action Input: National Anthem of Canada
Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa ... O Canada, national anthem of Canada. It was proclaimed the official national anthem on July 1, 1980. “God Save the Queen” remains the royal anthem of Canada ... O Canada! Our home and native land! True patriot love in all of us command. Car ton bras sait porter l'épée,. Il sait porter la croix! "O Canada" (French: Ô Canada) is the national anthem of Canada. The song was originally commissioned by Lieutenant Governor of Quebec Théodore Robitaille ... Feb 1, 2018 ... It was a simple tweak — just two words. But with that, Canada just voted to make its national anthem, “O Canada,” gender neutral, ... "O Canada" was proclaimed Canada's national anthem on July 1,. 1980, 100 years after it was first sung on June 24, 1880. The music. Patriotic music in Canada dates back over 200 years as a distinct category from British or French patriotism, preceding the first legal steps to ... Feb 4, 2022 ... English version: O Canada! Our home and native land! True patriot love in all of us command. With glowing hearts we ... Feb 1, 2018 ... Canada's Senate has passed a bill making the country's national anthem gender-neutral. If you're not familiar with the words to “O Canada,” ... | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-5 | Thought: I now know the final answer.
Final Answer: The national anthem of Canada is called "O Canada".
> Finished AgentExecutor chain.
'The national anthem of Canada is called "O Canada".'
We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada’s national anthem was.
For fun, let’s compare this to an agent that does NOT have memory.
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "agent_scratchpad"]
)
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_without_memory = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_without_memory.run("How many people live in canada?")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-6 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real- ... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its ... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the ... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations ... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. • Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada ... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population ... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.
Thought: I now know the final answer
Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.
> Finished AgentExecutor chain. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-7 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
agent_without_memory.run("what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I should look up the answer
Action: Search
Action Input: national anthem of [country] | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-8 | Action: Search
Action Input: national anthem of [country]
Observation: Most nation states have an anthem, defined as "a song, as of praise, devotion, or patriotism"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language of the country and translated into English, Aug 1, 2021 ... 1. Afghanistan, "Milli Surood" (National Anthem) · 2. Armenia, "Mer Hayrenik" (Our Fatherland) · 3. Azerbaijan (a transcontinental country with ... A national anthem is a patriotic musical composition symbolizing and evoking eulogies of the history and traditions of a country or nation. National Anthem of Every Country ; Fiji, “Meda Dau Doka” (“God Bless Fiji”) ; Finland, “Maamme”. (“Our Land”) ; France, “La Marseillaise” (“The Marseillaise”). You can find an anthem in the menu at the top alphabetically or you can use the search feature. This site is focussed on the scholarly study of national anthems ... Feb 13, 2022 ... The 38-year-old country music artist had the honor of singing the National Anthem during this year's big game, and she did not disappoint. Oldest of the World's National Anthems ; France, La Marseillaise (“The Marseillaise”), 1795 ; Argentina, Himno Nacional Argentino (“Argentine National Anthem”) ... Mar 3, 2022 ... Country music star Jessie James Decker gained the respect of music and hockey fans alike after a jaw-dropping rendition of "The Star-Spangled ... This list shows the country on the left, the national anthem in the ... There are many countries over the world who have a national anthem of their own. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
8948bd65c7f0-9 | Thought: I now know the final answer
Final Answer: The national anthem of [country] is [name of anthem].
> Finished AgentExecutor chain.
'The national anthem of [country] is [name of anthem].'
previous
How to add memory to a Multi-Input Chain
next
Adding Message Memory backed by a database to an Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html"
} |
3889333dbd7f-0 | .ipynb
.pdf
Adding Message Memory backed by a database to an Agent
Adding Message Memory backed by a database to an Agent#
This notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:
Adding memory to an LLM Chain
Custom Agents
Agent with Memory
In order to add a memory with an external message store to an agent we are going to do the following steps:
We are going to create a RedisChatMessageHistory to connect to an external database to store the messages in.
We are going to create an LLMChain useing that chat history as memory.
We are going to use that LLMChain to create a custom Agent.
For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the ConversationBufferMemory class.
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_memory import ChatMessageHistory
from langchain.memory.chat_message_histories import RedisChatMessageHistory
from langchain import OpenAI, LLMChain
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events"
)
]
Notice the usage of the chat_history variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory.
