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Removes unused `Params` in `libs/langchain/langchain/llms/mlflow.py`.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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The example code for `llms.Mlflow` is outdated.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** `MarkdownHeaderTextSplitter` currently strips header
lines from chunked content. Many applications require these header lines
are preserved. This adds an optional parameter to preserve those headers
in the chunked content.
- **Issue:** #2836 (relevant)
- **Dependencies:** -
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @finnless
Unit tests and new examples in notebook included.
cc @rlancemartin
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via
chat service in lightweight and secure WebAssembly containers. In this
PR, `WasmChatService` is introduced as the first step of the
integration. `WasmChatService` is driven by
[llama-api-server](https://github.com/second-state/llama-utils) and
[WasmEdge Runtime](https://wasmedge.org/).
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
Follow up on https://github.com/langchain-ai/langchain/pull/13048.
This PR intends to simplify the Qdrant async implementation by replacing
the internal GRPC methods with the `QdrantAsyncClient` methods.
This is a backward compatible change with no additional steps required
after merge.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes#14347
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- **Description:** Added the traceback of the previous error to keep the
initial error type,
- **Issue:** #14347 ,
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---------
Co-authored-by: Julien Raffy <julien.raffy@emeria.eu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** the ability to add all extra parameter of vectorstore
and using them SemanticSimilarityExampleSelector.
- **Issue:** #14583
- **Dependencies:** no dependensies
- **Tag maintainer:**
- **Twitter handle:** @AmirMalekiz
---------
Co-authored-by: Amir Maleki <amaleki@fb.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Add support for setting the `score_threshold` for
similarity search in SupabaseVectoreStore.
This pull request addresses issue #14438
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** changed json.py to handle additional cases of partial
json string to be parsed, basically by dropping the last character in
the string until a valid json string is found or the string is empty.
Also added additional test cases.
- **Issue:** function parse_partial_json could not parse cases where the
key is present but the value is not.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Because Milvus' collection_name doesn't support UFT8 characters in other
languages, I want the `collection_descriotion`.
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**Description:** Fix for processing for serpapi response for Google Maps
API
**Issue:** Due to the fact corresponding
[api](https://serpapi.com/google-maps-api) returns 'local_results' as
list, and old version requested `res["local_results"].keys()` of the
list. As the result we got exception: ```AttributeError: 'list' object
has no attribute 'keys'```.
Way to reproduce wrong behaviour:
```
params = {
"engine": "google_maps",
"type": "search",
"google_domain": "google.de",
"ll": "@51.1917,10.525,14z",
"hl": "de",
"gl": "de",
}
search = SerpAPIWrapper(params=params)
results = search.run("cafe")
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Ran <rccalman@gmail.com>
Because Milvus doesn't support nullable fields, but document metadata is
very rich, so it makes more sense to store it as json.
https://github.com/milvus-io/pymilvus/issues/1705#issuecomment-1731112372
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
BigQuery vector search lets you use GoogleSQL to do semantic search,
using vector indexes for fast but approximate results, or using brute
force for exact results.
This PR integrates LangChain vectorstore with BigQuery Vector Search.
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---------
Co-authored-by: Vlad Kolesnikov <vladkol@google.com>
- **Description:** replace score_threshold with args
- **Issue:** needs a way to pass more options to similarity search
- **Dependencies:** None
- **Twitter handle:** @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
- **Description:** Tool now supports querying over 200 million
scientific articles, vastly expanding its reach beyond the 2 million
articles accessible through Arxiv. This update significantly broadens
access to the entire scope of scientific literature.
