- Remove dynamic model creation in the `args()` property. _Only infer
for the decorator (and add an argument to NOT infer if someone wishes to
only pass as a string)_
- Update the validation example to make it less likely to be
misinterpreted as a "safe" way to run a repl
There is one example of "Multi-argument tools" in the custom_tools.ipynb
from yesterday, but we could add more. The output parsing for the base
MRKL agent hasn't been adapted to handle structured args at this point
in time
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Sometimes the LLM response (generated code) tends to miss the ending
ticks "```". Therefore causing the text parsing to fail due to not
enough values to unpack.
The 2 extra `_` don't add value and can cause errors. Suggest to simply
update the `_, action, _` to just `action` then with index.
Fixes issue #3057
re
https://github.com/hwchase17/langchain/issues/439#issuecomment-1510442791
I think it's not polite for a library to use the root logger
both of these forms are also used:
```
logger = logging.getLogger(__name__)
logger = logging.getLogger(__file__)
```
I am not sure if there is any reason behind one vs the other? (...I am
guessing maybe just contributed by different people)
it seems to me it'd be better to consistently use
`logging.getLogger(__name__)`
this makes it easier for consumers of the library to set up log
handlers, e.g. for everything with `langchain.` prefix
My recent pull request (#2729) neglected to update the
`reduce_openapi_spec` in spec.py to also accommodate PATCH and DELETE
added to planner.py and prompt_planner.py.
In #2399 we added the ability to set `max_execution_time` when creating
an AgentExecutor. This PR adds the `max_execution_time` argument to the
built-in pandas, sql, and openapi agents.
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
This PR proposes
- An NLAToolkit method to instantiate from an AI Plugin URL
- A notebook that shows how to use that alongside an example of using a
Retriever object to lookup specs and route queries to them on the fly
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR proposes an update to the OpenAPI Planner and Planner Prompts to
make Patch and Delete available to the planner and executor. I followed
the same patterns as for GET and POST, and made some updates to the
examples available to the Planner and Orchestrator.
Of note, I tried to write prompts for DELETE such that the model will
only execute that job if the User specifically asks for a 'Delete' (see
the Prompt_planner.py examples to see specificity), or if the User had
previously authorized the Delete in the Conversation memory. Although
PATCH also modifies existing data, I considered it lower risk and so did
not try to enforce the same restrictions on the Planner.
`AgentExecutor` already has support for limiting the number of
iterations. But the amount of time taken for each iteration can vary
quite a bit, so it is difficult to place limits on the execution time.
This PR adds a new field `max_execution_time` to the `AgentExecutor`
model. When called asynchronously, the agent loop is wrapped in an
`asyncio.timeout()` context which triggers the early stopping response
if the time limit is reached. When called synchronously, the agent loop
checks for both the max_iteration limit and the time limit after each
iteration.
When used asynchronously `max_execution_time` gives really tight control
over the max time for an execution chain. When used synchronously, the
chain can unfortunately exceed max_execution_time, but it still gives
more control than trying to estimate the number of max_iterations needed
to cap the execution time.
---------
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
The specs used in chat-gpt plugins have only a few endpoints and have
unrealistically small specifications. By contrast, a spec like spotify's
has 60+ endpoints and is comprised 100k+ tokens.
Here are some impressive traces from gpt-4 that string together
non-trivial sequences of API calls. As noted in `planner.py`, gpt-3 is
not as robust but can be improved with i) better retry, self-reflect,
etc. logic and ii) better few-shots iii) etc. This PR's just a first
attempt probing a few different directions that eventually can be made
more core.
`make me a playlist with songs from kind of blue. call it machine
blues.`
```
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to create a playlist with songs from Kind of Blue and name it Machine Blues
Observation: 1. GET /search to find the album ID for "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 ID.
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 tracks from "Kind of Blue" to the newly created "Machine Blues" playlist.
Thought:I have a plan to create the playlist. Now, I will execute the API calls.
Action: api_controller
Action Input: 1. GET /search to find the album ID for "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 ID.
