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https://api.github.com/repos/langchain-ai/langchain/issues/6642
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| 1,771,287,642 |
I_kwDOIPDwls5pk7Ra
| 6,642 |
Tokenize before OpenAI call issues
|
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[] | null | 2 | 2023-06-23T11:21:02 | 2023-07-12T13:21:20 | 2023-07-12T13:21:19 |
NONE
| null |
### Issue you'd like to raise.
I would like to know how many **tokens** the tokenizer would generate for the **prompt** doing the **OpenAI** call, but I'm finding issues in reproducing the real call.
Indeed I'm trying two methods (that internally use `tiktoken` library if I'm not wrong) found in the documentation:
- [`get_num_tokens`](https://api.python.langchain.com/en/latest/modules/llms.html#langchain.llms.AI21.get_num_tokens)
- [`get_num_tokens_from_messages`](https://api.python.langchain.com/en/latest/modules/llms.html#langchain.llms.AI21.get_num_tokens_from_messages)
Then I check the number of prompt tokens with the callback `get_openai_callback` the understand if the calculation was correct:
```python
from langchain.llms import OpenAI
from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain.callbacks import get_openai_callback
models_name = ["text-davinci-003", "gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613"]
for model_name in models_name:
print(f"----{model_name}----")
llm = OpenAI(model_name = model_name)
print(llm)
text = "Hello world"
tokens = llm.get_num_tokens(text)
print(f"1) get_num_tokens: {tokens}")
human_message = HumanMessage(content=text)
system_message = SystemMessage(content=text)
ai_message = AIMessage(content=text)
tokens = llm.get_num_tokens_from_messages([human_message]), llm.get_num_tokens_from_messages([system_message]), llm.get_num_tokens_from_messages([ai_message])
print(f"2) get_num_tokens_from_messages: {tokens}")
with get_openai_callback() as cb:
llm_response = llm(text)
print(f"3) callback: {cb}")
```
The output is:
```
----text-davinci-003----
OpenAI
Params: {'model_name': 'text-davinci-003', 'temperature': 0.7, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'request_timeout': None, 'logit_bias': {}}
1) get_num_tokens: 2
2) get_num_tokens_from_messages: (4, 4, 4)
3) callback: Tokens Used: 23
Prompt Tokens: 2
Completion Tokens: 21
Successful Requests: 1
Total Cost (USD): $0.00045999999999999996
----gpt-3.5-turbo-0301----
OpenAIChat
Params: {'model_name': 'gpt-3.5-turbo-0301'}
1) get_num_tokens: 2
2) get_num_tokens_from_messages: (4, 4, 4)
3) callback: Tokens Used: 50
Prompt Tokens: 10
Completion Tokens: 40
Successful Requests: 1
Total Cost (USD): $0.0001
----gpt-3.5-turbo-0613----
OpenAIChat
Params: {'model_name': 'gpt-3.5-turbo-0613'}
1) get_num_tokens: 2
2) get_num_tokens_from_messages: (4, 4, 4)
3) callback: Tokens Used: 18
Prompt Tokens: 9
Completion Tokens: 9
Successful Requests: 1
Total Cost (USD): $0.0
```
I understand that each model has a different way to count the tokens, for example **text-davinci-003** has the same number between `get_num_tokens` output and the callback. The other two models: **gpt-3.5-turbo-0301** and **gpt-3.5-turbo-0613** seems to have respectively 6 and 5 tokens more in the callback compared to `get_num_tokens_from_messages`.
So how I can reproduce exactly the calculation of the token in the real call? Which is the official function used in it?
### Suggestion:
_No response_
|
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| 1,771,224,942 |
I_kwDOIPDwls5pkr9u
| 6,641 |
是否有fine tuning LLAMA预训练模型功能
|
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[] | null | 1 | 2023-06-23T10:34:28 | 2023-09-29T16:05:43 | 2023-09-29T16:05:42 |
NONE
| null |
因为LLAMA没有中文数据,想用中文数据fine tuning LLAMA模型,请问langchain有这个功能吗?
|
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| 1,771,222,506 |
I_kwDOIPDwls5pkrXq
| 6,640 |
sql_db_list_tables returning different order each time making caching impossible
|
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[] | null | 2 | 2023-06-23T10:32:43 | 2023-09-30T16:05:48 | 2023-09-30T16:05:47 |
NONE
| null |
### System Info
langchain==0.0.207
### 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
```python
from langchain.sql_database import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///sample.db",)
db.get_usable_table_names() # Change table names order each time the application is retarted
```
Current implementation
```python
class SQLDatabase:
def get_usable_table_names(self) -> Iterable[str]:
if self._include_tables:
return self._include_tables
return self._all_tables - self._ignore_tables # THIS IS A SET
class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
def _run(self, tool_input: str = "", ...) -> str:
return ", ".join(self.db.get_usable_table_names()) # ORDER CHANGES EACH RUN
```
### Expected behavior
```python
class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
def _run(self, tool_input: str = "", ...) -> str:
return ", ".join(sorted(self.db.get_usable_table_names()))
```
|
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| 1,771,205,138 |
I_kwDOIPDwls5pknIS
| 6,639 |
Issue: Set doc_content_chars_max with WikipediaLoader
|
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[] | null | 2 | 2023-06-23T10:20:04 | 2023-10-30T09:11:52 | 2023-09-29T16:05:53 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
In order to read all the text of wikipedia page, we would need to allow overriding the hard limit of 4000 characters set in `WikipediaAPIWrapper`
### Suggestion:
Just add a new argument to `WikipediaLoader` named `doc_content_chars_max` (the very same name that uses `WikipediaAPIWrapper` under the hood and use it when instancing the client.
|
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| 1,771,189,299 |
I_kwDOIPDwls5pkjQz
| 6,638 |
how to pass additional variables using kwargs to CustomLLM
|
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open
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[] | null | 4 | 2023-06-23T10:09:31 | 2023-10-04T14:38:03 | null |
NONE
| null |
### System Info
langchain - 0.0.198
platform - ubuntu
python - 3.10.11
### Who can help?
@hwchase17 @agola11
### 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
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Objective is to pass additional variables to `_call` method in CustomLLM.
Colab Link - https://colab.research.google.com/drive/19VSmSEBq5D0MDXQ3CF0rrmOdGjdaELUj?usp=sharing
Sample code:
```
from langchain import PromptTemplate, LLMChain
from langchain.llms.base import LLM
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
model_name = "facebook/opt-350m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
class CustomLLM(LLM):
def _call(self, prompt, stop=None, **kwargs) -> str:
print("Kwargs: ", kwargs)
inputs = tokenizer([prompt], return_tensors="pt")
response = model.generate(**inputs, max_new_tokens=128)
response = tokenizer.decode(response[0])
return response
@property
def _identifying_params(self):
return {"name_of_model": model_name}
@property
def _llm_type(self) -> str:
return "custom"
llm = CustomLLM()
prompt_template = "Answer the question - {question}"
prompt = PromptTemplate(template=prompt_template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
```
### Expected behavior
### Scenario1
passing additional parameter `foo=123`
```
result = llm_chain.run({"question":"What is the weather in LA and SF"}, foo=123)
```
Following error is thrown
```
ValueError: `run` supported with either positional arguments or keyword arguments but not both. Got args: ({'question': 'What is the weather in LA and SF'},) and kwargs: {'foo': 123}.
```
### Scenario2
if we pass it as a dictionary - `{'foo': 123}`
```
result = llm_chain.run({"question":"What is the weather in LA and SF"}, {"foo":123})
```
Following error is thrown
```
ValueError: `run` supports only one positional argument.
```
### Scenario3
if we pass everything together
```
result = llm_chain.run({"question":"What is the weather in LA and SF", "foo":123})
```
The code works, but the kwargs in CustomLLM - `_call` is still empty. i guess, the chain is safely ignoring the variables which are not part of prompt template.
Is there any way to pass the additional parameter to the kwargs of CustomLLM - `_call` method without changing the prompt template?
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| 1,771,161,521 |
I_kwDOIPDwls5pkcex
| 6,637 |
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION finishes chain BEFORE using a tool
|
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open
| false | null |
[] | null | 14 | 2023-06-23T09:50:41 | 2023-11-22T03:42:06 | null |
NONE
| null |
### System Info
I've recently changed to use this agent since I started getting errors with `chat-conversational-react-description` (about it not being able to use multi-input tools). I've noticed that it often finishes a chain telling the user that it'll make a search/use a tool but it never does (because the chain is already finished).
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is how the agent is set up
```python
from langchain.chat_models import ChatOpenAI
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.agents import AgentType, initialize_agent
from agent_tools.comparables_tool import ComparablesTool
# from agent_tools.duck_search_tool import duck_search
from langchain.prompts import SystemMessagePromptTemplate, PromptTemplate
from agent_tools.python_repl_tool import PythonREPL
from token_counter import get_token_count
from langchain.prompts import MessagesPlaceholder
from langchain.memory import ConversationBufferMemory
tools = [PythonREPL(), ComparablesTool()]
chat_history = MessagesPlaceholder(variable_name="chat_history")
memory = ConversationBufferMemory(
memory_key="chat_history", return_messages=True)
gpt = ChatOpenAI(
temperature=0.2,
model_name='gpt-3.5-turbo-16k',
verbose=True
)
conversational_agent = initialize_agent(
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=tools,
llm=gpt,
verbose=True,
max_iterations=10,
memory=memory,
agent_kwargs={
"memory_prompts": [chat_history],
"input_variables": ["input", "agent_scratchpad", "chat_history"]
}
)
async def get_response(user_message: str) -> str:
return await conversational_agent.arun(user_message)
```
And this is what's on the terminal:
```python
FerAtTheFringe#1080 said: "Hey I need to find apartments in madrid with at least 3 rooms" (general)
←[1m> Entering new chain...←[0m
←[32;1m←[1;3mSure! I can help you find apartments in Madrid with at least 3 rooms. Let me search for some options for you.←[0m
←[1m> Finished chain.←[0m
```
### Expected behavior
```python
FerAtTheFringe#1080 said: "Hey I need to find apartments in madrid with at least 3 rooms" (general)
←[1m> Entering new chain...←[0m
←[32;1m←[1;3m "action": "get_comparables",
"action_input": {
"latitude": "38.9921979",
"longitude": "-1.878099",
"rooms": "5",
"nresults": "10"
}
```
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I_kwDOIPDwls5pkC_W
| 6,636 |
'ConversationSummaryMemory' has no attribute 'from_messages'
|
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[] | null | 2 | 2023-06-23T08:37:14 | 2023-09-29T16:05:58 | 2023-09-29T16:05:57 |
CONTRIBUTOR
| null |
### System Info
Ubuntu 20.04
Python 3.10
langchain 0.0.166
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This code is retrieved from the official website
https://python.langchain.com/docs/modules/memory/how_to/summary#initializing-with-messages
```python
from langchain.memory import ConversationSummaryMemory, ChatMessageHistory
from langchain.llms import OpenAI
from dotenv import load_dotenv
load_dotenv()
history = ChatMessageHistory()
history.add_user_message("hi")
history.add_ai_message("hi there!")
memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)
```
The above code will throw out an exception
```
AttributeError: type object 'ConversationSummaryMemory' has no attribute 'from_messages'
```
I guess the class method has been deprecated?
### Expected behavior
It passes.
|
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I_kwDOIPDwls5pj6vD
| 6,635 |
ValidationError: 1 validation error for ConversationalRetrievalChain chain_type_kwargs extra fields not permitted (type=value_error.extra)
|
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[] | null | 11 | 2023-06-23T08:13:12 | 2023-11-03T04:33:18 | 2023-07-14T05:54:55 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.206
python 3.11.3
### Who can help?
_No response_
### Information
- [ ] 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
Code
```
tfretriever = TFIDFRetriever.from_texts(
["My name is Luis Valencia",
"I am 70 years old",
"I like gardening, baking and hockey"])
template = """
Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question:
------
<ctx>
{context}
</ctx>
------
<hs>
{chat_history}
</hs>
------
{question}
Answer:
"""
prompt = PromptTemplate(
input_variables=["chat_history", "context", "question"],
template=template,
)
st.session_state['chain'] = chain = ConversationalRetrievalChain.from_llm(llm,
vectordb.as_retriever(),
memory=memory,
chain_type_kwargs={
"verbose": True,
"prompt": prompt,
"memory": ConversationBufferMemory(
memory_key="chat_history",
input_key="question"),
})
```
Error:
ValidationError: 1 validation error for ConversationalRetrievalChain chain_type_kwargs extra fields not permitted (type=value_error.extra)
### Expected behavior
I should be able to provide custom context to my conversational retrieval chain, without custom prompt it works and gets good answers from vector db, but I cant use custom prompts
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| 1,770,965,108 |
PR_kwDOIPDwls5TuOG7
| 6,634 |
PowerBI: catch outdated token
|
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[] | null | 2 | 2023-06-23T07:28:00 | 2023-06-23T22:01:09 | 2023-06-23T22:01:09 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
This adds just a small tweak to catch the error that says the token is expired rather then retrying.