- **Dependencies:** semantischolar
https://github.com/danielnsilva/semanticscholar
- **Twitter handle:** @shauryr
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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…tch]: import models from community
ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
```
- easier to write custom logic/loops with automatic tracing
- if you don't want to streaming support write a regular function and
pass to RunnableLambda
- if you do want streaming write a generator and pass it to
RunnableGenerator
```py
import json
from typing import AsyncIterator
from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable, RunnableGenerator, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.tools.render import format_tool_to_openai_function
def _get_tavily():
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
tavily_search = TavilySearchAPIWrapper()
return TavilySearchResults(api_wrapper=tavily_search)
async def _agent_executor_generator(
input: AsyncIterator[list[BaseMessage]],
*,
max_iterations: int = 10,
tools: dict[str, BaseTool],
agent: Runnable[list[BaseMessage], BaseMessage],
parser: Runnable[BaseMessage, AgentAction | AgentFinish],
) -> AsyncIterator[BaseMessage]:
messages = [m async for mm in input for m in mm]
for _ in range(max_iterations):
next_message = await agent.ainvoke(messages)
yield next_message
messages.append(next_message)
parsed = await parser.ainvoke(next_message)
if isinstance(parsed, AgentAction):
result = await tools[parsed.tool].ainvoke(parsed.tool_input)
next_message = FunctionMessage(name=parsed.tool, content=json.dumps(result))
yield next_message
messages.append(next_message)
elif isinstance(parsed, AgentFinish):
return
def get_agent_executor(tools: list[BaseTool], system_message: str):
llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_message),
MessagesPlaceholder(variable_name="messages"),
]
)
llm_with_tools = llm.bind(
functions=[format_tool_to_openai_function(t) for t in tools]
)
agent = {"messages": RunnablePassthrough()} | prompt | llm_with_tools
parser = OpenAIFunctionsAgentOutputParser()
executor = RunnableGenerator(_agent_executor_generator)
return executor.bind(
tools={tool.name for tool in tools}, agent=agent, parser=parser
)
agent = get_agent_executor([_get_tavily()], "You are a very nice agent!")
async def main():
async for message in agent.astream(
[HumanMessage(content="whats the weather in sf tomorrow?")]
):
print(message)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
results in this trace
https://smith.langchain.com/public/fa17f05d-9724-4d08-8fa1-750f8fcd051b/r
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- **Description:** SingleFileFacebookMessengerChatLoader did not handle
the case for when messages had stickers and/or photos so fixed that.
- **Issue:** #15356
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** updates/enhancements to IBM
[watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider
(prompt tuned models and prompt templates deployments support)
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan
- **Twitter handle:** details in comment below.
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally. ✅
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The fix#14221 has broken default gitlab url which is forcing the users
to specify GITLAB_URL for default one. With this fix if GITLAB_URL is
not set, the default gitlab url will be taken.
- **Description:** Add the GITHUB URL instead of None
- **Issue:** the issue #14221 has broken the default github URL
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** manjunath_shiva
- **Description:** This PR adds `api_base` to `_client_params` in the
`chat_model` of LiteLLM to ensure it's included in API calls.
Previously, `api_base` was set on the client but was not included in the
parameters passed to the completion function. This change ensures that
`api_base` is correctly passed to all API calls.
- **Issue:** #14338
- **Tag maintainer:** @hwchase17 @agola11
- **Twitter handle:** @LMS_David_RS
Sometimes, the tool_schema is like:
` {'action_name': 'search_items', 'action': {'term': 'pizza'}}`
sometimes, specially with gpt3.5 it comes like:
`{'action_name': 'search_items', 'term': 'pizza'}`
and it fails.
This PR is a way to make it work in both scenarios.
issues releated: #6624
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-->
Co-authored-by: Lucca Zenobio <lucca.zenobio@ifood.com.br>
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This change addresses the issue where DashScopeEmbeddingAPI limits
requests to 25 lines of data, and DashScopeEmbeddings did not handle
cases with more than 25 lines, leading to errors. I have implemented a
fix to manage data exceeding this limit efficiently.
---------
Co-authored-by: xuxiang <xuxiang@aliyun.com>
Adding to my previously, already merged PR I made some further
improvements:
* Added documentation to the existing Pydantic Parser notebook, with an
example using LCEL and `with_retry()` on `OutputParserException`.
* Added an additional output example to the prompt
* More lenient parser in terms of LLM output format
* Amended unit test
FYI @hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Update _retrieve_ref inside json_schema.py to include
an isdigit() check
- **Issue:** This library is used inside dereference_refs inside
langchain_community.agent_toolkits.openapi.spec. When I read in a yaml
file which has references for "400", "401" etc; the line "out =
out[component]" causes a KeyError. The isdigit() check ensures that if
it is an integer like "400" or "401"; it converts it into integer before
using it as a key to prevent the error.
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description: _python-lint_
This agent writes Python code that is formatted and linted using
`black`, `ruff`, and `mypy`, but does not execute the code. It writes
the code to a temporary file and then runs the linters. Once these
checks pass, the code is returned.
