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[
"hwchase17",
"langchain"
]
| I keep getting this error every time I try to ask my data a question using the code from "[chat_vector_db.ipynb](https://github.com/hwchase17/langchain/blob/f356cca1f278ac73f8e59f49da39854e1e47a205/docs/modules/chat/examples/chat_vector_db.ipynb)" notebook, so how can I fix this and is this from my stored data and if it is how can I encode it, also I'm using the UnstructuredFileLoader for a text file:
UnicodeEncodeError Traceback (most recent call last)
Cell In[97], line 3
1 chat_history = []
2 query = "who are you?"
----> 3 result = qa({"question": query, "chat_history": chat_history})
File [c:\Users\yousef\AppData\Local\Programs\Python\Python39\lib\site-packages\langchain\chains\base.py:116](file:///C:/Users/yousef/AppData/Local/Programs/Python/Python39/lib/site-packages/langchain/chains/base.py:116), in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File [c:\Users\yousef\AppData\Local\Programs\Python\Python39\lib\site-packages\langchain\chains\base.py:113](file:///C:/Users/yousef/AppData/Local/Programs/Python/Python39/lib/site-packages/langchain/chains/base.py:113), in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
...
-> 1258 values[i] = one_value.encode('latin-1')
1259 elif isinstance(one_value, int):
1260 values[i] = str(one_value).encode('ascii')
UnicodeEncodeError: 'latin-1' codec can't encode character '\u201c' in position 7: ordinal not in range(256) | UnicodeEncodeError Using Chat Vector and My Own Data | https://api.github.com/repos/langchain-ai/langchain/issues/2121/comments | 8 | 2023-03-28T23:02:07Z | 2023-09-26T16:12:59Z | https://github.com/langchain-ai/langchain/issues/2121 | 1,644,771,527 | 2,121 |
[
"hwchase17",
"langchain"
]
| OpenAI python client supports passing additional headers when invoking the following functions
`openai.ChatCompletion.create`
or
`openai.Completion.create`
For example: I can pass the headers as shown in the sample code below.
```
completion = openai.Completion.create(deployment_id=deployment_id,
prompt=payload_dict['prompt'], stop=payload_dict['stop'], temperature=payload_dict['temperature'], headers=headers, max_tokens=1000)
```
Langchain does not surface the capability to pass the headers when we need to include custom HTTPS headers from the client. It would very useful to include this capability especially when you have custom authentication scheme where the model is exposed as an endpoint. | Unable to pass headers to Completion, ChatCompletion, Embedding endpoints | https://api.github.com/repos/langchain-ai/langchain/issues/2120/comments | 4 | 2023-03-28T22:29:45Z | 2023-09-27T16:11:24Z | https://github.com/langchain-ai/langchain/issues/2120 | 1,644,740,925 | 2,120 |
[
"hwchase17",
"langchain"
]
| I'm trying to save embeddings in Redis vectorstore and when I try to execute getting the following error. Any idea if this is a bug or if anything is wrong with my code? Any help is appreciated.
langchain version - both 0.0.123 and 0.0.124
Python 3.8.2
File "/Users/aruna/PycharmProjects/redis-test/database.py", line 16, in init_redis_database
rds = Redis.from_documents(docs, embeddings, redis_url="redis://localhost:6379", index_name='link')
File "/Users/aruna/PycharmProjects/redis-test/venv/lib/python3.8/site-packages/langchain/vectorstores/base.py", line 116, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "/Users/aruna/PycharmProjects/redis-test/venv/lib/python3.8/site-packages/langchain/vectorstores/redis.py", line 224, in from_texts
if not _check_redis_module_exist(client, "search"):
File "/Users/aruna/PycharmProjects/redis-test/venv/lib/python3.8/site-packages/langchain/vectorstores/redis.py", line 23, in _check_redis_module_exist
return module in [m["name"] for m in client.info().get("modules", {"name": ""})]
File "/Users/aruna/PycharmProjects/redis-test/venv/lib/python3.8/site-packages/langchain/vectorstores/redis.py", line 23, in <listcomp>
return module in [m["name"] for m in client.info().get("modules", {"name": ""})]
**TypeError: string indices must be integers**
Sample code as follows.
