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I_kwDOIPDwls53v1-W
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Issue: please assist me in removing myself from the Discord blacklist.
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[] | null | 1 | 2023-11-24T03:13:29 | 2023-11-30T21:19:17 | null |
CONTRIBUTOR
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### Issue you'd like to raise.
it seems my discord account has been hacked, so it auto send advertisement like below, and i thought it was official ads just like telegram and the ads only show to me, so I don't pay attention to it.
<img width="503" alt="image" src="https://github.com/langchain-ai/langchain/assets/1664952/8a048285-bd18-408e-9a31-0d2c57e1ef17">
Now i can't join discord server as expect.
I have enable 2FA and change my password, so could your please assist me in removing myself from the Discord blacklist. my account id is "h3l1221", thanks a low .
### Suggestion:
_No response_
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I_kwDOIPDwls53vpSC
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Issue: can i define an agent using a llm chain or a conversationcain as a too?
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[] | null | 9 | 2023-11-24T02:15:35 | 2023-11-28T02:00:03 | null |
NONE
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### Issue you'd like to raise.
can i define an agent using a llm chain or a conversationcain as a too?
### Suggestion:
_No response_
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Mac nov23
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gQ88r
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Added SecureStr for NLP Cloud
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NONE
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Description: Updated nlpcloud.py with SecureStr
Issue: #12165
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`kaggle` document loader
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COLLABORATOR
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Added the `Kaggle` datasets as a DocumentLoader
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bump 0.0.339rc2
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[] | null | 1 | 2023-11-23T20:40:58 | 2023-11-23T20:50:10 | 2023-11-23T20:50:10 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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|
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PR_kwDOIPDwls5gQgIL
| 13,786 |
RELEASE: core 0.0.5
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[] | null | 1 | 2023-11-23T20:23:44 | 2023-11-23T20:23:52 | 2023-11-23T20:23:50 |
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I_kwDOIPDwls53uYsf
| 13,785 |
Azure OpenAI (with openai module 1.x.x) seems not working anymore
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[] | null | 7 | 2023-11-23T18:06:59 | 2024-01-03T16:10:19 | null |
NONE
| null |
### System Info
$ python3 --version
Python 3.11.6
$ pip show openai | grep Version
Version: 1.3.5
$ pip show langchain | grep Version
Version: 0.0.340
### Who can help?
Who wants to use AzureOpenai deployments with langchain, enabling last openai package versione 1.x.x.
### Information
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
### Reproduction
Hi @hwchase17, @agola11, all!
Thanks in advance for your huge work.
I so far successfully used langchain with the openai module 0.28.x.
Today I upgraded the openai package to the latest version (1.x.x), and I installed also the latest langchain package version.
I configured the environment variables (as described above) and I run the following simple program, as described in langchain documentation: https://python.langchain.com/docs/integrations/chat/azure_chat_openai
```
$ source path/.langchain_azure.env
$ cat path/.langchain_azure.env
# https://python.langchain.com/docs/integrations/llms/azure_openai
export OPENAI_API_TYPE=azure
export OPENAI_API_VERSION=2023-09-01-preview
export AZURE_OPENAI_ENDPOINT="https://xxxxxxxxxxx.openai.azure.com/"
export AZURE_OPENAI_API_KEY="XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
```
Program that generate a run-time exception:
```python
# langchain_simple.py
# this example follows the langchain documentation example at: https://python.langchain.com/docs/integrations/chat/azure_chat_openai
import os
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
model = AzureChatOpenAI(
openai_api_version="2023-05-15",
azure_deployment="gpt-35-turbo" # my existing deployment name
)
message = HumanMessage(content="Translate this sentence from English to French. I love programming.")
model([message])
```
I got the following exception:
```
$ py langchain_simple.py
/home/giorgio/.local/lib/python3.11/site-packages/langchain/chat_models/azure_openai.py:162: UserWarning: As of openai>=1.0.0, if `deployment_name` (or alias `azure_deployment`) is specified then `openai_api_base` (or alias `base_url`) should not be. Instead use `deployment_name` (or alias `azure_deployment`) and `azure_endpoint`.
warnings.warn(
/home/giorgio/.local/lib/python3.11/site-packages/langchain/chat_models/azure_openai.py:170: UserWarning: As of openai>=1.0.0, if `openai_api_base` (or alias `base_url`) is specified it is expected to be of the form https://example-resource.azure.openai.com/openai/deployments/example-deployment. Updating https://openai-convai.openai.azure.com/ to https://openai-convai.openai.azure.com/.
warnings.warn(
Traceback (most recent call last):
File "/home/giorgio/gpt/langchain/langchain_simple.py", line 9, in <module>
model = AzureChatOpenAI(
^^^^^^^^^^^^^^^^
File "/home/giorgio/.local/lib/python3.11/site-packages/langchain/load/serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "/home/giorgio/.local/lib/python3.11/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for AzureChatOpenAI
__root__
base_url and azure_endpoint are mutually exclusive (type=value_error)
```
BTW, I do not understand exactly the error sentence:
```base_url and azure_endpoint are mutually exclusive (type=value_error)```
Where am I wrong?
Thanks
Giorgio
### Expected behavior
I didn't expect to have run-time errors
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PR_kwDOIPDwls5gQGVD
| 13,784 |
Astra DB vector store, move constructor docstring to class docstring
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[] | null | 1 | 2023-11-23T17:54:43 | 2023-11-28T21:25:45 | 2023-11-28T21:25:45 |
CONTRIBUTOR
| null |
This PR rearranges the docstring for the `AstraDB` vector store class so as to have all useful information in the _class_ docstring for ease of reading.
(incidentally, due to an oversight, the docstring that was in the constructor ended up buried below some lines of code, thereby disappearing altogether from accessibility. Apologies.)
|
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| 2,008,609,445 |
I_kwDOIPDwls53uPKl
| 13,783 |
How to add json_object to ChatOpenAI class?
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[] | null | 5 | 2023-11-23T17:26:32 | 2023-12-11T20:59:25 | null |
NONE
| null |
### Issue you'd like to raise.
How can I add the new parameter **JSON MODE** ( https://platform.openai.com/docs/guides/text-generation/json-mode )
to this snippet of code?
```python
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(
model_name="gpt-3.5-turbo-1106",
temperature=1,
max_tokens=None
)
```
I see in openai it should be used in this way:
```python
response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
response_format={ "type": "json_object" },
messages=[
{"role": "system", "content": "You are a helpful assistant designed to output JSON."},
{"role": "user", "content": "Who won the world series in 2020?"}
]
)
```
Thanks!!
### Suggestion:
_No response_
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Support auth header, when underlying client support it.
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[] | null | 1 | 2023-11-23T16:46:27 | 2023-11-24T10:33:54 | null |
NONE
| null |
### Feature request
Support auth token headers in langchain, when underlying model supports it.
### Motivation
I'd like to use ChatAnthropic, but I'm using a custom proxy backend, where I need to provide Authorization header.
This is simple using AnthropicClient directly as it accepts auth_token parameter.
Problem is, langchain ChatAnthropic abstraction, doesn't accept this param.
It would be great to have an option to pass auth_token to model, when model supports it.
### Your contribution
I don't feel comfortable in this codebase to create a PR
|
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PR_kwDOIPDwls5gPy3A
| 13,781 |
Allow returning tokens usage counts
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[] | null | 2 | 2023-11-23T16:35:31 | 2023-11-29T03:09:02 | 2023-11-29T03:09:02 |
CONTRIBUTOR
| null |
Small change to allow returning tokens usage counts from OctoAI endpoints
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PR_kwDOIPDwls5gPgDr
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AstraDB VectorStore: implement pre_delete_collection
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[] | null | 3 | 2023-11-23T15:34:21 | 2023-12-03T20:06:20 | 2023-12-03T20:06:20 |
CONTRIBUTOR
| null |
- **Description:** some vector stores have a flag for try deleting the collection before creating it (such as ´vectorpg´). This is a useful flag when prototyping indexing pipelines and also for integration tests. Added the bool flag `pre_delete_collection ` to the constructor (default False)
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
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I_kwDOIPDwls53sufq
| 13,778 |
Issue: how to access to cached question and answer in InMemoryCache
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[] | null | 7 | 2023-11-23T13:14:19 | 2023-12-06T06:19:45 | 2023-12-06T06:19:45 |
NONE
| null |
### Issue you'd like to raise.
InMemoryCache works, but it doesn't print to stderr like all other LLM responses. I checked logs and response is stored. I don't know how to access to this stored values...
### Suggestion:
option to view object or option to return values.
|
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I_kwDOIPDwls53r7Di
| 13,776 |
DOC: Simplify CONTRIBUTING.md by removing conda and pip references
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[] | null | 1 | 2023-11-23T11:09:26 | 2023-11-23T11:09:50 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
if "our goal is to have the simplest developer setup possible", we shouldn't encourage mixing poetry with the use of conda and pip in [CONTRIBUTING.md](https://github.com/langchain-ai/langchain/blob/5ae51a8a85d1a37ea98afeaf639a72ac74a50523/.github/CONTRIBUTING.md). Mixing different tools for the same job is error prone and leads to confusion for new contributors.
Specifically, the following lines should be reconsidered:
❗Note: Before installing Poetry, if you use Conda, create and activate a new Conda env (e.g. conda create -n langchain python=3.9) ([link](https://github.com/langchain-ai/langchain/blob/5ae51a8a85d1a37ea98afeaf639a72ac74a50523/.github/CONTRIBUTING.md#:~:text=%E2%9D%97Note%3A%20Before%20installing%20Poetry%2C%20if%20you%20use%20Conda%2C%20create%20and%20activate%20a%20new%20Conda%20env%20(e.g.%20conda%20create%20%2Dn%20langchain%20python%3D3.9)))
❗Note: If you use Conda or Pyenv as your environment/package manager, after installing Poetry, tell Poetry to use the virtualenv python environment (poetry config virtualenvs.prefer-active-python true) ([link](https://github.com/langchain-ai/langchain/blob/5ae51a8a85d1a37ea98afeaf639a72ac74a50523/.github/CONTRIBUTING.md#:~:text=%E2%9D%97Note%3A%20If%20you%20use%20Conda%20or%20Pyenv%20as%20your%20environment/package%20manager%2C%20after%20installing%20Poetry%2C%20tell%20Poetry%20to%20use%20the%20virtualenv%20python%20environment%20(poetry%20config%20virtualenvs.prefer%2Dactive%2Dpython%20true)))
If the tests don't pass, you may need to pip install additional dependencies, such as numexpr and openapi_schema_pydantic. ([link](https://github.com/langchain-ai/langchain/blob/5ae51a8a85d1a37ea98afeaf639a72ac74a50523/.github/CONTRIBUTING.md#:~:text=If%20the%20tests%20don%27t%20pass%2C%20you%20may%20need%20to%20pip%20install%20additional%20dependencies%2C%20such%20as%20numexpr%20and%20openapi_schema_pydantic.))
