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| 1,865,308,594 |
PR_kwDOIPDwls5Ys878
| 9,704 |
bump 272
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PR_kwDOIPDwls5Ys1-R
| 9,703 |
Added a link to the dependencies document
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I_kwDOIPDwls5vLZ03
| 9,702 |
DOC: Custom Templates issue with Pydantic v2
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[] | null | 19 | 2023-08-24T14:13:41 | 2023-10-31T16:09:37 | null |
NONE
| null |
### Issue with current documentation:
The "Creating a Custom Prompt Template" documentation is outdated with Pydantic v2.
```
class FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel):
"""A custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function."""
@validator("input_variables")
def validate_input_variables(cls, v):
"""Validate that the input variables are correct."""
if len(v) != 1 or "function_name" not in v:
raise ValueError("function_name must be the only input_variable.")
return v
```
The above code raises following TypeError:
```
> class FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel):
E TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases
```
Frankly, I have no idea how to get the prompt templates to work with new Pydantic, even after changing @validator to @field_validator.
### Idea or request for content:
Update the documentation to include new version of Pydantic in examples.
|
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PR_kwDOIPDwls5Ysuek
| 9,701 |
Do not share executors between parent and child tasks
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[] | null | 1 | 2023-08-24T14:05:25 | 2023-08-24T14:17:08 | 2023-08-24T14:17:07 |
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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PR_kwDOIPDwls5YsYbM
| 9,700 |
Harrison/multi vector
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PR_kwDOIPDwls5YsW3g
| 9,699 |
adapt ChatGLM in llm model and chat model
|
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[] | null | 2 | 2023-08-24T13:15:12 | 2023-08-26T10:09:06 | 2023-08-26T10:09:06 |
CONTRIBUTOR
| null |
using sample:
```
endpoint_url = API URL
ChatGLM_llm = ChatGLM(
endpoint_url=endpoint_url,
api_key=Your API Key by ChatGLM
)
print(ChatGLM_llm("hello"))
```
```
model = ChatChatGLM(
chatglm_api_key="api_key",
chatglm_api_base="api_base_url",
model_name="model_name"
)
chain = LLMChain(llm=model)
```
Description: The call of ChatGLM has been adapted.
Issue: The call of ChatGLM has been adapted.
Dependencies: Need python package `zhipuai` and `aiostream`
Tag maintainer: @baskaryan, @hwchase17
Twitter handle: None
|
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| 1,865,041,669 |
I_kwDOIPDwls5vKkcF
| 9,697 |
Calude models not able to output more than 8191 tokens.
|
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[] | null | 3 | 2023-08-24T12:21:25 | 2023-10-22T02:02:36 | null |
NONE
| null |
### System Info
Platform - AWS
Python version: 3.11.4
OS - Mac
### Who can help?
@3coins , @hwchase17 @agola11
### Information
- [X] 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
The code sample which produces the bug:
```
body = json.dumps({"prompt": prompt, "max_tokens_to_sample": 8192})
accept = "application/json"
contentType = "application/json"
response_claudeV2 = boto3_bedrock.invoke_model(
body=body, modelId="anthropic.claude-v2", accept=accept, contentType=contentType
)
response_body_claudeV2 = json.loads(response_claudeV2.get("body").read())
print(response_body_claudeV2.get("completion"))
```
When the above code snippet is executed with boto3_client we get the error:
` botocore.errorfactory.ValidationException: An error occurred (ValidationException) when calling the InvokeModel operation: Malformed input request: 8192 is not less or equal to 8191, please reformat your input and try again.`
As per the doc of anthropic claude the claude-instant can output 9k tokens, claude-v1 can output 12k tokens and claude-v2 can output 12k tokens but when 'max_token_sample' parameter exceeds 8191 values they give the above error.
### Expected behavior
Expected behaviour is that the model should give the output as it can produce max 12K tokens.
|
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| 1,865,001,390 |
I_kwDOIPDwls5vKamu
| 9,696 |
No way to Close an open connection in PGVector.py
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[] | null | 3 | 2023-08-24T11:57:09 | 2023-11-15T20:34:38 | 2023-11-15T20:34:38 |
NONE
| null |
### Feature request
Initialization with Database Connection: When an instance of the PGVector class is created, it automatically establish a connection with the PostgreSQL Vector database.
Method for Closing Connection: we need to implement a method within the PGVector class that allows you to close the established connection with the PostgreSQL database.
`def __del__(self):
# Close the session (and thus the connection) when the instance is destroyed.
self.session.close()`
### Motivation
The problem is, I am unable to close a connection and the pool get overload with multiple connections and hence the service starts throwing error
### Your contribution
I guess, may be.
|
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I_kwDOIPDwls5vJu15
| 9,695 |
Agent by AgentType.OPENAI_FUNCTIONS cannot output message content.
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[] | null | 1 | 2023-08-24T10:03:15 | 2023-11-30T16:06:01 | 2023-11-30T16:06:00 |
NONE
| null |
### Issue you'd like to raise.
It will always output
```
responded: {content}
```
I suppose this [line](https://github.com/langchain-ai/langchain/blob/v0.0.271/libs/langchain/langchain/agents/openai_functions_agent/base.py#L130) is not correct.
### Suggestion:
Is the following code the way you need?
```
content_msg = f"responded: {message.content}\n" if message.content else "\n"
```
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| 1,864,811,327 |
PR_kwDOIPDwls5YrQDk
| 9,694 |
Add .with_config() method to Runnables, Add run_id, run_name to RunnableConfig
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[] | null | 4 | 2023-08-24T09:56:48 | 2023-09-01T14:48:48 | 2023-09-01T14:48:47 |
COLLABORATOR
| null |
- with_config() allows binding any config values to a Runnable, like .bind() does for kwargs
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,864,641,239 |
PR_kwDOIPDwls5YqrXI
| 9,692 |
Add AliCloudPaiEAS type in langchain.llms to access alicloud pai-eas service.
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[] | null | 1 | 2023-08-24T08:12:13 | 2023-08-31T06:52:10 | 2023-08-24T08:38:11 |
CONTRIBUTOR
| null |
### Description
This PR aims at adding new llm type AliCloudPaiEAS to support service accessing for alicloud product named pai-eas service. PAI-EAS can help users to deploy llm models on alicloud.
### Usage
The user needs to start an eas service on alicloud and then set the service's access url and token as a params for AliCloudPaiEAS. Then AliCloudPaiEAS object as a llm object is passed to the Chain to run an output.
The usage is like:
`llm = AliCloudPaiEAS(eas_service_url="your_access_url",eas_service_token="your_access_token"`
`llm_chain = LLMChain(prompt=prompt, llm=llm)
`
`llm_chain.run("Hello there.")`
### Test and example
The integration test case is located at libs/langchain/tests/integration_tests/llms/test_alicloud_eas.py
The example notebook is located at docs/extras/integrations/lls/alicloud_api_eas.ipynb
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PR_kwDOIPDwls5YqrIp
| 9,691 |
fix setting --only-extended or --only-core flags to False if not prov…
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[] | null | 3 | 2023-08-24T08:11:32 | 2023-08-29T08:00:21 | 2023-08-29T08:00:21 |
CONTRIBUTOR
| null |
Description:
`--only-extended` and `--only-core` flags are not set correctly to `False`, if not provided
Issue:
When trying to run `make coverage` I got the following issue:
```
INTERNALERROR> ValueError: no option named 'only_extended'
```
It turned out that `config.getoption("--only-extended")` in `pytest_collection_modifyitems` function was unable to get the `--only-extended` value
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| 1,864,609,720 |
PR_kwDOIPDwls5Yqkjg
| 9,690 |
Update cube_semantic.py to add new type
|
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[] | null | 4 | 2023-08-24T07:51:55 | 2023-09-19T23:32:09 | 2023-09-19T23:32:09 |
CONTRIBUTOR
| null |
the cube_semantic example jupyter notebook didn't work, if I use curl, it has following json with type=cube, so I add this type in the code to make it work:
(langchain) ➜ langchain git:(master) ✗ curl \
-H "Authorization:.xxx" \
-G \
--data-urlencode 'query={"measures":["LineItems.count"]}' \
https://xxx.cubecloudapp.dev/cubejs-api/v1/meta
{"cubes":[{"public":true,"name":"LineItems","type":"cube","title":"Line Items","measures":[{"name":"LineItems.count","title":"Line Items Count","shortTitle":"Count","cumulativeTotal":false,"cumulative":false,"type":"number","aggType":"count","drillMembers":[],"drillMembersGrouped":{"measures":[],"dimensions":[]},"isVisible":true},{"name":"LineItems.price","title":"Line Items Price","shortTitle":"Price","cumulativeTotal":fals
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: Update cube_semantic.py to add new type "cube",
- Issue: the cube_semantic example jupyter notebook didn't work,
- Dependencies: NA,
- Tag maintainer: NA
- Twitter handle: NA
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Yqd7w
| 9,689 |
Add the option to use separate LLMs for GraphCypherQA chain
|
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[] | null | 3 | 2023-08-24T07:32:03 | 2023-08-24T18:50:38 | 2023-08-24T18:50:38 |
CONTRIBUTOR
| null |
The Graph Chains are different in the way that it uses two LLMChains instead of one like the retrievalQA chains. Therefore, sometimes you want to use different LLM to generate the database query and to generate the final answer.
This feature would make it more convenient to use different LLMs in the same chain.
I have also renamed the Graph DB QA Chain to Neo4j DB QA Chain in the documentation only as it is used only for Neo4j. The naming was ambigious as it was the first graphQA chain added and wasn't sure how do you want to spin it.
|
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I_kwDOIPDwls5vIoXc
| 9,688 |
Issue: APIConnectionError: Error communicating with OpenAI
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[] | null | 2 | 2023-08-24T06:59:49 | 2023-11-30T02:15:30 | 2023-11-30T02:15:22 |
NONE
| null |
### Issue you'd like to raise.
eroory msg:
Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI: HTTPSConnectionPool(host='api.openai.com', port=443): Max retries exceeded with url: /v1/chat/completions (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response'))).
code:
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
chat_model.predict("hi!")
export OPENAI_API_KEY="sk-xxxxxxujOoH";
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5YqPsW
| 9,687 |
Fix docs for AssemblyAIAudioTranscriptLoader (shorter import path)
|
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[] | null | 1 | 2023-08-24T06:45:28 | 2023-08-24T14:24:53 | 2023-08-24T14:24:53 |
CONTRIBUTOR
| null |
Uses the shorter import path
`from langchain.document_loaders import` instead of the full path
`from langchain.document_loaders.assemblyai`
Applies those changes to the docs and the unit test.
See #9667 that adds this new loader.
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I_kwDOIPDwls5vIgGM
| 9,686 |
DQL DB Langchain : When running an db_run query based on the prompt it should execute sql query only when needed.
|
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[] | null | 4 | 2023-08-24T06:35:24 | 2023-11-30T16:06:06 | 2023-11-30T16:06:05 |
NONE
| null |
The Prompt was to explain tables in the data. in this case it should query into the SQL database.
|
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I_kwDOIPDwls5vIUW-
| 9,685 |
An error occurred: 'DDGS' object does not support the context manager protocol
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[] | null | 2 | 2023-08-24T05:50:09 | 2023-11-30T16:06:11 | 2023-11-30T16:06:10 |
NONE
| null |
### System Info
> Entering new AgentExecutor chain...
Invoking: `duckduckgo_search` with `2023年8月的新闻`
An error occurred: 'DDGS' object does not support the context manager protocol
### Who can help?
@hwchase17
@agola11
### 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
# 初始化OpenAI Functions代理
agent = initialize_agent(
# tools,
tools=load_tools(["ddg-search"]),
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory,
max_iterations=10,
early_stopping_method="generate",
handle_parsing_errors=True, # 初始化代理并处理解析错误
callbacks=[handler],
)
> Entering new AgentExecutor chain...
Invoking: `duckduckgo_search` with `2023年8月的新闻`
An error occurred: 'DDGS' object does not support the context manager protocol
### Expected behavior
该类提供了通过 [DuckDuckGo](https://duckduckgo.com/) 搜索引擎搜索的功能。
from langchain.tools import DuckDuckGoSearchRun
search = DuckDuckGoSearchRun()
search.run("Who is winner of FIFA worldcup 2018?")
你应该期望如下输出：
The 2018 FIFA World Cup was the 21st FIFA World Cup, ... Mario Zagallo (Brazil) and Franz Beckenbauer (Germany) have also achieved the feat.
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PR_kwDOIPDwls5YqCE0
| 9,684 |
Add dashvector self query retriever
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[] | null | 2 | 2023-08-24T05:47:04 | 2023-09-07T01:30:36 | 2023-09-04T03:51:04 |
CONTRIBUTOR
| null |
## Description
Add `Dashvector` retriever and self-query retriever
## How to use
```python
from langchain.vectorstores.dashvector import DashVector
vectorstore = DashVector.from_documents(docs, embeddings)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
```
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PR_kwDOIPDwls5YqBO9
| 9,683 |
Allow specifying arbitrary keyword arguments in `langchain.llms.VLLM`
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[] | null | 3 | 2023-08-24T05:42:57 | 2023-09-04T03:40:06 | 2023-09-04T03:40:06 |
CONTRIBUTOR
| null |
Description: add arbitrary keyword arguments for VLLM
Issue: https://github.com/langchain-ai/langchain/issues/9682
Dependencies: none
Tag maintainer: @hwchase17, @baskaryan
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I_kwDOIPDwls5vISbh
| 9,682 |
Allow specifying arbitrary keyword arguments in `langchain.llms.VLLM`
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[] | null | 2 | 2023-08-24T05:41:55 | 2023-11-30T16:06:16 | 2023-11-30T16:06:15 |
NONE
| null |
### Feature request
Enable the option to specify arbitrary keyword arguments (e.g. gpu_memory_utilization=0.98) in langchain.llms.VLLM() constructor
### Motivation
Currently, vllm library provides many useful keyword arguments which enables a lot of usages on a wide variety of devices, but langchain doesn't.
e.g. Some models do not work with GPUs with smaller memory as the default gpu_memory_utilization = 0.9, increasing this limit could enable the use of those models on the smaller GPUs.