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 tracks from "Kind of Blue" to the newly created "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 ids of all the tracks 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
Action Input: {"url": "https://api.spotify.com/v1/users/22rhrz4m4kvpxlsb5hezokzwi/playlists", "data": {"name": "Machine Blues"}, "output_instructions": "Extract the id of the newly created playlist"}
Observation: 48YP9TMcEtFu9aGN8n10lg
Thought:Action: requests_post
Action Input: {"url": "https://api.spotify.com/v1/playlists/48YP9TMcEtFu9aGN8n10lg/tracks", "data": {"uris": ["spotify:track:7q3kkfAVpmcZ8g6JUThi3o"]}, "output_instructions": "Confirm that the tracks were added to the playlist"}
Observation: The tracks were added to the playlist. The snapshot_id is "Miw4NTdmMWUxOGU5YWMxMzVmYmE3ZWE5MWZlYWNkMTc2NGVmNTI1ZjY5".
Thought:I am finished executing the plan.
Final Answer: The tracks from the "Kind of Blue" album have been added to the newly created "Machine Blues" playlist. The playlist ID is 48YP9TMcEtFu9aGN8n10lg.
> Finished chain.
Observation: The tracks from the "Kind of Blue" album have been added to the newly created "Machine Blues" playlist. The playlist ID is 48YP9TMcEtFu9aGN8n10lg.
Thought:I am finished executing the plan and have created the playlist with songs from Kind of Blue, named Machine Blues.
Final Answer: I have created a playlist called "Machine Blues" with songs from the "Kind of Blue" album. The playlist ID is 48YP9TMcEtFu9aGN8n10lg.
> Finished chain.
```
or
`give me a song in the style of tobe nwige`
```
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to get a song in the style of Tobe Nwigwe
Observation: 1. GET /search to find the artist ID for Tobe Nwigwe.
2. GET /artists/{id}/related-artists to find similar artists to Tobe Nwigwe.
3. Pick one of the related artists and use their artist ID in the next step.
4. GET /artists/{id}/top-tracks to get the top tracks of the chosen related artist.
Thought:
I'm ready to execute the API calls.
Action: api_controller
Action Input: 1. GET /search to find the artist ID for Tobe Nwigwe.
2. GET /artists/{id}/related-artists to find similar artists to Tobe Nwigwe.
3. Pick one of the related artists and use their artist ID in the next step.
4. GET /artists/{id}/top-tracks to get the top tracks of the chosen related artist.
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/search?q=Tobe%20Nwigwe&type=artist", "output_instructions": "Extract the artist id for Tobe Nwigwe"}
Observation: 3Qh89pgJeZq6d8uM1bTot3
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/artists/3Qh89pgJeZq6d8uM1bTot3/related-artists", "output_instructions": "Extract the ids and names of the related artists"}
Observation: [
{
"id": "75WcpJKWXBV3o3cfluWapK",
"name": "Lute"
},
{
"id": "5REHfa3YDopGOzrxwTsPvH",
"name": "Deante' Hitchcock"
},
{
"id": "6NL31G53xThQXkFs7lDpL5",
"name": "Rapsody"
},
{
"id": "5MbNzCW3qokGyoo9giHA3V",
"name": "EARTHGANG"
},
{
"id": "7Hjbimq43OgxaBRpFXic4x",
"name": "Saba"
},
{
"id": "1ewyVtTZBqFYWIcepopRhp",
"name": "Mick Jenkins"
}
]
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/artists/75WcpJKWXBV3o3cfluWapK/top-tracks?country=US", "output_instructions": "Extract the ids and names of the top tracks"}
Observation: [
{
"id": "6MF4tRr5lU8qok8IKaFOBE",
"name": "Under The Sun (with J. Cole & Lute feat. DaBaby)"
}
]
Thought:I am finished executing the plan.
Final Answer: The top track of the related artist Lute is "Under The Sun (with J. Cole & Lute feat. DaBaby)" with the track ID "6MF4tRr5lU8qok8IKaFOBE".