### Idea or request for content:
_No response_
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Ensure AstraDB integration tests clean up the environment
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[] | null | 3 | 2023-11-23T09:49:32 | 2023-12-03T19:14:43 | 2023-12-03T19:14:42 |
CONTRIBUTOR
| null |
- **Description:** currently astra_db integration tests might leave orphan collections
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
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I_kwDOIPDwls53q898
| 13,772 |
how to chain the lenght of context if i wanted use gpt-4 model because of increase token i can't use gpt 4 so how to chain the context
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[] | null | 1 | 2023-11-23T08:57:22 | 2023-11-23T09:04:02 | null |
NONE
| null |
### Issue with current documentation:
(import pandas as pd
import json
from IPython.display import Markdown, display
from langchain.agents import create_csv_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
import os
os.environ["OPENAI_API_KEY"] = ""
# Load the dataset
df = pd.read_csv('Loan Collections - Sheet1.csv')
def convert_columns_to_float_by_keywords(df):
# Convert date and time columns to float
for column in df.select_dtypes(include=['object']).columns:
# Check if the column name contains 'date' or 'time'
if 'date' in column.lower() or 'time' in column.lower() or 'dob' in column.lower() or 'date of birth' in column.lower():
try:
# Convert the column to datetime
df[column] = pd.to_datetime(df[column], errors='coerce')
# Convert datetime to numerical representation (e.g., days since a reference date)
reference_date = pd.to_datetime('1900-01-01')
df[column] = (df[column] - reference_date).dt.total_seconds() / (24 * 60 * 60)
except ValueError:
# Handle errors during conversion
print(f"Error converting column '{column}' to float.")
# Convert columns with specific keywords to float
keywords_to_convert = ["unique id", "reference id", "account id"]
for column in df.columns:
# Check if the column name contains any of the specified keywords
if any(keyword in column.lower() for keyword in keywords_to_convert):
try:
# Convert the column to float
df[column] = pd.to_numeric(df[column], errors='coerce')
except ValueError:
# Handle errors during conversion
print(f"Error converting column '{column}' to float.")
# Convert 'date' and 'time' columns to float
convert_columns_to_float_by_keywords(df)
# Extract unique values for each column
unique_values_per_column = {}
for column in df.select_dtypes(include=['object']).columns:
unique_values_per_column[column] = df[column].unique().tolist()
# Convert the dictionary to JSON
json_data_train = json.dumps(unique_values_per_column, indent=4)
testData_fname = "Mutual Funds 2023 - Mutual Funds Data Final File (1).csv"
# Load the dataset
df2 = pd.read_csv(testData_fname)
convert_columns_to_float_by_keywords(df2)
# Extract unique values for each column
unique_values_per_column = {}
for column in df2.select_dtypes(include=['object']).columns:
unique_values_per_column[column] = df2[column].unique().tolist()
# Convert the dictionary to JSON
json_data_test = json.dumps(unique_values_per_column, indent=4)
# Define user's question
user_question = "monthly growth analysis of Broker Commission ?"
# Define the prompt template
prompt_template = f'''If the dataset has the following columns: {json_data_train}'''+''' Understand user questions with different column names and convert them to a JSON format.
Question might not even mentioned column name at all, it would probably mention value of the column. so it has to figure it out columnn name based on that value.
Example1:
User Question1: top zone in the year 2019 with Loan Amt between 10k and 20k and tenure > 12 excluding Texas region?
{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": [],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "null",
"variables_grpby": ["Zone"],
"filters": {},
"not_in": {"Region": ["Texas"]},
"num_filter": {
"gt": [
["Loan Tenure", 12],
["Loan Amount", 10000]
],
"lt": [
["Loan Amount", 20000]
]
},
"percent": "false",
"top": "1",
"bottom": "null"
}
Note the following in the above example
- The word "top" in the User Question made the "top" key have the value as "1". If "highest" is mentioned in the User Question, even then "top" would have the value as "1". If "top" is not mentioned or not implied in the User Question, then it takes on the value "null". Similarly for "bottom" key in the System Response.
- The word "zone" in the User Question refers to a column "Zone" in the dataset and since it is a non-numeric column and we have to group by that column, the system response has it as one of the values of the list of the key "variables_grpby"
- The key "start_date" and "end_date" Since it is mentioned 2019 in the User Question as the timeframe, the "start_date" assumes the beginning of the year 2019 and "end_date" assumes the end of the year 2019. If no date related words are mentioned in the question, "start_date" would be "null" and "end_date" would be "null".
- The key "time_stamp_col" in the System Response should mention the relevant time related column name from the dataset according to the question if the question mentions a time related word.
- The key "agg_columns" in the System Response is a list of columns to be aggregated which should mention the numeric column names on which the question wants us to aggregate on.
- The key "trend" in the System Response, "trend" is set to "null" since the user question doesn't imply any trend analysis . If the question were about trends over time, this key would contain information about the trend, such as "upward," "downward," or "null" if no trend is specified.
- The key "filters" An empty dictionary in this case, as there are no explicit filters mentioned in the user question. If the user asked to filter data based on certain conditions (e.g. excluding a specific region), this key would contain the relevant filters.
- The key "to_start_date" and "to_end_date" Both set to "null" in this example because the user question specifies a single timeframe (2019). If the question mentioned a range (e.g. "from January 2019 to March 2019"), these keys would capture the specified range.
- The key "growth" Set to "null" in this example as there is no mention of growth in the user question. If the user inquired about growth or change over time, this key would provide information about the type of growth (e.g."monthly","yearly"," "absolute") or be set to "null" if not applicable.
- The key "not_in" Contains information about exclusion criteria based on the user's question. In this example, it excludes the "Texas" region. If the user question doesn't involve exclusions, this key would be an empty dictionary.
- The key "num_filter" Specifies numerical filters based on conditions in the user question. In this example, it filters loans with a tenure greater than 12 and loan amounts between 10k and 20k. If the user question doesn't involve numerical filters, this key would be an empty dictionary.
- The key "percent" Set to "false" in this example as there is no mention of percentage in the user question. If the user inquired about percentages, this key would contain information about the use of percentages in the response.
Similarly, below are more examples of user questions and their corresponding expected System Responses.
Example 2:
User Question: What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020
{
"start_date": "01-01-2020",
"end_date": "31-01-2020",
"time_stamp_col": "Due Date",
"agg_columns": ["Loan Amount", "Loan Outstanding"],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "null",
"variables_grpby": [],
"filters": {"RM Name": ["James"]},
"not_in": {},
"num_filter": {},
"percent": "false",
"top": "1",
"bottom": "null"
}
Example 3:
User Question: Which RM Name with respect to Region has the Highest Interest Outstanding and Principal Outstanding in the year 2019
{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Interest Outstanding", "Principal Outstanding"],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "null",
"variables_grpby": ["RM Name", "Region"],
"filters": {},
"not_in": {},
"num_filter": {},
"percent": "false",
"top": "1",
"bottom": "null"
}
Example 4:
User Question: Which Branch in North Carolina with respect to Cibil Score Bucket has the Highest Cibil Score in 2019
{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Cibil Score", "DPD Bucket"],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "null",
"variables_grpby": ["Branch"],
"filters": {"Region": ["North Carolina"]},
"not_in": {},
"num_filter": {},
"percent": "false",
"top": "1",
"bottom": "null"
}
''
Example 5:
User Question: With respect to Zone, Region, Branch, RM Name what is the Highest Loan Amount, Loan Tenure, Loan Outstanding, EMI Pending, Principal Outstanding
{
"start_date": "null",
"end_date": "null",
"time_stamp_col": "null",
"agg_columns": ["Loan Amount", "Loan Tenure", "Loan Outstanding", "EMI Pending", "Principal Outstanding"],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "null",
"variables_grpby": ["Zone", "Region", "Branch", "RM Name"],
"filters": {},
"not_in": {},
"num_filter": {},
"percent": "false",
"top": "1",
"bottom": "null"
}
Example 6:
User Question: Top 2 zones by Housing Loan in the year 2019
{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Housing Loan"],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "null",
"variables_grpby": ["Zone"],
"filters": {"Product": ["Home Loan"]},
"not_in": {},
"num_filter": {},
"percent": "false",
"top": "2",
"bottom": "null"
}
Example 7:
User Question: yearly growth analysis by Due Date of Loan Amount?
{
"start_date": "null",
"end_date": "null",
"time_stamp_col": "Due Date",
"agg_columns": ["Loan Amount"],
"trend": "null",
"to_start_date": "null",
"to_end_date": "null",
"growth": "yearly",
"variables_grpby": ["Due Date"],
"filters": {},
"not_in": {},
"num_filter": {},
"percent": "false",
"top": "null",
"bottom": "null"
}
'''+ f'''Our test dataset has the following columns: {json_data_test}
User Question (to be converted): {user_question}'''
# Set the context length
context_length = 3 # Set your desired context length here
# Load the agent with GPT-4 and the specified context length
gpt4_agent = create_csv_agent(ChatOpenAI(temperature=0, model_name="gpt-4"), testData_fname, context_length=context_length)
# Use the formatted question as the input to your agent
response = gpt4_agent.run(prompt_template)
# Print the response
print(user_question)
print(response))
and the error is ( RateLimitError: Rate limit reached for gpt-4 in organization org-bJurmVX4HBor6BJfUF9Q6miB on tokens per min (TPM): Limit 10000, Used 1269, Requested 9015. Please try again in 1.704s. Visit https://platform.openai.com/account/rate-limits to learn more.) so try to run gpt-4 model but chaining the lenght contex
### Idea or request for content:
_No response_
|
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| 2,007,681,270 |
I_kwDOIPDwls53qsj2
| 13,771 |
Issue: Conversational retrieval chain, response_if_no_docs_found not working
|
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open
| false | null |
[] | null | 2 | 2023-11-23T08:17:41 | 2023-11-23T08:24:54 | null |
NONE
| null |
### Issue you'd like to raise.
It stated in the docs that there's a parameter called response_if_no_docs_found, which If specified, the chain will return a fixed response if no docs
are found for the question.