### Your contribution
I can contribute by submitting a PR for that.
|
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I_kwDOIPDwls5vIP7A
| 9,681 |
initialize_agent not saving and returning messages in memory
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[] | null | 1 | 2023-08-24T05:30:13 | 2023-08-24T05:40:45 | 2023-08-24T05:40:45 |
NONE
| null |
### System Info
langchain version == 0.0.271
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
chat_history = MessagesPlaceholder(variable_name="chat_history")
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo')
agent_chain = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
memory=memory,
max_iterations=1,
early_stopping_method='generate',
handle_parsing_errors=True,
system_prompt="You are an assistant named Mikey",
agent_kwargs={
"memory_prompts": [chat_history],
"input_variables": ["input", "agent_scratchpad", "chat_history"],
}
)
agent_chain.run(input="My name is Dev")
agent_chain.run(input="What is my name")
### Expected behavior
Expected behavior
final answer: Your name is Dev
Output behavior:
Final answer: I don't have access to personal information like your name. Is there anything else I can help you with?
Note: What happens is that the memory is not being passed along, so the model doesn't know my name
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I_kwDOIPDwls5vINSE
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DOC: <Please write a comprehensive title after the 'DOC: ' prefix>
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[] | null | 2 | 2023-08-24T05:17:48 | 2023-11-30T16:06:21 | 2023-11-30T16:06:20 |
NONE
| null |
### Issue with current documentation:
The basic documents for the output parser are out of date for pydantic. Tried with python 3.9 and 3.11.
```
pip install langchain
pip install python-dotenv
```
The copied code:
```
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field, validator
from typing import List
load_dotenv()
model_name = 'text-davinci-003'
temperature = 0.0
model = OpenAI(model_name=model_name, temperature=temperature)
# Define your desired data structure.
class Joke(BaseModel):
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator('setup')
def question_ends_with_question_mark(cls, field):
if field[-1] != '?':
raise ValueError("Badly formed question!")
return field
# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=Joke)
prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
# And a query intended to prompt a language model to populate the data structure.
joke_query = "Tell me a joke."
_input = prompt.format_prompt(query=joke_query)
output = model(_input.to_string())
parser.parse(output)
```
results in:
```
<input>:21: PydanticDeprecatedSince20: Pydantic V1 style `@validator` validators are deprecated. You should migrate to Pydantic V2 style `@field_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.3/migration/
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevconsole.py", line 364, in runcode
coro = func()
File "<input>", line 16, in <module>
File "/Users/travisbarton/opt/anaconda3/envs/scratch39/lib/python3.9/site-packages/pydantic/_internal/_model_construction.py", line 130, in __new__
cls.__pydantic_decorators__ = DecoratorInfos.build(cls)
File "/Users/travisbarton/opt/anaconda3/envs/scratch39/lib/python3.9/site-packages/pydantic/_internal/_decorators.py", line 441, in build
res.validators[var_name] = Decorator.build(
File "/Users/travisbarton/opt/anaconda3/envs/scratch39/lib/python3.9/site-packages/pydantic/_internal/_decorators.py", line 249, in build
func = shim(func)
File "/Users/travisbarton/opt/anaconda3/envs/scratch39/lib/python3.9/site-packages/pydantic/_internal/_decorators_v1.py", line 77, in make_generic_v1_field_validator
raise PydanticUserError(
pydantic.errors.PydanticUserError: The `field` and `config` parameters are not available in Pydantic V2, please use the `info` parameter instead.
For further information visit https://errors.pydantic.dev/2.3/u/validator-field-config-info
```
### Idea or request for content:
Maybe fix the validator to match pydantic? Perhaps I'm mistaken?
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I_kwDOIPDwls5vIKkW
| 9,679 |
Initialize_agent not storing messages when memory is present
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[] | null | 1 | 2023-08-24T05:04:25 | 2023-08-24T05:25:36 | 2023-08-24T05:25:36 |
NONE
| null |
### System Info
langchain version == 0.0.271
### Who can help?
@hw
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
chat_history = MessagesPlaceholder(variable_name="chat_history")
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo')
agent_chain = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
memory=memory,
max_iterations=1,
early_stopping_method='generate',
handle_parsing_errors=True,
system_prompt="You are an assistant named Mikey",
agent_kwargs={
"memory_prompts": [chat_history],
"input_variables": ["input", "agent_scratchpad", "chat_history"],
}
)
agent_chain.run(input="What is my name")
### Expected behavior
final answer: Your name is ....
What happens is that the memory is not being returned(the name was given to the model in a previous run) so the model doesn't know my name
|
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I_kwDOIPDwls5vH8iG
| 9,678 |
Issue: ERNIE Bot is not able to call tool
|
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[] | null | 5 | 2023-08-24T03:51:14 | 2023-11-30T16:06:26 | 2023-11-30T16:06:25 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I tried to use Erine Bot as an agent like below
```python
tools = [faq_tool, query_order_tool]
FORMAT_INSTRUCTIONS = """要使用工具，请按照以下格式返回：
Thought: 我是否需要使用一个工具? 是
Action: 采取的action应该是以下之一：[{tool_names}]
Action Input: action的输入参数
Observation: action的返回结果
如果你不需要使用工具， 请按以下格式返回：
Thought: 我是否需要使用工具? 否
{ai_prefix}: [把你的回复放在这里]
"""
PREFIX = """用中文回答以下问题. 如果找不到答案回答 '我不知道'. 你可以使用以下工具:"""
prompt_ = ConversationalAgent.create_prompt(
tools,
prefix = PREFIX,
format_instructions = FORMAT_INSTRUCTIONS,
input_variables=["input", "chat_history","agent_scratchpad"]
)
memory = ConversationBufferWindowMemory(memory_key="chat_history", return_messages=True, k=3)
chatllmChain = LLMChain(llm=ErnieBotChat(), callbacks=[self.logHandler], verbose = True)
agent = ConversationalAgent(llm_chain=chatllmChain)
callback_manager = CallbackManager([self.logHandler])
self.conversation_agent = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools,memory=memory, callback_manager = callback_manager, verbose=True, max_iterations=3)
```
Then I asked a question "你好，我应该怎么申请赔偿"
The answer returned by the agent is:
Could not parse LLM output: `我不知道，但我会将你的问题传递给对应的工具来获取答案。我发送了一个消息到[FAQ](%E8%BF%94%E5%9B%9E)和[search_order](%E8%BF%94%E5%9B%9E)。请等待他们的回复。`
Which is not correct. The correct behavior is call the faq tool and get relevant information to form a reply. Which was proven to be work when using Open API chat model.
@axiangcoding Any idea of how to make Ernie Bot to be able to handle this situation? Thanks in advance!!!
### Suggestion:
_No response_
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I_kwDOIPDwls5vHxjb
| 9,677 |
Please share a complete document PDF OR markdown etc about langchain
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[] | null | 2 | 2023-08-24T03:07:12 | 2023-11-30T16:06:31 | 2023-11-30T16:06:30 |
NONE
| null |
### Issue with current documentation:
_No response_
### Idea or request for content:
_No response_
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I_kwDOIPDwls5vHxHZ
| 9,676 |
Support load ChatPromptTemplate from config dict
|
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[] | null | 2 | 2023-08-24T03:04:38 | 2023-11-30T16:06:36 | 2023-11-30T16:06:35 |
CONTRIBUTOR
| null |
### Feature request
We are currently using OpenAI's ChatCompletion API with custom ChatPromptTemplate, as converting langchain's ChatPromptTemplate to dict (or vice versa) seems not working well.
So I'd like to suggest new feature, to enable loading [ChatPromptTemplate](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/prompts/chat.py) from config dict,
by adding loading function (or classmethod in ChatPromptTemplate) to [prompts/loading.py](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/prompts/loading.py#L151-L155).
Or if there is standardize way to easily convert dict to ChatPromptTemplate, please let me know.
### Motivation
We are currently using OpenAI's ChatCompletion API with custom ChatPromptTemplate, as converting langchain's ChatPromptTemplate to dict (or vice versa) seems not working well.
As Utilizing OpenAI's ChatCompletion API (other than Completion API) becoming mainstream, I thought it might be good to support standard loading method for ChatPromptTemplate.
### Your contribution
If you allow me, I'd like to make a contribution related to this feature.
|
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PR_kwDOIPDwls5Ypiii
| 9,675 |
Resolve circular imports in runnables
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[] | null | 1 | 2023-08-24T02:52:24 | 2023-08-24T05:05:52 | 2023-08-24T05:05:51 |
COLLABORATOR
| null |
These are about to cause circular imports.
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PR_kwDOIPDwls5Ypepr
| 9,674 |
Make document serializable, create utility to create a docstore
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[] | null | 3 | 2023-08-24T02:30:46 | 2023-08-30T13:45:06 | 2023-08-30T13:45:05 |
COLLABORATOR
| null |
This PR makes the following changes:
1. Documents become serializable using langhchain serialization
2. Make a utility to create a docstore kw store
Will help to address issue here: https://github.com/langchain-ai/langchain/issues/9345
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PR_kwDOIPDwls5YpaN2
| 9,673 |
Wfh/whatsapp loader
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[] | null | 1 | 2023-08-24T02:04:33 | 2023-08-24T15:28:33 | 2023-08-24T15:28:31 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- Issue: the issue # it fixes (if applicable),
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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.
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If you're adding a new integration, please include:
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2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5YpTeG
| 9,672 |
Update ChatOpenAI._stream to respect finish_reason
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[] | null | 2 | 2023-08-24T01:31:40 | 2023-08-24T23:35:02 | 2023-08-24T05:58:14 |
CONTRIBUTOR
| null |
Currently, ChatOpenAI._stream does not reflect finish_reason to generation_info. Change it to reflect that.
Same patch as https://github.com/langchain-ai/langchain/pull/9431 , but also applies to _stream.
|
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PR_kwDOIPDwls5YpRzS
| 9,671 |
docs:misc fixes
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[] | null | 2 | 2023-08-24T01:20:52 | 2023-08-24T05:46:54 | 2023-08-24T05:36:54 |
CONTRIBUTOR
| null |
Improve internal consistency in LangChain documentation
- Change occurrences of eg and eg. to e.g.
- Fix headers containing unnecessary capital letters.
- Change instances of "few shot" to "few-shot".
- Add periods to end of sentences where missing.
- Minor spelling and grammar fixes.
|
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Wfh/whatsapp loader
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[] | null | 1 | 2023-08-24T00:11:27 | 2023-08-24T00:21:52 | 2023-08-24T00:13:07 |
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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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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PR_kwDOIPDwls5YpETa
| 9,669 |
fix integration test imports
|
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[] | null | 1 | 2023-08-23T23:43:52 | 2023-08-23T23:47:02 | 2023-08-23T23:47:01 |
COLLABORATOR
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I_kwDOIPDwls5vHKEN
| 9,668 |
Issue: vectorstore hence indexes and embeddings are persisting when they should not be.
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[] | null | 3 | 2023-08-23T23:32:29 | 2023-11-30T16:06:41 | 2023-11-30T16:06:40 |
NONE
| null |
### Issue you'd like to raise.
langchain==0.0.250
platform = macOS
Python version 3.11
I am not sure if this is by design, I think it is not and hence reporting this as an issue.
When looping through a list of pdf files to get a summary for each I am creating an index using - VectorstoreIndexCreator().from_documents(pages).
The issue is for each subsequent file, data(documents) from previous file are also being retrieved to be passed on to GPT and end up in the summary.
Code:
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
for file in files:
loader = PyPDFLoader(file['path'])
pages = loader.load_and_split()
index = VectorstoreIndexCreator().from_documents(pages)
retriever = index.vectorstore.as_retriever(search_type='mmr')
retriever.search_kwargs['k'] = 10
llm = ChatOpenAI()
aca_qa = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
chain_type='stuff',
# return_source_documents=True,
)
result = aca_qa({'query': summary_query})
### Suggestion:
Temp fix:
The resolution is to include - index = '' at the beginning of each loop cycle
|
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PR_kwDOIPDwls5Yoe17
| 9,667 |
Add new document_loader: AssemblyAIAudioTranscriptLoader
|
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closed
| false | null |
[] | null | 1 | 2023-08-23T20:41:43 | 2023-08-24T05:51:19 | 2023-08-24T05:51:19 |
CONTRIBUTOR
| null |
This PR adds a new document loader `AssemblyAIAudioTranscriptLoader` that allows to transcribe audio files with the [AssemblyAI API](https://www.assemblyai.com) and loads the transcribed text into documents.