REDIS_URL = 'redis://localhost:6379'
def init_redis_database(docs):
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
rds = Redis.from_documents(docs, embeddings, redis_url=REDIS_URL, index_name='link')
| Unable save embeddings in Redis vectorstore | https://api.github.com/repos/langchain-ai/langchain/issues/2113/comments | 16 | 2023-03-28T20:22:00Z | 2024-03-15T05:05:26Z | https://github.com/langchain-ai/langchain/issues/2113 | 1,644,606,036 | 2,113 |
[
"hwchase17",
"langchain"
]
| while using llama_index GPTSimpleVectorIndex
I am reading a pdf file using SimpleDirectoryReader.
I am unable to create index for the file and it is generating the below error:
**INFO:openai:error_code=None error_message='Too many inputs for model None. The max number of inputs is 1. We hope to increase the number of inputs per request soon. Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions.' error_param=None error_type=invalid_request_error message='OpenAI API error received' stream_error=False**
The code works for some files and fails for others with the above error.
Please suggest what does it mean by **Too many inputs for model** that comes as a error only for some files. | openai:error_code=None error_message='Too many inputs for model None. The max number of inputs is 1. | https://api.github.com/repos/langchain-ai/langchain/issues/2096/comments | 9 | 2023-03-28T14:48:14Z | 2023-09-28T16:10:17Z | https://github.com/langchain-ai/langchain/issues/2096 | 1,644,118,649 | 2,096 |
[
"hwchase17",
"langchain"
]
| I need to supply a 'where' value to filter on metadata to Chromadb `similarity_search_with_score` function. I can't find a straightforward way to do it. Is there some way to do it when I kickoff my chain? Any hints, hacks, plans to support? | How to pass filter down to Chroma db when using ConversationalRetrievalChain | https://api.github.com/repos/langchain-ai/langchain/issues/2095/comments | 23 | 2023-03-28T14:47:44Z | 2024-01-01T09:39:52Z | https://github.com/langchain-ai/langchain/issues/2095 | 1,644,117,753 | 2,095 |
[
"hwchase17",
"langchain"
]
| Hi,
There seems to be a bug when trying to load a serialize faiss index when using Azure through OpenAIEmbeddings.
I get the following error:
```{python}
AttributeError: Can't get attribute 'Document' on <module 'langchain.schema' from '/langchain/schema.py'>
```
| Can't load faiss index when using Azure embeddings | https://api.github.com/repos/langchain-ai/langchain/issues/2094/comments | 2 | 2023-03-28T14:23:38Z | 2023-09-10T16:40:07Z | https://github.com/langchain-ai/langchain/issues/2094 | 1,644,073,204 | 2,094 |
[
"hwchase17",
"langchain"
]
| As the title says, there's a full implementation of ConversationalChatAgent which however is not in the __init__ file of agents, thus could not import it by
`from langchain.agents import ConversationalChatAgent`
I'm going to fix this right now. | ConversationalChatAgent is not in agent.__init__.py | https://api.github.com/repos/langchain-ai/langchain/issues/2093/comments | 0 | 2023-03-28T13:43:34Z | 2023-03-28T15:14:24Z | https://github.com/langchain-ai/langchain/issues/2093 | 1,643,984,599 | 2,093 |
[
"hwchase17",
"langchain"
]
| Hello i'm trying to provide a diifferent API key for each profile, but it seems that the last profile API key i set is the one used by all the profiles, is there a way to force use each profile its dedicated key? | Multiple openai keys | https://api.github.com/repos/langchain-ai/langchain/issues/2091/comments | 9 | 2023-03-28T12:34:07Z | 2023-10-28T16:07:45Z | https://github.com/langchain-ai/langchain/issues/2091 | 1,643,856,338 | 2,091 |
[
"hwchase17",
"langchain"
]
| I am using the latest version and i get this error message while only trying to:
from langchain.chains.chat_index.prompts import CONDENSE_QUESTION_PROMPT
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[5], line 3
1 from langchain.chains import LLMChain
2 from langchain.chains.question_answering import load_qa_chain
----> 3 from langchain.chains.chat_index.prompts import CONDENSE_QUESTION_PROMPT