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| 1,770,856,353 |
I_kwDOIPDwls5pjR-h
| 6,631 |
Github issues instead of Github repo?
|
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[] | null | 1 | 2023-06-23T06:21:35 | 2023-09-29T16:06:04 | 2023-09-29T16:06:03 |
NONE
| null |
### Feature request
Why does the langchain js version have a github repo document loader and this one can only load github issues?
### Motivation
-
### Your contribution
-
|
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| 1,770,853,281 |
I_kwDOIPDwls5pjROh
| 6,630 |
Can't use arun acall, no return and can't stop
|
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[] | null | 1 | 2023-06-23T06:18:54 | 2023-09-29T16:06:09 | 2023-09-29T16:06:08 |
NONE
| null |
### System Info
langchain-0.0.209
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
`import asyncio
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
async def async_generate(chain):
resp = await chain.arun(product="toothpaste")
print(resp)
async def generate_concurrently():
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
tasks = [async_generate(chain) for _ in range(3)]
await asyncio.gather(*tasks)
asyncio.run(generate_concurrently())`
no error and no answer until timeout
Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds...
I know this message mean it is working but no retrun and can't stop
and the same problem in Jupyter, code like this
my code in Jupyter
`import asyncio
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
async def async_generate(chain):
resp = await chain.arun(product="toothpaste")
print(resp)
async def generate_concurrently():
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
tasks = [async_generate(chain) for _ in range(3)]
await asyncio.gather(*tasks)
await generate_concurrently()`
### Expected behavior
no error and no answer until timeout
Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds...
I know this message mean it is working but no retrun and can't stop
and the same problem in Jupyter
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| 1,770,776,581 |
PR_kwDOIPDwls5TtpRj
| 6,629 |
Update to RunOnDataset helper functions to accept evaluator callbacks
|
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[] | null | 1 | 2023-06-23T04:58:01 | 2023-06-27T06:58:14 | 2023-06-27T06:58:13 |
CONTRIBUTOR
| null |
Also improve docstrings and update the tracing datasets notebook to focus on "debug, evaluate, monitor"
|
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I_kwDOIPDwls5pi16R
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Issue: How to print the complete prompt that chain used
|
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[] | null | 6 | 2023-06-23T04:03:35 | 2023-11-16T13:33:39 | null |
NONE
| null |
### Issue you'd like to raise.
qa = ConversationalRetrievalChain.from_llm(AzureChatOpenAI(deployment_name="gpt-35-turbo"), db.as_retriever(), memory=memory)
print(qa.combine_docs_chain.llm_chain.prompt)
ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context'], output_parser=None, partial_variables={}, template="Use the following pieces of context to answer the users question. \nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n----------------\n{context}", template_format='f-string', validate_template=True), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}', template_format='f-string', validate_template=True), additional_kwargs={})])
How can I get the complete prompt includes questions and context?
### Suggestion:
_No response_
|
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| 6,627 |
Critical Vulnerability Blocking Deployment
|
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[] | null | 10 | 2023-06-23T03:47:23 | 2023-08-28T21:35:45 | 2023-08-28T21:35:45 |
NONE
| null |
### System Info
Langchain 0.0.209, Python 3.8.17
https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5725807
Hi, we are deploying an app in our environment to production with langchain as one of the packages.
Today, on Snyk this critical vulnerability showed up, and as a result we're blocked from deploying as Snyk flagged this out as critical.
Are there any plans to fix this soon?
Thank you very much.
<img width="1383" alt="image" src="https://github.com/hwchase17/langchain/assets/1635202/81aa2179-7c10-4f3c-9fa4-11042f43a9be">
### Who can help?
@hwchase17 @dev2049 @vowelparrot @bborn @Jflick58 @duckdoom4 @verm
_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
My requirements.txt
#
# This file is autogenerated by pip-compile with Python
# by the following command:
#
# pip-compile --allow-unsafe --output-file=requirements.txt --resolver=backtracking requirements.in
#
aioboto3==11.2.0
# via python-commons
aiobotocore[boto3]==2.5.0
# via aioboto3
aiohttp==3.8.4
# via
# aiobotocore
# langchain
# openai
aioitertools==0.11.0
# via aiobotocore
aiosignal==1.3.1
# via aiohttp
alembic==1.10.4
# via -r requirements.in
anyio==3.7.0
# via
# httpcore
# starlette
# watchfiles
async-timeout==4.0.2
# via
# aiohttp
# langchain
asyncpg==0.27.0
# via -r requirements.in
attrs==23.1.0
# via
# aiohttp
# pytest
boto3==1.26.76
# via aiobotocore
botocore==1.29.76
# via
# aiobotocore
# boto3
# s3transfer
certifi==2023.5.7
# via
# httpcore
# httpx
# python-commons
# requests
cffi==1.15.1
# via cryptography
charset-normalizer==3.1.0
# via
# aiohttp
# python-commons
# requests
click==8.1.3
# via uvicorn
coverage==7.2.7
# via pytest-cov
cryptography==41.0.1
# via
# pyopenssl
# python-commons
dataclasses-json==0.5.8
# via langchain
dnspython==2.3.0
# via email-validator
email-validator==1.3.1
# via -r requirements.in
exceptiongroup==1.1.1
# via anyio
fastapi==0.95.2
# via -r requirements.in
frozenlist==1.3.3
# via
# aiohttp
# aiosignal
greenlet==2.0.2
# via sqlalchemy
gunicorn==20.1.0
# via python-commons
h11==0.14.0
# via
# httpcore
# uvicorn
httpcore==0.17.2
# via httpx
httptools==0.5.0
# via uvicorn
httpx==0.24.1
# via python-commons
idna==3.4
# via
# anyio
# email-validator
# httpx
# requests
# yarl
iniconfig==2.0.0
# via pytest
jmespath==1.0.1
# via
# boto3
# botocore
langchain==0.0.209
# via -r requirements.in
langchainplus-sdk==0.0.16
# via langchain
loguru==0.7.0
# via python-commons
mako==1.2.4
# via alembic
markdown-it-py==3.0.0
# via rich
markupsafe==2.1.3
# via mako
marshmallow==3.19.0
# via
# dataclasses-json
# marshmallow-enum
marshmallow-enum==1.5.1
# via dataclasses-json
mdurl==0.1.2
# via markdown-it-py
multidict==6.0.4
# via
# aiohttp
# yarl
mypy-extensions==1.0.0
# via typing-inspect
numexpr==2.8.4
# via langchain
numpy==1.24.3
# via
# -r requirements.in
# langchain
# numexpr
openai==0.27.8
# via -r requirements.in
openapi-schema-pydantic==1.2.4
# via langchain
packaging==23.1
# via
# marshmallow
# pytest
pluggy==1.2.0
# via pytest
py==1.11.0
# via pytest
pycparser==2.21
# via cffi
pydantic==1.10.9
# via
# fastapi
# langchain
# langchainplus-sdk
# openapi-schema-pydantic
# python-commons
pygments==2.15.1
# via rich
pyopenssl==23.2.0
# via python-commons
pytest==6.2.5
# via
# -r requirements.in
# pytest-asyncio
# pytest-cov
# pytest-mock
pytest-asyncio==0.18.3
# via -r requirements.in
pytest-cov==2.12.1
# via -r requirements.in
pytest-mock==3.6.1
# via -r requirements.in
python-commons @ ## masked internal repo ##
# via -r requirements.in
python-dateutil==2.8.2
# via botocore
python-dotenv==1.0.0
# via
# -r requirements.in
# uvicorn
pyyaml==6.0
# via
# langchain
# uvicorn
regex==2023.6.3
# via tiktoken
requests==2.31.0
# via
# langchain
# langchainplus-sdk
# openai
# tiktoken
rich==13.4.2
# via python-commons
s3transfer==0.6.1
# via boto3
six==1.16.0
# via python-dateutil
sniffio==1.3.0
# via
# anyio
# httpcore
# httpx
sqlalchemy[asyncio]==2.0.16
# via
# -r requirements.in
# alembic
# langchain
sse-starlette==1.6.1
# via -r requirements.in
starlette==0.27.0
# via
# fastapi
# python-commons
# sse-starlette
tenacity==8.2.2
# via
# langchain
# langchainplus-sdk
tiktoken==0.4.0
# via -r requirements.in
toml==0.10.2
# via
# pytest
# pytest-cov
tqdm==4.65.0
# via openai
typing-extensions==4.6.3
# via
# aioitertools
# alembic
# pydantic
# sqlalchemy
# starlette
# typing-inspect
typing-inspect==0.9.0
# via dataclasses-json
urllib3==1.26.16
# via
# botocore
# python-commons
# requests
uvicorn[standard]==0.21.1
# via
# -r requirements.in
# python-commons
uvloop==0.17.0
# via uvicorn
watchfiles==0.19.0
# via uvicorn
websockets==11.0.3
# via uvicorn
wrapt==1.15.0
# via aiobotocore
yarl==1.9.2
# via aiohttp
# The following packages are considered to be unsafe in a requirements file:
setuptools==68.0.0
# via
# gunicorn
# python-commons
### Expected behavior
Critical vulnerability would have to be fixed for us to deploy, thanks.
|
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PR_kwDOIPDwls5Ttdoy
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docstrings: top-level 1
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[] | null | 2 | 2023-06-23T03:34:22 | 2023-07-05T04:33:41 | 2023-06-23T22:49:45 |
COLLABORATOR
| null |
This PR targets the `API Reference` documentation.
- Several classes and functions missed `docstrings`. These docstrings were created.
- In several places this
```
except ImportError:
raise ValueError(
```
was replaced to
```
except ImportError:
raise ImportError(
```
@hwchase17 @dev2049 - please review :)
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Fix the markdown rendering issue with a code block inside a markdown code block
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[] | null | 2 | 2023-06-23T03:27:00 | 2023-07-12T20:29:26 | 2023-07-12T20:29:25 |
CONTRIBUTOR
| null |
### Description
- Fix the markdown rendering issue with a code block inside a markdown, using a different number of backticks for the delimiters.
Current doc site: <https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/code_splitter#markdown>
After fix:
<img width="480" alt="image" src="https://github.com/hwchase17/langchain/assets/3115235/d9921d59-64e6-4a34-9c62-79743667f528">
### Who can review
PTAL @dev2049
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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OpenAIMultiFunctionsAgent KeyError: 'actions' on custom tools
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[] | null | 10 | 2023-06-23T02:49:01 | 2023-11-13T23:23:26 | null |
CONTRIBUTOR
| null |
### System Info
Langchain 0.0.209
The recent commit #6518 provided an OpenAIMultiFunctionsAgent class.
This MultiFunctions agent fails often when using Custom Tools that worked fine with the OpenAIFunctionsAgent.
```
File "/home/gene/endpoints/app/routers/query.py", line 44, in query3
result = await agent.acall(inputs={"input":query.query})
File "/home/gene/endpoints/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 215, in acall
raise e
File "/home/gene/endpoints/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 209, in acall
await self._acall(inputs, run_manager=run_manager)
File "/home/gene/endpoints/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1006, in _acall
next_step_output = await self._atake_next_step(
File "/home/gene/endpoints/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 853, in _atake_next_step
output = await self.agent.aplan(
File "/home/gene/endpoints/venv/lib/python3.10/site-packages/langchain/agents/openai_functions_multi_agent/base.py", line 301, in aplan
agent_decision = _parse_ai_message(predicted_message)
File "/home/gene/endpoints/venv/lib/python3.10/site-packages/langchain/agents/openai_functions_multi_agent/base.py", line 110, in _parse_ai_message
tools = json.loads(function_call["arguments"])["actions"]
KeyError: 'actions'
```
**Example tool that FAILS:**
```
from typing import Optional, Type
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun
class ProductInput(BaseModel):
prod_name: str = Field(description="Product Name or Type of Product")
class CustomProductTool(BaseTool):
name : str = "price_lookup"
description : str = "useful to look up pricing for a specific product or product type and shopping url of products offered by the Company's online website."
args_schema: Type[BaseModel] = ProductInput
def _run(self, prod_name: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> dict:
# custom code here
products = {}
return products
async def _arun(self, prod_name: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> dict:
return self._run(prod_name)
```
**Example tool that WORKS:**
```
from typing import Optional, Type
from langchain.tools import BaseTool
from ..src.OrderStatus import func_get_order_status, afunc_get_order_status
from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun
from pydantic import BaseModel, Field
class OrderInput(BaseModel):
order_num: str = Field(description="order number")
class CustomOrderTool(BaseTool):
name = "order_status"
description = "useful for when you need to look up the shipping status of an order."
args_schema: Type[BaseModel] = OrderInput
def _run(self, order_num: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> dict:
# Your custom logic here
return func_get_order_status(order_num)
async def _arun(self, order_num: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> dict:
return await afunc_get_order_status(order_num)
```
### 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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Instantiate a OpenAIMultiFunctionAgent:
`agent = initialize_agent(tools,llm,agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=True)`
Create a custom tool (example above):
```
tools=[
CustomOrderTool(return_direct=False),
CustomQAToolSources(llm=llm,vectorstore=general_vectorstore),
CustomProductTool(return_direct=False),
CustomEscalateTool(return_direct=False)
]
```
Call agent:
```
result = await agent.acall(inputs={"input":query.query})
```
### Expected behavior
Tools are very similar to each other, not sure why one would work and the other fails. Might have something to do with the different description lengths? As far as I can tell the structure of the args_schema are the same between the two tools. Both tools work fine on OpenAIFunctionAgent.