- Add new document_loader with class `AssemblyAIAudioTranscriptLoader`
- Add optional dependency `assemblyai`
- Add unit tests (using a Mock client)
- Add docs notebook
This is the equivalent to the JS integration already available in LangChain.js. See the [LangChain JS docs AssemblyAI page](https://js.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_loaders/assemblyai_audio_transcription).
At its simplest, you can use the loader to get a transcript back from an audio file like this:
```python
from langchain.document_loaders.assemblyai import AssemblyAIAudioTranscriptLoader
loader = AssemblyAIAudioTranscriptLoader(file_path="./testfile.mp3")
docs = loader.load()
```
To use it, it needs the `assemblyai` python package installed, and the
environment variable `ASSEMBLYAI_API_KEY` set with your API key. Alternatively, the API key can also be passed as an argument.
Twitter handles to shout out if so kindly 🙇
[@AssemblyAI](https://twitter.com/AssemblyAI) and [@patloeber](https://twitter.com/patloeber)
|
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PR_kwDOIPDwls5YoSKR
| 9,666 |
Whatsapp + slack loaders
|
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[] | null | 1 | 2023-08-23T19:51:38 | 2023-08-23T19:59:53 | 2023-08-23T19:59:52 |
COLLABORATOR
| null |
Add whatsapp and slack loaders
|
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PR_kwDOIPDwls5YoB4R
| 9,665 |
Initial commit for comprehend moderator
|
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[] | null | 14 | 2023-08-23T18:51:39 | 2023-08-28T23:01:06 | 2023-08-25T22:11:27 |
CONTRIBUTOR
| null |
This PR implements a custom chain that wraps Amazon Comprehend API calls. The custom chain is aimed to be used with LLM chains to provide moderation capability that let’s you detect and redact PII, Toxic and Intent content in the LLM prompt, or the LLM response. The implementation accepts a configuration object to control what checks will be performed on a LLM prompt and can be used in a variety of setups using the LangChain expression language to not only detect the configured info in chains, but also other constructs such as a retriever.
The included sample notebook goes over the different configuration options and how to use it with other chains.
### Usage sample
```python
from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters
moderation_config = {
"filters":[
BaseModerationFilters.PII,
BaseModerationFilters.TOXICITY,
BaseModerationFilters.INTENT
],
"pii":{
"action": BaseModerationActions.ALLOW,
"threshold":0.5,
"labels":["SSN"],
"mask_character": "X"
},
"toxicity":{
"action": BaseModerationActions.STOP,
"threshold":0.5
},
"intent":{
"action": BaseModerationActions.STOP,
"threshold":0.5
}
}
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, #specify the configuration
client=comprehend_client, #optionally pass the Boto3 Client
verbose=True
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)
llm_chain = LLMChain(prompt=prompt, llm=llm)
chain = (
prompt
| comp_moderation_with_config
| {llm_chain.input_keys[0]: lambda x: x['output'] }
| llm_chain
| { "input": lambda x: x['text'] }
| comp_moderation_with_config
)
response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
print(response['output'])
```
### Output
```
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii validation...
Found PII content..stopping..
The prompt contains PII entities and cannot be processed
```
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PR_kwDOIPDwls5Yn8LP
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mv embedding cache docs
|
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[] | null | 2 | 2023-08-23T18:31:03 | 2023-08-23T18:46:05 | 2023-08-23T18:46:04 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Yn6nb
| 9,663 |
docs: `DeepLake` example
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[] | null | 3 | 2023-08-23T18:25:22 | 2023-09-04T17:08:22 | 2023-09-04T03:42:52 |
COLLABORATOR
| null |
Updated the `Deep Lake` example. Added a link to an example provided by Activeloop.
|
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Runnable locals
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PR_kwDOIPDwls5YnpUc
| 9,661 |
Revert "Locals in config"
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[] | null | 1 | 2023-08-23T17:24:40 | 2023-08-23T17:25:01 | 2023-08-23T17:25:00 |
COLLABORATOR
| null |
Reverts langchain-ai/langchain#9007
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PR_kwDOIPDwls5YnlH6
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Wfh/whatsapp loader
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Ynhdc
| 9,659 |
docs: fix PromptGuard docs
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[] | null | 1 | 2023-08-23T16:57:29 | 2023-08-23T17:07:45 | 2023-08-23T17:04:54 |
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
Fix PromptGuard docs. Noticed several trivial issues on the docs when integrating the new class.
cc @baskaryan
|
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I_kwDOIPDwls5vFAI2
| 9,658 |
Could not parse LLM output: `Action: json_spec_list_keys(data)`
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[] | null | 2 | 2023-08-23T15:39:38 | 2023-11-29T16:06:24 | 2023-11-29T16:06:23 |
NONE
| null |
### System Info
- Version: 0.0.271
- Platform: Macbook Pro M1 macos 13.5
- Python Version: 3.11.4
### Who can help?
@...
### 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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior:
1. Copy the existing code from [Langchain Document - Json Agent](https://python.langchain.com/docs/integrations/toolkits/json)
2. Replace the llm model from OpenAI to GPT4All
3. Use the model [ggml-gpt4all-j-v1.3-groovy.bin](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin)
4. Download the [JSON FIle](https://github.com/OAI/OpenAPI-Specification/blob/main/examples/v2.0/json/petstore.json) and provide the path in the script
5. Replace the question with `What are the required parameters in the request body to the /pets endpoint?`.
6. Output: `File venv/lib/python3.11/site-packages/langchain/agents/mrkl/output_parser.py", line 61, in parse
raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: 'Action: json_spec_list_keys(data)'`
### Expected behavior
The agent should parse through and provide the answer.
|
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| 1,863,545,736 |
PR_kwDOIPDwls5YnAZZ
| 9,657 |
Add CrateDB prompt
|
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[] | null | 2 | 2023-08-23T15:17:50 | 2023-08-23T17:33:47 | 2023-08-23T17:33:38 |
CONTRIBUTOR
| null |
Adds a prompt template for the CrateDB SQL dialect.
|
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I_kwDOIPDwls5vEw4x
| 9,656 |
Error in the code given in the prompt template
|
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[] | null | 3 | 2023-08-23T15:02:55 | 2023-12-04T16:05:28 | 2023-12-04T16:05:27 |
NONE
| null |
### Issue with current documentation:
Modules -> prompt templates
This set code which is there in documentation is throwing error
`from langchain import PromptTemplate`
`invalid_prompt = PromptTemplate(`
` input_variables=["adjective"],`
` template="Tell me a {adjective} joke about {content}."`
`)`
Because here in the input variables 'content' word is missing , as it is also one of the input variable to the prompt template.
error -
**update the code -**
`from langchain import PromptTemplate`
`invalid_prompt = PromptTemplate(`
` input_variables=["adjective","content"],`
` template="Tell me a {adjective} joke about {content}."`
`)`
|
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| 1,863,382,118 |
PR_kwDOIPDwls5YmcfQ
| 9,655 |
Cleanup of ruff warnings use isinstance() instead of type()
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[] | null | 2 | 2023-08-23T13:49:51 | 2023-08-27T07:18:37 | 2023-08-23T14:14:32 |
CONTRIBUTOR
| null |
Minor cosmetic PR just cleanup of `ruff` warnings use `isinstance()` instead of `type()`
|
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| 1,863,187,495 |
I_kwDOIPDwls5vDfwn
| 9,654 |
output_parser has a bug while the output string has ``` code ```
|
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[] | null | 1 | 2023-08-23T11:58:25 | 2023-11-29T16:06:34 | 2023-11-29T16:06:33 |
NONE
| null |
### System Info
langchain 0.0.235 / python 3.10.11 /
File: libs/langchain/langchain/output_parsers/json.py
There is a bug in the function `parse_json_markdown`.
When the input json_string contains \`\`\` $code \`\`\` . It mistakenly interprets $code as json format and fails to parse.
### Who can help?
_No response_
### Information
- [X] 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
1. make LLM answer with text and code (in \`\`\` $code \`\`\` format)
2. The output_parser will parse the $code instead of \`\`\` json \`\`\`
3. FAIL
### Expected behavior
maybe use re.findall and get the last search result
EX:
```
match = re.findall(r"```(json)?(.*?)```", json_string, re.DOTALL)
```
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| 1,863,141,243 |
I_kwDOIPDwls5vDUd7
| 9,653 |
DOC: Not working: <OpenAPI spec and create + execute valid requests against the API>
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[] | null | 2 | 2023-08-23T11:28:20 | 2023-11-29T16:06:39 | 2023-11-29T16:06:38 |
NONE
| null |
### Issue with current documentation:
Link not working: [OpenAPI](https://python.langchain.com/docs/use_cases/apis/openapi_openai)
where is the link? https://python.langchain.com/docs/modules/chains/how_to/openai_functions
### Idea or request for content:
_No response_
|
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| 1,863,049,747 |
I_kwDOIPDwls5vC-IT
| 9,651 |
sql_cmd Error: SQLDatabaseChain with ChatGLM2-6B
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[] | null | 2 | 2023-08-23T10:31:14 | 2023-11-30T16:06:51 | 2023-11-30T16:06:50 |
NONE
| null |
I wrote a ChatGLM class that inherits LLM class as below.
```
class ChatGLM(LLM):
def _call(self, prompt: str,
stop: Optional[List[str]] = None) -> str:
message = [{"role": "user", "content": prompt}]
payload = {"model": "string", ...}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
return response.json()['choices'][0]['message']['content']
```
And I hope to use the ChatGLM class I wrote to replace the previously used OpenAI class during sql querying with SQLDatabaseChain:
```
llm = ChatGLM(temperature=0, verbose=True)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True,
return_intermediate_steps=False)
db_chain.run("What tables are in the database?")
```
This is the error reported by the code, does anyone know what caused it?
```
> Entering new SQLDatabaseChain chain...
What tables are in the database?
SQLQuery: Here
I'm sorry, but I don't see any SQLite query provided in your message. Could you please provide the SQLite query so I can review it for any common mistakes?
---------------------------------------------------------------------------
OperationalError Traceback (most recent call last)
File C:\Language\Anaconda3\lib\site-packages\sqlalchemy\engine\base.py:1963, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1962 if not evt_handled:
-> 1963 self.dialect.do_execute(
1964 cursor, str_statement, effective_parameters, context
1965 )
1967 if self._has_events or self.engine._has_events:
File C:\Language\Anaconda3\lib\site-packages\sqlalchemy\engine\default.py:920, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
919 def do_execute(self, cursor, statement, parameters, context=None):
--> 920 cursor.execute(statement, parameters)
OperationalError: near "I": syntax error
......
```
I have located the error to `langchain/chains/sql_database/base.py` ln125, from here chatglm cannot output the correct `sql_cmd`, but I cannot further check what is causing this problem. Any help would be greatly appreciated, thank you.
### 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
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import langchain
from langchain.llms.base import LLM
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
db = SQLDatabase.from_uri('sqlite:///music.db')
class ChatGLM(LLM):
url = "http://region-9.seetacloud.com:23361/v1/chat/completions"
@property
def _llm_type(self) -> str:
return "chatglm2-6b"
def _call(self, prompt: str,
stop: Optional[List[str]] = None) -> str:
message = [{
"role": "user",
"content": prompt
}]
url = "http://region-9.seetacloud.com:23361/v1/chat/completions"
payload = {
"model": "string",
"messages": message,
"temperature":1,
"top_p": 0,
"n": 1,
"max_tokens": 0,
"stream": False
}
headers = {
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
return response.json()['choices'][0]['message']['content']
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters.
"""
_param_dict = {
"url": self.url
}
return _param_dict
llm = ChatGLM(temperature=0, verbose=True)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True,
return_intermediate_steps=False)
db_chain.run("What tables are in the database?")
```
### Expected behavior
return correct sql query
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I_kwDOIPDwls5vCxZq
| 9,650 |
PydanticOutputParser not called by chain
|
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[] | null | 2 | 2023-08-23T10:00:55 | 2023-08-23T11:45:35 | 2023-08-23T11:44:13 |
NONE
| null |
### System Info
langchain 0.0.268
python 3.9
Windows 10
### 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
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
class Actor(BaseModel):
name: str = Field(description="name of an actor")
film_names: List[str] = Field(description="list of names of films they starred in")
parser = PydanticOutputParser(pydantic_object=Actor)
prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
chain = LLMChain(llm=ChatModel.get_model(), prompt=prompt, verbose=True)
output = chain.run(query="Generate the filmography for a random actor.", output_parser=parser)
```
Problem: _output_ is a JSON string, and not an Actor object.
### Expected behavior
The method _parse()_ from the OutputParser passed to the chain should be automatically called and return the parsed object, which is not the case. One has to call explicitly `parser.parse(output)` to retrieve an Actor object.
For custom parsers, it seems to work, but not for PydanticOutputParser. I expect the chain's behavior to be consistent across all parsers.
|
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I_kwDOIPDwls5vCuA-
| 9,649 |
Issue: How to stop extra LLM call in ConversationalRetrievalChain for question rephrasing
|
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[] | null | 13 | 2023-08-23T09:53:18 | 2024-01-16T17:58:05 | null |
NONE
| null |
### Issue you'd like to raise.
I am using ConversationalRetrievalChain for the RAG question-answer bot.
There is one LLM call that I have not configured and it is reducing the quality of responses and increasing the time.
The prompt in the LLM call is:
> Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
It is taking around 1 second to complete this call, and it is reducing the quality of the response as well.