I have tried it and asked another person, who says they tried it and it doesn't work,
how do i get it to work
because its a very important aspect
### Suggestion:
_No response_
|
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| 2,007,675,246 |
I_kwDOIPDwls53qrFu
| 13,770 |
JSON response support for Ollama
|
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[] | null | 1 | 2023-11-23T08:13:02 | 2023-11-23T08:13:19 | null |
NONE
| null |
### Feature request
JS LangChain (https://js.langchain.com/docs/integrations/chat/ollama_functions) supports Ollama Functions and allows to return JSON output from Ollama. It would be good to have the same functionality in Python LangChain.
### Motivation
JSON response from LangChain Ollama would be very useful, when integrating LangChain into API pipelines running on local environments
### Your contribution
I could test the implementation.
|
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PR_kwDOIPDwls5gMs2S
| 13,769 |
Repair Wikipedia document loader `load_max_docs` and improve test coverage.
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[] | null | 2 | 2023-11-23T07:25:37 | 2023-11-29T07:07:52 | 2023-11-29T01:26:40 |
CONTRIBUTOR
| null |
**Description:**
Repair Wikipedia document loader `load_max_docs` and improve test coverage.
**Issue:**
The Wikipedia document loader was not respecting the `load_max_docs` paramater (not reported) and would always return a maximum of 10 documents. This is because the API wrapper (in `utilities/wikipedia.py`) wasn't passing `top_k_results` to the underlying [Wikipedia library](https://wikipedia.readthedocs.io/en/latest/code.html#module-wikipedia). By default this library returns 10 results.
The default number of results for the document loader has been reduced from 100 to 25. This is because loading 100 results takes a very long time and is an inconvenient default. It should possibly be 10.
In addition, the documentation for the loader reported that there was a hard limit (300) on the number of documents returned. In actuality 300 is the maximum Wikipedia query character length set by the API wrapper.
Tests have been added for the document loader (previously missing) and to test the correct numbers of documents are being returned by each class, both by default, and when overridden. Also repaired is the `assert_docs` test which has been updated to correctly test for the default metadata (which includes `source` in recent releases).
**Dependencies:**
nil
**Tag maintainer:**
@leo-gan
**Twitter handle:**
@queenvictoria
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PR_kwDOIPDwls5gMsHn
| 13,768 |
Fix Azure Openai function calling in streaming mode
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[] | null | 1 | 2023-11-23T07:22:34 | 2023-12-03T20:07:16 | 2023-12-03T20:07:15 |
CONTRIBUTOR
| null |
- **Description**: This PR addresses an issue with the OpenAI API streaming response, where initially the key (arguments) is provided but the value is None. Subsequently, it updates with {"arguments": "{\n"}, leading to a type inconsistency that causes an exception. The specific error encountered is ValueError: additional_kwargs["arguments"] already exists in this message, but with a different type. This change aims to resolve this inconsistency and ensure smooth API interactions.
- **Issue**: None.
- **Dependencies**: None.
- **Tag maintainer**: @eyurtsev
This is an updated version of #13229 based on the refactored code. Credit goes to @superken01.
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PR_kwDOIPDwls5gMpp-
| 13,767 |
Update Hologres vector store: use hologres-vector
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[] | null | 1 | 2023-11-23T07:12:04 | 2023-12-03T19:50:46 | 2023-12-03T19:50:45 |
CONTRIBUTOR
| null |
Hi,
I made some code changes on the Hologres vector store to improve the data insertion performance.
Also, this version of the code uses `hologres-vector` library. This library is more convenient for us to update, and more efficient in performance.
The code has passed the format/lint/spell check. I have run the unit test for Hologres connecting to my own database.
Please check this PR again and tell me if anything needs to change.
Best,
Changgeng,
Developer @ Alibaba Cloud
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I_kwDOIPDwls53qUlP
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_TextGenerationModel.predict() got an unexpected keyword argument 'functions'Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 4 | 2023-11-23T06:51:29 | 2023-12-04T04:13:10 | null |
NONE
| null |
### Issue you'd like to raise.
I am trying to create a vertex ai and langchain based entity extraction program. I have stored my documents in form of vector embeddings in chromadb. I have been trying to extract the attributes/features like name, price, etc but whenever i run chains I'm getting this error.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gMGl0
| 13,764 |
Update agents\conversational\output_parser.py
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NONE
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The regular expression regex = r"Action: (.*?)[\n]*Action Input: (.*)", with .* being the default greedy match.This results in the action_input variable not including the part after the newline character.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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small fix matching engine AttributeError - object has no attribute
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[] | null | 2 | 2023-11-23T03:48:41 | 2023-11-29T03:42:30 | 2023-11-29T03:42:30 |
CONTRIBUTOR
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This PR is fixing an attributeError: object endpoint has no attribute "_public_match_client" when using gcp matching engine with private VPC network.
@baskaryan, @eyurtsev, @hwchase17.
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| 2,007,439,169 |
I_kwDOIPDwls53pxdB
| 13,762 |
Changing Model Param after Initialization VLLM Model
|
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| false | null |
[] | null | 2 | 2023-11-23T03:45:30 | 2024-01-15T12:38:27 | null |
NONE
| null |
### Feature request
Introduce a more flexible method to specify model parameters directly within the LLMChain or VLLM method, allowing users to modify these parameters without creating a new model instance each time.
### Motivation
Currently, when utilizing the Langchain library's VLLM class, modifying model parameters (such as temperature, top_k, top_p, etc.) requires creating a new instance of the VLLM class. This process becomes cumbersome, especially when frequent adjustments to model parameters are necessary.
Current Situation:
`
from langchain.llms import VLLM
llm = VLLM(
model="mosaicml/mpt-7b",
trust_remote_code=True, # mandatory for hf models
max_new_tokens=128,
top_k=10,
top_p=0.95,
temperature=0.8,
)
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "Who was the US president in the year the first Pokemon game was released?"
print(llm_chain.run(question))
`
It is very tedious to keep creating new model when i want to change model params like (temperature and etc) .
In naive VLLM, this can be done so easily:
llm = LLM(model="qwen/Qwen-7B-Chat", revision="v1.1.8", trust_remote_code=True)
outputs = llm.generate(prompts, model_params)
outputs = llm.generate(prompts, model_params)
### Your contribution
NA
|
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| 2,007,425,696 |
PR_kwDOIPDwls5gMDHS
| 13,761 |
RFC: Serialization tests
|
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[] | null | 1 | 2023-11-23T03:25:32 | 2023-12-14T02:53:39 | 2023-12-14T02:53:39 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 2,007,415,465 |
I_kwDOIPDwls53prqp
| 13,760 |
Combine Tools Together in a Chain
|
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| false | null |
[] | null | 1 | 2023-11-23T03:12:58 | 2023-11-23T03:17:25 | null |
NONE
| null |
### Issue you'd like to raise.
I got 2 functions and 2 related tools:
def Func_Tool_Extractor(parameters):
print("\n", parameters)
print("触发Func_Tool_Extractor插件")
pdf_id, extractor_id_position = parameters.split(",")
print(f"获取到pdf id为{pdf_id}，这篇文档用户想要查询第{extractor_id_position}个分子")
test_smiles = "C1C2=C3CCC3=C2CC1"
extractor_id = 1876
print(f"查询到第{extractor_id_position}个分子的smiles为{test_smiles}, extractor_id为{extractor_id}")
answer = f"查询到分子的SMILES为{test_smiles}, extractor_id为{extractor_id}, pdf_id为{pdf_id}"
return answer
def Func_Tool_ADMET(parameters):
print("\n", parameters)
print("触发Func_Tool_ADMET插件")
print("........正在解析ADMET属性..........")
data = {
"id": "2567",
"smiles": "C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1",
"humanIntestinalAbsorption": "HIA+|0.73",
"caco2Permeability": "None",
"caco2PermeabilityIi": "Caco2+|0.70",
"pGlycoproteinInhibitorI": "Pgp_nonInhibitor|0.51",
"pGlycoproteinInhibitorIi": "Pgp_Inhibitor|0.68",
"pGlycoproteinSubstrate": "substrate|0.56",
"bloodBrainBarrier": "BBB+|0.73",
"cyp4501a2Inhibitor": "Inhibitor|0.73",
"cyp4502c19Inhibitor": "Inhibitor|0.68",
"cyp4502c9Inhibitor": "Non_Inhibitor|0.53",
"cyp4502c9Substrate": "non-substrate|0.59",
"cyp4502d6Inhibitor": "Non_Inhibitor|0.65",
"cyp4502d6Substrate": "substrate|0.55",
"cyp4503a4Inhibitor": "Non_Inhibitor|0.71",
"cyp4503a4Substrate": "non_substrate|0.52",
"cypInhibitorPromiscuity": "High CYP Inhibitory Promiscuity|0.61",
"biodegradation": "Not ready biodegradable|0.64",
"renalOrganicCationTransporter": "inhibitor|0.64",
"amesToxicity": "Non AMES toxic|0.66",
"carcinogens": "non_carcinogens|0.71",
"humanEtherAGoGoRelatedGeneInhibitionI": "Weak inhibitor|0.60",
"humanEtherAGoGoRelatedGeneInhibitionIi": "Weak inhibitor|0.53",
"honeyBeeToxicity": "highAT|0.56",
"tetrahymenaPyriformisToxicity": "None",
"tetrahymenaPyriformisToxicityIi": "non-TPT|0.73",
"fishToxicity": "None",
"fishToxicityIi": "High FHMT|0.73",
"aqueousSolubility": "None",
"savePath": "/profile/chemicalAppsResult/admetResult/2023/11/10/2567",
"status": "1",
"favoriteFlag": "0"
}
return data
# Define the tools
tool1 = Tool(
name="Tool_Extractor",
func=Func_Tool_Extractor,
description="""
useful when you want to get a molecule from a document.
like: get the 3rd molecule of the document.
The input of this tool should be comma separated string of two, representing the pdf_id and the number of the molecule to be gotten.
"""
)
tool2 = Tool(
name="Tool_ADMET",
func=Func_Tool_ADMET,
description="""
useful when you want to obtain the ADMET data for a molecule.
like: get the ADMET data for molecule X
The input to this tool should be a string, representing the SMILES of the molecule.