ModuleNotFoundError: No module named 'langchain.chains.chat_index' | On the latest version 0.0.123 i get No module named 'langchain.chains.chat_index' | https://api.github.com/repos/langchain-ai/langchain/issues/2090/comments | 13 | 2023-03-28T09:35:57Z | 2023-12-08T16:08:40Z | https://github.com/langchain-ai/langchain/issues/2090 | 1,643,564,375 | 2,090 |
[
"hwchase17",
"langchain"
]
| No module 'datasets' found in langchain.evaluation | ModuleNotFoundError: No module named 'datasets' | https://api.github.com/repos/langchain-ai/langchain/issues/2088/comments | 2 | 2023-03-28T08:22:52Z | 2023-06-16T15:37:47Z | https://github.com/langchain-ai/langchain/issues/2088 | 1,643,446,461 | 2,088 |
[
"hwchase17",
"langchain"
]
| I'm testing on windows where default encoding is cp1252 and not utf-8 and I still have encoding problems that cannot overcome. | Add optional encoding parameter on each Loader | https://api.github.com/repos/langchain-ai/langchain/issues/2087/comments | 2 | 2023-03-28T08:10:59Z | 2023-09-18T16:22:19Z | https://github.com/langchain-ai/langchain/issues/2087 | 1,643,429,036 | 2,087 |
[
"hwchase17",
"langchain"
]
| Was trying to follow the document to run summarization, here's my code:
```python
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.prompts import PromptTemplate
llm = OpenAI(temperature=0, engine='text-davinci-003')
text_splitter = CharacterTextSplitter()
with open("./state_of_the_union.txt") as f:
state_of_the_union = f.read()
texts = text_splitter.split_text(state_of_the_union)
from langchain.docstore.document import Document
docs = [Document(page_content=t) for t in texts[:3]]
from langchain.chains.summarize import load_summarize_chain
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)
```
Got errors like below:
<img width="1273" alt="image" src="https://user-images.githubusercontent.com/30015018/228138989-1808a102-4246-412b-a86a-388d60579543.png">
Any ideas how to fix this? langchain version is 0.0.123 | load_summarize_chain cannot run | https://api.github.com/repos/langchain-ai/langchain/issues/2081/comments | 4 | 2023-03-28T05:41:15Z | 2023-04-14T11:41:02Z | https://github.com/langchain-ai/langchain/issues/2081 | 1,643,238,154 | 2,081 |
[
"hwchase17",
"langchain"
]
| Trying to run a simple script:
```
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.9)
text = "What would be a good company name for a company that makes colorful socks?"
print(llm(text))
```
I'm running into this error:
`ModuleNotFoundError: No module named 'langchain.llms'; 'langchain' is not a package`
I've got a virtualenv installed with langchains downloaded.
```โ pip show langchain
Name: langchain
Version: 0.0.39
Summary: Building applications with LLMs through composability
Home-page: https://www.github.com/hwchase17/langchain
Author:
Author-email:
License: MIT
Location: /Users/jkaye/dev/langchain-tutorial/venv/lib/python3.11/site-packages
Requires: numpy, pydantic, PyYAML, requests, SQLAlchemy
```
```
โ python --version
Python 3.11.0
```
I'm using zsh so I ran `pip install 'langchain[all]'` | 'langchain' is not a package | https://api.github.com/repos/langchain-ai/langchain/issues/2079/comments | 29 | 2023-03-28T04:42:16Z | 2024-04-26T03:52:15Z | https://github.com/langchain-ai/langchain/issues/2079 | 1,643,187,784 | 2,079 |
[
"hwchase17",
"langchain"
]
| from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='docs/whats-new-latest.csv', csv_args={
'fieldnames': ['Line of Business',
'Short Description'
]
})
data = loader.load()
print(data)
/.pyenv/versions/3.9.2/envs/s4-hana-chatbot/lib/python3.9/site-packages/langchain/document_loaders/csv_loader.py", line 53, in <genexpr>
content = "\n".join(f"{k.strip()}: {v.strip()}" for k, v in row.items())
AttributeError: 'NoneType' object has no attribute 'strip'
Can anyone assist how to solve this? | 'NoneType' object has no attribute 'strip' | https://api.github.com/repos/langchain-ai/langchain/issues/2074/comments | 9 | 2023-03-28T03:06:17Z | 2023-11-17T18:19:35Z | https://github.com/langchain-ai/langchain/issues/2074 | 1,643,123,069 | 2,074 |
[
"hwchase17",
"langchain"
]
| E.g. running
```python
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True, return_intermediate_steps=True)
agent.run("What is 2 raised to the 0.43 power?")