How do I stop this call?
### Suggestion:
_No response_
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| 1,862,966,004 |
PR_kwDOIPDwls5YlBUi
| 9,648 |
ElasticsearchStore: improve error logging for adding documents
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[] | null | 2 | 2023-08-23T09:43:51 | 2023-08-23T15:38:35 | 2023-08-23T14:04:09 |
CONTRIBUTOR
| null |
Not obvious what the error is when you cannot index. This pr adds the ability to log the first errors reason, to help the user diagnose the issue.
Also added some more documentation for when you want to use the vectorstore with an embedding model deployed in elasticsearch.
Credit: @elastic and @phoey1
|
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PR_kwDOIPDwls5Yk78s
| 9,647 |
Fixings grammatical errors in Doc Files
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[] | null | 2 | 2023-08-23T09:31:30 | 2023-08-23T14:04:30 | 2023-08-23T14:04:30 |
CONTRIBUTOR
| null |
Fixing some typos and grammatical error is doc file.
@eyurtsev , @baskaryan
Thanks
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I_kwDOIPDwls5vCYFK
| 9,646 |
VLLM from langchain.llms
|
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[] | null | 3 | 2023-08-23T09:05:03 | 2023-11-29T16:06:49 | 2023-11-29T16:06:48 |
NONE
| null |
### System Info
VLLM from langchain gives the below error and stops executing:
code: `python
from langchain.llms import VLLM
llm = VLLM(model="facebook/opt-125m"
tensor_parallel_size=2,
trust_remote_code=True, # mandatory for hf models
max_new_tokens=128,
top_k=10,
top_p=0.95,
temperature=0.8,
)
print(llm("What is the capital of France ?"))
`
gives the below error when setting tensor_parallel_size=2 and runs successfully if we comment out tensor_parallel_size argument:
error =>
**2023-08-23 08:52:55,683 ERROR services.py:1207 -- Failed to start the dashboard, return code -11
2023-08-23 08:52:55,685 ERROR services.py:1232 -- Error should be written to 'dashboard.log' or 'dashboard.err'. We are printing the last 20 lines for you. See 'https://docs.ray.io/en/master/ray-observability/ray-logging.html#logging-directory-structure' to find where the log file is.
2023-08-23 08:52:55,687 ERROR services.py:1276 --
The last 20 lines of /tmp/ray/session_2023-08-23_08-52-52_882632_28/logs/dashboard.log (it contains the error message from the dashboard):
2023-08-23 08:52:55,607 INFO head.py:242 -- Starting dashboard metrics server on port 44227**
**2023-08-23 08:52:56,847 INFO worker.py:1636 -- Started a local Ray instance.**
VM Details:
CPU : 4 Core
RAM : 13 GB
GPU: Nvidia T4 *2
Instance : Kaggle Kernal
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] 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
### code:
`python
from langchain.llms import VLLM
llm = VLLM(model="facebook/opt-125m"
tensor_parallel_size=2,
trust_remote_code=True, # mandatory for hf models
max_new_tokens=128,
top_k=10,
top_p=0.95,
temperature=0.8,
)
print(llm("What is the capital of France ?"))
`
### On Kaggle Kernal
VM Details:
CPU : 4 Core
RAM : 13 GB
GPU: Nvidia T4 *2
Instance : Kaggle Kernal
### Expected behavior
2023-08-23 08:52:55,683 ERROR services.py:1207 -- Failed to start the dashboard , return code -11
2023-08-23 08:52:55,685 ERROR services.py:1232 -- Error should be written to 'dashboard.log' or 'dashboard.err'. We are printing the last 20 lines for you. See 'https://docs.ray.io/en/master/ray-observability/ray-logging.html#logging-directory-structure' to find where the log file is.
2023-08-23 08:52:55,687 ERROR services.py:1276 --
The last 20 lines of /tmp/ray/session_2023-08-23_08-52-52_882632_28/logs/dashboard.log (it contains the error message from the dashboard):
2023-08-23 08:52:55,607 INFO head.py:242 -- Starting dashboard metrics server on port 44227
2023-08-23 08:52:56,847 INFO worker.py:1636 -- Started a local Ray instance.
and Cell stops running i.e. execution stops
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| 1,862,851,384 |
I_kwDOIPDwls5vCNs4
| 9,644 |
Load a jpg using UnstructuredImageLoader from Langchain.
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[] | null | 2 | 2023-08-23T08:39:20 | 2023-11-29T16:06:54 | 2023-11-29T16:06:53 |
NONE
| null |
### System Info
win10
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Load a jpg using UnstructuredImageLoader from Langchain.
2. An error occurred.
### Expected behavior
got the error here：
```
loader:<langchain.document_loaders.image.UnstructuredImageLoader object at 0x000002926EA8EFB0>
Exception in thread Thread-3 (_handle_results):
Traceback (most recent call last):
File "D:\ProgramData\anaconda3\envs\3.10.11\lib\threading.py", line 1016, in _bootstrap_inner
self.run()
File "D:\ProgramData\anaconda3\envs\3.10.11\lib\threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "D:\ProgramData\anaconda3\envs\3.10.11\lib\multiprocessing\pool.py", line 579, in _handle_results
task = get()
File "D:\ProgramData\anaconda3\envs\3.10.11\lib\multiprocessing\connection.py", line 251, in recv
return _ForkingPickler.loads(buf.getbuffer())
TypeError: TesseractNotFoundError.__init__() takes 1 positional argument but 2 were given
```
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| 1,862,705,124 |
PR_kwDOIPDwls5YkJF6
| 9,643 |
docs: fix link url
|
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[] | null | 2 | 2023-08-23T07:07:49 | 2023-08-23T14:05:03 | 2023-08-23T14:05:03 |
CONTRIBUTOR
| null |
This pull request corrects the URL links in the Async API documentation to align with the updated project layout. The links had not been updated despite the changes in layout.
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,862,704,214 |
PR_kwDOIPDwls5YkI5E
| 9,642 |
Update ChatOpenAI._stream to respect finish_reason
|
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[] | null | 1 | 2023-08-23T07:07:08 | 2023-08-23T07:33:53 | 2023-08-23T07:33:53 |
CONTRIBUTOR
| null |
Currently, ChatOpenAI._stream does not reflect finish_reason to generation_info. Change it to reflect that.
Same patch as https://github.com/langchain-ai/langchain/pull/9431 , but also applies to _stream.
|
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| 1,862,687,289 |
I_kwDOIPDwls5vBlo5
| 9,641 |
Issue: I am working with SQLChain and initialize_agent it is not answering from the connected database.
|
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[] | null | 4 | 2023-08-23T06:54:38 | 2023-11-29T16:06:59 | 2023-11-29T16:06:59 |
NONE
| null |
### Issue you'd like to raise.
def get_connection():
user=os.environ.get('DB_USERNAME')
database= os.environ.get('DB_DATABASE')
password=os.environ.get('DB_PASSWORD')
host= os.environ.get('DB_HOST')
port= os.environ.get('DB_PORT')
return create_engine(
url="mysql+pymysql://{0}:{1}@{2}:{3}/{4}".format(
user, password, host, port, database
)
)
ef get_whole_conversation(question):
try:
llm = ChatOpenAI(temperature=0,openai_api_key=env('OPENAI_API_KEY'),model='gpt-3.5-turbo')
engine = get_connection()
input_db = SQLDatabase(engine)
db_chain = SQLDatabaseChain.from_llm(llm, input_db, verbose=True)
prompt="""
"""
tools = [Tool(name="Foo-Bar-db",func=db_chain.run,description=prompt)]
agent_kwargs = {"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],}
memory = ConversationBufferWindowMemory(memory_key="memory",k=4,return_messages=True)
agent = initialize_agent(tools,llm,agent=AgentType.OPENAI_FUNCTIONS,verbose=True,agent_kwargs=agent_kwargs,memory=memory)
agent.run(question)
### Suggestion:
_No response_
|
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| 1,862,651,370 |
I_kwDOIPDwls5vBc3q
| 9,640 |
When using Langchain to upload a .docx file to the Milvus database, an error occurs.
|
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[] | null | 2 | 2023-08-23T06:25:55 | 2023-11-29T16:07:05 | 2023-11-29T16:07:04 |
NONE
| null |
### System Info
Win10
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
embeddings = HuggingFaceEmbeddings( model_name='sentence-transformers/LaBSE')
vectordb = Milvus(embedding_function=embeddings, connection_args=milvus_settings)
vectordb.add_documents(documents=spilt_results)
```
I got this Error:
Traceback (most recent call last):
File "E:\文件\programs\test_jieba\ingest.py", line 44, in <module>
main()
File "E:\文件\programs\test_jieba\ingest.py", line 39, in main
vectordb.add_documents(documents=spilt_results)
File "E:\文件\programs\test_jieba\.venv\lib\site-packages\langchain\vectorstores\base.py", line 92, in add_documents
return self.add_texts(texts, metadatas, **kwargs)
File "E:\文件\programs\test_jieba\.venv\lib\site-packages\langchain\vectorstores\milvus.py", line 454, in add_texts
insert_list = [insert_dict[x][i:end] for x in self.fields]
File "E:\文件\programs\test_jieba\.venv\lib\site-packages\langchain\vectorstores\milvus.py", line 454, in <listcomp>
insert_list = [insert_dict[x][i:end] for x in self.fields]
KeyError: 'file_path'
### Expected behavior
why is this happen?
|
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| 1,862,640,640 |
I_kwDOIPDwls5vBaQA
| 9,639 |
DOC: Broken notebook link
|
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[] | null | 2 | 2023-08-23T06:17:03 | 2023-11-29T16:07:11 | 2023-11-29T16:07:10 |
NONE
| null |
### Issue with current documentation:
There is a notebook link in [QA using a Retriever
](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa)
that should open a [notebook](https://python.langchain.com/docs/modules/chains/additional/question_answering.html)
But link is boken. Page does not exist.
Can you fix it?
Thanks
### Idea or request for content:
_No response_
|
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| 1,862,618,906 |
PR_kwDOIPDwls5Yj2n1
| 9,638 |
adapt ChatGLM using api(chatglm.py)
|
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closed
| false | null |
[] | null | 4 | 2023-08-23T05:57:06 | 2023-08-24T13:01:54 | 2023-08-24T13:01:54 |
CONTRIBUTOR
| null |
using sample:
```
endpoint_url = API URL
ChatGLM_llm = ChatGLM(
endpoint_url=endpoint_url,
api_key=Your API Key by ChatGLM
)
print(ChatGLM_llm("hello"))
```
Replace this entire comment with:
- Description: The call of ChatGLM has been adapted.
- Issue: The call of ChatGLM has been adapted.
- Dependencies: Need python package `zhipuai`
- Tag maintainer: @baskaryan, @hwchase17
- Twitter handle: None
|
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| 1,862,610,190 |
PR_kwDOIPDwls5Yj0wU
| 9,637 |
Fix typo in tigris
|
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[] | null | 2 | 2023-08-23T05:47:49 | 2023-08-23T14:15:19 | 2023-08-23T14:15:18 |
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
The link has a **typo** in [tigirs docs](https://python.langchain.com/docs/integrations/providers/tigris), so I couldn't access it. So, I have corrected it.
Thanks! ☺️
|
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| 1,862,450,431 |
I_kwDOIPDwls5vArz_
| 9,636 |
PipelinePromptTemplate does not respect partial_variables
|
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[] | null | 2 | 2023-08-23T02:31:08 | 2023-11-29T16:07:15 | 2023-11-29T16:07:14 |
NONE
| null |
### System Info
langchain 0.0.271
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This raises a key error because `word` is treated like an input variable even though it's a partial variables
```python
p = PipelinePromptTemplate(
final_prompt=PromptTemplate.from_template("this {word} work"),
pipeline_prompts=[],
input_variables=[],
partial_variables={"word": "does"}
)
print(p.format())
# or
print(p.partial().format())
```
### Expected behavior
partial_variables should be interpolated properly
|
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| 1,862,447,487 |
PR_kwDOIPDwls5YjSGx
| 9,635 |
Allow specifying dtype in `langchain.llms.VLLM`
|
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| false | null |
[] | null | 2 | 2023-08-23T02:27:04 | 2023-08-23T03:30:09 | 2023-08-23T03:21:57 |
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
- Description: add `dtype` argument for VLLM
- Issue: #9593
- Dependencies: none
- Tag maintainer: @hwchase17, @baskaryan
|
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| 1,862,387,605 |
I_kwDOIPDwls5vAceV
| 9,634 |
create_openapi_agent always generating same chain no matter what question
|
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[] | null | 2 | 2023-08-23T00:54:53 | 2023-08-23T05:08:57 | 2023-08-23T05:08:56 |
NONE
| null |
### System Info
LangChain version: 0.0.266
Python3.11.4
Sample python snippet:
```with open("openapi_request_body.yaml") as f:
raw_api_spec = yaml.load(f, Loader=yaml.Loader)
api_spec = reduce_openapi_spec(raw_api_spec)
requests_wrapper = RequestsWrapper()
from langchain.llms.openai import OpenAI
from langchain.agents.agent_toolkits.openapi import planner
llm = OpenAI(temperature=0.0)
agent = planner.create_openapi_agent(api_spec, requests_wrapper, llm)
while True:
user_inp_query = input("Ask me anything: ")
print('user_inp_query: ', user_inp_query)
user_query = (user_inp_query)
agent.run(user_query)
```
The sample API spec contains 3 APIs: count_cars, locate_license_plate, and timestamps_of_matching_car_configs. However, I notice no matter what question I ask I see the following API planner response:
```Ask me anything: Tell me the timestamps whenever a green car came
-----------------------------------------------
user_inp_query: Tell me the timestamps whenever a green car came
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to get the timestamps of when a green car came
Observation: 1. POST /count_cars with a query param to search for green cars
2. POST /timestamps_of_matching_car_configs with the query param from the previous call
3. POST /locate_license_plate with the query param from the previous call
Thought: I'm ready to execute the API calls.