I expected tools would work on OpenAIMultiFunctionAgent. Instead, **KeyError: 'actions'** results. Somehow the transformation of langchain tools to OpenAI function schema is not working as expected for OpenAIMultiFunctionAgent.
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Merge pull request #1 from hwchase17/master
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NONE
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test
<!-- Thank you for contributing to LangChain!
Replace this 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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If you're adding a new integration, please include:
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2. an example notebook showing its use.
Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5TtI86
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fix(docs): broken link for OpenLLM
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[] | null | 3 | 2023-06-23T01:27:58 | 2023-06-24T00:48:33 | 2023-06-23T20:59:17 |
CONTRIBUTOR
| null |
This link for the notebook of OpenLLM is not migrated to the new format
Signed-off-by: Aaron <[email protected]>
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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I_kwDOIPDwls5piT-r
| 6,621 |
Variable or Conditional Toolbox
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[] | null | 1 | 2023-06-23T01:26:22 | 2023-09-29T16:06:14 | 2023-09-29T16:06:13 |
NONE
| null |
### Feature request
How can I make a toolset be dependent on some situation?
In the examples I have seen so far and from what I have been able to piece together from reading code, awhile ago (you guys work fast)... is there a way to make it so that I can only make certain tools available to an Agent at certain times beyond changing the actual tools stack myself in the Agent?
### Motivation
The idea is to keep the agent prompt template as lean as possible. The goal is to be able to do something like leading an agent through a process with varying toolboxes given the step that it is on. I have seen that it is possible using function calling, but is it just possible in any type of Agent?
### Your contribution
This is a feature request. I can make a contribution of looking at the code and adding this, but I am not sure if it is already possible or planned seeing as you move fast!
|
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I_kwDOIPDwls5piOth
| 6,620 |
How can I implement a custom LangChain class wrapper (LLM model/Agent) for replit-code-v1-3b model?
|
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[] | null | 1 | 2023-06-23T00:59:38 | 2023-09-29T16:06:18 | 2023-09-29T16:06:18 |
NONE
| null |
### Issue you'd like to raise.
Is it possible to integrate [replit-code-v1-3b](https://replicate.com/replit/replit-code-v1-3b) as an [LLM Model](https://python.langchain.com/en/latest/modules/models.html) or an [Agent](https://python.langchain.com/en/latest/modules/agents.html) with [LangChain](https://github.com/hwchase17/langchain), and [chain](https://python.langchain.com/en/latest/modules/chains.html) it in a complex usecase?
### Suggestion:
Any help / hints on the same would be appreciated!
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I_kwDOIPDwls5piKgd
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Truncate HF pipeline response
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[] | null | 1 | 2023-06-23T00:30:59 | 2023-09-29T16:06:24 | 2023-09-29T16:06:23 |
NONE
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For models like -> "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2" the generated output doesn't contain the prompt. So it is wrong to filter the first characters of the response.
https://github.com/hwchase17/langchain/blob/9d42621fa4385e519f702b7005d475781033188c/langchain/llms/huggingface_pipeline.py#L172C13-L172C64
https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2/blob/main/h2oai_pipeline.py
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I_kwDOIPDwls5piGQJ
| 6,618 |
How can I integrate SerpAPI with custom ChatGLM model
|
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[] | null | 2 | 2023-06-23T00:05:19 | 2023-10-01T16:05:53 | 2023-10-01T16:05:53 |
NONE
| null |
### Issue you'd like to raise.
Hello, Team
How can I integrate SerpAPI with custom ChatGLM model. It looks like my code is not correct and I can't find useful information from internet. Hope post here can help me to resolve this issue. Thanks in advcance.
```
import time
import logging
import requests
from typing import Optional, List, Dict, Mapping, Any
import langchain
from langchain.llms.base import LLM
# from langchain.cache import InMemoryCache
#------------------------------
import os
os.environ["SERPAPI_API_KEY"] = '44eafc5bc26834f931324798f8e370e5c5039578dde6ef7a67918f24ed00599f'
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
#------------------------------
logging.basicConfig(level=logging.INFO)
# 启动llm的缓存
# langchain.llm_cache = InMemoryCache()
class ChatGLM(LLM):
# 模型服务url
url = "http://18.183.251.31:8000"
@property
def _llm_type(self) -> str:
return "chatglm"
def _construct_query(self, prompt: str) -> Dict:
"""构造请求体
"""
query = {
"prompt": prompt
}
return query
@classmethod
def _post(cls, url: str, query: Dict) -> Any:
"""POST请求
"""
_headers = {"Content_Type": "application/json"}
with requests.session() as sess:
resp = sess.post(url,
json=query,
headers=_headers,
timeout=60)
return resp
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""_call
"""
# construct query
query = self._construct_query(prompt=prompt)
print(query)
# post
resp = self._post(url=self.url, query=query)
if resp.status_code == 200:
resp_json = resp.json()
predictions = resp_json["response"]
return predictions
else:
return "请求模型"
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters.
"""
_param_dict = {
"url": self.url
}
return _param_dict
if __name__ == "__main__":
llm = ChatGLM()
# ------------------------------
tools = load_tools(["serpapi"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What's the date today? What great events have taken place today in history?")
# ------------------------------
# while True:
# prompt = input("Human: ")
#
# begin_time = time.time() * 1000
# # 请求模型
# response = llm(prompt, stop=["you"])
# end_time = time.time() * 1000
# used_time = round(end_time - begin_time, 3)
# # logging.info(f"chatGLM process time: {used_time}ms")
# print("chatGLM process time %s" % {used_time})
# print(f"ChatGLM: {response}")
```
### Suggestion:
_No response_
|
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| 1,770,528,462 |
I_kwDOIPDwls5piB7O
| 6,617 |
Dynamic fields for BaseModels in PydanticOutputParser?
|
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[] | null | 5 | 2023-06-22T23:40:52 | 2023-12-21T18:16:43 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I have the following BaseModel:
```python
class MainMagnetClass(BaseModel):
main_materials: List[str] = Field(description="main material")
additional_doping_elements: List[str] = Field(description="doping")
```
which can be instantiated as:
```python
instance = PydanticOutputParser(pydantic_object=MainMagnetClass)
```
I would like to know if there is a way to dynamically load the description of the two fields.
I tried with `construct()` but it doesn't seems to work.
The reason is that I'm generating a set of queries and for each of them I want to have different "description" for the PydanticOutputParser that is going to be used.
### Suggestion:
I would load a dict with the fields and their description and pass it to the object so that I could override the default descriptions.
|
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| 1,770,523,892 |
PR_kwDOIPDwls5Ts3wl
| 6,616 |
Kendra retriever api
|
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[] | null | 8 | 2023-06-22T23:32:57 | 2023-06-23T22:33:35 | 2023-06-23T21:59:36 |
CONTRIBUTOR
| null |
## Description
Replaces [Kendra Retriever](https://github.com/hwchase17/langchain/blob/master/langchain/retrievers/aws_kendra_index_retriever.py) with an updated version that uses the new [retriever API](https://docs.aws.amazon.com/kendra/latest/dg/searching-retrieve.html) which is better suited for retrieval augmented generation (RAG) systems.
**Note**: This change requires the latest version (1.26.159) of boto3 to work. `pip install -U boto3` to upgrade the boto3 version.
cc @hupe1980
cc @dev2049
<!-- Thank you for contributing to LangChain!
Replace this 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,770,522,784 |
PR_kwDOIPDwls5Ts3hf
| 6,615 |
Update String Evaluator
|
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[] | null | 1 | 2023-06-22T23:31:00 | 2023-06-26T21:16:16 | 2023-06-26T21:16:15 |
CONTRIBUTOR
| null |
- Add protocol for `evaluate_strings`
- Move the criteria evaluator out so it's not restricted to being applied on traced runs
|
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| 1,770,474,562 |
PR_kwDOIPDwls5TstLs
| 6,614 |
Add enum fields to parameters when constructing openai function requests
|
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[] | null | 3 | 2023-06-22T22:29:03 | 2023-11-07T03:56:32 | 2023-11-07T03:56:32 |
NONE
| null |
**Description:** Added `add_enum_properties` into `format_tool_to_openai_function` which adds enum details from `tools.arg_schema.schema()` into OpenAI Functions API requests, to make the response more relevant; similar to how enum is used in [this tutorial](https://platform.openai.com/docs/guides/gpt/function-calling) for the `["celsius", "fahrenheit"]`.
There are several cases where an enum in the parameters where pydantic handles differently not as a simple `"enum"` key in the schema, most were covered in the unit test here `tests/unit_tests/tools/test_convert_to_openai.py`.
**Issue:** None
**Dependencies:** N/A
**Tag maintainers:** @vowelparrot
**Twitter handle:** ZhiHaoLow
It's my first time here, if there are any things that need amendment please let me know.
OpenAI Functions seems promising and fun and hope I can contribute more! 😄
|
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PR_kwDOIPDwls5TsikQ
| 6,612 |
GuardrailsOutputParser, added pydantic support.
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NONE
| null |
**Description**: Added `from_rail_pydantic` to `GuardrailsOutputParser` which maps to the `guardrails.Guard.from_pydantic` method recently added and offers the option to use pydantic BaseModel classes instead of RAIL spec to define the guardrails Guard.
**Issue**: None
**Dependencies**: N/A
**Tag maintainers**: @dev2049 @hwchase17
This is my first time contributing to this repo, lmk if anything else needs adding.
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PR_kwDOIPDwls5TsfVr
| 6,611 |
Fix grammar mistake in base.py in planners
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[] | null | 2 | 2023-06-22T21:18:28 | 2023-06-23T22:23:07 | 2023-06-23T21:47:10 |
CONTRIBUTOR
| null |
Fix a typo in `langchain/experimental/plan_and_execute/planners/base.py`, by changing "Given input, decided what to do." to "Given input, decide what to do."
This is in the docstring for functions running LLM chains which shall create a plan, "decided" does not make any sense in this context.
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I_kwDOIPDwls5pheb2
| 6,610 |
ChatVertexAI Error: _ChatSessionBase.send_message() got an unexpected keyword argument 'context'
|
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[] | null | 0 | 2023-06-22T20:56:38 | 2023-06-26T17:21:02 | 2023-06-26T17:21:02 |
NONE
| null |
### System Info
langchain version: 0.0.209
### Who can help?
_No response_
### Information
- [X] 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
https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm
### Expected behavior
I get an error saying "TypeError: _ChatSessionBase.send_message() got an unexpected keyword argument 'context'" when I run `chat(messages)` command mentioned in https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm.
This is probably because ChatSession.send_message does not have the argument 'context' and ChatVertexAI._generate automatically adds the context argument to params since chat-bison being a non-code model.
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| 1,770,378,439 |
PR_kwDOIPDwls5TsYsI
| 6,609 |
Session deletion method in motorhead memory
|
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[] | null | 2 | 2023-06-22T20:53:25 | 2023-06-24T04:27:43 | 2023-06-24T04:27:43 |
CONTRIBUTOR
| null |
Motorhead Memory module didn't support deletion of a session. Added a method to enable deletion.
@hwchase17
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I_kwDOIPDwls5phW6X
| 6,608 |
RetrievalQA.from_chain_type does not release GPU memory when given ChromaDB context
|
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[] | null | 7 | 2023-06-22T20:31:11 | 2024-01-10T07:16:05 | null |
NONE
| null |
### System Info
When querying with no context (emptyStore below) GPU memory goes up to 8GB and after the chain completes, GPU memory goes back down to 630MB.
When using a ChromaDB to provide vector context, GPU memory is never released. Memory usage goes up to 8GB and stays there. Once enough calls have been made, the program will crash with an out of memory error.
I have tried manually deleting the variables associated with the DB and langchain, running garbage collection... I am unable to free this GPU memory. Is there a manual method to free this memory that I could employ or some other workaround?