```
gives the error
```
203 """Run the chain as text in, text out or multiple variables, text out."""
204 if len(self.output_keys) != 1:
--> 205 raise ValueError(
206 f"`run` not supported when there is not exactly "
207 f"one output key. Got {self.output_keys}."
208 )
210 if args and not kwargs:
211 if len(args) != 1:
ValueError: `run` not supported when there is not exactly one output key. Got ['output', 'intermediate_steps'].
```
Is this supposed to be called differently or how else can the intermediate outputs ("Observations") be retrieved? | `initialize_agent` does not work with `return_intermediate_steps=True` | https://api.github.com/repos/langchain-ai/langchain/issues/2068/comments | 18 | 2023-03-28T00:50:15Z | 2024-02-23T16:09:08Z | https://github.com/langchain-ai/langchain/issues/2068 | 1,643,029,722 | 2,068 |
[
"hwchase17",
"langchain"
]
| This definition:
"purchase_order": """CREATE TABLE purchase_order (
id SERIAL NOT NULL,
name VARCHAR NOT NULL,
origin VARCHAR,
partner_ref VARCHAR,
date_order TIMESTAMP NOT NULL,
date_approve DATE,
partner_id INTEGER NOT NULL,
state VARCHAR,
notes TEXT,
amount_untaxed NUMERIC,
amount_tax NUMERIC,
amount_total NUMERIC,
user_id INTEGER,
company_id INTEGER NOT NULL,
create_uid INTEGER,
create_date TIMESTAMP,
write_uid INTEGER,
write_date TIMESTAMP,
CONSTRAINT PRIMARY KEY (id),
CONSTRAINT FOREIGN KEY(company_id) REFERENCES res_company (id) ,
CONSTRAINT FOREIGN KEY(partner_id) REFERENCES res_partner (id)
can be reduced to:
"purchase_order": """TABLE purchase_order (
id SERIAL NN PK,
name VC NN,
origin VC,
partner_ref VC,
date_order TIMESTAMP,
date_approve DATE,
partner_id INT NN,
state VC,
notes TX,
amount_untaxed NUM,
amount_tax NUM,
amount_total NUM,
user_id INT,
company_id INT NN,
create_uid INT,
create_date TIMESTAMP,
write_uid INT,
write_date TIMESTAMP,
FK(company_id) REF res_company (id) ,
FK(partner_id) REF res_partner (id)
and save a lot of space.
If need we can add some instruction for the aliases such as: VC=VARCHAR, etc... | DB Tools - Table definitions cam be shortened with some asumptions in order to keep used token low. | https://api.github.com/repos/langchain-ai/langchain/issues/2067/comments | 1 | 2023-03-28T00:36:30Z | 2023-08-25T16:14:11Z | https://github.com/langchain-ai/langchain/issues/2067 | 1,643,021,881 | 2,067 |
[
"hwchase17",
"langchain"
]
| I tried to map my db that have a lot of views that can leverage the whoole work. But the initial check does not allow this, also by having custom_table_info that describe the view structure. | DB Tools - Allow to reference also views thru custom_table_info | https://api.github.com/repos/langchain-ai/langchain/issues/2066/comments | 2 | 2023-03-28T00:31:19Z | 2023-09-10T16:40:17Z | https://github.com/langchain-ai/langchain/issues/2066 | 1,643,018,821 | 2,066 |
[
"hwchase17",
"langchain"
]
| ### Alpaca-LoRA
[Alpaca-LoRA](https://github.com/tloen/alpaca-lora) and Stanford Alpaca are NLP models that use the GPT architecture, but there are some critical differences between them. Here are three:
- **Training data**: Stanford Alpaca was trained on a larger dataset that includes a variety of sources, including webpages, books, and more. Alpaca-LoRA, on the other hand, was trained on a smaller dataset (but one that has been curated for quality) and uses low-rank adaptation (LoRA) to fine-tune the model for specific tasks.
- **Model size**: Stanford Alpaca is a larger model, with versions ranging from 774M to 1.5B parameters. Alpaca-LoRA, on the other hand, provides a smaller, 7B parameter model that is specifically optimized for low-cost devices such as the Raspberry Pi.