Action: api_controller
Action Input: 1. POST /count_cars with a query param to search for green cars
2. POST /timestamps_of_matching_car_configs with the query param from the previous call
3. POST /locate_license_plate with the query param from the previous call
> Entering new AgentExecutor chain...
I need to make a POST request to the /count_cars endpoint with the query params.
```
openAPI spec definition is as follows:
```openapi: 3.0.0
servers:
- url: http://127.0.0.1:5001
info:
title: Car Traffic MetaData Analyzer
description: REST API service which can be used to fetch details like car's color, car's type [like suv, sedan. coupe etc i.e. type of vehicle body] and car's make [toyota, honda, porche etc i.e. manufacturing company's name]. We also store the license plate of the individual cars for each entry.
version: 1.0.0
paths:
/count_cars:
post:
summary: This API takes as an input car's color (like red, green etc), car's vehicle body type (like suv, sedan. coupe etc) and car's make (like toyota, honda, porche etc). All the input values are optional. This API then returns a count of total number of cars which are of that certain color/type/make.
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/car_attributes'
# examples:
# examples1:
# $ref: '#/components/examples/car_attributes_example1'
# examples2:
# $ref: '#/components/examples/car_attributes_example2'
required: True
responses:
'200':
description: Successful response
content:
application/json:
examples:
example1:
summary: example 1
value:
total_count: "15"
example2:
summary: example 2
value:
total_count: "0"
'400':
description: Bad Request
'404':
description: Resource Not Found
/timestamps_of_matching_car_configs:
post:
summary: This API takes as an input car's color (like red, green etc), car's type (like suv, sedan. coupe etc) and car's make (like toyota, honda, porche etc). All the input values are optional. This API then returns two values, found and timestamps. When found=true it means that car config in query has been found and the corresponding timestamps key stores a list of all the timestamp at which this car config was found. If found=False, it means that not such car config can be found.
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/car_attributes'
# examples:
# examples1:
# $ref: '#/components/examples/car_attributes_example1'
# examples2:
# $ref: '#/components/examples/car_attributes_example2'
required: True
responses:
'200':
description: Successful response when the API found none or some entries where the car's color, type and make matches with the parameters passed to it.
content:
application/json:
examples:
example1:
summary: When API endpoint found none relevant entries
value:
found: "False"
example2:
summary: When API endpoint found some relevant entries
value:
found: "True"
timestamps: ["2021-10-04T16:53:11Z", "2021-11-22T06:50:14Z"]
example3:
summary: Another example of when API endpoint found some relevant entries
value:
found: "True"
timestamps: ["2001-10-04T12:23:43Z", "2011-01-29T23:23:29Z", "2001-11-30T00:01:01Z", "2011-01-09T23:59:00Z"]
'400':
description: Bad Request
'404':
description: Resource Not Found
/locate_license_plate:
post:
summary: This API takes as an input license plate of a car. If the car's license plate is present in the database, it return with found=true, and it also returns that car's color, vehicle body type and make i.e. manufacturing company's name. Else if found=false, it means no such car is found in the database.
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/license_plate'
# examples:
# examples1:
# $ref: '#/components/examples/license_plate_example1'
# examples2:
# $ref: '#/components/examples/license_plate_example2'
required: True
responses:
'200':
description: Successful response from the API. From the response found=False means that the license plate was not found in the databse. If found=True it means that the license plate was found.
content:
application/json:
examples:
example1:
summary: When API endpoint can't find license plate in the databse
value:
found: "False"
example2:
summary: When API endpoint found license plate, it returns car's color, type and make.
value:
found: "True"
color: "red"
type: "suv"
make: "honda"
example3:
summary: When API endpoint found license plate, it returns car's color, type and make.
value:
found: "True"
color: "grey"
type: "sedan"
make: "hyundai"
'400':
description: Bad Request
'404':
description: Resource Not Found
components:
schemas:
car_attributes:
properties:
color:
description: color of the car you want to query for.
type: string
type:
description: style of the vehicle body type on whether the car is one of suv, sedan, convertible, truck etc.
type: string
make:
description: manufacturing company of the car
type: string
license_plate:
properties:
license_plate:
description: lincense plate of the car
type: string
parameters:
color:
name: color
in: query
description: color of the car you want to query for.
schema:
type: string
examples:
red:
value: red
green:
value: green
blue:
value: blue
yellow:
value: yellow
type:
name: type
in: query
description: style of the vehicle body type on whether the car is one of suv, sedan, convertible, truck etc.
schema:
type: string
examples:
suv:
value: suv
sedan:
value: sedan
convertible:
value: convertible
truck:
value: truck
make:
name: make
in: query
description: Manufacturing company of the car
schema:
type: string
examples:
subaru:
value: subaru
hyundai:
value: hyundai
toyota:
value: toyota
porche:
value: porche
license_plate:
name: license_plate
in: query
description: Lincense plate of the car
schema:
type: string
examples:
6FFR593:
value: 6FFR593
KVT6282:
value: KVT6282
BHT9205:
value: BHT9205```
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
run the code snippet as it is and point the openapi.yaml to the yaml file provided.
### Expected behavior
The API chain is expected to classify the query into one of these APIs and only run that API instead of calling all the APIs in the same sequence everytime.
|
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PR_kwDOIPDwls5YjCMc
| 9,633 |
Fixed the error on ConfluenceLoader when content_format=VIEW and `keep_markdown_format`=True
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[] | null | 3 | 2023-08-23T00:26:46 | 2023-08-23T04:00:16 | 2023-08-23T04:00:16 |
CONTRIBUTOR
| null |
- Description: a description of the change
when I set `content_format=ContentFormat.VIEW` and `keep_markdown_format=True` on ConfluenceLoader, it shows the following error:
```
langchain/document_loaders/confluence.py", line 459, in process_page
page["body"]["storage"]["value"], heading_style="ATX"
KeyError: 'storage'
```
The reason is because the content format was set to `view` but it was still trying to get the content from `page["body"]["storage"]["value"]`.
Also added the other content formats which are supported by Atlassian API
https://stackoverflow.com/questions/34353955/confluence-rest-api-expanding-page-body-when-retrieving-page-by-title/34363386#34363386
- Issue: the issue # it fixes (if applicable),
Not applicable.
- Dependencies: any dependencies required for this change,
Added optional dependency `markdownify` if anyone wants to extract in markdown format.
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PR_kwDOIPDwls5Yi7Bc
| 9,632 |
Update ChatOpenAI docs with fine-tuning example
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[] | null | 1 | 2023-08-22T23:34:29 | 2023-08-22T23:56:54 | 2023-08-22T23:56:54 |
CONTRIBUTOR
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PR_kwDOIPDwls5YivEJ
| 9,631 |
docs: `integrations/providers`
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[] | null | 2 | 2023-08-22T22:21:13 | 2023-08-23T16:04:38 | 2023-08-23T03:28:12 |
COLLABORATOR
| null |
Added missed pages for `integrations/providers` from `vectorstores`.
Updated several `vectorstores` notebooks.
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I_kwDOIPDwls5vAAA_
| 9,630 |
Monitoring the progress of long running vectorDB index updates
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[] | null | 1 | 2023-08-22T22:08:51 | 2023-11-19T23:49:28 | null |
NONE
| null |
### Feature request
Add the option to output progress bar Methods that add documents to an index, e.g., `from_documents`.
### Motivation
Methods that add documents to an index can take a long time to execute, e.g., `from_documents`. When executing from a notebook or CLI it would be very convenient to be able to track the progressing using something like tqdm.
### Your contribution
No
|
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PR_kwDOIPDwls5Yifww
| 9,629 |
Return feedback
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[] | null | 1 | 2023-08-22T21:08:39 | 2023-08-28T16:15:06 | 2023-08-28T16:15:05 |
COLLABORATOR
| null |
Return the feedback values in an eval run result
Also made a helper method to display as a dataframe but it may be overkill
|
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PR_kwDOIPDwls5YicLG
| 9,628 |
Adds ChatOllama
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[] | null | 6 | 2023-08-22T20:54:39 | 2023-08-23T20:02:27 | 2023-08-23T20:02:26 |
CONTRIBUTOR
| null |
@rlancemartin
|
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I_kwDOIPDwls5u_bZM
| 9,627 |
SQLDatabaseChain: sqlite3.Warning: You can only execute one statement at a time.
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[] | null | 3 | 2023-08-22T20:04:19 | 2023-11-29T16:07:20 | 2023-11-29T16:07:19 |
NONE
| null |
### System Info
langchain 0.0.246
Python 3.11.4
SQLAlchemy 1.4.39
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chat_models import ChatOpenAI
from langchain.callbacks import get_openai_callback
from dotenv import load_dotenv, find_dotenv
from sqlalchemy import create_engine, MetaData, Table, Column, Integer, String, ForeignKey
from langchain.agents import initialize_agent
from langchain.sql_database import SQLDatabase
from langchain.chains import SQLDatabaseChain
from langchain.agents import Tool
def count_tokens(agent, query):
with get_openai_callback() as cb:
result = agent(query)
print(f'Spent a total of {cb.total_tokens} tokens')
return result
custom_dotenv_path = './openai.env'
_ = load_dotenv(custom_dotenv_path)
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
engine = create_engine('sqlite:///college.db', echo=True)
meta = MetaData()
students = Table(
'students', meta,
Column('id', Integer, primary_key = True),
Column('firstname', String),
Column('lastname', String),
)
addresses = Table(
'addresses', meta,
Column('id', Integer, primary_key = True),
Column('st_id', Integer, ForeignKey('students.id')),
Column('zipcode', String),
Column('email', String))
meta.create_all(engine)
conn = engine.connect()
conn.execute(students.insert(), [
{'id': 1, 'firstname': 'John', 'lastname': 'Smith'},
{'id': 2, 'firstname': 'Emily', 'lastname': 'Johnson'},
{'id': 3, 'firstname': 'Michael', 'lastname': 'Rodriguez'},
{'id': 4, 'firstname': 'Sarah', 'lastname': 'Kim'},
{'id': 5, 'firstname': 'William', 'lastname': 'Brown'}
])
conn.execute(addresses.insert(), [
{'st_id': 1, 'zipcode': '90210', 'email': '[email protected]'},
{'st_id': 2, 'zipcode': '30301', 'email': '[email protected]'},
{'st_id': 3, 'zipcode': '77001', 'email': '[email protected]'},
{'st_id': 4, 'zipcode': '94101', 'email': '[email protected]'},
{'st_id': 5, 'zipcode': '10001', 'email': '[email protected]'}
])
db = SQLDatabase(engine)
from langchain.prompts.prompt import PromptTemplate
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
When a user requests to add a new record, adhere to the following steps:
Only have single quotes on any sql command sent to the engine. If you generate 'INSERT' statement for adding any record to the table. Please 'EXECUTE' one statement at a time.
Step 1.Student Table Entry:
Navigate to the 'students' table.
Input the desired first name and last name for the new record.
Step 2.Address Table Entry:
Once the student record is created, retrieve its 'id'.
Move to the 'addresses' table.
Using the retrieved 'id', prepare a new entry ensuring it consists of the 'student id', 'zipcode', and 'email' as initially provided.
Question: {input}"""
PROMPT = PromptTemplate(
input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE
)
db = SQLDatabase(engine)
sql_chain = SQLDatabaseChain.from_llm(llm=llm, db=db, verbose=True,
#use_query_checker=True,
prompt=PROMPT
)
tools =[
Tool(
name='student',
func=sql_chain.run,
description="Useful for when you need to answer questions about new student record"
)
]
zero_shot_agent = initialize_agent(
agent="zero-shot-react-description",
tools=tools,
llm=llm,
verbose=True,
max_iterations=5,
)
result = count_tokens(
zero_shot_agent,
"insert a new record with name Jane Everwood. Her email is '[email protected]' and her zipcode is '99999'."
)
```
### Expected behavior
I asked ChatGPT to generate SQL for me to insert a new student record into the database. This database comprises two tables: students (cols: id, firstname, lastname) and addresses (cols: id, st_id, zipcode, email). When I input "insert a new record with the name Jane Everwood. Her email is '[email protected]' and her zipcode is '99999'", the system should add one record to the students table and one corresponding record to the addresses table. Instead, I received a "sqlite3.Warning: You can only execute one statement at a time" message.
The generated SQL scripts from SQLDatabaseChain appear correct. Could the issue be related to calling "cursor.execute(statement, parameters)" to execute multiple statements? Thanks!
Here is the full message that I got:
```
> Entering new AgentExecutor chain...
I need to insert a new student record with the given information.
Action: student
Action Input: insert new record with name Jane Everwood, email '[email protected]', and zipcode '99999'
> Entering new SQLDatabaseChain chain...
insert new record with name Jane Everwood, email '[email protected]', and zipcode '99999'
SQLQuery:2023-08-22 15:44:43,547 INFO sqlalchemy.engine.Engine SELECT students.id, students.firstname, students.lastname
FROM students
LIMIT ? OFFSET ?