I started with using langchain 201 and noticed the issue. The issue persists when using the latest 209.
```
def queryGpt(query):
# Get our llm and embeddings
llm = get_llm()
embeddings = get_embeddings()
# Even if the user does not specify a vector store to use, it is necessary
# to pass in a retriever to the RetrievalQA chain.
docs = [
Document(page_content=""),
Document(page_content=""),
Document(page_content=""),
Document(page_content=""),
]
emptyStore = Chroma.from_documents(docs)
retriever = emptyStore.as_retriever()
if request.content_type == "application/json":
data = request.get_json()
store_id = data.get("store_id")
store_collection = data.get("store_collection")
if store_id and store_collection:
vector_stores = load_vector_stores()
found: VectorStore | None = None
for store in vector_stores:
if store["id"] == store_id:
found = store
if not found:
print(f"Warning: vector store not found id:{store_id}")
else:
# print(f"Using vector store '{found['name']}' id:{found['id']} collection {store_collection}")
client = get_chroma_instance(found["dirname"])
# embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
db = Chroma(
client=client,
embedding_function=embeddings,
collection_name=store_collection,
)
retriever = db.as_retriever()
print('Answering question')
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
# Get the answer from the chain
res = qa(query)
```
We are using the latest Vicuna 13b. With `all-MiniLM-L6-v2` used for the embeddings.
We are in Azure using Tesla GPU's. Ubuntu 20.04. Cuda 12.1.
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run a QA chain with a ChromaDB enabled.
### Expected behavior
I would expect the memory to be freed upon completion of the chain.
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PR_kwDOIPDwls5TsPms
| 6,607 |
Fix ray-project/Aviary integration
|
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[] | null | 3 | 2023-06-22T20:17:20 | 2023-06-23T21:49:55 | 2023-06-23T21:49:54 |
CONTRIBUTOR
| null |
- Description: The aviary integration has changed url link. This PR provide fix for those changes and also it makes providing the input URL optional to the API (since they can be set via env variables).
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @dev2049, @hwchase17
- Twitter handle: N/A
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I_kwDOIPDwls5phQq1
| 6,606 |
For JSON loaders - like a Figma Design - similarity does not work, and ends up with the wrong output.
|
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[] | null | 1 | 2023-06-22T20:09:41 | 2023-09-30T16:05:58 | 2023-09-30T16:05:57 |
NONE
| null |
### System Info
Tried on Colab.
Version: [v0.0.209](https://github.com/hwchase17/langchain/releases/tag/v0.0.209)
Platform: Google Colab
Python: 3.10
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Take a Figma file and use Langchain's Figma plugin to get the JSON from API.
2. Use the index = VectorstoreIndexCreator().from_loaders([figma_loader]) to get the index.
3. And then create doc retriever using figma_doc_retriever = index.vectorstore.as_retriever()
When we query ChatGPT/LLMs, the way code works is that it breaks the original document into parts, and finds similarity. Great for unstructured documents, but bad for JSON - breaks the structure and does not carry the right content either. So, for example, this function
relevant_nodes = figma_doc_retriever.get_relevant_documents("Slack Integration")
which calculates similarity to get the nearest nodes, gave me this output:
[Document(page_content='name: Dark Mode\nlastModified: 2023-06-19T07:26:34Z\nthumbnailUrl: \nversion: 3676773001\nrole: owner\neditorType: figma\nlinkAccess: view\nnodes: \n10:138: \ndocument: \nid: 10:138\nname: Slack Integration\ntype: FRAME\nscrollBehavior: SCROLLS\nblendMode: PASS_THROUGH\nchildren: \nid: 10:139\nname: div.sc-dwFxSa\ntype: FRAME\nscrollBehavior: SCROLLS\nblendMode: PASS_THROUGH\nchildren: \nid: 10:140\nname: Main\ntype: FRAME\nscrollBehavior: SCROLLS\nblendMode: PASS_THROUGH\nchildren: \nid: 10:141\nname: div.sc-iAVVkm\ntype: FRAME\nscrollBehavior: SCROLLS\nblendMode: PASS_THROUGH\nchildren: \nid: 10:142\nname: div.sc-bcXHqe\ntype: FRAME\nscrollBehavior: SCROLLS\nblendMode: PASS_THROUGH', metadata={'source': ''}),
Document(page_content='id: 10:178\nname: Send project updates to a Slack channel\ntype: TEXT\nscrollBehavior: SCROLLS\nblendMode: PASS_THROUGH\nabsoluteBoundingBox: \nx: -3084.0\ny: 3199.0\nwidth: 250.0\nheight: 16.0\n\nabsoluteRenderBounds: \nx: -3083.335205078125\ny: 3202.1669921875\nwidth: 247.89453125\nheight: 12.4921875\n\nconstraints: \nvertical: TOP\nhorizontal: LEFT\n\nlayoutAlign: INHERIT\nlayoutGrow: 0.0\nfills: \nblendMode: NORMAL\ntype: SOLID\ncolor: \nr: 0.9960784316062927\ng: 1.0\nb: 0.9960784316062927\na: 1.0\n\n\nstrokes: \nstrokeWeight: 1.0\nstrokeAlign: OUTSIDE\neffects: \ncharacters: Send project updates to a Slack channel\nstyle: \nfontFamily: Inter\nfontPostScriptName: None\nfontWeight: 500\ntextAutoResize: WIDTH_AND_HEIGHT\nfontSize: 13.0\ntextAlignHorizontal: LEFT\ntextAlignVertical: CENTER\nletterSpacing: 0.0\nlineHeightPx: 15.732954025268555\nlineHeightPercent: 100.0\nlineHeightUnit: INTRINSIC_%\n\nlayoutVersion: 3\ncharacterStyleOverrides: \nstyleOverrideTable: \n\nlineTypes: NONE\nlineIndentations: 0', metadata={'source': ''}),
(2 more such nodes)
### Expected behavior
For JSON, it should start from the innermost json, and then work the way backwards (especially for Figma) to enable LLM with more precise understanding of the structure and also get the output as desired.
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PR_kwDOIPDwls5Tr8S5
| 6,605 |
feat: add MultiStrategy output parser
|
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[] | null | 8 | 2023-06-22T19:08:37 | 2023-11-23T16:49:23 | 2023-11-23T16:49:23 |
CONTRIBUTOR
| null |
**tldr**: fancy name for brute forcing multiple parsing functions before giving up with an exception.
#### Description:
- This PR introduces a new type of output parser interface and implementation that can try multiple strategies
- A strategy is a tuple-like class of `(parser, predicate, name=None)` *see strategies.py*
- Strategies are tried if predicate is True
- Strategies are tried in order, allows for fallbacks
- New strategies can be added to handle new output errors and coverd by unit tests.
- Base interface allows existing parsers to implement it
```python
# strategies can be defined like this
parse_strategies = (
ParseStrategy(parse_raw_json, is_json),
ParseStrategy(code_block, lambda text: text.find('```') != -1), name="md_code_block"),
...,
ParseStrategy(fallback, lambda _:True)
)
# then passed to the implementer:
my_parser = MyMultiParser(parse_strategies)
```
In this first pass I implemented a ConvoOutputParser that implements multi strategy. I also implemented some example parsing strategies that can handle most exceptions on nested code blocks in json. *see the tests and test outputs*
Looking for feedback if this is something worth pursuing and whether the code organization is right.
@hwchase17, @dev2049
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PR_kwDOIPDwls5Tr6xf
| 6,604 |
fix minor typo in vector_db_qa.mdx
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[] | null | 2 | 2023-06-22T19:03:33 | 2023-06-23T21:57:38 | 2023-06-23T21:57:37 |
CONTRIBUTOR
| null |
- Description: minor typo fixed - doesn't instead of does. No other changes.
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PR_kwDOIPDwls5TrsJM
| 6,603 |
Just corrected a small inconsistency on a doc page
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[] | null | 1 | 2023-06-22T18:13:05 | 2023-06-23T23:09:29 | 2023-06-23T23:09:29 |
CONTRIBUTOR
| null |
### Just corrected a small inconsistency on a doc page (not exactly a typo, per se)
- Description: There was inconsistency due to the use of single quotes at one place on the [Squential Chains](https://python.langchain.com/docs/modules/chains/foundational/sequential_chains) page of the docs,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @dev2049,
- Twitter handle: kambleakash0
|
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PR_kwDOIPDwls5Trh_3
| 6,602 |
Add an MlflowRetrieverEvaluator Chain
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[] | null | 3 | 2023-06-22T17:37:54 | 2023-07-11T23:17:44 | 2023-07-11T23:17:44 |
CONTRIBUTOR
| null |
Description:
This PR adds a new Chain called `MlflowRetrieverEvaluator`, which is a wrapper of a retriever object used in the `RetrievalQA` chain. The wrapper is used for logging the retriever part to MLflow, in order to evaluate the retrieval model separately.
A bit more context about what the MlflowRetrieverEvaluator chain is supposed to be used for: When a user is developing a `RetrievalQA` chain, they may want to evaluate the retriever by itself, without sending the documents to an llm. Tools will be provided to help the evaluation in `mlflow.evaluate()`. In order to use the `mlflow.evaluate()`, the retriever needs to be logged as an mlflow langchain flavor model, which accepts a Chain. So we introduce the `MlflowRetrieverEvaluator` Chain, which is essentially a wrapper around the retriever object.
Some notes:
1. If a user doesn't want to evaluate the retriever by itself, their code would not be affected. (We are not proposing to force all the retrievers to be wrapped in this chain.)
2. If a user want to evaluate the retriever by itself, they can leverage the new `MlflowRetrieverEvaluator` chain to do the evaluation. However, this doesn't change how their `RetrievalQA` chain works.
See https://github.com/mlflow/mlflow/pull/8808 for more context.
@dev2049 @hwchase17
This PR enables the following use case with MLflow:
```python
# I have a retrieval model: db
embeddings = OpenAIEmbeddings()
db = FAISS.load_local(local_dir, embeddings)
# I can log db using mlflow langchain flavor:
# First, wrap the retriever in the MlflowRetrieverEvaluator Chain
# Added to LangChain in this PR https://github.com/hwchase17/langchain/pull/6602
from langchain.chains.retriever import MlflowRetrieverEvaluator
retriever_evaluator = MlflowRetrieverEvaluator(retriever=db.as_retriever())
# Second, define the loader_fn and persist_dir, as with RetrievalQA Chain
def load_retriever(persist_directory):
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local(persist_directory, embeddings)
return vectorstore.as_retriever()
local_dir = '/Users/liang.zhang/faiss_index'
# Third, log the model
logged_model = mlflow.langchain.log_model(retriever_evaluator, "retriever_evaluator", loader_fn=load_retriever, persist_dir=local_dir)
# You can load the model in pyfunc flavor:
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
# You can retrieve the documents by running predict:
loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}])
# The return value is the same as
# db.as_retriever().get_relevant_documents("What did the president say about Ketanji Brown Jackson")
```
The return value of the last statement:
```
[[Document(lc_kwargs={'page_content': 'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', 'metadata': {'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}}, page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}),
Document(lc_kwargs={'page_content': 'A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', 'metadata': {'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}}, page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}),
Document(lc_kwargs={'page_content': 'And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', 'metadata': {'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}}, page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}),
Document(lc_kwargs={'page_content': 'Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', 'metadata': {'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'}}, page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '/Users/liang.zhang/langchain/docs/modules/state_of_the_union.txt'})]]
```
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Replace this comment with:
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PR_kwDOIPDwls5TrV8D
| 6,601 |
Fix typo in myscale_self_query.ipynb
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closed
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[] | null | 1 | 2023-06-22T16:58:26 | 2023-06-23T21:57:13 | 2023-06-23T21:57:13 |
CONTRIBUTOR
| null |
lenth -> length
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5TrQQ_
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feat: added tqdm progress bar to UnstructuredURLLoader
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CONTRIBUTOR
| null |
- Description: Adds a simple progress bar with tqdm when using UnstructuredURLLoader. Exposes new paramater `show_progress_bar`. Very simple PR.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @rlancemartin @eyurtsev
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response does not call the plugin
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[] | null | 2 | 2023-06-22T16:03:31 | 2023-10-23T16:07:42 | 2023-10-23T16:07:41 |
NONE
| null |
### System Info
Hello,
When using AIPluginTool with ChatOpenAI
sometimes the chain call the plugin and sometimes the response is like "the user can call the url ... to get the response" . Why is it?