- **Pretrained models**: Both models offer pre-trained models that can be used out-of-the-box, but the available options are slightly different. Stanford Alpaca provides several models with different sizes and degrees of finetuning, while Alpaca-LoRA provides an Instruct model of similar quality to `text-davinci-003`.
### Similar To
https://github.com/hwchase17/langchain/issues/1777
### Resources
- [alpaca.cpp](https://github.com/antimatter15/alpaca.cpp), a native client for running Alpaca models on the CPU
- [Alpaca-LoRA-Serve](https://github.com/deep-diver/Alpaca-LoRA-Serve), a ChatGPT-style interface for Alpaca models
- [AlpacaDataCleaned](https://github.com/gururise/AlpacaDataCleaned), a project to improve the quality of the Alpaca dataset
- Various adapter weights (download at own risk):
- 7B:
- <https://huggingface.co/tloen/alpaca-lora-7b>
- <https://huggingface.co/samwit/alpaca7B-lora>
- ๐ง๐ทย <https://huggingface.co/22h/cabrita-lora-v0-1>
- ๐จ๐ณย <https://huggingface.co/qychen/luotuo-lora-7b-0.1>
- ๐ฏ๐ตย <https://huggingface.co/kunishou/Japanese-Alapaca-LoRA-7b-v0>
- ๐ซ๐ทย <https://huggingface.co/bofenghuang/vigogne-lora-7b>
- ๐น๐ญย <https://huggingface.co/Thaweewat/thai-buffala-lora-7b-v0-1>
- ๐ฉ๐ชย <https://huggingface.co/thisserand/alpaca_lora_german>
- ๐ฎ๐นย <https://huggingface.co/teelinsan/camoscio-7b-llama>
- 13B:
- <https://huggingface.co/chansung/alpaca-lora-13b>
- <https://huggingface.co/mattreid/alpaca-lora-13b>
- <https://huggingface.co/samwit/alpaca13B-lora>
- ๐ฏ๐ตย <https://huggingface.co/kunishou/Japanese-Alapaca-LoRA-13b-v0>
- ๐ฐ๐ทย <https://huggingface.co/chansung/koalpaca-lora-13b>
- ๐จ๐ณย <https://huggingface.co/facat/alpaca-lora-cn-13b>
- 30B:
- <https://huggingface.co/baseten/alpaca-30b>
- <https://huggingface.co/chansung/alpaca-lora-30b>
- ๐ฏ๐ตย <https://huggingface.co/kunishou/Japanese-Alapaca-LoRA-30b-v0>
- [alpaca-native](https://huggingface.co/chavinlo/alpaca-native), a replication using the original Alpaca code | Alpaca-LoRA | https://api.github.com/repos/langchain-ai/langchain/issues/2063/comments | 4 | 2023-03-27T23:41:12Z | 2023-09-29T16:09:41Z | https://github.com/langchain-ai/langchain/issues/2063 | 1,642,978,571 | 2,063 |
[
"hwchase17",
"langchain"
]
| 
Moreover, I cannot use multiple prompts using `ChatPromptTemplate.from_messages` | map_rerank custom prompts don't work with SystemMessagePromptTemplate | https://api.github.com/repos/langchain-ai/langchain/issues/2053/comments | 1 | 2023-03-27T19:42:33Z | 2023-08-21T16:07:44Z | https://github.com/langchain-ai/langchain/issues/2053 | 1,642,705,217 | 2,053 |
[
"hwchase17",
"langchain"
]
| I was reading this blog post: https://blog.langchain.dev/retrieval/
There is this link:
> Other types of indexes, [like graphs](https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/graph_qa.html), have piqued user's interests
Currently goes to a RTD 404 page
If I google for "langchain graph qa" the top result also goes to a 404
I can view a cached copy here https://webcache.googleusercontent.com/search?q=cache:obQTh41ZBRoJ:https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/graph_qa.html&cd=1&hl=en&ct=clnk&gl=uk | Graph QA docs links are broken currently | https://api.github.com/repos/langchain-ai/langchain/issues/2049/comments | 3 | 2023-03-27T16:37:18Z | 2023-08-14T20:20:49Z | https://github.com/langchain-ai/langchain/issues/2049 | 1,642,438,709 | 2,049 |
[
"hwchase17",
"langchain"
]
| I understand that brace brackets "{ }" are used for the text replacement. I was wondering if we are able to use the brace brackets in our prompts where we want them to act as just text and do not want them to replace text, for example with c++ classes.