"""
)
Give me a CONVERSATIONAL_REACT_DESCRIPTION code example to use these two tools, and the following requirements should be met:
1. Use initialize_agent method to initialize the agent as needed;
2. The output of tool2 should not be modified by LLM or further processed by the agent chain, avoiding data elimination caused by the thoughts made by LLM models;
3. Use memory to keep history chat messages;
4. Use prompt templates to customize the outputs, especially for the tool2.
### Suggestion:
_No response_
|
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| 2,007,339,223 |
PR_kwDOIPDwls5gLwpe
| 13,759 |
Revert "INFRA: temp rm master condition (#13753)"
|
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[] | null | 1 | 2023-11-23T01:21:59 | 2023-11-24T08:03:53 | 2023-11-23T01:22:07 |
COLLABORATOR
| null | null |
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| 2,007,320,637 |
I_kwDOIPDwls53pUg9
| 13,758 |
Pinecone MultiqueryRetriver with multiple OR metadata filters
|
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[] | null | 2 | 2023-11-23T00:55:10 | 2023-11-23T01:59:43 | null |
NONE
| null |
### Issue you'd like to raise.
HI there, I am trying to use Multiquery retiever on pinecone vectordb with multiple filters. I specifically need to use an OR operator.
For example, I would like to retrieve documents with metadata having the metadata "category" : 'value1' OR 'value2' OR 'value3'
this is my current implementation:
```
import pinecone
from langchain.llms import Cohere
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.vectorstores import Pinecone
pinecone.init(api_key=pine_api_key, environment=pinecone_env)
index_name = "index_name"
index = pinecone.Index(index_name)
vectorstore = Pinecone(index, embeddings, "text")
llm = Cohere(cohere_api_key=cohere_api_key)
retriever_from_llm = MultiQueryRetriever.from_llm(
retriever=vectorstore.as_retriever(search_kwargs={"k": 10,
'filter': {'user_id': '42',
'category': "c1"}}), llm=llm
)
```
At this point I am able to sucessfully filter by user_id and category when it is only one value, but when I add more values as such
'category': ["c1", "c2", "c3"].
It does not work, when retrieving documents as such:
```
question = "what is foo?"
unique_docs = retriever_from_llm.get_relevant_documents(query=question)
```
Error:
```
ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'date': 'Thu, 23 Nov 2023 00:24:58 GMT', 'x-envoy-upstream-service-time': '0', 'content-length': '108', 'server': 'envoy'})
HTTP response body: {"code":3,"message":"illegal condition for field category, got ["c1","c2"]","details":[]}.
```
How can I add a filters that returns documents with category c1 Or c2 Or c3, using pincone and MultiqueryRetriver?
### Suggestion:
_No response_
|
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I_kwDOIPDwls53pTZR
| 13,757 |
How to use a different OpenAI endpoint when Azure content filter applys ?
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[] | null | 3 | 2023-11-23T00:47:25 | 2023-11-24T12:07:04 | null |
CONTRIBUTOR
| null |
### Feature request
When I receive empty text from the Azure content filter, I need to temporarily switch to OpenAI and try again to get an answer.
How can I achieve this functionality?
### Motivation
Error handling for prod. High availability
### Your contribution
pr
|
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PR_kwDOIPDwls5gLqZP
| 13,756 |
refactor: Reduce duplicate code of import openai
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1. a test for the integration, preferably unit tests that do not rely on network access,
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| 2,007,263,917 |
I_kwDOIPDwls53pGqt
| 13,755 |
MessageHistory type field serialized twice twice
|
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[] | null | 1 | 2023-11-22T23:55:16 | 2023-11-22T23:58:07 | null |
NONE
| null |
```python
from langchain.memory import RedisChatMessageHistory
history = RedisChatMessageHistory("foo")
history.add_user_message("hi!")
history.add_ai_message("whats up?")
```
{**<ins>"type": "human"**</ins>, "data": {"content": "hi!", "additional_kwargs": {}, <ins>**"type": "human"**</ins>, "example": false}}
I suspect it's a bug in the serialization or does it serve any purpose being in that structure with a duplicate k/v?
https://github.com/langchain-ai/langchain/blob/163bf165ed2d6ae453aad1bca4ed56814d81bf5b/libs/core/langchain_core/messages/base.py#L113C1-L115C1
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PR_kwDOIPDwls5gLj4p
| 13,754 |
IMPROVEMENT: filter global warnings properly
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PR_kwDOIPDwls5gLgdq
| 13,753 |
rm master condition
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[] | null | 1 | 2023-11-22T23:30:27 | 2023-11-24T08:04:18 | 2023-11-23T00:59:50 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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| 13,752 |
Bagatur/patch 0.0.340
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[] | null | 1 | 2023-11-22T23:11:54 | 2023-11-23T01:19:51 | 2023-11-23T01:19:51 |
COLLABORATOR
| null | null |
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| 2,007,226,855 |
I_kwDOIPDwls53o9nn
| 13,751 |
GenericLoader.from_filesystem "exclude" not working
|
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| false | null |
[] | null | 4 | 2023-11-22T23:08:00 | 2023-11-27T18:43:20 | null |
NONE
| null |
### System Info
Python 3.9.6, Langchain 0.0.334
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm experimenting with some simple code to load a local repository to test CodeLlama, but the "exclude" in GenericLoader.from_filesystem seems not working:
`from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import LanguageParser
from langchain.text_splitter import Language
repo_path = "../../my/laravel/project/"
# Load
loader = GenericLoader.from_filesystem(
repo_path,
glob="**/*",
suffixes=[".php"],
parser=LanguageParser(
parser_threshold=2000,
),
exclude=["../../my/laravel/project/vendor/", "../../my/laravel/project/node_modules/", "../../my/laravel/project/storage/", "../../my/laravel/project/public/", "../../my/laravel/project/tests/", "../../my/laravel/project/resources/"]
)
documents = loader.load()
len(documents)
`
Am I missing something obvious? I cannot find any example...with or without the exclude, the length of docs is the same (and if I just print "documents" I see files in the folders I excluded).
### Expected behavior
I would expect that listing subpaths from the main path then these would be excluded.
|
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| 2,007,218,514 |
I_kwDOIPDwls53o7lS
| 13,750 |
Random Application Lockdown on ECS Fargate with Langchain and FastAPI
|
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| false | null |
[] | null | 2 | 2023-11-22T22:56:07 | 2023-11-22T23:17:05 | null |
NONE
| null |
### System Info
**Operating system/architecture:**
Linux/X86_64
**CPU | Memory**
8 vCPU | 16 GB
**Platform version**
1.4.0
**Launch type**
FARGATE
**Project libraries:**
snowflake-sqlalchemy==1.4.6
python-dotenv==0.21.0
openai==0.27.2
langchain==0.0.336
pandas==2.0.2
boto3==1.26.144
colorama==0.4.6
fastapi==0.100.1
pydantic~=1.10.8
pytest~=7.1.2
uvicorn~=0.17.6
cassio==0.1.3
sentry-sdk==1.29.2
langsmith==0.0.66
numpy==1.24.3
SQLAlchemy==1.4.46
psycopg2-binary==2.9.7
tiktoken==0.4.0
httpx==0.24.1
unidecode==1.3.7
transformers==4.28.0
transformers[torch]
tensorflow==2.12.1
keras==2.12.0
**Python version of the project:**
python:3.10-slim-bullseye
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's quite challenging to replicate the error as it appears to be rather random. After a few requests, FastAPI stops responding following the OPTIONS query of the endpoint. This issue seems to be attributable to one of the libraries in use. I observed this error after a code refactoring in the project, moving from legacy chains to chains with LCEL. Since this refactoring, the ECS system has exhibited peculiar behavior. Extensive debugging has been conducted throughout the codebase, yet there are no indications of the error's origin. It's worth noting that everything functions flawlessly in local emulation, with no occurrence of any unusual errors. The problem arises when the code is deployed to the ECS Fargate instance, and I want to emphasize that this issue did not exist before the aforementioned changes were made.
<img width="996" alt="Captura de pantalla 2023-11-22 a la(s) 5 51 24 p m" src="https://github.com/langchain-ai/langchain/assets/122487744/15c12aeb-6016-468d-81de-b31a4204ca78">
### Expected behavior
I need someone to help me with new ways to debug this extremely rare bug, to give me ideas on what to do, what to show from my machine, what can be done, or if it's some incompatibility between the libraries. I haven't been able to pinpoint the specific point where the program stops, and it's proving to be very challenging.
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| 2,007,212,254 |
I_kwDOIPDwls53o6De
| 13,749 |
UTF-8 content is not loaded correctly with RecursiveUrlLoader
|
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| false | null |
[] | null | 1 | 2023-11-22T22:47:26 | 2023-11-22T22:51:19 | null |
NONE
| null |
### System Info
Langchain version: 0.0.339
Python version: 3.11.5
Running on Ubuntu 22.04.3 LTS via WSL 2
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Running the steps described in the guide for RecursiveUrlLoader: [https://python.langchain.com/docs/integrations/document_loaders/recursive_url](url)
2. Loading some content with UTF-8 encoding, the Python docs for example: [https://docs.python.org/3/](url)
Exact code used:
```python
from pprint import pprint
from langchain.document_loaders import RecursiveUrlLoader
from bs4 import BeautifulSoup
def load_python_docs():
url = "https://docs.python.org/3/"
loader = RecursiveUrlLoader(
url=url, max_depth=2, extractor=lambda x: BeautifulSoup(x, "html.parser").text
)
return loader.load()
pprint([doc.metadata for doc in load_python_docs()])
```
3. If you print the loaded documents, you should be able to see these kind of encoding issues:
```
{'description': 'Editor, Adam Turner,. This article explains the new features '
'in Python 3.12, compared to 3.11. Python 3.12 was released '
'on October 2, 2023. For full details, see the changelog. '
'Summary â\x80\x93 Release hi...',
'language': None,
'source': 'https://docs.python.org/3/whatsnew/3.12.html',
'title': 'Whatâ\x80\x99s New In Python 3.12 — Python 3.12.0 documentation'}
```
### Expected behavior
Should load content correctly, using the right encoding to parse the document. I suppose the issue is due to the fact that the `_get_child_links_recursive` method is calling `requests.get` and not specifying the encoding for the response. A quick fix that worked for me was to include the following line, just after the GET request: `response.encoding = response.apparent_encoding`
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| 2,007,206,054 |
PR_kwDOIPDwls5gLXbH
| 13,748 |
docs: top menu
|
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[] | null | 2 | 2023-11-22T22:38:52 | 2023-11-29T01:21:46 | 2023-11-29T00:35:38 |
COLLABORATOR
| null |
Addressed this issue with the top menu: It allocates too much space. If the screen is small, then the top menu items are split into two lines and look unreadable.