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input} | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-1 | suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"]
)
Now we can create the ChatMessageHistory backed by the database.
message_history = RedisChatMessageHistory(url='redis://localhost:6379/0', ttl=600, session_id='my-session')
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=message_history)
We can now construct the LLMChain, with the Memory object, and then create the agent.
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
agent_chain.run(input="How many people live in canada?")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-2 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real- ... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its ... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the ... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations ... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. • Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada ... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population ... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.
Thought: I now know the final answer
Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.
> Finished AgentExecutor chain. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-3 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly.
agent_chain.run(input="what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I need to find out what the national anthem of Canada is called.
Action: Search
Action Input: National Anthem of Canada | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-4 | Action: Search
Action Input: National Anthem of Canada
Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa ... O Canada, national anthem of Canada. It was proclaimed the official national anthem on July 1, 1980. “God Save the Queen” remains the royal anthem of Canada ... O Canada! Our home and native land! True patriot love in all of us command. Car ton bras sait porter l'épée,. Il sait porter la croix! "O Canada" (French: Ô Canada) is the national anthem of Canada. The song was originally commissioned by Lieutenant Governor of Quebec Théodore Robitaille ... Feb 1, 2018 ... It was a simple tweak — just two words. But with that, Canada just voted to make its national anthem, “O Canada,” gender neutral, ... "O Canada" was proclaimed Canada's national anthem on July 1,. 1980, 100 years after it was first sung on June 24, 1880. The music. Patriotic music in Canada dates back over 200 years as a distinct category from British or French patriotism, preceding the first legal steps to ... Feb 4, 2022 ... English version: O Canada! Our home and native land! True patriot love in all of us command. With glowing hearts we ... Feb 1, 2018 ... Canada's Senate has passed a bill making the country's national anthem gender-neutral. If you're not familiar with the words to “O Canada,” ... | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-5 | Thought: I now know the final answer.
Final Answer: The national anthem of Canada is called "O Canada".
> Finished AgentExecutor chain.
'The national anthem of Canada is called "O Canada".'
We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada’s national anthem was.
For fun, let’s compare this to an agent that does NOT have memory.
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "agent_scratchpad"]
)
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_without_memory = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_without_memory.run("How many people live in canada?")
> Entering new AgentExecutor chain...
Thought: I need to find out the population of Canada
Action: Search
Action Input: Population of Canada | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-6 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real- ... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its ... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the ... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations ... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. • Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada ... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population ... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.
Thought: I now know the final answer
Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.
> Finished AgentExecutor chain. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-7 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
agent_without_memory.run("what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I should look up the answer
Action: Search
Action Input: national anthem of [country] | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
3889333dbd7f-8 | Action: Search
Action Input: national anthem of [country]
Observation: Most nation states have an anthem, defined as "a song, as of praise, devotion, or patriotism"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language of the country and translated into English, Aug 1, 2021 ... 1. Afghanistan, "Milli Surood" (National Anthem) · 2. Armenia, "Mer Hayrenik" (Our Fatherland) · 3. Azerbaijan (a transcontinental country with ... A national anthem is a patriotic musical composition symbolizing and evoking eulogies of the history and traditions of a country or nation. National Anthem of Every Country ; Fiji, “Meda Dau Doka” (“God Bless Fiji”) ; Finland, “Maamme”. (“Our Land”) ; France, “La Marseillaise” (“The Marseillaise”). You can find an anthem in the menu at the top alphabetically or you can use the search feature. This site is focussed on the scholarly study of national anthems ... Feb 13, 2022 ... The 38-year-old country music artist had the honor of singing the National Anthem during this year's big game, and she did not disappoint. Oldest of the World's National Anthems ; France, La Marseillaise (“The Marseillaise”), 1795 ; Argentina, Himno Nacional Argentino (“Argentine National Anthem”) ... Mar 3, 2022 ... Country music star Jessie James Decker gained the respect of music and hockey fans alike after a jaw-dropping rendition of "The Star-Spangled ... This list shows the country on the left, the national anthem in the ... There are many countries over the world who have a national anthem of their own. | {
"url": "https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html"
} |
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