```c++
#include <string>
class BankAccount {
public:
// Constructor to initialize the account
BankAccount(const std::string& account_holder_name, const std::string& account_number, double initial_balance);
// Method to deposit money into the account
void deposit(double amount);
// Method to withdraw money from the account
bool withdraw(double amount);
// Method to display account details
void display_account_details() const;
private:
std::string account_holder_name_;
std::string account_number_;
double balance_;
};
``` | How to use brace brackets | https://api.github.com/repos/langchain-ai/langchain/issues/2048/comments | 2 | 2023-03-27T16:21:43Z | 2023-10-24T07:51:46Z | https://github.com/langchain-ai/langchain/issues/2048 | 1,642,415,345 | 2,048 |
[
"hwchase17",
"langchain"
]
| Is there a way to redirect the verbose logs away from stdout? I'm working on a frontend app, and I'd like to collect the logs to display in the frontend, but couldn't find in the docs a way to capture them into a variable or similar. | Redirect vebose logs | https://api.github.com/repos/langchain-ai/langchain/issues/2045/comments | 4 | 2023-03-27T15:27:23Z | 2023-07-18T03:25:03Z | https://github.com/langchain-ai/langchain/issues/2045 | 1,642,322,662 | 2,045 |
[
"hwchase17",
"langchain"
]
| Many OpenAI plugin implementations are starting to use yaml for the api doc.
```python
class AIPluginTool(BaseTool):
api_spec: str
def from_plugin_url(cls, url: str) -> "AIPluginTool":
response = requests.get(url)
if response.headers["Content-Type"] == "application/json":
response_data = response.json()
elif response.headers["Content-Type"] == "application/x-yaml":
response_data = yaml.safe_load(response.text)
else:
raise ValueError("Unsupported content type")
...
```
| Support also yaml files for the AIPluginTool | https://api.github.com/repos/langchain-ai/langchain/issues/2042/comments | 1 | 2023-03-27T15:01:41Z | 2023-03-28T07:54:03Z | https://github.com/langchain-ai/langchain/issues/2042 | 1,642,275,010 | 2,042 |
[
"hwchase17",
"langchain"
]
| Hey team,
As you can in the `get_docs` method there is no option to provide kwargs arguments, even the top_k is not updating. Do I have to make any changes on how to pass this info, or will it get fixed. | kwargs for ConversationalRetrievalChain | https://api.github.com/repos/langchain-ai/langchain/issues/2038/comments | 1 | 2023-03-27T13:41:57Z | 2023-03-28T06:46:16Z | https://github.com/langchain-ai/langchain/issues/2038 | 1,642,090,994 | 2,038 |
[
"hwchase17",
"langchain"
]
| Since `llms.OpenAIChat` is deprecated and `chat_models.ChatOpenAI` is sugguested in latest releases, I think it is necessary to add `prefix_messages` to `ChatOpenAI`, just like it in `OpenAIChat` so that we can provide messages to this chat model, for example, provide a system message helps set the behavior of the assistant.