# Dependencies
- black
- ruff
- mypy
# Demo
The functionality can be seen here:
https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo
Added some Headers in steam tool notebook to match consistency with the
other toolkit notebooks
- Dependencies: no new dependencies
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
`integrations/document_loaders/` `Excel` and `OneNote` pages in the
navbar were in the wrong sort order. It is because the file names are
not equal to the page titles.
- renamed `excel` and `onenote` file names
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** Using PGVector vector store, it was only possible to
filter for values equals, in or not in metadata. Extended this feature
to work with the following keywords : IN, NIN, BETWEEN, GT, LT, NE, EQ,
LIKE, CONTAINS, OR, AND
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The regex used to match "Action" and "Action Input" in the output parser
has been updated. Previously, the regex did not correctly handle
multi-line inputs for "Action Input". The updated code uses the
're.DOTALL' flag to ensure multi-line inputs are correctly captured.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:**
- This PR introduces a significant enhancement to the LangChain project
by integrating a new chat model powered by the third-generation base
large model, ChatGLM3, via the zhipuai API.
- This advanced model supports functionalities like function calls, code
interpretation, and intelligent Agent capabilities.
- The additions include the chat model itself, comprehensive
documentation in the form of Python notebook docs, and thorough testing
with both unit and integrated tests.
- **Dependencies:** This update relies on the ZhipuAI package as a key
dependency.
- **Twitter handle:** If this PR receives spotlight attention, we would
be honored to receive a mention for our integration of the advanced
ChatGLM3 model via the ZhipuAI API. Kindly tag us at @kaiwu.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
TO DO: Continue refining and enhancing both the unit tests and
integrated tests.
---------
Co-authored-by: jing <jingguo92@gmail.com>
Co-authored-by: hyy1987 <779003812@qq.com>
Co-authored-by: jianchuanqi <qijianchuan@hotmail.com>
Co-authored-by: lirq <whuclarence@gmail.com>
Co-authored-by: whucalrence <81530213+whucalrence@users.noreply.github.com>
Co-authored-by: Jing Guo <48378126+JaneCrystall@users.noreply.github.com>
Description: Volcano Ark is an enterprise-grade large-model service
platform for developers, providing a full range of functions and
services such as model training, inference, evaluation, fine-tuning. You
can visit its homepage at https://www.volcengine.com/docs/82379/1099455
for details. This change could help developers use the platform for
embedding.
Issue: None
Dependencies: volcengine
Tag maintainer: @baskaryan
Twitter handle: @hinnnnnnnnnnnns
---------
Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>
Updated prompt input suggestions
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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- **Description:** updated the outdated code in the document that was
generating the error,
- **Issue:** #15086 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
**Description:** the MWDumpLoader implementation currently does not
support the lazy_load method, and the files are usually very large. We
are proposing refactoring the load function, extracting two private
functions with the functionality of loading the dump file and parsing a
single page, to reuse the code in the lazy_load implementation.
**Description:**
This PR adds the `**kwargs` parameter to six calls in the `chroma.py`
package. All functions already were able to receive `kwargs` but they
were discarded before.
**Issue:**
When passing `kwargs` to functions in the `chroma.py` package they are
being ignored.
For example:
```
chroma_instance.similarity_search_with_score(
query,
k=100,
include=["metadatas", "documents", "distances", "embeddings"], # this parameter gets ignored
)
```
The `include` parameter does not get passed on to the next function and
does not have any effect.
**Dependencies:**
None
The quickstart doc is missing a few but very simple things that without
them, the code does not work. This PR fixes that by
- Adding commands to install `tiktoken` and `langchainhub`
- Adds a comma between 2 parameters for one of the methods
- **Description:** Fix a few spelling and grammar issues
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** @donovancmuller
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- **Description:** This PR corrects a documentation error in the
`ollama` usage tutorial. Specifically, it fixes a missing `])` in the
`CallbackManager()` example, ensuring that the code snippet is
syntactically correct and can be successfully executed.
- **Issue:** N/A
- **Dependencies:** No additional dependencies are required for this
change.
- **Twitter handle:** My twitter is @yhzhu99
Updated comment for better understanding
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- **Description:**
- support custom kwargs in object initialization. For instantance, QPS
differs from multiple object(chat/completion/embedding with diverse
models), for which global env is not a good choice for configuration.