> Finished chain.
Observation: The top track of the related artist Lute is "Under The Sun (with J. Cole & Lute feat. DaBaby)" with the track ID "6MF4tRr5lU8qok8IKaFOBE".
Thought:I am finished executing the plan and have the information the user asked for.
Final Answer: The song "Under The Sun (with J. Cole & Lute feat. DaBaby)" by Lute is in the style of Tobe Nwigwe.
> Finished chain.
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.
The new enum members include:
- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION
In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.
Currently, `agent_toolkits.sql.create_sql_agent()` passes kwargs to the
`ZeroShotAgent` that it creates but not to `AgentExecutor` that it also
creates. This prevents the caller from providing some useful arguments
like `max_iterations` and `early_stopping_method`
This PR changes `create_sql_agent` so that it passes kwargs to both
constructors.
---------
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
### Motivation / Context
When exploring `load_tools(["requests"] )`, I would have expected all
request method tools to be imported instead of just `RequestsGetTool`.
### Changes
Break `_get_requests` into multiple functions by request method. Each
function returns the `BaseTool` for that particular request method.
In `load_tools`, if the tool name "requests_all" is encountered, we
replace with all `_BASE_TOOLS` that starts with `requests_`.
This way, `load_tools(["requests"])` returns:
- RequestsGetTool
- RequestsPostTool
- RequestsPatchTool
- RequestsPutTool
- RequestsDeleteTool
Hello!
I've noticed a bug in `create_pandas_dataframe_agent`. When calling it
with argument `return_intermediate_steps=True`, it doesn't return the
intermediate step. I think the issue is that `kwargs` was not passed
where it needed to be passed. It should be passed into
`AgentExecutor.from_agent_and_tools`
Please correct me if my solution isn't appropriate and I will fix with
the appropriate approach.
Co-authored-by: alhajji <m.alhajji@drahim.sa>
Fix issue#1645: Parse either whitespace or newline after 'Action Input:'
in llm_output in mrkl agent.
Unittests added accordingly.
Co-authored-by: ₿ingnan.ΞTH <brillliantz@outlook.com>
I have found that when the user has not asked an explicit question the
agent might have trouble answering the latest comment and might instead
try to answer a question that came before in the conversation which
would not be what is desired.
I also found that the agent might get confused with the current prompt
and talk about the tools themselves instead of the results obtained from
them.
I added two changes to the tool prompt so that the agent answers only
the last comment/question and only returns information from tool
results.
Use the following code to test:
```python
import os
from langchain.llms import OpenAI
from langchain.chains.api import podcast_docs
from langchain.chains import APIChain
# Get api key here: https://openai.com/pricing
os.environ["OPENAI_API_KEY"] = "sk-xxxxx"
# Get api key here: https://www.listennotes.com/api/pricing/
listen_api_key = 'xxx'
llm = OpenAI(temperature=0)
headers = {"X-ListenAPI-Key": listen_api_key}
chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)
chain.run("Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results")
```
Known issues: the api response data might be too big, and we'll get such
error:
`openai.error.InvalidRequestError: This model's maximum context length
is 4097 tokens, however you requested 6733 tokens (6477 in your prompt;
256 for the completion). Please reduce your prompt; or completion
length.`
While testing out `VectorDBQA` as a `Tool` for one of the conversation,
I happened to get a response from LLM (OpenAI) like this
<code>
Could not parse LLM output: Here's a response using the Product Search
tool:
```json
{
"action": "Product Search",
"action_input": "pots for plants"
}
```
This will allow you to search for pots for your plants and find a
variety of options that are available for purchase. You can use this
information to choose the pots that best fit your needs and preferences.
</code>
i.e. The response had a text before & *after* the expected JSON, leading
to `JSONDecodeError`. It's fixed now, by removing text after '```' to
remove unwanted text.