My code:
import os
import openai
from dotenv import load_dotenv, find_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.tools import AIPluginTool
from langchain.agents import load_tools, ConversationalChatAgent, ZeroShotAgent
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.agents.agent import AgentExecutor
tool = AIPluginTool.from_plugin_url("http://localhost:5003/.well-known/ai-plugin.json")
tools2 = load_tools(["requests_get"] )
tools = [tool,tools2[0]]
_ = load_dotenv(find_dotenv()) #read local .env file
openai.api_key = os.getenv('OPENAI_API_KEY')
llm=ChatOpenAI(
openai_api_key=os.getenv('OPENAI_API_KEY'),
temperature=0,
model_name='gpt-3.5-turbo'
)
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
memory = ConversationBufferWindowMemory(
memory_key="chat_history",
k=5,
return_messages=True
)
custom_agent = ConversationalChatAgent.from_llm_and_tools(llm=llm, tools=tools, system_message=prefix)
agent_executor = AgentExecutor.from_agent_and_tools(agent=custom_agent, tools=tools, memory=memory)
agent_executor.verbose = True
print(
agent_executor.agent.llm_chain.prompt
)
resp = agent_executor.run(input="What are my store orders for userId Leo ?")
print(
resp
)
### 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
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
execute the code , two or three times. you will get a different response
### Expected behavior
call the plugin and get the response from http://localhost:5003/order/Leo
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I_kwDOIPDwls5pf5nV
| 6,597 |
Issue: why SQLAlchemy 2.0 is forced?
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[] | null | 1 | 2023-06-22T15:48:15 | 2023-09-28T16:05:39 | 2023-09-28T16:05:38 |
NONE
| null |
### Issue you'd like to raise.
I need to upgrade our Langchain version due to a security issue flagged in version 0.0.27 (see https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5411357).
However, I can't do this because Langchain depends on SQLAlchemy 2.0, while we use 1.4.
1. Why is SQLALchemy 2.0 needed? It might be useful for a tiny feature out of all the Langchain functionality...
2. SQLAlchemy 1.4 is still more widely used than 2.0
### Suggestion:
Not forcing 2.0, but >1.4 - should support the same syntax -> https://docs.sqlalchemy.org/en/14/
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I_kwDOIPDwls5pf5R6
| 6,596 |
ImportError: cannot import name 'RetrievalQAWithSourcesChain' from 'langchain.chains'
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[] | null | 1 | 2023-06-22T15:47:32 | 2023-06-28T16:18:28 | 2023-06-28T16:18:27 |
NONE
| null |
### System Info
Langchain 0.27
Python 3.7
Amazon Linux
### Who can help?
@ag
### 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
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
At launch, after recreating python venv and reinstalling latest version of langchain the error message is:
ImportError: cannot import name 'RetrievalQAWithSourcesChain' from 'langchain.chains'
### Expected behavior
This import should not cause an error.
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PR_kwDOIPDwls5Tq8Ty
| 6,595 |
Fix Typo
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[] | null | 2 | 2023-06-22T15:40:50 | 2023-06-23T21:56:55 | 2023-06-23T21:56:54 |
CONTRIBUTOR
| null |
Resolves #6582
|
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PR_kwDOIPDwls5TqzIT
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feat: Add `UnstructuredRSTLoader`
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[] | null | 1 | 2023-06-22T15:19:17 | 2023-06-25T19:41:58 | 2023-06-25T19:41:58 |
CONTRIBUTOR
| null |
### Summary
Adds an `UnstructuredRSTLoader` for loading [reStructuredText](https://en.wikipedia.org/wiki/ReStructuredText) file.
### Testing
```python
from langchain.document_loaders import UnstructuredRSTLoader
loader = UnstructuredRSTLoader(
file_path="example_data/README.rst", mode="elements"
)
docs = loader.load()
print(docs[0])
```
### Reviewers
- @hwchase17
- @rlancemartin
- @eyurtsev
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bump 209
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[] | null | 1 | 2023-06-22T15:11:11 | 2023-06-22T15:18:21 | 2023-06-22T15:18:20 |
CONTRIBUTOR
| null | null |
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I_kwDOIPDwls5pfZIg
| 6,592 |
Issue: Summarization using Vertex AI - returns 400 Error on certain cases
|
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[] | null | 1 | 2023-06-22T14:44:12 | 2023-06-29T12:59:38 | 2023-06-29T12:59:10 |
NONE
| null |
### Issue you'd like to raise.
We are trying to summarize contents of some URLs using Vertex AI. Below is the code snippet
```
from langchain.llms import VertexAI
from langchain.chains.summarize import load_summarize_chain
llm = VertexAI(temperature=0.5, max_output_tokens=1024)
chain = load_summarize_chain(llm, chain_type="map_reduce")
def digest(url, driver):
# Get page source HTML
html = driver.page_source
# Parse HTML with BeautifulSoup
soup = BeautifulSoup(html, 'html.parser')
get_html_and_add(soup, url)
def get_html_and_add(soup: BeautifulSoup, url: str):
text = soup.get_text()
if soup.title:
title = str(soup.title.string)
else:
title = ""
vector_store.add_documents(summary_text(text, url, title))
vector_store.persist()
def summary_text(docs: str, url: str, title: str):
metadata: Dict[str, Union[str, None]] = {
"source": url,
"title": title,
}
docs = [Document(page_content=docs, metadata=metadata)]
val = chain.run(docs)
print(f'summary for url {url} is \n {val}')
return [Document(page_content=val, metadata=metadata)]
```
The code works fine for most of the URLs, however, for few URLs, we are receiving the attached error, when ```chain.run(docs)``` is invoked in the method ```summary_text```
[Error.txt](https://github.com/hwchase17/langchain/files/11835133/Error.txt)
Not able to identify the root cause, any help is appreciated.
Thank you!
PS: Unable to share the URL as it is an internal URL. The langchain version that we use is the latest as of today i.e., 0.0.208.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5TqFs5
| 6,591 |
beautifulsoup get_text kwargs in WebBaseLoader
|
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[] | null | 1 | 2023-06-22T13:36:34 | 2023-06-25T19:42:28 | 2023-06-25T19:42:27 |
CONTRIBUTOR
| null |
# beautifulsoup get_text kwargs in WebBaseLoader
- Description: this PR introduces an optional `bs_get_text_kwargs` parameter to `WebBaseLoader` constructor. It can be used to pass kwargs to the downstream BeautifulSoup.get_text call. The most common usage might be to pass a custom text separator, as seen also in `BSHTMLLoader`.
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: jtolgyesi
|
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I_kwDOIPDwls5pehXQ
| 6,590 |
Custom prompts for chain types that aren't "stuff" in RetrievalQA
|
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[] | null | 6 | 2023-06-22T12:48:37 | 2023-11-25T16:08:59 | 2023-11-25T16:08:58 |
NONE
| null |
### Feature request
Consider the following example:
```python
# All the dependencies being used
import openai
import os
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.prompts import PromptTemplate
load_dotenv()
openai.organization = os.getenv("OPENAI_ORG_ID_")
openai.api_key = os.getenv("OPENAI_API_KEY")
# Load up a text file
loader = TextLoader("foo.txt")
documents = loader.load()
# Split text into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
# Set up chroma
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
# I want a custom prompt that asks for the output in JSON format
prompt_template = """
Use the following pieces of context to answer the question at the end.
If you don't know the answer, output 'N/A', don't try to
make up an answer.
{context}
Question: {question}
Answer in JSON format:
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
llm = ChatOpenAI(model_name="gpt-3.5", temperature=0)
# This is what's done in the Python docs
chain_type_kwargs = {'prompt': PROMPT}
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)
query = "Foo bar"
res = qa.run(query)
```
If we use anything other than `"stuff"` for the `chain_type` parameter in `RetrievalQA.from_chain_type`, we'll get the following error from that line:
```terminal
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py", line 91, in from_chain_type
combine_documents_chain = load_qa_chain(
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/question_answering/__init__.py", line 238, in load_qa_chain
return loader_mapping[chain_type](
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/question_answering/__init__.py", line 196, in _load_refine_chain
return RefineDocumentsChain(
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/load/serializable.py", line 61, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for RefineDocumentsChain
prompt
extra fields not permitted (type=value_error.extra)
```
Is there anything in particular that prevents custom prompts being used for different chain types? Am I missing something? Open to any help and/or guidance.
### Motivation
I'm trying to perform QA on a large block of text and so using map_reduce or refine is preferable over stuff. I also want to perform the QA with a custom prompt as I need the chain's output to be in JSON format for parsing. When using stuff for text that doesn't surpass the token limit, it works as expected.
### Your contribution
Happy to contribute via a PR if someone identifies that what I'm suggesting isn't impossible.
|
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I_kwDOIPDwls5pehUD
| 6,589 |
DOC: RecursiveTextSplitter function doesn't exist
|
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[] | null | 3 | 2023-06-22T12:48:29 | 2023-11-26T16:08:54 | 2023-11-26T16:08:53 |
NONE
| null |
### Issue with current documentation:
https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter
this function isn't to be found in the text_splitter.py file :
from langchain.text_splitter import RecursiveCharacterTextSplitter
this returns an error.
### Idea or request for content:
_No response_
|
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| 6,587 |
Add missing word in comment
|
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[] | null | 2 | 2023-06-22T12:16:14 | 2023-06-22T14:56:05 | 2023-06-22T14:54:29 |
CONTRIBUTOR
| null |
Changed
```
# Do this so we can exactly what's going on under the hood
```
to
```
# Do this so we can see exactly what's going on under the hood
```
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I_kwDOIPDwls5peDy8
| 6,586 |
Pre-built tool list
|
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[] | null | 2 | 2023-06-22T11:29:09 | 2023-09-28T16:05:43 | 2023-09-28T16:05:43 |
NONE
| null |
### Issue with current documentation:
I haven't been able to find any documentation on a comprehensive list of pre-built tools available on Langchain, for example, there is nothing in the documentation that suggests we're able to load the "llm-math" tool?
### Idea or request for content:
It would be good to have a list of all pre-built tools!
|
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Not able to clear Conversationbuffermemory.
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[] | null | 4 | 2023-06-22T11:23:00 | 2023-07-17T14:42:27 | 2023-07-17T14:42:16 |
NONE
| null |
### Feature request
The current implementation of ConversationBufferMemory lacks the capability to clear the memory history. When using the load_qa_chain function with ConversationBufferMemory and uploading the abc.pdf file for the first time, subsequent questions based on that document yield expected answers. However, if I then change the file to 123.pdf and ask the same questions as before, the system provides the same answers as those given for the previous pdf.
Unfortunately, I have not found a clear_history function within the ConversationBufferMemory, which would enable me to reset or remove the previous memory records.
### Motivation
add that clear_history under ConversationBufferMemory to clear all previous saved messages.
### Your contribution
no
|
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| 6,584 |
Fix callback forwarding in async plan method for OpenAI function agent
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[] | null | 2 | 2023-06-22T11:13:33 | 2023-06-22T15:18:32 | 2023-06-22T15:18:32 |
CONTRIBUTOR
| null |
The callback argument was missing, preventing me to get callbacks to work properly when using it async
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I_kwDOIPDwls5pds8Z
| 6,583 |
Create_documents for MarkdownHeaderTextSplitter?
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[] | null | 1 | 2023-06-22T10:26:00 | 2023-09-02T03:34:02 | 2023-09-02T03:34:01 |
NONE
| null |
### Feature request
Can we have create_document function for MarkdownHeaderTextSplitter to create documents based on the splits?
### Motivation
MarkdownHeaderTextSplitter only has split_text. Not sure how to get documents from the list of dict.
### Your contribution
...
|
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I_kwDOIPDwls5pdXNb
| 6,582 |
Typo
|
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[] | null | 0 | 2023-06-22T09:34:08 | 2023-06-23T21:56:55 | 2023-06-23T21:56:55 |
CONTRIBUTOR
| null |
### System Info
latest version
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Typo on :
https://github.com/hwchase17/langchain/blob/d50de2728f95df0ffc59c538bd67e116a8e75a53/langchain/vectorstores/weaviate.py#L49
Instal - > install
### Expected behavior
typo corrected
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I_kwDOIPDwls5pdVzP
| 6,581 |
Improve PubMedAPIWrapper to get the PubMed ID and/or DOI and/or journal information returned.
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[] | null | 1 | 2023-06-22T09:30:14 | 2023-09-28T16:05:48 | 2023-09-28T16:05:47 |
NONE
| null |
### Feature request
Improve PubMedAPIWrapper to get the PubMed ID and/or DOI and/or journal information returned.
Please rename
langchain/utilities/pu**p**med.py
to
langchain/utilities/pu**b**med.py
### Motivation
A user of a chat model can ask the model to provide a link to the original literature to verify if the answer of the model makes sense.
### Your contribution
None.