- **Issue:** no
- **Dependencies:** no
- **Twitter handle:** no
@baskaryan PTAL
These can happen for edge cases not covered by `default` handler (eg.
"strange" keys in dicts)
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- Any direct usage of ThreadPoolExecutor or asyncio.run_in_executor
needs manual handling of context vars
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- **Description:** fix parse issue for AIMessageChunk when using
- **Issue:** https://github.com/langchain-ai/langchain/issues/14511
- **Dependencies:** none
- **Twitter handle:** none
Taken from this fix:
https://github.com/gpt-engineer-org/gpt-engineer/issues/804#issuecomment-1769853850
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@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
removed bad comments
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- **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi
Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream
generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should
be properly handled when set by environment variables or initialization
parameters.
- Changed the `dashscope` response to incremental output by setting the
parameter `incremental_output`, which eliminates the need for the
prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`,
`_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @hwchase17
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and
`Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke
the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Correcting a small typo ('the' instead of 'then') and changing another
'the' (instead of 'then' too, it was a hard day for the 'n' key :D) to
'also' to match better with what is done in the code
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- **Description:** in the code_understanding.ipynb example, the loader
errors out on the
langchain/libs/community/tests/examples/non-utf8-encoding.py file, so I
updated the loader to exclude that file. Excluding that file allows the
example to run.
- **Issue:** not applicable
- **Dependencies:** none
- do not match text after - in the middle of a sentence
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-->
…parse
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```shell
Python 3.11.6 (main, Nov 2 2023, 04:39:43) [Clang 14.0.3 (clang-1403.0.22.14.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> s = {'name': 'gc', 'arguments': '{"prompt":"hi\nbob."}'}
>>> import json
>>> json.loads(s['arguments'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
^^^^^^^^^^^^^^^^^^^^^^
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 14 (char 13)
>>> json.loads(s['arguments'].replace('\n', '\\n'))
{'prompt': 'hi\nbob.'}
>>>
```
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
While using `chain.batch`, the default implementation uses a
`ThreadPoolExecutor` and run the chains in separate threads. An issue
with this approach is that that [the token counting
callback](https://python.langchain.com/docs/modules/callbacks/token_counting)
fails to work as a consequence of the context not being propagated
between threads. This PR adds context propagation to the new threads and
adds some thread synchronization in the OpenAI callback. With this
change, the token counting callback works as intended.
Having the context propagation change would be highly beneficial for
those implementing custom callbacks for similar functionalities as well.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
- Enables strict=False by default
- Uses partial json recovery logic by default
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"If you want to use the provided folder, then simply opt for a [pdf loader](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf) for the document:\n",
"Add raw docs and doc summaries to [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary): \n",
"\n",
"* Store the raw texts, tables, and images in the `docstore`.\n",
"* Store the texts, table summaries, and image summaries in the `vectorstore` for semantic retrieval."
"* Store the texts, table summaries, and image summaries in the `vectorstore` for efficient semantic retrieval."
" \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",
" 1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
"2. Question-Answering:\n",
" 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
"This notebook builds off of [this notebook](/docs/modules/agents/how_to/custom_llm_agent) and assumes familiarity with how agents work.\n",
"\n",
"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.\n",
"\n",
"In this notebook we will create a somewhat contrived 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."
"- To use Azure embeddings with OpenAI V1, you'll need to use the new `AzureOpenAIEmbeddings` instead of the existing `OpenAIEmbeddings`. `OpenAIEmbeddings` continue to work when using Azure with `openai<1`.\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m <tool>search</tool>\n",
"<tool_input>weather in new york\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"\n",
"<final_answer>The weather in New York is 32 degrees\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m <tool>search</tool><tool_input>weather in New York\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m <tool>search</tool>\n",
"<tool_input>weather in New York\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m <final_answer>The weather in New York is 32 degrees\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -159,17 +159,17 @@
{
"data": {
"text/plain": [
"{'question': 'whats the weather in New york?',\n",
"{'input': 'whats the weather in New york?',\n",
" 'output': 'The weather in New York is 32 degrees'}"
]
},
"execution_count": 9,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.invoke({\"question\": \"whats the weather in New york?\"})"
"agent_executor.invoke({\"input\": \"whats the weather in New york?\"})"
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