2023-08-22 15:44:43,548 INFO sqlalchemy.engine.Engine [generated in 0.00041s] (3, 0)
2023-08-22 15:44:43,551 INFO sqlalchemy.engine.Engine SELECT addresses.id, addresses.st_id, addresses.zipcode, addresses.email
FROM addresses
LIMIT ? OFFSET ?
2023-08-22 15:44:43,551 INFO sqlalchemy.engine.Engine [generated in 0.00025s] (3, 0)
INSERT INTO students (firstname, lastname) VALUES ('Jane', 'Everwood');
INSERT INTO addresses (st_id, zipcode, email) VALUES ((SELECT id FROM students WHERE firstname = 'Jane' AND lastname = 'Everwood'), '99999', '[email protected]');2023-08-22 15:44:45,160 INFO sqlalchemy.engine.Engine BEGIN (implicit)
2023-08-22 15:44:45,162 INFO sqlalchemy.engine.Engine INSERT INTO students (firstname, lastname) VALUES ('Jane', 'Everwood');
INSERT INTO addresses (st_id, zipcode, email) VALUES ((SELECT id FROM students WHERE firstname = 'Jane' AND lastname = 'Everwood'), '99999', '[email protected]');
2023-08-22 15:44:45,162 INFO sqlalchemy.engine.Engine [generated in 0.00026s] ()
2023-08-22 15:44:45,162 INFO sqlalchemy.engine.Engine ROLLBACK
Traceback (most recent call last):
File "C:\_PyCharmProject\openai\bug_report.py", line 117, in <module>
result = count_tokens(
File "C:\_PyCharmProject\openai\bug_report.py", line 13, in count_tokens
result = agent(query)
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\base.py", line 258, in __call__
raise e
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\agents\agent.py", line 1029, in _call
next_step_output = self._take_next_step(
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\agents\agent.py", line 890, in _take_next_step
observation = tool.run(
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\tools\base.py", line 320, in run
raise e
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\tools\base.py", line 292, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\tools\base.py", line 444, in _run
self.func(
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\base.py", line 451, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\base.py", line 258, in __call__
raise e
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\sql_database\base.py", line 186, in _call
raise exc
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\chains\sql_database\base.py", line 131, in _call
result = self.database.run(sql_cmd)
File "C:\Users\xl\AppData\Roaming\Python\Python39\site-packages\langchain\utilities\sql_database.py", line 390, in run
cursor = connection.execute(text(command))
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\engine\base.py", line 1306, in execute
return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS)
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\sql\elements.py", line 332, in _execute_on_connection
return connection._execute_clauseelement(
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\engine\base.py", line 1498, in _execute_clauseelement
ret = self._execute_context(
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\engine\base.py", line 1862, in _execute_context
self._handle_dbapi_exception(
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\engine\base.py", line 2047, in _handle_dbapi_exception
util.raise_(exc_info[1], with_traceback=exc_info[2])
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\util\compat.py", line 208, in raise_
raise exception
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\engine\base.py", line 1819, in _execute_context
self.dialect.do_execute(
File "C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\engine\default.py", line 732, in do_execute
cursor.execute(statement, parameters)
sqlite3.Warning: You can only execute one statement at a time.
Process finished with exit code 1
```
|
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PR_kwDOIPDwls5YiLbx
| 9,626 |
Use new Python setup approach for scheduled tests.
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[] | null | 1 | 2023-08-22T20:03:59 | 2023-08-22T20:07:55 | 2023-08-22T20:07:53 |
COLLABORATOR
| null |
Using the same new unified Python setup as the regular tests and the lint job, as set up in #9625.
|
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| 1,862,058,792 |
PR_kwDOIPDwls5Yh9zf
| 9,625 |
Cache poetry install + unify Python/Poetry setup for lint and test jobs.
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[] | null | 1 | 2023-08-22T19:18:41 | 2023-08-22T19:59:23 | 2023-08-22T19:59:22 |
COLLABORATOR
| null |
With this PR:
- All lint and test jobs use the exact same Python + Poetry installation approach, instead of lints doing it one way and tests doing it another way.
- The Poetry installation itself is cached, which saves ~15s per run.
- We no longer pass shell commands as workflow arguments to a workflow that just runs them in a shell. This makes our actions more resilient to shell code injection.
If y'all like this approach, I can modify the scheduled tests workflow and the release workflow to use this too.
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I_kwDOIPDwls5u_H3Z
| 9,624 |
DOC: Langchain Supabase Vectorstore ID Incompatability
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[] | null | 6 | 2023-08-22T19:07:33 | 2023-12-04T20:18:50 | 2023-12-04T20:18:42 |
NONE
| null |
### Issue with current documentation:
The current documentation recommends running the following SQL function to create a table and function for using a supabase postgres database as a vector store.
```postgres
-- Enable the pgvector extension to work with embedding vectors
create extension vector;
-- Create a table to store your documents
create table documents (
id bigserial primary key,
content text, -- corresponds to Document.pageContent
metadata jsonb, -- corresponds to Document.metadata
embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
);
-- Create a function to search for documents
create function match_documents (
query_embedding vector(1536),
match_count int default null,
filter jsonb DEFAULT '{}'
) returns table (
id bigint,
content text,
metadata jsonb,
similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
return query
select
id,
content,
metadata,
1 - (documents.embedding <=> query_embedding) as similarity
from documents
where metadata @> filter
order by documents.embedding <=> query_embedding
limit match_count;
end;
$$;
```
This creates a table with a column named id of type `bigint`
However when using the `SupabaseVectorStore` class and trying to run methods such as `add_documents` or `add_texts`, an attempt is made to insert `Document` objects into the database. However it uses the uuid generated as the id which is incompatible with the `bigint` column type resulting in errors such as
```bash
postgrest.exceptions.APIError: {'code': '22P02', 'details': None, 'hint': None, 'message': 'invalid input syntax for type bigint: "dbf8aa60-8295-450c-83bc-7395e2836a6a"'}
```
### Idea or request for content:
Instead the documentation should recommend using the column type of uuid which is natively supported within supabase
|
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Document Pipeline
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[] | null | 5 | 2023-08-22T18:49:17 | 2023-09-21T22:12:37 | null |
COLLABORATOR
| null |
Create a simple pipeline to help compose loaders and transformers to yield document loaders.
|
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| 1,861,880,436 |
PR_kwDOIPDwls5YhWxc
| 9,621 |
Test experimental package with `langchain` on `master` branch.
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[] | null | 2 | 2023-08-22T17:13:10 | 2023-08-24T16:57:24 | 2023-08-22T17:35:21 |
COLLABORATOR
| null |
It's possible that langchain-experimental works fine with the latest *published* langchain, but is broken with the langchain on `master`. Unfortunately, you can see this is currently the case — this is why this PR also includes a minor fix for the `langchain` package itself.
We want to catch situations like that *before* releasing a new langchain, hence this test.
|
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PR_kwDOIPDwls5YhEwA
| 9,620 |
Add batch util
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[] | null | 1 | 2023-08-22T16:18:03 | 2023-08-22T16:31:46 | 2023-08-22T16:31:18 |
COLLABORATOR
| null |
Add `batch` utility to langchain
|
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PR_kwDOIPDwls5Yg6wm
| 9,619 |
Reduce cache timeouts to ensure faster builds on timeout.
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[] | null | 2 | 2023-08-22T15:55:33 | 2023-08-22T16:11:39 | 2023-08-22T16:11:38 |
COLLABORATOR
| null |
The current timeouts are too long, and mean that if the GitHub cache decides to act up, jobs get bogged down for 15min at a time. This has happened 2-3 times already this week -- a tiny fraction of our total workflows but really annoying when it happens to you. We can do better.
Installing deps on cache miss takes about ~4min, so it's not worth waiting more than 4min for the deps cache. The black and mypy caches save 1 and 2min, respectively, so wait only up to that long to download them.
|
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python repl improvement for csv agent
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PR_kwDOIPDwls5Ygz6K
| 9,617 |
Explicitly add the `contents: write` permission for publishing releases.
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[] | null | 1 | 2023-08-22T15:35:44 | 2023-08-22T15:38:19 | 2023-08-22T15:38:18 |
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I_kwDOIPDwls5u9vez
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duckdb too small to be a Parquet file
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[] | null | 2 | 2023-08-22T15:12:51 | 2023-10-20T22:21:22 | null |
NONE
| null |
### System Info
langchain 0.0.193
python 3.8.10
### Who can help?
@agola11
@hwchase17
### 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
- [ ] Document Loaders
- [x] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Im creating multiple vector stores using Chroma DB and saved these inside subdirectories inside a directory DB/ , all occurring inside of a docker container. I'm using persist method as you detail in the docs.
2. save and load work as expected, however, after container restart (an expected operation during development), I got the following error related to parquet file in the DB subdirectory: Invalid Input Error: File '/app/DB/SIC/chroma-embeddings.parquet' too small to be a Parquet file. During interaction with Chroma DB app only reads data not adds or overwrites data.
it seems that the reading operation overwrites the chroma-embeddings.parquet file.
### Expected behavior
I expect that after the container restarts the DB could be loaded and not require to be rebuilt.
|
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| 1,861,665,316 |
PR_kwDOIPDwls5Ygnfb
| 9,615 |
bump 271
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PR_kwDOIPDwls5YgdWs
| 9,614 |
Add indexing support
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[] | null | 4 | 2023-08-22T14:43:20 | 2023-08-24T01:41:39 | 2023-08-24T01:41:39 |
COLLABORATOR
| null |
This PR introduces a persistence layer to help with indexing workflows into
vectostores.
The indexing code helps users to:
1. Avoid writing duplicated content into the vectostore
2. Avoid over-writing content if it's unchanged
Importantly, this keeps on working even if the content being written is derived
via a set of transformations from some source content (e.g., indexing children
documents that were derived from parent documents by chunking.)
The two main components are:
1. Persistence layer that keeps track of which keys were updated and when.
Keeping track of the timestamp of updates, allows to clean up old content
safely, and with minimal complexity.
2. HashedDocument which is used to hash the contents (including metadata) of
the documents. We rely on the hashes for identifying duplicates.
The indexing code works with **ANY** document loader. To add transformations
to the documents, users for now can add a custom document loader
that composes an existing loader together with document transformers.
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| 1,861,609,494 |
PR_kwDOIPDwls5Ygbcy
| 9,613 |
Bagatur/litellm model name
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I_kwDOIPDwls5u9RJS
| 9,611 |
Croma Db Wrapper not considering the **kwargs for quering the collection
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[] | null | 2 | 2023-08-22T14:12:14 | 2023-11-29T16:07:30 | 2023-11-29T16:07:29 |
NONE
| null |
### System Info
langchain-0.0.270
Croma Db Wrapper not considering the **kwargs for querying the collection
Method: similarity_search_with_score
Code :
results = self.__query_collection(
query_embeddings=[query_embedding], n_results=k, where=filter
)
the query collection doesn't uses **kwargs which will be helpful to query the document that contains "string"
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
langchain-0.0.270
Croma Db Wrapper not considering the **kwargs for querying the collection
Method: similarity_search_with_score
Code :
results = self.__query_collection(
query_embeddings=[query_embedding], n_results=k, where=filter
)
the query collection doesn't uses **kwargs which will be helpful to query the document that contains "string"
### Expected behavior
langchain-0.0.270
Croma Db Wrapper not considering the **kwargs for querying the collection
Method: similarity_search_with_score
Code :
results = self.__query_collection(
query_embeddings=[query_embedding], n_results=k, where=filter
)
the query collection doesn't uses **kwargs which will be helpful to query the document that contains "string"
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| 1,861,513,967 |
PR_kwDOIPDwls5YgGr6
| 9,610 |
Add to support polars
|
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[] | null | 2 | 2023-08-22T13:51:50 | 2023-08-22T14:36:24 | 2023-08-22T14:36:24 |
CONTRIBUTOR
| null |
### Description
Polars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust.
Polars is faster to read than pandas, so I'm looking forward to seeing it added to the document loader.