Another issue is with several top menu items: "Chat our docs" and "Also by LangChain". They are compound of several words which also hurts readability. The top menu items should be 1-word size.
Updates:
- "Chat our docs" -> "Chat" (the meaning is clean after clicking/opening the item)
- "Also by LangChain" -> "🦜️🔗"
- "🦜️🔗" moved before "Chat" item. This new item is partially copied from the first left item, the "🦜️🔗 LangChain". This design (with two 🦜️🔗 elements, visually splits the top menu into two parts. The first item in each part holds the 🦜️🔗 symbols and, when we click the second 🦜️🔗 item, it opens the drop-down menu. So, we've got two visually similar parts, which visually split the top menu on the right side: the LangChain Docs (and Doc-related items) and the lift side: other LangChain.ai (company) products/docs.
CC @efriis
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PR_kwDOIPDwls5gLVmh
| 13,747 |
DOCS: core editable dep api refs
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RELEASE: 0.0.339rc1
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Bagatur/bump core 4
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PR_kwDOIPDwls5gK7Sh
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DOCS: fix core api ref build
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Langchain-cli Does not install a usable binary.
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[] | null | 6 | 2023-11-22T19:54:42 | 2023-12-04T02:56:26 | 2023-12-04T02:56:26 |
NONE
| null |
### System Info
Langchain version: 0.0.339
Laangserve version: 0.0.30
Langchain-cli version: 0.0.19
Running on apple silicone: MacBook Pro M3 max using a zsh shell.
### Who can help?
@erick
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [x] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Running the installation steps in the guide with
`pip3 install -U langchain-cli`
And then trying to run `langchain app` just results in zsh responding with `zsh: command not found: langchain`.
### Expected behavior
Running the installation steps in the guide with
`pip3 install -U langchain-cli`
Should allow me to run
`langchain app new my-app`
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PR_kwDOIPDwls5gKrj4
| 13,742 |
INFRA: run LC ci after core changes
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PR_kwDOIPDwls5gKrew
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docs: moved up Chains menu
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[] | null | 3 | 2023-11-22T19:35:39 | 2023-11-29T03:07:59 | 2023-11-29T03:07:58 |
COLLABORATOR
| null |
`Chains` now is inside `More` menu. It is the most important module of LangChain, no? The whole package is "Lang CHAIN" and the Chains are hidden somewhere, no kidding.
`Chains` menu is moved up in the top menu hierarchy.
|
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PR_kwDOIPDwls5gKkmW
| 13,740 |
docs[patch]: link to LangSmith docs
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[] | null | 2 | 2023-11-22T19:15:11 | 2023-11-29T01:19:26 | 2023-11-29T00:44:45 |
COLLABORATOR
| null |
It happens that there is no link to the LangSmith Docs from the LangChain Docs.
Added this link
|
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PR_kwDOIPDwls5gKkRa
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[wip] BUGFIX: backwards compatible core namespacing
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gKXCm
| 13,738 |
Keep lc_id the same in core
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[] | null | 1 | 2023-11-22T18:38:30 | 2023-11-29T00:41:37 | 2023-11-29T00:41:37 |
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| null | null |
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PR_kwDOIPDwls5gKTvt
| 13,737 |
DOCS: added missing imports (#13736)
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[] | null | 1 | 2023-11-22T18:28:26 | 2023-11-29T03:42:44 | 2023-11-29T03:42:43 |
CONTRIBUTOR
| null |
- **Description:** Fixed missing imports in docs
- **Issue:** [#13736](https://github.com/langchain-ai/langchain/issues/13736)
- **Dependencies:** N/A
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I_kwDOIPDwls53nl4r
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DOC: missing imports in "Types of MessagePromptTemplate"
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[] | null | 1 | 2023-11-22T18:08:15 | 2023-11-22T18:08:38 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
Some langchain objects are used but not imported in [Types of MessagePromptTemplate](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates) documentation.
Specifically, it lacks the following:
```python
from langchain.prompts import HumanMessagePromptTemplate
from langchain.prompts import ChatPromptTemplate
```
and
```python
from langchain.schema.messages import HumanMessage, AIMessage
```
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5gKIqE
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Updated langchain stack svg
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I_kwDOIPDwls53nhCZ
| 13,734 |
Issue: <Handling Python Package Conflicts in Langchain When Integrating External APIs for Agent Tools>
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[] | null | 1 | 2023-11-22T17:59:25 | 2023-11-22T18:10:12 | null |
NONE
| null |
### Issue you'd like to raise.
**Description:**
Hi, sir. I am currently developing an Agent for scientific tasks using Langchain. The first tool I intend to integrate is a function for generating a mesh, implemented through the external API `dolfinx`. However, I've encountered a compatibility issue between my Langchain packages and the `dolfinx` package. This seems to be a broader problem related to designing an Agent that leverages diverse external APIs.
The concern here is that as I continue to expand the Agent's capabilities with more tools utilizing various external APIs, I might face additional conflicts. This issue could be a common challenge in the development process of creating Agents with multiple, diverse external integrations. Here's a snippet of the code for reference:
```
python
from mpi4py import MPI
from dolfinx import mesh, io
@tool("create_mesh")
def create_mesh_tool(text: str):
"""Returns a dolfinx.mesh.Mesh object, useful for generating meshes for PDEs.
The input should always be a string."""
domain = mesh.create_unit_square(MPI.COMM_WORLD, 8, 8, mesh.CellType.quadrilateral)
return domain
tools = [make_random_num, create_mesh_tool]
```
I am seeking advice or solutions on how to effectively manage and resolve these package conflicts within the Langchain framework, particularly when integrating tools that depend on external APIs like dolfinx.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gKGI7
| 13,733 |
REFACTOR: combine core documents files
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[] | null | 1 | 2023-11-22T17:52:05 | 2023-11-22T18:10:27 | 2023-11-22T18:10:26 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5gJ3V2
| 13,732 |
Add Nasa tool
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[] | null | 1 | 2023-11-22T17:07:56 | 2023-12-04T20:37:11 | 2023-12-04T20:37:11 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
**Description:**
This PR adds a new NASA toolkit that provides users access to the NASA's Image and Video Library API, with potential to expand and include other available NASA APIs in the future.
Integration tests and an example notebook showing the toolkit's use have been added.
Who can review?
@baskaryan , @eyurtsev , @hwchase17
**We also ran the following checks:**
make format passed ✅
make lint passed ✅
Files touched by this PR passed the linter ✅
passed integration tests via local jupyter notebook testing on NasaAPIWrapper ✅
make spell_check passed ✅
The original issue that this ticket addresses can be found here: https://github.com/langchain-ai/langchain/issues/11787
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PR_kwDOIPDwls5gJrpU
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DB-bound (or, "Convertor-based") prompt templates + Cassandra and Feast implementation
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[] | null | 1 | 2023-11-22T16:34:24 | 2023-12-08T01:02:26 | null |
CONTRIBUTOR
| null |
This PR adds a new (sub)type of Prompt Template to reduce boilerplate around "retrieving from a DB to fill a prompt".
A generic subclass of `StringPromptTemplate` provides the common behaviour, which is then further subclassed as a Cassandra-specific and a Feast-specific ready-to-use classes. The accompanying notebook/doc well shows rationale and usage and points to the way for a developer to expand with other DB-specific implementations.
A picture to summarize the decoupling of inputs / template variables with a DB-lookup acting as a bridge:
#### Remarks
- I could not test building the docs due to a `No docs found in "templates"` from Docusaurus - might be related to the recent langchain/langchain_core refactor. Sorry about that.
- The notebook includes the same image I left here; however it is linked from `user-images.githubusercontent.com` - I am not sure of the right place for those. I'd ask for advice on this point.
I'd like to draw @eyurtsev 's attention to this PR - Thank you very much!
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I_kwDOIPDwls53m5u6
| 13,730 |
chain input not pass into Python_REPL tool input by '\n'
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[] | null | 2 | 2023-11-22T16:15:52 | 2023-11-23T03:41:35 | null |
NONE
| null |
### System Info
langchain==0.0.339
windows11
python==3.10
### Who can help?
simple chain with tool code ,but not pass full input to tool cause '\n'
`
model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, verbose=False, streaming=True)
custom_tool_list = [PythonREPLTool(),#CustomPythonExec()
]
memory = ConversationBufferMemory(memory_key="chat_history")
agent_executor = initialize_agent(
custom_tool_list,
llm=model,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
max_iterations=1,
memory=memory,
early_stopping_method="generate",
agent_kwargs={"prefix": custom_prefix},
handle_parsing_errors="Check your output and make sure it conforms",
agent_executor.run("some prompt")
`
logs:
[chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [4.94s] Exiting Chain run with output:
{
"text": "Thought: Do I need to use a tool? Yes\nAction: Python_REPL\nAction Input: import pandas as pd\ndf = pd.read_csv('./statics/20231123_000614_test.csv')\nline_count = len(df
)\nline_count"
}
[tool/start] [1:chain:AgentExecutor > 4:tool:Python_REPL] Entering Tool run with input:
"import pandas as pd"
2023-11-23 00:06:19 - Python REPL can execute arbitrary code. Use with caution.
[tool/end] [1:chain:AgentExecutor > 4:tool:Python_REPL] [4ms] Exiting Tool run with output:
""
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
[chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [4.94s] Exiting Chain run with output:
{
"text": "Thought: Do I need to use a tool? Yes\nAction: Python_REPL\nAction Input: import pandas as pd\ndf = pd.read_csv('./statics/20231123_000614_test.csv')\nline_count = len(df
)\nline_count"
}
[tool/start] [1:chain:AgentExecutor > 4:tool:Python_REPL] Entering Tool run with input:
"import pandas as pd"
2023-11-23 00:06:19 - Python REPL can execute arbitrary code. Use with caution.