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PR_kwDOIPDwls5ToaqH
| 6,580 |
Dev2049/azure endpoint
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[] | null | 1 | 2023-06-22T08:34:10 | 2023-06-22T08:46:02 | 2023-06-22T08:46:02 |
CONTRIBUTOR
| null |
### Description
We have added a new LLM integration `azureml_endpoint` that allows users
to leverage models from the AzureML platform. Microsoft recently
announced the release of [Azure Foundation
Models](https://learn.microsoft.com/en-us/azure/machine-learning/concept-foundation-models?view=azureml-api-2)
which users can find in the AzureML Model Catalog. The Model Catalog
contains a variety of open source and Hugging Face models that users can
deploy on AzureML. The `azureml_endpoint` allows LangChain users to use
the deployed Azure Foundation Models.
### Dependencies
No added dependencies were required for the change.
### Tests
Integration tests were added in
`tests/integration_tests/llms/test_azureml_endpoint.py`.
### Notebook
A Jupyter notebook demonstrating how to use `azureml_endpoint` was added
to `docs/modules/llms/integrations/azureml_endpoint_example.ipynb`.
### Twitters
[Prakhar Gupta](https://twitter.com/prakhar_in)
[Matthew DeGuzman](https://twitter.com/matthew_d13)
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I_kwDOIPDwls5pdAHP
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HuggingFace Models as Azure ML Managed endpoints
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CONTRIBUTOR
| null |
### Feature request
You now support Hugging Face Inference endpoints, could you support also HF Models deployed in Azure ML as Managed endpoints?
It should be a similar implementation, its a REST API
### Motivation
My company would like to use Azure services only :) and many companies are like this
### Your contribution
I could help with some guidance.
|
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Dev2049/openllm
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CONTRIBUTOR
| null |
update poetry lock
|
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unable to use matching engine
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[] | null | 2 | 2023-06-22T07:26:32 | 2023-12-06T08:17:35 | 2023-09-28T16:05:53 |
NONE
| null |
### System Info
langchain 0.0.207
platform ubuntu
python 3.9
### Who can help?
@hwaking @eyurtsev @tomaspiaggio
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
python script
```
from langchain.embeddings import VertexAIEmbeddings
embeddings = VertexAIEmbeddings()
llm = VertexAI(model_name='text-bison')
from langchain.vectorstores import MatchingEngine
texts = ['The cat sat on', 'the mat.', 'I like to', 'eat pizza for', 'dinner.', 'The sun sets', 'in the west.']
vector_store = MatchingEngine.from_components(
project_id=project,
region=location,
gcs_bucket_name='bucket_name',
index_id="index_id",
endpoint_id="endpoint_id",
embedding=embeddings
)
```
error message
[error.txt](https://github.com/hwchase17/langchain/files/11830103/error.txt)
### Expected behavior
expected behavior is pushing the vectors to vectorstore
|
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I_kwDOIPDwls5pbyej
| 6,574 |
error when creating SQLDatabase agent with Amazon Athena
|
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[] | null | 1 | 2023-06-22T04:08:00 | 2023-09-28T16:05:59 | 2023-09-28T16:05:58 |
NONE
| null |
### System Info
Version:
PyAthena[SQLAlchemy]==2.25.2
langchain==0.0.166
sqlalchemy==1.4.47
Python==3.10.10
### Who can help?
@hwchase17 @agola11 @ey
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1- I'm creating the engine_athena with the following connection string
engine_athena = create_engine('awsathena+rest:/@athena.us-east-1.amazonaws.com:443/<schema>?s3_staging_dir=<S3 directory>&work_group=primary')
2- db = SQLDatabase(engine_athena)
3- However, I'm getting an error NoSuchTableError: <table_name>
4- I confirmed that the table exists and I'm able to query it directly using:
with engine_athena.connect() as connection:
result = connection.execute(text("SELECT * FROM <table_name> limit 10"))
for row in result:
print(row)
### Expected behavior
Expect to receive a Connection is established successfully message.
Any pointers to how I can resolve this issue. Here is the full error
---------------------------------------------------------------------------
NoSuchTableError Traceback (most recent call last)
Cell In[7], line 3
1 # Create the connection string (SQLAlchemy engine)
2 engine_athena = create_engine('awsathena+rest://:443/<table_name>?s3_staging_dir=<S3 directory>/&work_group=primary')
----> 3 db = SQLDatabase(engine_athena)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/sql_database.py:98, in SQLDatabase.__init__(self, engine, schema, metadata, ignore_tables, include_tables, sample_rows_in_table_info, indexes_in_table_info, custom_table_info, view_support)
96 self._metadata = metadata or MetaData()
97 # including view support if view_support = true
---> 98 self._metadata.reflect(
99 views=view_support,
100 bind=self._engine,
101 only=list(self._usable_tables),
102 schema=self._schema,
103 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/sql/schema.py:4901, in MetaData.reflect(self, bind, schema, views, only, extend_existing, autoload_replace, resolve_fks, **dialect_kwargs)
4899 for name in load:
4900 try:
-> 4901 Table(name, self, **reflect_opts)
4902 except exc.UnreflectableTableError as uerr:
4903 util.warn("Skipping table %s: %s" % (name, uerr))
File <string>:2, in __new__(cls, *args, **kw)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/util/deprecations.py:375, in deprecated_params.<locals>.decorate.<locals>.warned(fn, *args, **kwargs)
368 if m in kwargs:
369 _warn_with_version(
370 messages[m],
371 versions[m],
372 version_warnings[m],
373 stacklevel=3,
374 )
--> 375 return fn(*args, **kwargs)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/sql/schema.py:618, in Table.__new__(cls, *args, **kw)
616 return table
617 except Exception:
--> 618 with util.safe_reraise():
619 metadata._remove_table(name, schema)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70, in safe_reraise.__exit__(self, type_, value, traceback)
68 self._exc_info = None # remove potential circular references
69 if not self.warn_only:
---> 70 compat.raise_(
71 exc_value,
72 with_traceback=exc_tb,
73 )
74 else:
75 if not compat.py3k and self._exc_info and self._exc_info[1]:
76 # emulate Py3K's behavior of telling us when an exception
77 # occurs in an exception handler.
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/util/compat.py:211, in raise_(***failed resolving arguments***)
208 exception.__cause__ = replace_context
210 try:
--> 211 raise exception
212 finally:
213 # credit to
214 # https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/
215 # as the __traceback__ object creates a cycle
216 del exception, replace_context, from_, with_traceback
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/sql/schema.py:614, in Table.__new__(cls, *args, **kw)
612 metadata._add_table(name, schema, table)
613 try:
--> 614 table._init(name, metadata, *args, **kw)
615 table.dispatch.after_parent_attach(table, metadata)
616 return table
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/sql/schema.py:689, in Table._init(self, name, metadata, *args, **kwargs)
685 # load column definitions from the database if 'autoload' is defined
686 # we do it after the table is in the singleton dictionary to support
687 # circular foreign keys
688 if autoload:
--> 689 self._autoload(
690 metadata,
691 autoload_with,
692 include_columns,
693 _extend_on=_extend_on,
694 resolve_fks=resolve_fks,
695 )
697 # initialize all the column, etc. objects. done after reflection to
698 # allow user-overrides
700 self._init_items(
701 *args,
702 allow_replacements=extend_existing or keep_existing or autoload
703 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/sql/schema.py:724, in Table._autoload(self, metadata, autoload_with, include_columns, exclude_columns, resolve_fks, _extend_on)
722 insp = inspection.inspect(autoload_with)
723 with insp._inspection_context() as conn_insp:
--> 724 conn_insp.reflect_table(
725 self,
726 include_columns,
727 exclude_columns,
728 resolve_fks,
729 _extend_on=_extend_on,
730 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:789, in Inspector.reflect_table(self, table, include_columns, exclude_columns, resolve_fks, _extend_on)
787 # NOTE: support tables/views with no columns
788 if not found_table and not self.has_table(table_name, schema):
--> 789 raise exc.NoSuchTableError(table_name)
791 self._reflect_pk(
792 table_name, schema, table, cols_by_orig_name, exclude_columns
793 )
795 self._reflect_fk(
796 table_name,
797 schema,
(...)
804 reflection_options,
805 )
NoSuchTableError: <table_name>
|
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PR_kwDOIPDwls5TnNvi
| 6,573 |
Use modern OpenAI endpoints for embeddings
|
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[] | null | 4 | 2023-06-22T03:16:05 | 2023-07-13T05:23:18 | 2023-07-13T05:23:18 |
CONTRIBUTOR
| null |
- Description:
LangChain passes [engine](https://github.com/hwchase17/langchain/blob/master/langchain/embeddings/openai.py#L256) and not `model` as a field when making OpenAI requests. Within the `openai` Python library, for OpenAI requests, this [makes a call](https://github.com/openai/openai-python/blob/main/openai/api_resources/abstract/engine_api_resource.py#L58) to an endpoint of the form `https://api.openai.com/v1/engines/{engine_id}/embeddings`.
These endpoints are [deprecated](https://help.openai.com/en/articles/6283125-what-happened-to-engines) in favor of endpoints of the format `https://api.openai.com/v1/embeddings`, where `model` is passed as a parameter in the request body.
While these deprecated endpoints continue to function for now, they may not be supported indefinitely and should be avoided in favor of the newer API format.
It appears that `engine` was passed in instead of `model` to make both Azure OpenAI and OpenAI calls work similarly. However, the inclusion of `engine` [causes](https://github.com/openai/openai-python/blob/main/openai/api_resources/abstract/engine_api_resource.py#L58) OpenAI to use the deprecated endpoint, requiring a diverging code path for Azure OpenAI calls where `engine` is passed in additionally (Azure OpenAI requires `engine` to specify a deployment, and can optionally take in `model`).
In the long-term, it may be worth considering spinning off Azure OpenAI embeddings into a separate class for ease of use and maintenance, similar to the [implementation for chat models](https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/azure_openai.py).
- Tag maintainer: @dev2049 @hwchase17
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PR_kwDOIPDwls5TnF0A
| 6,572 |
add motherduck docs
|
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[] | null | 1 | 2023-06-22T02:23:10 | 2023-06-22T06:13:47 | 2023-06-22T06:13:46 |
COLLABORATOR
| null | null |
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| 1,768,738,178 |
PR_kwDOIPDwls5Tm0Cw
| 6,571 |
MD header text splitter returns Documents
|
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[] | null | 1 | 2023-06-22T01:02:19 | 2023-06-22T16:25:39 | 2023-06-22T16:25:39 |
COLLABORATOR
| null |
Return `Documents` from MD header text splitter to simplify UX.
Updates the test as well as example notebooks.
|
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PR_kwDOIPDwls5TmtJ9
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Refactor vector storage to correctly handle relevancy scores
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CONTRIBUTOR
| null |
Description: This pull request aims to support generating the correct generic relevancy scores for different vector stores by refactoring the relevance score functions and their selection in the base class and subclasses of VectorStore. This is especially relevant with VectorStores that require a distance metric upon initialization. Note many of the current implenetations of `_similarity_search_with_relevance_scores` are not technically correct, as they just return `self.similarity_search_with_score(query, k, **kwargs)` without applying the relevant score function
Also includes changes associated with: https://github.com/hwchase17/langchain/pull/6564 and https://github.com/hwchase17/langchain/pull/6494
See more indepth discussion in thread in #6494
Issue:
https://github.com/hwchase17/langchain/issues/6526
https://github.com/hwchase17/langchain/issues/6481
https://github.com/hwchase17/langchain/issues/6346
Dependencies: None
The changes include:
- Properly handling score thresholding in FAISS `similarity_search_with_score_by_vector` for the corresponding distance metric.
- Refactoring the `_similarity_search_with_relevance_scores` method in the base class and removing it from the subclasses for incorrectly implemented subclasses.
- Adding a `_select_relevance_score_fn` method in the base class and implementing it in the subclasses to select the appropriate relevance score function based on the distance strategy.
- Updating the `__init__` methods of the subclasses to set the `relevance_score_fn` attribute.
- Removing the `_default_relevance_score_fn` function from the FAISS class and using the base class's `_euclidean_relevance_score_fn` instead.
- Adding the `DistanceStrategy` enum to the `utils.py` file and updating the imports in the vector store classes.
- Updating the tests to import the `DistanceStrategy` enum from the `utils.py` file.
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| 1,768,689,538 |
PR_kwDOIPDwls5TmpaB
| 6,569 |
Remove duplicate databricks entries in ecosystem integrations
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[] | null | 3 | 2023-06-22T00:02:11 | 2023-06-22T02:14:34 | 2023-06-22T02:14:34 |
CONTRIBUTOR
| null |
Currently, there are two Databricks entries in https://python.langchain.com/docs/ecosystem/integrations/
<img width="277" alt="image" src="https://github.com/hwchase17/langchain/assets/1097932/86ab4ad2-6bce-4459-9d56-1ab2fbb69f6d">
The reason is that there are duplicated notebooks for Databricks integration:
* https://github.com/hwchase17/langchain/blob/master/docs/extras/ecosystem/integrations/databricks.ipynb
* https://github.com/hwchase17/langchain/blob/master/docs/extras/ecosystem/integrations/databricks/databricks.ipynb
This PR is to remove the second one for simplicity.