### Issue
N/A
### Dependencies
polars (https://pola-rs.github.io/polars-book/user-guide/)
### Tag maintainer
x
### Twitter handle
N/A
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I_kwDOIPDwls5u84Dg
| 9,609 |
import DirectoryLoader generate error
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[] | null | 3 | 2023-08-22T13:19:30 | 2023-08-22T17:00:35 | 2023-08-22T17:00:34 |
NONE
| null |
### System Info
langchain version: 0.0.270
python version: 3.11.0
os: Ubuntu 20.04.6 LTS
### Who can help?
@hwchase17
### 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
in a python file, there is only one line of code like following:
`from langchain.document_loaders import DirectoryLoader`
when run it, it generate following errorss:
```
Traceback (most recent call last):
File "/home/tq/code/langchain/python_311/project/01_test.py", line 1, in <module>
from langchain.document_loaders import DirectoryLoader
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/agents/__init__.py", line 31, in <module>
from langchain.agents.agent import (
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/agents/agent.py", line 14, in <module>
from langchain.agents.agent_iterator import AgentExecutorIterator
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/agents/agent_iterator.py", line 30, in <module>
from langchain.tools import BaseTool
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/tools/__init__.py", line 41, in <module>
from langchain.tools.gmail import (
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/tools/gmail/__init__.py", line 3, in <module>
from langchain.tools.gmail.create_draft import GmailCreateDraft
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/tools/gmail/create_draft.py", line 7, in <module>
from langchain.tools.gmail.base import GmailBaseTool
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/langchain/tools/gmail/base.py", line 16, in <module>
from googleapiclient.discovery import Resource
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/googleapiclient/discovery.py", line 57, in <module>
from googleapiclient import _auth, mimeparse
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/googleapiclient/_auth.py", line 34, in <module>
import oauth2client.client
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/oauth2client/client.py", line 47, in <module>
from oauth2client import crypt
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/oauth2client/crypt.py", line 55, in <module>
from oauth2client import _pycrypto_crypt
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/oauth2client/_pycrypto_crypt.py", line 17, in <module>
from Crypto.PublicKey import RSA
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/Crypto/PublicKey/__init__.py", line 29, in <module>
from Crypto.Util.asn1 import (DerSequence, DerInteger, DerBitString,
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/Crypto/Util/asn1.py", line 33, in <module>
from Crypto.Util.number import long_to_bytes, bytes_to_long
File "/home/tq/anaconda3/envs/py311/lib/python3.11/site-packages/Crypto/Util/number.py", line 398
s = pack('>I', n & 0xffffffffL) + s
^
SyntaxError: invalid hexadecimal literal
```
please help, thanks!
### Expected behavior
import DirectoryLoader should be work.
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TypeError: Object of type ConversationalRetrievalChain is not JSON serializable
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[] | null | 3 | 2023-08-22T12:48:52 | 2023-10-15T00:51:39 | null |
NONE
| null |
### System Info
langchain==0.0.249
### Who can help?
@hwchase17 @agola11 @eyurtsev
### Information
- [X] 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chains import ConversationalRetrievalChain
qa = ConversationalRetrievalChain.from_llm(with some values such as vector store, memory etc)
now i want to serialize qa, or store it, the key is to be able to store the qa and pass it wherever i want, ideally store it in one endpoint and pass to another endpoint
but i get errors, about not being able to serialize, which makes it difficult
i have tried flask session, json.dumps, pickle, cloud pickle, tried to store in a database etc, still get an error
### Expected behavior
I should be able to serialize or save the QA, so i can pass it from one endpoint to another, to keep user in session, and isolate a user's QA from others
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I_kwDOIPDwls5u8n4B
| 9,606 |
Upload ChromDB vectordb from s3
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[] | null | 7 | 2023-08-22T12:43:32 | 2023-10-25T03:36:56 | null |
NONE
| null |
### Feature request
Greetings everyone,
I'm interested in directly saving the chromaDB vector store to an S3 bucket. Is there a way to accomplish this?
### Motivation
I want to run my LLM directly in the AWS cloud, but firstly I need to deal with implementation of efficient loading and saving of my db.
### Your contribution
I already implemented function to load data from s3 and creating the vector store.
```
import boto3
from langchain.document_loaders import S3DirectoryLoader
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
# Initialize the S3 client
s3 = boto3.client('s3')
# Specify the S3 bucket and directory path
bucket_name = 'bucket_name'
directory_key = 's3_path'
# List objects with a delimiter to get only common prefixes (directories)
response = s3.list_objects_v2(Bucket=bucket_name, Prefix=directory_path, Delimiter='/')
# Extract the common prefixes (directories) from the response
common_prefixes = response.get('CommonPrefixes', [])
# Print the directory names
for prefix in common_prefixes:
print(prefix['Prefix'])
def create_chromadb_from_s3():
# Load data from s3
docs = []
for key in s3.list_objects_v2(Bucket=bucket_name, Prefix=directory_path, Delimiter='/').get('CommonPrefixes', []):
loader = S3DirectoryLoader(bucket_name, key['Prefix'])
docs.extend(loader.load())
# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings(openai_api_key=open_ai_secret)
db = Chroma.from_documents(
texts, embedding=embeddings
)
return db
```
Unfortunately there is no way to set "persist_dir" as connection to the s3 bucket. Any idea how we can implement it?
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Add del vector pgvector + adding modification time to confluence and google drive docs
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CONTRIBUTOR
| null |
Description:
- adding implementation of delete for pgvector
- adding modification time in docs metadata for confluence and google drive.
Issue:
https://github.com/langchain-ai/langchain/issues/9312
Tag maintainer: @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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| false | null |
[] | null | 2 | 2023-08-22T11:22:34 | 2023-08-22T22:12:25 | 2023-08-22T22:12:24 |
CONTRIBUTOR
| null |
deeplake.mdx was using old links and was not working properly, in the PR we fix the issue.
|
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| 1,861,223,410 |
I_kwDOIPDwls5u8APy
| 9,601 |
Expose x-ratelimit-* headers from OpenAI API with Langchain
|
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| false | null |
[] | null | 2 | 2023-08-22T11:04:51 | 2023-12-10T18:09:33 | null |
NONE
| null |
### Describe the feature or improvement you're requesting
When making a direct call to OpenAI's POST /v1/chat/completions endpoint, we receive valuable headers that provide information about the rate limiting, including:
```
x-ratelimit-limit-requests: 3500
x-ratelimit-remaining-requests: 3499
x-ratelimit-reset-requests: 17ms
```
However, when using the `ChatOpenAI` and `OpenAIEmbeddings` APIs, these rate limit headers are not returned in the response or as part of the `OpenAICallbackHandler` object (as total_tokens or total_cost values). This makes it challenging to track and manage rate limits programmatically.
Can we get rate headers values using `ChatOpenAI` and `OpenAIEmbeddings`?
It would be nice if we could get access to these headers, or have them returned in a helpful format along with the response.
### Motivation
Having access to rate limit information is crucial for developers to effectively manage their API usage, especially when using these APIs in production environments. This addition would greatly enhance the usability and monitoring capabilities of the OpenAI API.
|
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I_kwDOIPDwls5u7zOv
| 9,600 |
MultiRouteChain not working as expected
|
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[] | null | 3 | 2023-08-22T10:31:29 | 2023-11-29T16:07:35 | 2023-11-29T16:07:34 |
NONE
| null |
### System Info
python==3.9
langchain==0.0.246
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to use GPT4ALL model with MultiRouterChain and it is throwing some weird error.
## Utility Function:
```
def create_transform_func(remove_index):
def my_transform_func(inputs: dict):
return transform_func(inputs, remove_index)
return my_transform_func
def transform_func(inputs: dict, remove_index = 5) -> dict:
text = inputs['input'].strip()
accumulate = ""
for i, s in enumerate(text.split(' ')):
i = i + 1
if i % remove_index != 0:
accumulate += f"{s} "
return {"input": accumulate.strip()}
```
```
"""Callback Handler that prints to std out."""
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish
from langchain.schema import LLMResult
from pathlib import Path
from datetime import datetime
import re
class FileCallbackHandler(BaseCallbackHandler):
"""Callback Handler that prints to std out."""
def __init__(self,
path: Path,
print_prompts: bool=False,
print_class: bool=False,
title: Optional[str] = "Conversation Log",
color: Optional[str] = None
) -> None:
"""Initialize callback handler."""
self.color = color
self.print_prompts = print_prompts
self.print_class = print_class
self.path = path
self.file_handle = open(path, 'w')
self.title = title
self.output_keys = []
self.output_values = []
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Print out the prompts."""
if self.print_prompts:
self.file_handle.write(f"=============== PROMPTS ==================\n")
for prompt in prompts:
self.file_handle.write(f"{prompt}\n")
self.file_handle.write("\n")
self.file_handle.flush()
self.file_handle.write(f"============ END PROMPTS =================\n\n")
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Do nothing."""
self.file_handle.write(f"=============== LLM END ==================\n")
pass
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Do nothing."""
pass
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
pass
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""Print out that we are entering a chain."""
if self.print_class:
self.file_handle.write(f"================ CLASS ===================\n")
class_name = serialized["name"]
self.file_handle.write(f">>> class: {class_name}\n")
self.file_handle.write(f"============== END CLASS =================\n\n")
self.file_handle.flush()
self.output_keys.append(list(inputs.keys()))
self.output_values.append(list(inputs.values()))
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""Print out that we finished a chain."""
# print("\n\033[1m> Finished chain.\033[0m")
# self.file_handle.close()
self.file_handle.write(f"================ OUTPUT ==================\n")
keys = []
values = []
for k, v in outputs.items():
keys.append(k)
values.append(v)
self.file_handle.write(f"{k}:\n")
self.file_handle.write(f"{v}\n\n")
self.output_keys.append(keys)
self.output_values.append(values)
self.file_handle.write(f"================ OUTPUT ==================\n")
self.file_handle.flush()
def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
pass
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Do nothing."""
self.file_handle.write(datetime.today().strftime('%Y-%m-%d'))
self.file_handle.write("\n========")
self.file_handle.flush()
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
) -> Any:
"""Run on agent action."""
self.file_handle.write(f">>> action: {action.log}")
def on_tool_end(
self,
output: str,
color: Optional[str] = None,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
"""If not the final action, print out observation."""
if observation_prefix is not None:
self.file_handle.write(f"\n{observation_prefix}")
self.file_handle.write(output)
if llm_prefix is not None:
self.file_handle.write(f"\n{llm_prefix}")
self.file_handle.flush()
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
pass
def on_text(self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any) -> None:
"""Run when agent ends."""
self.file_handle.write(f"================ TEXT ===================\n")
self.file_handle.write(f"{text}\n")
self.file_handle.flush()
self.file_handle.write(f"============== END TEXT =================\n\n")
agent = extract_agent(text)
if agent != "":
self.output_keys.append([agent])
self.output_values.append([text])
def on_agent_finish(
self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
) -> None:
"""Run on agent end."""
self.file_handle.write(f"{finish.log}\n")
self.file_handle.flush()
self.file_handle.close()
def create_html(self):
table: str = """
<table class="table table-striped">
<tr>
<th>
Agent
</th>
<th>
Type
</th>
<th>
Output
</th>
</tr>
"""
dedup_hash = set()
for keys, values in zip(self.output_keys, self.output_values):
for key, val in zip(keys, values):
if val not in dedup_hash:
dedup_hash.add(val)
else:
continue
agent = extract_agent(val)
table += (f"""
<tr>
<td>
</td>
<td>
<pre>{key}</pre>
</td>
<td>
<pre>{val}</pre>
</td>
</tr>
""" if agent == "" else f"""
<tr>
<td>{agent}</td>
<td></td>
<td></td>
</tr>
""")
table += "</table>"
target_file = f"{self.path.stem}.html"
with open(target_file, "w", encoding='utf-8') as f:
f.write(f"""
<html>
<head>
<meta charset="UTF-8" />
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-9ndCyUaIbzAi2FUVXJi0CjmCapSmO7SnpJef0486qhLnuZ2cdeRhO02iuK6FUUVM" crossorigin="anonymous">
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js" integrity="sha384-geWF76RCwLtnZ8qwWowPQNguL3RmwHVBC9FhGdlKrxdiJJigb/j/68SIy3Te4Bkz" crossorigin="anonymous"></script>
<style>
pre {{
white-space: pre-wrap;
}}
</style>
</head>
<body>
<div class="container-fluid">
<h1>{self.title}</h1>
<h2>{generate_timestamp()}</h2>
{table}
</div>
</body>
</html>
""")
print(f"Saved chat content to {target_file}")
def generate_timestamp():
# Get the current date and time
now = datetime.now()
# Get the weekday, day, month, year, and time in English
weekday = now.strftime("%A")
day = now.strftime("%d")
month = now.strftime("%B")
year = now.strftime("%Y")
time = now.strftime("%H:%M:%S")
# Create the timestamp string
timestamp = f"{weekday}, {day} {month} {year} {time}"
return timestamp
def extract_input(text):
return re.sub(r".+?'input':\s*'(.+)'}", r"\1", text)
def extract_agent(text):
regex = r"^([a-z\s]+)\:.+"
match = re.search(regex, text)
if match is None:
return ""
return re.sub(regex, r"\1", text)
```
## Driver Code
```
from langchain.chains.router import MultiRouteChain, RouterChain
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE
from langchain.chains import SimpleSequentialChain, TransformChain
from prompt_toolkit import HTML, prompt
import langchain.callbacks
from replace_function import create_transform_func
from langchain.callbacks import StdOutCallbackHandler
from FileCallbackHandler import FileCallbackHandler
from pathlib import Path
from typing import Mapping, List, Union
import openai, os
from langchain.llms import GPT4All, LlamaCpp, OpenAI, AzureOpenAI, SelfHostedHuggingFaceLLM, HuggingFacePipeline
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
file_ballback_handler = FileCallbackHandler(Path('router_chain.txt'), print_prompts=True)
class GPT4_Config():
current_path = os.path.dirname(os.path.realpath(__file__))
llm = GPT4All(
model=os.path.abspath(os.path.join(current_path, r"../../llms/gpt4all/ggml-gpt4all-j-v1.3-groovy.bin")),
# n_ctx=1000,
verbose=True,
backend='gptj',
callbacks=[StreamingStdOutCallbackHandler()]
)
cfg = GPT4_Config()
class PromptFactory():
developer_template = """You are a very smart Python programmer. \
You provide answers for algorithmic and computer problems in Python. \
You explain the code in a detailed manner. \
Here is a question:
{input}
Answer:"""
python_test_developer_template = """You are a very smart Python programmer who writes unit tests using pytest. \
You provide test functions written in pytest with asserts. \
You explain the code in a detailed manner. \
Here is a input on which you create a test:
{input}
Answer:"""
kotlin_developer_template = """You are a very smart Kotlin programmer. \
You provide answers for algorithmic and computer science problems in Kotlin. \
You explain the code in a detailed manner. \
Here is a question:
{input}
Answer:"""
kotlin_test_developer_template = """You are a very smart Kotlin programmer who writes unit tests using JUnit 5. \
You provide test functions written in JUnit 5 with JUnit asserts. \
You explain the code in a detailed manner. \
Here is a input on which you create a test:
{input}
Answer:"""
poet_template = """You are a poet who replies to creative requests with poems in English. \
You provide answers which are poems in the style of Lord Byron or Shakespeare. \
Here is a question:
{input}
Answer:"""
wiki_template = """You are a Wikipedia expert. \
You answer common knowledge questions based on Wikipedia knowledge. \
Your explanations are detailed and in plain English.