[tool/end] [1:chain:AgentExecutor > 4:tool:Python_REPL] [4ms] Exiting Tool run with output:
""
### Expected behavior
pass full input string to tool
|
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I_kwDOIPDwls53m1ll
| 13,728 |
Issue: Error `Got invalid JSON object. Error: Extra data:`when using `SelfQueryRetriever`
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[] | null | 3 | 2023-11-22T16:07:12 | 2023-11-24T17:09:20 | 2023-11-24T17:09:20 |
NONE
| null |
### Issue you'd like to raise.
I followed the lecture by Harrison on langchain on deeplearning.ai and when i follow it I am getting error `Got invalid JSON object. Error: Extra data:`when using `SelfQueryRetriever` to `get_relevant_documents`.
Using the following code:
```python
document_content_description = "Lecture notes"
retriever = SelfQueryRetriever.from_llm(
llm=model,
vectorstore=vectordb,
metadata_field_info=metadata_field_info,
document_contents=document_content_description,
verbose=True,
handle_parsing_errors=True,
)
docs = retriever.get_relevant_documents(question)
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gJgkN
| 13,727 |
Tqdm while waiting
|
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[] | null | 2 | 2023-11-22T16:06:17 | 2023-12-01T17:26:22 | 2023-12-01T17:26:22 |
COLLABORATOR
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| 2,006,618,922 |
PR_kwDOIPDwls5gJVxw
| 13,726 |
[docs] fix reduce prompt in summarization example
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[] | null | 1 | 2023-11-22T15:43:56 | 2023-11-29T03:22:42 | 2023-11-29T03:22:42 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Small fix to _summarization_ example, `reduce_template` should use `{docs}` variable.
Bug likely introduced as following code suggests using `hub.pull("rlm/map-prompt")` instead of defined prompt.
|
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| 2,006,596,477 |
PR_kwDOIPDwls5gJQwu
| 13,725 |
Fix locking
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[] | null | 1 | 2023-11-22T15:33:22 | 2023-11-22T15:37:26 | 2023-11-22T15:37:25 |
COLLABORATOR
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PR_kwDOIPDwls5gJIgG
| 13,724 |
Merge branch 'master' into pr/xsa-dev/1
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[] | null | 2 | 2023-11-22T15:13:08 | 2023-11-22T15:24:14 | 2023-11-22T15:21:38 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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| 2,006,525,842 |
I_kwDOIPDwls53mSeS
| 13,723 |
RunnableBranch doesnt stream correctly
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[] | null | 7 | 2023-11-22T14:57:38 | 2023-12-16T23:54:54 | null |
NONE
| null |
### System Info
LangChain Version: 0.0.339
Platform: Windows 10
Python Version: 3.10.10
### Who can help?
@agol
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
I've implemented this `RunnableBranch` example: https://python.langchain.com/docs/expression_language/how_to/routing
using this `MyCallbackHandler` example: https://python.langchain.com/docs/modules/agents/how_to/streaming_stdout_final_only
For streamming I've used this `streamming` example: https://python.langchain.com/docs/modules/model_io/chat/streaming
the result becomes something like this:
```
##
#Other#
##
(Verse 1)... <the output of the question as a whole, not separated by tokens, generated all at once>
```
### Expected behavior
every single token be surrounded by ##, like this:
```
#token1#
#token2#
...
```
|
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PR_kwDOIPDwls5gI0js
| 13,722 |
fix: MergedDataLoader is compatible with load with parameters such as ConfluenceLoader
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[] | null | 4 | 2023-11-22T14:27:17 | 2023-12-03T22:09:18 | 2023-12-03T22:09:18 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** [a description of the change, ](fix: MergedDataLoader is compatible with load with parameters such as ConfluenceLoader)
- **Issue:** https://github.com/langchain-ai/langchain/issues/12923
- **Dependencies:** no
- **Tag maintainer:**
- **Twitter handle:** @chyroc_
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gIPxU
| 13,720 |
Update summarization.ipynb
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| null |
After testing the example, I saw that the input_variables was docs and not doc_summaries
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I_kwDOIPDwls53lVFA
| 13,719 |
get token count for other LLM Models
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[] | null | 3 | 2023-11-22T12:50:02 | 2023-11-22T13:01:38 | null |
CONTRIBUTOR
| null |
### Feature request
it could be better where i can add a callback to get the total token spend on other LLMS apart from OPENAI ?
### Motivation
since there is a call back for openai why don't we have for other models too just to see how much token is consumed ?
### Your contribution
Happy to contribute to the issue with PR later
|
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PR_kwDOIPDwls5gIAPM
| 13,718 |
Feat: Robocorp Action Server
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[] | null | 1 | 2023-11-22T12:25:02 | 2023-11-23T12:43:28 | 2023-11-23T12:43:28 |
CONTRIBUTOR
| null |
[do not merge]
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Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 3 | 2023-11-22T11:54:27 | 2023-11-23T08:40:22 | null |
NONE
| null |
### Issue you'd like to raise.
Hi I am running the create_pandas_dataframe_agent agent, and the issue I'm facing is with the call back FinalStreamingStdOutCallbackHandler for some reason when I only have one civ it works fine as soon as I pass through multiple then it doesn't stream the data it just prints it all in one go.
here is my code:
```
def _get_llm(callback):
return OpenAI(
callbacks=[callback],
streaming=True,
temperature=0
)
file_path = ['people-100.csv','organizations-100.csv']
dataframes = [pd.read_csv(path) for path in file_path]
agent = create_pandas_dataframe_agent(
llm = _get_llm(FinalStreamingStdOutCallbackHandler()),
df = dataframes,
# agent_executor_kwargs={"memory": memory},
verbose=False,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
print(agent.run('go in deatil on the explination of the data and give me a statistical list aswell'))
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gHydR
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feat: Add ERNIE-Bot-8K model support for ErnieBotChat.
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[] | null | 1 | 2023-11-22T11:47:38 | 2023-11-29T03:22:24 | 2023-11-29T03:22:23 |
CONTRIBUTOR
| null |
- **Description:** According to the document https://cloud.baidu.com/doc/WENXINWORKSHOP/s/6lp69is2a, add ERNIE-Bot-8K model support for ErnieBotChat.
- **Dependencies:** Before using the ERNIE-Bot-8K, you should have the model's access authority.
|
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| 2,006,088,794 |
PR_kwDOIPDwls5gHghP
| 13,715 |
FIX #13709 rename xml.py-> xml_parser.py
|
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[] | null | 1 | 2023-11-22T11:02:37 | 2023-11-22T11:21:24 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** rename langchain/output_parsers/xml.py -> langchain/output_parsers/xml_parser.py, fix xml.py and the imported module xml.etree.ElementTree as ET is causing the ModuleNotFoundError
- **Issue:** [ModuleNotFoundError](https://github.com/langchain-ai/langchain/issues/13709),
- - **Dependencies:** no dependencies change,
|
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| 2,006,029,378 |
PR_kwDOIPDwls5gHTnb
| 13,714 |
Add option to prefix config keys in configurable_alts
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[] | null | 2 | 2023-11-22T10:29:09 | 2023-11-27T15:25:18 | 2023-11-27T15:25:17 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gG1a8
| 13,712 |
info sql tool remove whitespaces in table names
|
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[] | null | 1 | 2023-11-22T09:13:37 | 2023-12-06T04:07:39 | 2023-12-06T04:07:38 |
CONTRIBUTOR
| null |
Remove whitespaces from the input of the ListSQLDatabaseTool for better support.
for example, the input "table1,table2,table3" will throw an exception whiteout the change although it's a valid input.
|
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I_kwDOIPDwls53jw1J
| 13,711 |
Agent as tool gives wrong result ( pandas agent)
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[] | null | 2 | 2023-11-22T08:56:25 | 2023-11-22T09:28:00 | null |
NONE
| null |
### Issue you'd like to raise.
I'm using
```python
from langchain.agents import create_pandas_dataframe_agent
pd_agent = create_pandas_dataframe_agent(OpenAI(temperature=0),
df,
verbose=True)
pd_agent.run('some question on dataframe')
```
```python
english_tools = [
Tool(name="SomeNAME_1",
func=lambda q: app.finance_chain.run(q),
description=" Some app related description ",
return_direct=True,
coroutine=lambda q: app.finance_chain.arun(q),
),
Tool(name="SomeNAME_2",
func=lambda q: app.rqa(q),
description=" Some app related description ",
coroutine=lambda q: app.rqa_english.arun(q),
return_direct=True
),
Tool.from_function(
name="SomeNAME_3",
func=lambda q: app.pd_agent(q),
description=" Some app related description",
coroutine=lambda q: app.pd_agent.arun(q),
)
]
```
when we call this agent seperatly it's gives me a good result
but when i use this agent as a tool along with other tools it's not gives consistent results, mostly wrong answer
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gGreR
| 13,710 |
Update DeepSparse LLM Tests and Docs
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[] | null | 2 | 2023-11-22T08:45:45 | 2023-12-05T04:59:03 | 2023-12-04T20:48:55 |
NONE
| null |
Update the documentation and tests for the DeepSparse LangChain integration
|
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ModuleNotFoundError: No module named 'xml.etree'; 'xml' is not a package
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[] | null | 6 | 2023-11-22T08:36:24 | 2023-11-22T11:10:41 | null |
NONE
| null |
### System Info
Traceback (most recent call last):
File "/Users/xxx/anaconda3/envs/kaoyan-chat/lib/python3.10/site-packages/langchain/output_parsers/xml.py", line 2, in <module>
import xml.etree.ElementTree as ET
File "/Users/xxx/anaconda3/envs/kaoyan-chat/lib/python3.10/site-packages/langchain/output_parsers/xml.py", line 2, in <module>
import xml.etree.ElementTree as ET
ModuleNotFoundError: No module named 'xml.etree'; 'xml' is not a package
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm = AzureChatOpenAI(model_name="gpt-35-turbo",
deployment_name="gpt-35-turbo", temperature=0.3)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
### Expected behavior
i run this code and hope it can run normally, but it throw the exception. xml is not a package , i think this is beacause the python file is named xml.py , it confilct with import xml.etree.ElementTree as ET
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PR_kwDOIPDwls5gGkzS
| 13,708 |
Fix typo/line break in python code
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[] | null | 1 | 2023-11-22T08:26:23 | 2023-11-22T17:10:07 | 2023-11-22T17:10:07 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** simple modify on markdown file
- **Dependencies:** None
|
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I_kwDOIPDwls53jkX8
| 13,707 |
Add system_fingerprint in OpenAI callbacks
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CONTRIBUTOR
| null |
### Feature request
Given that the seed is already in beta phase, the system_fingerprint would also be highly useful in tracking deterministic responses. Suggest to add a `system_fingerprint` as part of the callbacks. https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html#langchain.callbacks.openai_info.OpenAICallbackHandler
### Motivation
Given that the seed is already in beta phase, the system_fingerprint would also be highly useful
### Your contribution
Proposed suggestion
|
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PR_kwDOIPDwls5gGcVO
| 13,705 |
Fix Pydantic V2 Errors
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NONE
| null | ERROR: type should be string, got "\r\nhttps://docs.pydantic.dev/latest/migration/#migration-guide\r\n\r\n- **Description:** response=response.dict() to response=response.model_dump()" |
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| 13,704 |
Added annotations support to AOAI
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NONE
| null |
- **Description:** Added Azure OpenAI Annotations (content filtering results) to ChatResult
- **Issue:** 13090
- **Twitter handle:** ElazarShay
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| 13,703 |
fix merge None value and non None value error
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[] | null | 2 | 2023-11-22T07:31:11 | 2023-11-28T20:49:56 | 2023-11-28T20:49:56 |
CONTRIBUTOR
| null |
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| 2,005,693,735 |
PR_kwDOIPDwls5gGKXY
| 13,702 |
BUGFIX: add prompt imports for backwards compat
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PR_kwDOIPDwls5gFzC-
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IMPROVEMENT: Conditionally import for type hints
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PR_kwDOIPDwls5gFxm9
| 13,699 |
Migrate mlflow and databricks classes to deployments APIs.