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PR_kwDOIPDwls5Tmehx
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Change Data Loader Namespace
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[] | null | 2 | 2023-06-21T23:01:58 | 2023-09-17T18:54:18 | 2023-06-22T02:24:04 |
CONTRIBUTOR
| null |
Description:
Update the artifact name of the xml file and the namespaces. Co-authored with @tjaffri
Maintainers:
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5Tma1Y
| 6,567 |
docs: update `ecosystem/Dependents` 3 - 1 file
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COLLABORATOR
| null |
updated dependents (3rd update)
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
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PR_kwDOIPDwls5TmU2N
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Expanding support for graphql tooling for more diverse graphql use cases.
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NONE
| null |
### Expanding the previous support for graphql tooling to fix some bugs and make it more accessible to diverse graphql use cases.
Changes:
1. Previous 'custom_headers' flag did not work as it wasn't hooked into the _EXTRA_LLM_TOOLS dict correctly, fixed that
2. Addition of flag for custom_transport_auth which enables signing request with sigV4 (aws appsync benefits from this)
3. Addition of automatic schema loading
- runs introspection query when tool is initialized and injects the schema into the tool description for model use
- removes redundant information from the schema before adding to prompt to avoid model confusion
- flag disable_schema_load can be used to disable and revert to previous functionality
4. Adds a try ... except around execution as not all graphql errors are serializable as langchain wants, prevents chain from crashing in these cases
#### Examples
Users can now use more diverse auth modes and have a more streamlined code implementation.
```
# Using an Api Key
appsync_tool = load_tools(
['graphql'],
graphql_endpoint="...",
custom_headers={'x-api-key': '...'},
llm=OpenAI()
)
```
```
# Using AWS IAM signing
credentials = boto3.Session().get_credentials()
region = os.getenv("AWS_REGION", "us-east-1")
awsauth = AWS4Auth(
credentials.access_key,
credentials.secret_key,
region,
"appsync",
session_token=credentials.token,
)
appsync_signed_requests_tool = load_tools(
['graphql'],
graphql_endpoint="...",
custom_transport_auth=awsauth,
llm=OpenAI()
)
```
In both cases there is no longer a need for the schema to be provided, its now pulled automatically.
```
# Using an agent like this
agent = initialize_agent(
appsync_tool,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
```
We can then query with:
```
agent.run('Who is not friends with bailey?')
```
Then results in a prompt like below:
```
Answer the following questions as best you can. You have access to the following tools:
query_graphql: Input to this tool is a detailed and correct GraphQL query, output is a result from the API.
If the query is not correct, an error message will be returned.
If an error is returned with 'Bad request' in it, rewrite the query and try again.
If an error is returned with 'Unauthorized' in it, do not try again, but tell the user to change their authentication.
Use the following schema for your queries:
type Query {
""" Gets your own account info"""
self: User
""" Get all accounts"""
users: [User]
}
type User {
id: String
name: String
friends: [UserRelation]
}
type UserRelation {
user: User
}
type Mutation {
""" Takes in a parameter of post which is a new post object. Its assumed to be created by you
"""
createPost(post: NewPost): Post
}
""" A post on the system"""
type Post {
id: String
title: String
content: String
author: User
}
""" A partial object that is used to create a new post"""
input NewPost {
title: String
content: String
}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [query_graphql]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
```
#### Who can review?
@hwchase17
|
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PR_kwDOIPDwls5TmUQ9
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Upgrade the version of AwaDB and add some new interfaces
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[] | null | 2 | 2023-06-21T22:21:43 | 2023-06-22T06:49:56 | 2023-06-22T06:15:18 |
CONTRIBUTOR
| null |
1. upgrade the version of AwaDB
2. add some new interfaces
3. fix bug of packing page content error
@dev2049 please review, thanks!
|
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PR_kwDOIPDwls5TmTFb
| 6,564 |
[VectorStores][FAISS] Score threshold implementation fix
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[] | null | 2 | 2023-06-21T22:16:38 | 2023-06-22T00:27:14 | 2023-06-22T00:27:14 |
CONTRIBUTOR
| null |
# Description:
Support different metric types for FAISS (and adds generic enum for different vector stores) and fixes bug for score threshold filtering
Currently FAISS implementation will filter out scores that are less than the score threshold (for L2, but it should keep these scores, see issue below). If the index is constructed using inner product or jaccard e.g. it should use ge.
# Issue
https://github.com/hwchase17/langchain/issues/6526
# Tag maintainer:
DataLoaders / VectorStores / Retrievers:
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5Tlz4I
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feat: interfaces for async embeddings, implement async openai
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[] | null | 2 | 2023-06-21T20:41:52 | 2023-06-22T22:19:01 | 2023-06-22T06:16:33 |
CONTRIBUTOR
| null |
Since it seems like #6111 will be blocked for a bit, I've forked @tyree731's fork and implemented the requested changes.
This change adds support to the base Embeddings class for two methods, aembed_query and aembed_documents, those two methods supporting async equivalents of embed_query and
embed_documents respectively. This ever so slightly rounds out async support within langchain, with an initial implementation of this functionality being implemented for openai.
Implements https://github.com/hwchase17/langchain/issues/6109
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PR_kwDOIPDwls5TlkGO
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Detailed using the Twilio tool to send messages with 3rd party apps incl. WhatsApp
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CONTRIBUTOR
| null |
Everything needed to support sending messages over WhatsApp Business Platform (GA), Facebook Messenger (Public Beta) and Google Business Messages (Private Beta) was present. Just added some details on leveraging it.
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PR_kwDOIPDwls5Tlfm7
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Using batches for update document with a new function in ChromaDB
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[] | null | 9 | 2023-06-21T19:44:30 | 2023-09-13T18:39:57 | 2023-09-13T18:39:56 |
CONTRIBUTOR
| null |
https://github.com/hwchase17/langchain/blob/2a4b32dee24c22159805f643b87eece107224951/langchain/vectorstores/chroma.py#L355-L375
Currently, the defined update_document function only takes a single document and its ID for updating. However, Chroma can update multiple documents by taking a list of IDs and documents for batch updates. If we update 'update_document' function both document_id and document can be `Union[str, List[str]]` but we need to do type check. Because embed_documents and update functions takes List for text and document_ids variables. I believe that, writing a new function is the best option.
I update the Chroma vectorstore with refreshed information from my website every 20 minutes. Updating the update_document function to perform simultaneous updates for each changed piece of information would significantly reduce the update time in such use cases.
For my case I update a total of 8810 chunks. Updating these 8810 individual chunks using the current function takes a total of 8.5 minutes. However, if we process the inputs in batches and update them collectively, all 8810 separate chunks can be updated in just 1 minute. This significantly reduces the time it takes for users of actively used chatbots to access up-to-date information.
I can add an integration test and an example for the documentation for the new update_document_batch function.
@hwchase17
[berkedilekoglu](https://twitter.com/berkedilekoglu)
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PR_kwDOIPDwls5TlcmI
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Page per class-style api reference
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[] | null | 1 | 2023-06-21T19:32:55 | 2023-06-30T16:23:34 | 2023-06-30T16:23:33 |
CONTRIBUTOR
| null |
can make it prettier, but what do we think of overall structure?
https://api.python.langchain.com/en/dev2049-page_per_class/api_ref.html
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PR_kwDOIPDwls5TlRzM
| 6,559 |
Add tags in agent initialization
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[] | null | 1 | 2023-06-21T19:11:25 | 2023-06-23T05:35:01 | 2023-06-23T05:35:00 |
CONTRIBUTOR
| null |
Add better docstrings for agent executor as well
Inspo: https://github.com/hwchase17/langchainjs/pull/1722

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PR_kwDOIPDwls5TlLYH
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docs: New experimental UI for Mendable Search
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[] | null | 1 | 2023-06-21T19:01:13 | 2023-07-03T19:52:14 | 2023-07-03T19:52:14 |
CONTRIBUTOR
| null |
This PR introduces a new Mendable UI tailored to a better search experience.
We're more closely integrating our traditional search with our AI generation.
With this change, you won't have to tab back and forth between the mendable bot and the keyword search. Both types of search are handled in the same bar. This should make the docs easier to navigate. while still letting users get code generations or AI-summarized answers if they so wish. Also, it should reduce the cost.
Would love to hear your feedback :)
Cc: @dev2049 @hwchase17
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| 1,768,102,983 |
PR_kwDOIPDwls5Tkv8B
| 6,556 |
docs: `ecosystem` change
|
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[] | null | 7 | 2023-06-21T17:38:59 | 2023-07-26T17:13:29 | 2023-07-26T17:13:28 |
COLLABORATOR
| null |
move `Additional_resources` items into `Ecosystem` group
Rationale: ToC groups composed from <3 items are too small. These small groups spent precious ToC space and distract user attention.
Here I merge two small groups.
Maintainer responsibilities:
@dev2049
|
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PR_kwDOIPDwls5Tku7c
| 6,555 |
Fix whatsappchatloader - enable parsing new datetime format on WhatsApp chat
|
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[] | null | 2 | 2023-06-21T17:35:54 | 2023-06-22T02:11:50 | 2023-06-22T02:11:49 |
CONTRIBUTOR
| null |
- Description: observed new format on WhatsApp exported chat - example: `[2023/5/4, 16:17:13] ~ Carolina: 🥺`
- Dependencies: no additional dependencies required
- Tag maintainer: @rlancemartin, @eyurtsev
|
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PR_kwDOIPDwls5Tko2T
| 6,554 |
Wait for all futures
|
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[] | null | 1 | 2023-06-21T17:17:07 | 2023-06-22T01:20:18 | 2023-06-22T01:20:17 |
CONTRIBUTOR
| null |
- Expose method to wait for all futures
- Wait for submissions in the run_on_dataset functions to ensure runs are fully submitted before cleaning up
|
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PR_kwDOIPDwls5TkfSq
| 6,552 |
update pr tmpl
|
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[] | null | 1 | 2023-06-21T16:54:20 | 2023-06-21T17:03:53 | 2023-06-21T17:03:52 |
CONTRIBUTOR
| null | null |
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I_kwDOIPDwls5pYYsZ
| 6,551 |
Unable to utilize AzureCognitiveSearch retriever without error.
|
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[] | null | 0 | 2023-06-21T16:43:04 | 2023-06-27T17:14:06 | 2023-06-27T17:14:06 |
NONE
| null |
### System Info
Python 3
Langchain: 0.0.199
### Who can help?
@hwchase17
@agola11
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am attempting to create a chatbot as a customer service assistant for a hotel agency. This is so I can experiment with azure cognitive search's sample data.
However, I keep running into issues utilizing the retriever.
```
from langchain import OpenAI, PromptTemplate
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferWindowMemory
from sandbox.hotels_demo.hotels_retreiver import HotelRetriever
from sandbox.hotels_demo.intent_classification import get_customer_intent
template = """
Assistant is a large language model trained by OpenAI.
Assistant is to act as a customer service agent for a hotel agency.
Assistant will describe hotel and discuss pricing if user is attempting to book a hotel.
General questions will be answered with summarization.
{history}
Human: {human_input}
Assistant:"""
hotel_retriever = HotelRetriever()
while True:
user_input = input("You: ")
if user_input == "EXIT":
print("Exiting...")
break
customer_intent = get_customer_intent(user_input).strip()
if customer_intent == "book_hotel" or customer_intent == "new_hotel_question":
hotel_retriever.refresh_relevant_hotels(user_input)
chatgpt_chain = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0),
prompt=PromptTemplate(
input_variables=["history", "human_input"], template=template
),
retriever=hotel_retriever.vectordb.as_retriever(),
memory=ConversationBufferWindowMemory(k=2),
)
print(
f"AI: {chatgpt_chain.run(human_input=user_input)}"
)
```
```
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import AzureCognitiveSearchRetriever
from langchain.vectorstores import Chroma
class HotelRetriever:
def __init__(self):
self.vectordb = None
self.retriever = AzureCognitiveSearchRetriever(content_key="Description")
def refresh_relevant_hotels(self, prompt):
docs = self.retriever.get_relevant_documents(prompt)
for document in docs:
for key, value in document.metadata.items():
if not isinstance(value, (int, float, str)):
document.metadata[key] = str(value)
self.vectordb = Chroma.from_documents(
documents=docs,
embedding=OpenAIEmbeddings(),
persist_directory="hotels-store",
)
```
The error that I keep getting is this or some variation of this:
```
Traceback (most recent call last):
File "C:\Users\naste\PycharmProjects\altairgpt\sandbox\hotels_demo\app.py", line 30, in <module>
chatgpt_chain = RetrievalQA.from_chain_type(
File "C:\Users\naste\PycharmProjects\altairgpt\venv\lib\site-packages\langchain\chains\retrieval_qa\base.py", line 94, in from_chain_type
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
File "C:\Users\naste\PycharmProjects\altairgpt\venv\lib\site-packages\langchain\load\serializable.py", line 61, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for RetrievalQA
prompt
extra fields not permitted (type=value_error.extra)
```
### Expected behavior
My goal is to be able to create a gpt chatbot that can query Azure cognitive search data for it's responses in summarizing and providing information about booking hotels based off of Azure's sample data.
|
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PR_kwDOIPDwls5TkPPO
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Update README.md
|
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NONE
| null |
change to capital letter
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
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I_kwDOIPDwls5pYJQh
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GPU Usage with GPT4All Integration
|
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[] | null | 4 | 2023-06-21T16:04:07 | 2023-12-02T08:05:38 | null |
NONE
| null |
### Issue you'd like to raise.
Hi guys, I'm wanting to use the
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
and know if I can make it use the GPU instead of the CPU.