Here is a question:
{input}
Answer:"""
image_creator_template = """You create a creator of images. \
You provide graphic representations of answers using SVG images.
Here is a question:
{input}
Answer:"""
legal_expert_template = """You are a UK or US legal expert. \
You explain questions related to the UK or US legal systems in an accessible language \
with a good number of examples.
Here is a question:
{input}
Answer:"""
word_filler = """Your job is to fill the words in a sentence in which words seems to be missing.
Here is the input:
{input}
Answer:"""
python_programmer = 'python programmer'
kotlin_programmer = 'kotlin programmer'
programmer_test_dict = {
python_programmer: python_test_developer_template,
kotlin_programmer: kotlin_test_developer_template
}
word_filler_name = 'word filler'
prompt_infos = [
{
'name': python_programmer,
'description': 'Good for questions about coding and algorithms in Python',
'prompt_template': developer_template
},
{
'name': 'python tester',
'description': 'Good for for generating Python tests from existing Python code',
'prompt_template': python_test_developer_template
},
{
'name': kotlin_programmer,
'description': 'Good for questions about coding and algorithms in Kotlin',
'prompt_template': kotlin_developer_template
},
{
'name': 'kotlin tester',
'description': 'Good for for generating Kotlin tests from existing Kotlin code',
'prompt_template': kotlin_test_developer_template
},
{
'name': 'poet',
'description': 'Good for generating poems for creatinve questions',
'prompt_template': poet_template
},
{
'name': 'wikipedia expert',
'description': 'Good for answering questions about general knwoledge',
'prompt_template': wiki_template
},
{
'name': 'graphical artist',
'description': 'Good for answering questions which require an image output',
'prompt_template': image_creator_template
},
{
'name': 'legal expert',
'description': 'Good for answering questions which are related to UK or US law',
'prompt_template': legal_expert_template
},
{
'name': word_filler_name,
'description': 'Good at filling words in sentences with missing words',
'prompt_template': word_filler
}
]
class MyMultiPromptChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst prompts."""
router_chain: RouterChain
"""Chain for deciding a destination chain and the input to it."""
destination_chains: Mapping[str, Union[LLMChain, SimpleSequentialChain]]
"""Map of name to candidate chains that inputs can be routed to."""
default_chain: LLMChain
"""Default chain to use when router doesn't map input to one of the destinations."""
@property
def output_keys(self) -> List[str]:
return ["text"]
def generate_destination_chains():
"""
Creates a list of LLM chains with different prompt templates.
Note that some of the chains are sequential chains which are supposed to generate unit tests.
"""
prompt_factory = PromptFactory()
destination_chains = {}
for p_info in prompt_factory.prompt_infos:
print("="*70)
name = p_info['name']
print(f"======= Prompt Name =======: \n{name}")
prompt_template = p_info['prompt_template']
print(f"======= Prompt Template =======: \n{prompt_template}")
# callbacks = [StdOutCallbackHandler]
chain = LLMChain(
llm=cfg.llm,
prompt=PromptTemplate(template=prompt_template, input_variables=['input']),
output_key='text',
callbacks=[file_ballback_handler],
verbose=True
)
if name not in prompt_factory.programmer_test_dict.keys() and name != prompt_factory.word_filler_name:
print("Addition using 1st case")
destination_chains[name] = chain
elif name == prompt_factory.word_filler_name:
print("Addition using 2nd case")
transform_chain = TransformChain(
input_variables=["input"], output_variables=["input"], transform=create_transform_func(3), callbacks=[file_ballback_handler]
)
destination_chains[name] = SimpleSequentialChain(
chains=[transform_chain, chain], verbose=True, output_key='text', callbacks=[file_ballback_handler]
)
else:
print("Addition using 3rd case")
# Normal chain is used to generate code
# Additional chain to generate unit tests
template = prompt_factory.programmer_test_dict[name]
prompt_template = PromptTemplate(input_variables=["input"], template=template)
test_chain = LLMChain(llm=cfg.llm, prompt=prompt_template, output_key='text', callbacks=[file_ballback_handler])
destination_chains[name] = SimpleSequentialChain(
chains=[chain, test_chain], verbose=True, output_key='text', callbacks=[file_ballback_handler]
)
print("="*70)
print("\n\n\n")
print("============= Destination Chains =============")
pprint(destination_chains)
default_chain = ConversationChain(llm=cfg.llm, output_key="text")
return prompt_factory.prompt_infos, destination_chains, default_chain
def generate_router_chain(prompt_infos, destination_chains, default_chain):
"""
Generats the router chains from the prompt infos.
:param prompt_infos The prompt informations generated above.
:param destination_chains The LLM chains with different prompt templates
:param default_chain A default chain
"""
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
print("====================== DESTINATIONS ======================")
print(destinations)
destinations_str = '\n'.join(destinations)
print("====================== DESTINATIONS STRINGS ======================")
print(destinations_str)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)
print("====================== ROUTER TEMPLATE ======================")
print(router_template)
router_prompt = PromptTemplate(
template=router_template,
input_variables=['input'],
output_parser=RouterOutputParser()
)
print("====================== PROMPT TEMPLATE ======================")
print(router_prompt)
router_chain = LLMRouterChain.from_llm(cfg.llm, router_prompt)
multi_route_chain = MyMultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True,
callbacks=[file_ballback_handler]
)
print("====================== MULTI ROUTER CHAIN ======================")
print(multi_route_chain)
return multi_route_chain
if __name__ == "__main__":
prompt_infos, destination_chains, default_chain = generate_destination_chains()
chain = generate_router_chain(prompt_infos, destination_chains, default_chain)
with open('conversation.log', 'w') as f:
while True:
question = prompt(
HTML("<b>Type <u>Your question</u></b> ('q' to exit, 's' to save to html file): ")
)
if question == 'q':
break
if question in ['s', 'w'] :
file_ballback_handler.create_html()
continue
result = chain.run(question)
f.write(f"Q: {question}\n\n")
f.write(f"A: {result}")
f.write('\n\n ====================================================================== \n\n')
print(result)
print()
```
## Error:
```
> Entering new MyMultiPromptChain chain...
/home/gpu-titan/anaconda3/envs/Seer/lib/python3.9/site-packages/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
warnings.warn(
{
"destination": "UK",
"next_inputs": "['Inheritance Tax', 'Legal System']"}UK: {'input': "['Inheritance Tax', 'Legal System']"}Traceback (most recent call last):
File "/home/gpu-titan/Desktop/Ramish/Seer/seer_main/complex_chain/complex_chain.py", line 360, in <module>
result = chain.run(question)
File "/home/gpu-titan/anaconda3/envs/Seer/lib/python3.9/site-packages/langchain/chains/base.py", line 451, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/gpu-titan/anaconda3/envs/Seer/lib/python3.9/site-packages/langchain/chains/base.py", line 258, in __call__
raise e
File "/home/gpu-titan/anaconda3/envs/Seer/lib/python3.9/site-packages/langchain/chains/base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/gpu-titan/anaconda3/envs/Seer/lib/python3.9/site-packages/langchain/chains/router/base.py", line 106, in _call
raise ValueError(
ValueError: Received invalid destination chain name 'UK'
```
### Expected behavior
Prompt is correctly routed to correct chain and returns the correct answer.
## Expected Output:
```
> Finished chain.
{'question': 'What are the main differences between the UK and US legal systems in terms of the inheritance tax?', 'text': ' The
main difference between the UK and US legal systems regarding inheritance taxes lies in their respective approaches to
calculating, taxing, and paying them out. In both countries, there may be variations depending on factors such as wealth or
family structure. However, some key differences include:\n1) Inheritance Tax Rates - While the rates of inheritance tax are similar
between the two jurisdictions (7% for assets over £325k in England/Wales; 7.5% for assets over $675k in Scotland), there may be
variations depending on factors such as wealth or family structure, which can affect how much is owed and when it should be
paid out to beneficiaries.\n2) Inheritance Tax Rules - The UK has a more complex inheritance tax system than the US, with
different rules governing who qualifies for an exemption from paying inheritance tax (e.g., married couples vs unmarried
individuals). In addition, there may be variations in how assets are taxed and when they should be transferred to beneficiaries
before death.\n3) Inheritance Tax Planning - Both countries offer various strategies such as trusts or wills that can help reduce
the amount of inheritance tax owed by transferring wealth out-of-estate at a lower rate than would otherwise apply, but with
different rules governing how these plans are set up'}
```
|
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I_kwDOIPDwls5u7uaj
| 9,599 |
_to_args_and_kwargs() failed to handle tool definition with no arguments
|
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[] | null | 2 | 2023-08-22T10:20:56 | 2023-11-28T16:07:40 | 2023-11-28T16:07:39 |
NONE
| null |
### System Info
(.venv) yale@LAPTOP-MATEBOOK:~/work/llm-app$ python --version
Python 3.10.12
(.venv) yale@LAPTOP-MATEBOOK:~/work/llm-app$ pip list|grep langchain
langchain 0.0.262
langchainplus-sdk 0.0.20
### 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
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Update the official example of Tool https://python.langchain.com/docs/modules/agents/tools/custom_tools with a tool without argument:
`
class NoParameterInput(BaseModel):
pass
tools = [
Tool(
name="GetToday",
func=lambda: "2023-09-30",
description="Get today's date",
args_schema=NoParameterInput
),
`
When running, a error was triggered:
`
Traceback (most recent call last):
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/gradio/routes.py", line 437, in run_predict
output = await app.get_blocks().process_api(
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/gradio/blocks.py", line 1352, in process_api
result = await self.call_function(
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/gradio/blocks.py", line 1077, in call_function
prediction = await anyio.to_thread.run_sync(
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/anyio/to_thread.py", line 33, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
return await future
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 807, in run
result = context.run(func, *args)
File "/home/yale/work/llm-app/local_tests/gradio/langchain_chatbot.py", line 356, in qa_answer_question
qa_answer = chain(question)
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
raise e
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 276, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/chains/router/base.py", line 100, in _call
return self.destination_chains[route.destination](
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
raise e
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 276, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1036, in _call
next_step_output = self._take_next_step(
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 891, in _take_next_step
observation = tool.run(
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/tools/base.py", line 340, in run
raise e
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/tools/base.py", line 331, in run
tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
File "/home/yale/work/llm-app/.venv/lib/python3.10/site-packages/langchain/tools/base.py", line 488, in _to_args_and_kwargs
raise ToolException(
langchain.tools.base.ToolException: Too many arguments to single-input tool GetToday. Args: []
`
The source of _to_args_and_kwargs():
`
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
"""Convert tool input to pydantic model."""
args, kwargs = super()._to_args_and_kwargs(tool_input)
# For backwards compatibility. The tool must be run with a single input
all_args = list(args) + list(kwargs.values())
if len(all_args) != 1:
raise ToolException(
f"Too many arguments to single-input tool {self.name}."
f" Args: {all_args}"
)
return tuple(all_args), {}
`
Which does not accept tools without argument.
### Expected behavior
Calls GetToday() successfully.
|
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I_kwDOIPDwls5u7d7d
| 9,597 |
Schedule for supporting plugins with auth
|
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[] | null | 1 | 2023-08-22T09:49:48 | 2023-11-28T16:07:45 | 2023-11-28T16:07:44 |
NONE
| null |
Hi Team,
Is there schedule to support plugins with auth?
thanks.
|
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| 1,861,060,406 |
I_kwDOIPDwls5u7Yc2
| 9,595 |
List in ChatMessageHistory is not correctly initialized
|
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[] | null | 1 | 2023-08-22T09:38:59 | 2023-08-25T12:08:49 | 2023-08-25T12:08:48 |
CONTRIBUTOR
| null |
### System Info
langchain version 0.0.270
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] 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
Create multiple instances of ChatMessageHistory and add history. They shared the list.
### Expected behavior
`messages: List[BaseMessage] = []
`
to
`messages: List[BaseMessage] = Field(default_factory=list)`
Fix [here](https://github.com/langchain-ai/langchain/pull/9594)
|
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| 1,861,053,619 |
PR_kwDOIPDwls5YehQX
| 9,594 |
Fix ChatMessageHistory
|
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[] | null | 2 | 2023-08-22T09:35:26 | 2023-08-22T14:36:36 | 2023-08-22T14:36:36 |
CONTRIBUTOR
| null |
The initialization of the array of ChatMessageHistory is buggy.
The list is shared with all instances.
|
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