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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## Description
Related to https://github.com/mlflow/mlflow/pull/10420. MLflow AI gateway will be deprecated and replaced by the `mlflow.deployments` module. Happy to split this PR if it's too large.
```
pip install git+https://github.com/langchain-ai/langchain.git@refs/pull/13699/merge#subdirectory=libs/langchain
```
## Dependencies
Install mlflow from https://github.com/mlflow/mlflow/pull/10420:
```
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/10420/merge
```
## Testing plan
The following code works fine on local and databricks:
<details><summary>Click</summary>
<p>
```python
"""
Setup
-----
mlflow deployments start-server --config-path examples/gateway/openai/config.yaml
databricks secrets create-scope <scope>
databricks secrets put-secret <scope> openai-api-key --string-value $OPENAI_API_KEY
Run
---
python /path/to/this/file.py secrets/<scope>/openai-api-key
"""
from langchain.chat_models import ChatMlflow, ChatDatabricks
from langchain.embeddings import MlflowEmbeddings, DatabricksEmbeddings
from langchain.llms import Databricks, Mlflow
from langchain.schema.messages import HumanMessage
from langchain.chains.loading import load_chain
from mlflow.deployments import get_deploy_client
import uuid
import sys
import tempfile
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
###############################
# MLflow
###############################
chat = ChatMlflow(
target_uri="http://127.0.0.1:5000", endpoint="chat", params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
embeddings = MlflowEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
llm = Mlflow(
target_uri="http://127.0.0.1:5000",
endpoint="completions",
params={"temperature": 0.1},
)
print(llm("I am"))
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
print(llm_chain.run(adjective="funny"))
# serialization/deserialization
with tempfile.TemporaryDirectory() as tmpdir:
print(tmpdir)
path = f"{tmpdir}/llm.yaml"
llm_chain.save(path)
loaded_chain = load_chain(path)
print(loaded_chain("funny"))
###############################
# Databricks
###############################
secret = sys.argv[1]
client = get_deploy_client("databricks")
# External - chat
name = f"chat-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "gpt-4",
"provider": "openai",
"task": "llm/v1/chat",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
chat = ChatDatabricks(
target_uri="databricks", endpoint=name, params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
finally:
client.delete_endpoint(endpoint=name)
# External - embeddings
name = f"embeddings-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "text-embedding-ada-002",
"provider": "openai",
"task": "llm/v1/embeddings",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
embeddings = DatabricksEmbeddings(target_uri="databricks", endpoint=name)
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
finally:
client.delete_endpoint(endpoint=name)
# External - completions
name = f"completions-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "gpt-3.5-turbo-instruct",
"provider": "openai",
"task": "llm/v1/completions",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
llm = Databricks(
endpoint_name=name,
model_kwargs={"temperature": 0.1},
)
print(llm("I am"))
finally:
client.delete_endpoint(endpoint=name)
# Foundation model - chat
chat = ChatDatabricks(
endpoint="databricks-llama-2-70b-chat", params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
# Foundation model - embeddings
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
print(embeddings.embed_query("hello")[:3])
# Foundation model - completions
llm = Databricks(
endpoint_name="databricks-mpt-7b-instruct", model_kwargs={"temperature": 0.1}
)
print(llm("hello"))
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
print(llm_chain.run(adjective="funny"))
# serialization/deserialization
with tempfile.TemporaryDirectory() as tmpdir:
print(tmpdir)
path = f"{tmpdir}/llm.yaml"
llm_chain.save(path)
loaded_chain = load_chain(path)
print(loaded_chain("funny"))
```
Output:
```
content='Hello! How can I assist you today?'
[-0.025058426, -0.01938856, -0.027781019]
[-0.025058426, -0.01938856, -0.027781019]
sorry, but I cannot continue the sentence as it is incomplete. Can you please provide more information or context?
Sure, here's a classic one for you:
Why don't scientists trust atoms?
Because they make up everything!
/var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmpx_4no6ad
{'adjective': 'funny', 'text': "Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"}
content='Hello! How can I assist you today?'
[-0.025058426, -0.01938856, -0.027781019]
[-0.025058426, -0.01938856, -0.027781019]
a 23 year old female and I am currently studying for my master's degree
content="\nHello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
[0.051055908203125, 0.007221221923828125, 0.003879547119140625]
[0.051055908203125, 0.007221221923828125, 0.003879547119140625]
hello back
Well, I don't really know many jokes, but I do know this funny story...
/var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmp7_ds72ex
{'adjective': 'funny', 'text': " Well, I don't really know many jokes, but I do know this funny story..."}
```
</p>
</details>
The existing workflow doesn't break:
<details><summary>click</summary>
<p>
```python
import uuid
import mlflow
from mlflow.models import ModelSignature
from mlflow.types.schema import ColSpec, Schema
class MyModel(mlflow.pyfunc.PythonModel):
def predict(self, context, model_input):
return str(uuid.uuid4())
with mlflow.start_run():
mlflow.pyfunc.log_model(
"model",
python_model=MyModel(),
pip_requirements=["mlflow==2.8.1", "cloudpickle<3"],
signature=ModelSignature(
inputs=Schema(
[
ColSpec("string", "prompt"),
ColSpec("string", "stop"),
]
),
outputs=Schema(
[
ColSpec(name=None, type="string"),
]
),
),
registered_model_name=f"lang-{uuid.uuid4()}",
)
# Manually create a serving endpoint with the registered model and run
from langchain.llms import Databricks
llm = Databricks(endpoint_name="<name>")
llm("hello") # 9d0b2491-3d13-487c-bc02-1287f06ecae7
```
</p>
</details>
## Follow-up tasks
(This PR is too large. I'll file a separate one for follow-up tasks.)
- Update `docs/docs/integrations/providers/mlflow_ai_gateway.mdx` and `docs/docs/integrations/providers/databricks.md`.
|
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PR_kwDOIPDwls5gFny1
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BUGFIX: llm backwards compat imports
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COLLABORATOR
| null | null |
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I_kwDOIPDwls53iX-P
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Issue: how to remain the chain in different api calls?
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NONE
| null |
### Issue you'd like to raise.
This question may be stupid but I find nothing to refer to.
I'm using langchain.chains.LLMChain in flask api, but I found that the chain will be reloaded and reconstructed if I call the api after a call. So the first question I wanna ask is how to make my LLMChain be remembered so that I can use it directly in the next call.
I tried to use session in flask, but get ```Object of type LLMChain is not JSON serializable```.
Thanks.
### Suggestion:
_No response_
|
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I_kwDOIPDwls53iWoC
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DOC: how to use load_qa_chain with memory
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[] | null | 6 | 2023-11-22T03:29:40 | 2023-11-22T06:30:33 | 2023-11-22T06:30:33 |
CONTRIBUTOR
| null |
### Issue with current documentation:
There is a lack of comprehensive documentation on how to use load_qa_chain with memory.
I've found this: https://cheatsheet.md/langchain-tutorials/load-qa-chain-langchain.en but does not cover other memories, like for example BufferedWindowMemory.
I have the following memory:
```
memory = {ConversationBufferWindowMemory} chat_memory=ChatMessageHistory(messages=[HumanMessage(content='What is the main material? Keept he answer short'), AIMessage(content='The main material is R 2 Fe 14 B.'), HumanMessage(content='What are its properties? '), AIMessage(content='The properties
Config = {type} <class 'langchain.schema.memory.BaseMemory.Config'>
ai_prefix = {str} 'AI'
buffer = {str} 'Human: What is the main material? Keept he answer short\nAI: The main material is R 2 Fe 14 B.\nHuman: What are its properties? \nAI: The properties of the material being discussed are not explicitly mentioned in the given context. However, some information
buffer_as_messages = {list: 4} [content='What is the main material? Keept he answer short', content='The main material is R 2 Fe 14 B.', content='What are its properties? ', content='The properties of the material being discussed are not explicitly mentioned in the given context. Howeve
buffer_as_str = {str} 'Human: What is the main material? Keept he answer short\nAI: The main material is R 2 Fe 14 B.\nHuman: What are its properties? \nAI: The properties of the material being discussed are not explicitly mentioned in the given context. However, some information
chat_memory = {ChatMessageHistory} messages=[HumanMessage(content='What is the main material? Keept he answer short'), AIMessage(content='The main material is R 2 Fe 14 B.'), HumanMessage(content='What are its properties? '), AIMessage(content='The properties of the material being discussed
human_prefix = {str} 'Human'
input_key = {NoneType} None
k = {int} 4
lc_attributes = {dict: 0} {}
lc_secrets = {dict: 0} {}
memory_key = {str} 'history'
memory_variables = {list: 1} ['history']
output_key = {NoneType} None
return_messages = {bool} False
```
And the messages are the following:
```
0 = {HumanMessage} content='What is the main material? Keept he answer short'
1 = {AIMessage} content='The main material is R 2 Fe 14 B.'