Specifically the GPT4All integration, I saw that it does not have any parameters that indicate the use of GPUs so I wanted to know if it is possible to use Langchain loading this model "ggml-gpt4all-l13b-snoozy.bin" with the activation of GPUs?
Outside of Langchain I was able to load the model in GPU!
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5TkJ3y
| 6,548 |
Added function _list_all_collections() to milvus.py
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[] | null | 1 | 2023-06-21T15:55:07 | 2023-08-25T17:36:26 | 2023-08-25T17:36:25 |
NONE
| null |
Added a function to list all the available collections in the connected Milvus database.
Can be useful when users want to get rid of old collections to make space for new collections.
Also, agents can autonomously pick which collection to search in based on the name of the collection without needing users to specify it.
Twitter handle: JacksonZheng_
#### Who can review?
@hwchase17 - project lead
VectorStores / Retrievers / Memory
- @dev2049
|
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I_kwDOIPDwls5pX-QL
| 6,547 |
Prebuild a dev container image to improve build time
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[] | null | 3 | 2023-06-21T15:40:23 | 2023-11-18T16:06:32 | 2023-11-18T16:06:31 |
CONTRIBUTOR
| null |
### Feature request
Thanks so much for merging the PR to update the dev container in this repo https://github.com/hwchase17/langchain/pull/6189!
While the dev container now builds and runs successfully, it can take some time to build. One recommendation is for the LangChain team to pre-build an image.
### Motivation
We recommend pre-building images with the tools you need rather than creating and building a container image each time you open your project in a dev container. Using pre-built images will result in a faster container startup, simpler configuration, and allows you to pin to a specific version of tools to improve supply-chain security and avoid potential breaks. You can automate pre-building your image by scheduling the build using a DevOps or continuous integration (CI) service like GitHub Actions.
There's further info in our docs: https://containers.dev/implementors/reference/#prebuilding.
### Your contribution
We're more than happy to answer any questions and would love to hear feedback if you're interested in hosting a pre-built image! On the dev container team side, we're also looking to even better document pre-building: https://github.com/devcontainers/spec/issues/261, which should help as a reference for scenarios like this too.
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I_kwDOIPDwls5pXt1e
| 6,546 |
Set doc_content_chars_max with ArxivLoader
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[] | null | 1 | 2023-06-21T15:07:33 | 2023-09-27T16:05:34 | 2023-09-27T16:05:33 |
NONE
| null |
### Issue you'd like to raise.
In order to read all the text of an arxiv article, we want to specify the number of characters that can be read by the ArxivLoader.
Is there a way to achieve this in the current code?
### Suggestion:
If not, we will create a PR that exposes doc_content_chars_max so that it can be specified on the ArxivLoader side.
Thank you!
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PR_kwDOIPDwls5TjsE3
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Update defaults for text splitters
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[] | null | 5 | 2023-06-21T14:47:36 | 2023-11-07T03:59:53 | 2023-11-07T03:59:53 |
COLLABORATOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
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<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
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#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
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PR_kwDOIPDwls5TjqpW
| 6,544 |
Relax string input mapper check
|
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[] | null | 1 | 2023-06-21T14:44:31 | 2023-06-21T15:01:43 | 2023-06-21T15:01:42 |
CONTRIBUTOR
| null |
for run evaluator. It could be that an evalutor doesn't need the output
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Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 2 | 2023-06-21T14:38:16 | 2023-10-06T16:07:09 | 2023-10-06T16:07:08 |
NONE
| null |
### Issue you'd like to raise.
I try to use this code:
`from langchain.agents import create_csv_agent
from langchain.llms import AzureOpenAI
agent = create_csv_agent(AzureOpenAI(temperature=0, deployment_name="text-davinci-003"), 'data.csv', sep='|', on_bad_lines='skip', verbose=True)
print(agent.run("how many rows are there?"))`
but when I execute them I obtain a ParserError:
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 679, saw 2
but if I try to open the same file with pandas.read_csv function I do not have any error.
Can you help me please?
Thank you
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5TjgHv
| 6,541 |
Remove unintended double negation in docstring
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closed
| false | null |
[] | null | 2 | 2023-06-21T14:23:12 | 2023-06-22T12:07:50 | 2023-06-21T17:01:29 |
CONTRIBUTOR
| null |
Small typo fix.
`ImportError: If importing vertexai SDK didn't not succeed.` -> `ImportError: If importing vertexai SDK did not succeed.`.
|
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| 1,767,722,835 |
PR_kwDOIPDwls5Tje7R
| 6,540 |
bump to ver 208
|
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[] | null | 1 | 2023-06-21T14:20:54 | 2023-06-21T14:32:37 | 2023-06-21T14:32:36 |
COLLABORATOR
| null | null |
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| 1,767,713,412 |
I_kwDOIPDwls5pXSqE
| 6,539 |
Issue: ImportError: cannot import name 'create_csv_agent' from 'langchain.agents'
|
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[] | null | 2 | 2023-06-21T14:17:45 | 2023-06-21T14:43:58 | 2023-06-21T14:43:58 |
NONE
| null |
### Issue you'd like to raise.
I try to use the create_csv_agent from langchain.agents but I receive the ImportError that it can't be imported.
Do you have a solution for this problem?
I have installed langchain with pip
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5TjXZx
| 6,538 |
Don't raise error if parent not found
|
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[] | null | 1 | 2023-06-21T14:07:59 | 2023-06-27T05:57:53 | 2023-06-27T05:57:52 |
CONTRIBUTOR
| null |
Done so that you can pass in a run from the low level api
|
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| 1,767,683,454 |
PR_kwDOIPDwls5TjWLX
| 6,537 |
feat: faiss filter from list
|
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[] | null | 2 | 2023-06-21T14:05:27 | 2023-06-21T17:49:02 | 2023-06-21T17:49:02 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
### Feature
Using FAISS on a retrievalQA task, I found myself wanting to allow in multiple sources. From what I understood, the filter feature takes in a dict of form {key: value} which then will check in the metadata for the exact value linked to that key.
I added some logic to be able to pass a list which will be checked against instead of an exact value. Passing an exact value will also work.
Here's an example of how I could then use it in my own project:
```
pdfs_to_filter_in = ["file_A", "file_B"]
filter_dict = {
"source": [f"source_pdfs/{pdf_name}.pdf" for pdf_name in pdfs_to_filter_in]
}
retriever = db.as_retriever()
retriever.search_kwargs = {"filter": filter_dict}
```
I added an integration test based on the other ones I found in `tests/integration_tests/vectorstores/test_faiss.py` under `test_faiss_with_metadatas_and_list_filter()`.
It doesn't feel like this is worthy of its own notebook or doc, but I'm open to suggestions if needed.
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
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- @agola11
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-->
VectorStores related: @dev2049
|
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PR_kwDOIPDwls5TjWAY
| 6,536 |
Minor Grammar Fixes in Docs and Comments
|
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CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (issue)
Just some grammar fixes: I found "retriver" instead of "retriever" in several comments across the documentation and in the comments. I fixed it.
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
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-->
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17, @agola11, @eyurtsev, @dev2049
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
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- @eyurtsev
Models
- @hwchase17
- @agola11
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- @hwchase17
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I_kwDOIPDwls5pXLD6
| 6,535 |
S3 Directory Loader reads prefix directory as file_path
|
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[] | null | 5 | 2023-06-21T14:04:53 | 2024-01-06T10:33:02 | 2023-09-26T12:46:20 |
NONE
| null |
### System Info
Langchain version: 0.0.190
boto3: 1.26.156
python: 3.11.4
Linux OS
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code:
```
from langchain.document_loaders import S3DirectoryLoader
loader = S3DirectoryLoader("my-bucket", prefix="folder contains document files")
print(loader.load())
```
Error msg:

The code to fix is within the for statement of line 29 in s3_directory.py:
```
docs = []
for obj in bucket.objects.filter(Prefix=self.prefix):
loader = S3FileLoader(self.bucket, obj.key)
docs.extend(loader.load())
return docs
```
It needs to catch out the prefix path that is the first obj.key in the loop, which is not a file (obj.key) that the S3FileLoader (in s3_file.py) can download.
A solution could be to bypass any directory/prefix paths and collect only files.
```
docs = []
for obj in bucket.objects.filter(Prefix=self.prefix):
if obj.key.endswith("/"): # bypass the prefix directory
continue
else:
loader = S3FileLoader(self.bucket, obj.key)
docs.extend(loader.load())
return docs
```
### Expected behavior
I expect the obj.key for S3FileLoader to point to an obj.key that is a file_path e.g. prefix/file_name.docx that will create a temporary file e.g., /tmp/tmp0rlkir33/prefix/file_name.docx and download will be successful into the loader object.
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I_kwDOIPDwls5pW40A
| 6,534 |
Issue: in "Tool()" seperate the chat models input to "func"
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[] | null | 4 | 2023-06-21T13:28:23 | 2023-09-22T17:22:16 | 2023-09-22T17:15:36 |
NONE
| null |
### Issue you'd like to raise.
`realistic_vision_tool = Tool(
name="realistic_vision_V1.4 image generating",
func=realistic_vision_v1_4, #would like to pass some params here
description="""Use when you want to generate an image of something with realistic_vision model. Input like "a dog standing on a rock" is decent for this tool so input not-so-detailed prompts to this tool. If an image is generated, tool will return "Successfully generated image.\". Say something like "Generated. Hope it helps.\" if you use this tool. Always input english prompts for the input, even if the user is not speaking english. Enter the inputs in english to this tool.""",
)`
And i tried to give the chat model params, but it behaived badly
### Suggestion:
maybe you can add a param to isolate some params to the "func". Maybe i dont know the solution or im so dumb.
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| 1,767,575,817 |
I_kwDOIPDwls5pWxEJ
| 6,533 |
Keep retrying and writing output to local file in an unstructured way in AutoGPT implemented in LangChain
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[] | null | 1 | 2023-06-21T13:11:42 | 2023-09-27T16:05:39 | 2023-09-27T16:05:39 |
NONE
| null |
### System Info
AutoGPT on LangChain implementation has shown bugs in internal steps and file output processing phase. In particular, I noticed occasional inconsistencies within the internal processes of the AutoGPT implementation. These slight hitches, though infrequent, interrupt the seamless flow of operations. Also, in the file output processing phase, the actual data output seems to diverge from the expected format, hinting at a potential misalignment during the conversion or translation stages.
<img width="1003" alt="AutoGPT LangChain Issues Screenshot 1" src="https://github.com/hwchase17/langchain/assets/63427721/d5bc1ecf-35fb-4636-828c-52f8782b8f3d">
<img width="1272" alt="AutoGPT LangChain Issues Screenshot 2" src="https://github.com/hwchase17/langchain/assets/63427721/4d037c55-b572-4c6d-b479-374927c2ec45">
<img width="1552" alt="AutoGPT LangChain Issues Screenshot 3" src="https://github.com/hwchase17/langchain/assets/63427721/fbb82a50-6600-49f4-8a8d-dd075a209893">
### Who can help?
@TransformerJialinWang @alon
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
import faiss
embedding_size = 1536 #openai embeddings has 1536 dimensions
index = faiss.IndexFlatL2(embedding_size) #Index that stores the full vectors and performs exhaustive search
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
agent = AutoGPT.from_llm_and_tools(
ai_name="Tom",
ai_role="Assistant",
tools=tools,
llm=ChatOpenAI(temperature=0),
memory=vectorstore.as_retriever()
)
# Set verbose to be true
agent.chain.verbose = True
agent.run(["Recommend 5 best books to read in Python"])
### Expected behavior
The primary goal is to solve the infinite loop in AutoGPT. The minor one is to provide structured output int local file.
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