The error I encountered in this Jupyter Notebook -
[link](https://github.com/anselm94/chatbot-llm-ecommerce/blob/main/chatcommerce.ipynb)
<details>
<summary>Error encountered</summary>
<code>
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/conversational_chat/base.py:104,
in ConversationalChatAgent._extract_tool_and_input(self, llm_output)
103 try:
--> 104 response = self.output_parser.parse(llm_output)
105 return response["action"], response["action_input"]
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/conversational_chat/base.py:49,
in AgentOutputParser.parse(self, text)
48 cleaned_output = cleaned_output.strip()
---> 49 response = json.loads(cleaned_output)
50 return {"action": response["action"], "action_input":
response["action_input"]}
File
/opt/homebrew/Cellar/python@3.11/3.11.2_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py:346,
in loads(s, cls, object_hook, parse_float, parse_int, parse_constant,
object_pairs_hook, **kw)
343 if (cls is None and object_hook is None and
344 parse_int is None and parse_float is None and
345 parse_constant is None and object_pairs_hook is None and not kw):
--> 346 return _default_decoder.decode(s)
347 if cls is None:
File
/opt/homebrew/Cellar/python@3.11/3.11.2_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py:340,
in JSONDecoder.decode(self, s, _w)
339 if end != len(s):
--> 340 raise JSONDecodeError("Extra data", s, end)
341 return obj
JSONDecodeError: Extra data: line 5 column 1 (char 74)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
Cell In[22], line 1
----> 1 ask_ai.run("Yes. I need pots for my plants")
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/chains/base.py:213,
in Chain.run(self, *args, **kwargs)
211 if len(args) != 1:
212 raise ValueError("`run` supports only one positional argument.")
--> 213 return self(args[0])[self.output_keys[0]]
215 if kwargs and not args:
216 return self(kwargs)[self.output_keys[0]]
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/chains/base.py:116,
in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/chains/base.py:113,
in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/agent.py:499,
in AgentExecutor._call(self, inputs)
497 # We now enter the agent loop (until it returns something).
498 while self._should_continue(iterations):
--> 499 next_step_output = self._take_next_step(
500 name_to_tool_map, color_mapping, inputs, intermediate_steps
501 )
502 if isinstance(next_step_output, AgentFinish):
503 return self._return(next_step_output, intermediate_steps)
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/agent.py:409,
in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping,
inputs, intermediate_steps)
404 """Take a single step in the thought-action-observation loop.
405
406 Override this to take control of how the agent makes and acts on
choices.
407 """
408 # Call the LLM to see what to do.
--> 409 output = self.agent.plan(intermediate_steps, **inputs)
410 # If the tool chosen is the finishing tool, then we end and return.
411 if isinstance(output, AgentFinish):
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/agent.py:105,
in Agent.plan(self, intermediate_steps, **kwargs)
94 """Given input, decided what to do.
95
96 Args:
(...)
102 Action specifying what tool to use.
103 """
104 full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
--> 105 action = self._get_next_action(full_inputs)
106 if action.tool == self.finish_tool_name:
107 return AgentFinish({"output": action.tool_input}, action.log)
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/agent.py:67,
in Agent._get_next_action(self, full_inputs)
65 def _get_next_action(self, full_inputs: Dict[str, str]) ->
AgentAction:
66 full_output = self.llm_chain.predict(**full_inputs)
---> 67 parsed_output = self._extract_tool_and_input(full_output)
68 while parsed_output is None:
69 full_output = self._fix_text(full_output)
File
~/Git/chatbot-llm-ecommerce/.venv/lib/python3.11/site-packages/langchain/agents/conversational_chat/base.py:107,
in ConversationalChatAgent._extract_tool_and_input(self, llm_output)
105 return response["action"], response["action_input"]
106 except Exception:
--> 107 raise ValueError(f"Could not parse LLM output: {llm_output}")
ValueError: Could not parse LLM output: Here's a response using the
Product Search tool:
```json
{
"action": "Product Search",
"action_input": "pots for plants"
}
```
This will allow you to search for pots for your plants and find a
variety of options that are available for purchase. You can use this
information to choose the pots that best fit your needs and preferences.
</details>
Harrison Chase