2 = {HumanMessage} content='What are its properties? '
3 = {AIMessage} content='The properties of the material being discussed are not explicitly mentioned in the given context. However, some information about the magnetic properties and microstructure of the material is provided. It is mentioned that the magnetic properties of the specimens were measured using a BH tracer or a vibrating sample magnetometer (VSM). The microstructure and crystal structure were analyzed using various techniques such as focused ion beam-scanning electron microscopy (FIB-SEM), electron probe microanalyses (EPMA), scanning transmission electron microscope-energy dispersive spectroscopy (STEM-EDS), and X-ray diffraction (XRD). The area ratio of each phase and the coverage ratio of the grains by the grain-boundary phase were calculated. Additionally, it is mentioned that the material contains rare-earth elements (Nd, Y, and Ce) and Fe, and that the coercivity can be improved by controlling the grain boundaries using certain phases (R6Fe13Ga and RFe2).'
```
And then I pass it to a load_qa_chain which is instantiated as follow (I'm summarising)
```
self.chain = load_qa_chain(llm, chain_type=qa_chain_type)
[...]
self.chain.run(input_documents=relevant_documents,
question=query,
memory=memory)
```
however, in the prompt, there seems to be no memory information.
I would like to avoid having to rewrite the prompt with a custom one. Maybe the memory does not follow the required conventions (e.g. how the human / assitant message should be characterized), which are not clear to me, I haven't found any documentation in regard.
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5gFfKw
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Support Azure gov cloud in Azure Cognitive Search retriever
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[] | null | 2 | 2023-11-22T03:29:35 | 2023-12-04T20:56:35 | 2023-12-04T20:56:35 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** The existing version hardcoded search.windows.net in the base url. This is not compatible with the gov cloud. I am allowing the user to override the default for gov cloud support.,
- **Issue:** N/A, did not write up in an issue,
- **Dependencies:** None
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gFeTA
| 13,694 |
Set default region from boto3 session for Bedrock
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[] | null | 2 | 2023-11-22T03:24:07 | 2023-11-29T01:52:07 | 2023-11-29T01:26:54 |
CONTRIBUTOR
| null |
- **Description:** Set default region from boto3 session for Bedrock
- **Issue:** #13683
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PR_kwDOIPDwls5gFSzE
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fix merge None value and non None value error
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gFQra
| 13,692 |
Added SQL report based querying as an option on the chain
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CONTRIBUTOR
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Summary:
1. During indexing, get description of all tables if they exist and put them in the pickle
2. When running the chain, create sql query agents for any tables that exist
3. Other improvements like fixing the "validation error" on chat history (just make it optional) and running ingestion in batches
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Template Metadata pt 1
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PR_kwDOIPDwls5gE6W0
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bump core dep 0.0.3
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PR_kwDOIPDwls5gE3td
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add callback import test
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Bagatur/core utils
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PR_kwDOIPDwls5gE0Qx
| 13,687 |
added CrossEncoderReranker with HuggingFaceCrossEncoder and SagemakerEndpointCrossEncoder
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[] | null | 5 | 2023-11-21T23:06:50 | 2023-12-18T19:04:28 | null |
CONTRIBUTOR
| null |
- **Description:** Support reranking based on cross encoder models available from HuggingFace.
- Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and `SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it. Please let me know if anything else needs to be done to make it visible on the table-of-contents navigation bar on the left, or on the card list on [retrievers documentation page](https://python.langchain.com/docs/integrations/retrievers).
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
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I_kwDOIPDwls53hPNQ
| 13,686 |
While utlizing the create_csv_agent function, the output is not returned in the JSON format
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[] | null | 1 | 2023-11-21T21:51:45 | 2023-11-21T21:56:09 | null |
NONE
| null |
### Issue with current documentation:
Hey guys! Below is the code which i'm using to get the output
```
import pandas as pd
from IPython.display import Markdown, display
from langchain.agents import create_csv_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
import os
os.environ["OPENAI_API_KEY"] = ""
# Load the dataset
df = pd.read_csv('https://gist.githubusercontent.com/armgilles/194bcff35001e7eb53a2a8b441e8b2c6/raw/92200bc0a673d5ce2110aaad4544ed6c4010f687/pokemon.csv')
df.to_csv('pokemon.csv', index=False)
# Extract columns for the prompt template
columns = list(df.columns)
json_data = ', '.join(columns)
# Define the prompt template
prompt_template = f'''If the dataset has the following columns: {json_data}
Understand user questions with different column names and convert them to a JSON format. Here's an example:
Example 1:
User Question: Top 2 zones by Housing Loan in the West and South in the year 2019 excluding small loans and with Discount > 5 and Term Deposit between 10k and 15k
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Month",
"agg_columns": ["Housing Loan"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone"],
"filters": {{"Zone": ["West", "South"]}},
"not_in": {{"Loan Type": ["Small"]}},
"num_filter": {{
"gt": [
["Discount", 5],
["Term Deposit", 10000]
],
"lt": [
["Term Deposit", 15000]
]
}},
"percent": false,
"top": "2",
"bottom": null
}}
Example 2:
User Question: What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020
{{
"start_date": "01-01-2020",
"end_date": "31-01-2020",
"time_stamp_col": "Due Date",
"agg_columns": ["Loan Amount", "Loan Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": [],
"filters": {{"RM Name": ["James"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 3:
User Question: Which RM Name with respect to Region has the Highest Interest Outstanding and Principal Outstanding in the year 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Interest Outstanding", "Principal Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["RM Name", "Region"],
"filters": {{}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 4:
User Question: Which Branch in North Carolina with respect to Cibil Score Bucket has the Highest Cibil Score in 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Cibil Score", "DPD Bucket"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Branch"],
"filters": {{"Region": ["North Carolina"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 5:
User Question: With respect to Zone, Region, Branch, RM Name what is the Highest Loan Amount, Loan Tenure, Loan Outstanding, EMI Pending, Principal Outstanding
{{
"start_date": null,
"end_date": null,
"time_stamp_col": null,
"agg_columns": ["Loan Amount", "Loan Tenure", "Loan Outstanding", "EMI Pending", "Principal Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone", "Region", "Branch", "RM Name"],
"filters": {{}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 6:
User Question: Top 2 zones by Housing Loan in the year 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Housing Loan"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone"],
"filters": {{"Product": ["Home Loan"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "2",
"bottom": null
}}
Our test dataset has the following columns: {json_data}
User Question (to be converted): {{{{user_question}}}}'''
prompt_template = prompt_template.replace("{", "{{").replace("}", "}}").replace("{{{{", "{").replace("}}}}", "}")
# Define user's question
user_question = "Which pokemon has the highest attack and which has lowest defense? I need the output in the json format"
# Format the user's question within the template
formatted_question = prompt_template.format(user_question=user_question)
# Load the agent
agent = create_csv_agent(OpenAI(temperature=0), "pokemon.csv", verbose=True)
gpt4_agent = create_csv_agent(ChatOpenAI(temperature=0, model_name="gpt-4"), "pokemon.csv", verbose=True)
# Use the formatted question as the input to your agent
response = agent.run(formatted_question)
# Print the response
print(response)
```
Below is the output which i got
```
> Entering new chain...
Thought: I need to find the highest attack and lowest defense values in the dataset
Action: python_repl_ast
Action Input: df[['Name', 'Attack', 'Defense']].sort_values(by=['Attack', 'Defense'], ascending=[False, True]).head(1).to_json()
Observation: {"Name":{"163":"MewtwoMega Mewtwo X"},"Attack":{"163":190},"Defense":{"163":100}}
Thought: I now know the final answer
Final Answer: The pokemon with the highest attack is MewtwoMega Mewtwo X with an attack of 190 and the pokemon with the lowest defense is MewtwoMega Mewtwo X with a defense of 100.
> Finished chain.
The pokemon with the highest attack is MewtwoMega Mewtwo X with an attack of 190 and the pokemon with the lowest defense is MewtwoMega Mewtwo X with a defense of 100.
```
But the output i need is in the JSON format whichi've mentioned in the prompt_template. Can anyone assist me?
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls53hM4u
| 13,685 |
OpenAIModerationChain with OpenAI SDK >= v1.0.0 Broken
|
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[] | null | 6 | 2023-11-21T21:45:08 | 2023-11-28T17:38:22 | null |
NONE
| null |
### System Info
It appears that OpenAI's SDK v1.0.0 update introduced some needed migrations.
Running the Langchain OpenAIModerationChain with OpenAI SDK >= v1.0.0 provides the following error:
```
You tried to access openai.Moderation, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API.
You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface.
Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`
A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742
```
After briefly reading the mitigation steps I believe the suggested migration is from `openai.Moderation.create()` -> `client.moderations.create()`.
I believe the `validate_environment` of `OpenAIModerationChain` will want updating from ```values["client"] = openai.Moderation``` to using the recommended `client.moderations` syntax. (https://api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html#OpenAIModerationChain)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
With OpenAI SDK >= v1.0.0 try to use `OpenAIModerationChain` to moderate a piece of content.
Error appears:
```
You tried to access openai.Moderation, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API.
You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface.
Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`
A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742
```
### Expected behavior
When using `OpenAIModerationChain` with OpenAI SDK >= v1.0.0 I expect the chain to properly moderate content and not fail with an error.
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Llama support bedrock chat fix
|
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[] | null | 1 | 2023-11-21T21:39:37 | 2023-11-21T21:41:59 | 2023-11-21T21:41:54 |
CONTRIBUTOR
| null |
- **Description:** Bug fix for `if` statement in provider select, potentially leading to `NotImplementedError`
- **Issue:** /
- **Dependencies:** /
- **Tag maintainer:** @baskaryan
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[Issue] Error with Bedrock when AWS region is not specified or not in environment variable
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[] | null | 2 | 2023-11-21T21:28:42 | 2023-11-30T03:55:47 | 2023-11-30T03:55:47 |
NONE
| null |
### System Info
langchain version: 0.0.339
python: 3.9.17
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When creating a Bedrock model without `region_name`
```py
llm_bedrock = Bedrock(model_id='anthropic.claude-instant-v1')
```
It errors out
```py
ValidationError: 1 validation error for Bedrock
__root__
Could not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. (type=value_error)
```
This works
```py
llm_bedrock = Bedrock(model_id='anthropic.claude-instant-v1', region_name='us-west-2')
```
The problem is from https://github.com/langchain-ai/langchain/blob/bfb980b96800020d90c9362aaad40d8817636711/libs/langchain/langchain/llms/bedrock.py#L203-L207
where `get_from_dict_or_env` raises an exception if region is neither specified nor in env and default is not set.
To fix it, change default from `None` to `session.region_name`.
### Expected behavior
No error is raised.
|
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