```
m = ChatOpenAI(prefix_messages=[SystemMessage(content='you are a helpful assistant')])
``` | Add prefix_messages property to ChatOpenAI | https://api.github.com/repos/langchain-ai/langchain/issues/2036/comments | 4 | 2023-03-27T13:20:40Z | 2023-03-30T06:43:02Z | https://github.com/langchain-ai/langchain/issues/2036 | 1,642,034,579 | 2,036 |
[
"hwchase17",
"langchain"
]
| Since `llms.OpenAIChat` is deprecated and `chat_models.ChatOpenAI` is sugguested in latest releases, I think it is necessary to add `prefix_messages` to `ChatOpenAI`, just like it in `OpenAIChat` so that we can provide messages to this chat model, for example, provide a system message helps set the behavior of the assistant. | Add prefix_messages property to ChatOpenAI | https://api.github.com/repos/langchain-ai/langchain/issues/2035/comments | 0 | 2023-03-27T13:17:19Z | 2023-03-27T13:24:21Z | https://github.com/langchain-ai/langchain/issues/2035 | 1,642,026,037 | 2,035 |
[
"hwchase17",
"langchain"
]
| Since `llms.OpenAIChat` is deprecated and `chat_models.ChatOpenAI` is sugguested in latest releases, I think it is necessary to add `prefix_messages` to `ChatOpenAI`, just like it in `OpenAIChat` so that we can provide messages to this chat model, for example, provide a system message helps set the behavior of the assistant. | Add prefix_messages property to ChatOpenAI | https://api.github.com/repos/langchain-ai/langchain/issues/2034/comments | 0 | 2023-03-27T13:15:06Z | 2023-03-27T13:24:10Z | https://github.com/langchain-ai/langchain/issues/2034 | 1,642,025,652 | 2,034 |
[
"hwchase17",
"langchain"
]
| Since `llms.OpenAIChat` is deprecated and `chat_models.ChatOpenAI` is sugguested in latest releases, I think it is necessary to add `prefix_messages` to `ChatOpenAI`, just like it in `OpenAIChat` so that we can provide messages to this chat model, for example, provide a system message helps set the behavior of the assistant. | Add prefix_messages property to ChatOpenAI | https://api.github.com/repos/langchain-ai/langchain/issues/2033/comments | 0 | 2023-03-27T13:14:20Z | 2023-03-27T13:24:01Z | https://github.com/langchain-ai/langchain/issues/2033 | 1,642,025,513 | 2,033 |
[
"hwchase17",
"langchain"
]
| Since `llms.OpenAIChat` is deprecated and `chat_models.ChatOpenAI` is sugguested in latest releases, I think it is necessary to add `prefix_messages` to `ChatOpenAI`, just like it in `OpenAIChat` so that we can provide messages to this chat model, for example, provide a system message helps set the behavior of the assistant. | Add prefix_messages property to ChatOpenAI | https://api.github.com/repos/langchain-ai/langchain/issues/2032/comments | 0 | 2023-03-27T13:14:06Z | 2023-03-27T13:23:42Z | https://github.com/langchain-ai/langchain/issues/2032 | 1,642,025,480 | 2,032 |
[
"hwchase17",
"langchain"
]
| Since `langchain.llms.OpenAIChat` is deprecated and `langchain.chat_models.ChatOpenAI` is suggested in the latest versions,
but `ChatOpenAI` misses property `prefix_messages`, for example we can use this property to provide [a system message](https://platform.openai.com/docs/guides/chat/introduction) that helps set the behavior of the assistant.
| Add missing property prefix_messages for ChatOpenAI | https://api.github.com/repos/langchain-ai/langchain/issues/2031/comments | 0 | 2023-03-27T13:07:57Z | 2023-03-27T13:22:49Z | https://github.com/langchain-ai/langchain/issues/2031 | 1,642,024,275 | 2,031 |
[
"hwchase17",
"langchain"
]
| I just installed langchain and when I try to import WolframAlphaAPIWrapper using "from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper", this error message is returned. I checked site-packages to see if the utilities is there, these are the list of files and no utilities:
VERSION chains formatting.py prompts sql_database.py
__init__.py docstore input.py py.typed text_splitter.py
__pycache__ embeddings llms python.py utils.py
agents example_generator.py model_laboratory.py serpapi.py vectorstores
| ModuleNotFoundError: No module named 'langchain.utilities' | https://api.github.com/repos/langchain-ai/langchain/issues/2029/comments | 7 | 2023-03-27T11:19:48Z | 2023-03-29T02:37:31Z | https://github.com/langchain-ai/langchain/issues/2029 | 1,641,927,118 | 2,029 |
[
"hwchase17",
"langchain"
]
| It looks like the result returned from the `predict` call to generate the query is returned surrounded by double quotes, so when passed to the db it's taken as a malformed query.
Example (using a postgres db):
```
llm = ChatOpenAI(temperature=0, model_name="gpt-4") # type: ignore
db = SQLDatabase.from_uri("<URI>")
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
print(db_chain.run("give me any row"))
```
Result:
```
Traceback (most recent call last):
File "main.py", line 26, in <module>
print(db_chain.run("give me any row"))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 213, in run
return self(args[0])[self.output_keys[0]]
^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 116, in __call__
raise e
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 113, in __call__
outputs = self._call(inputs)
^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/sql_database/base.py", line 88, in _call
result = self.database.run(sql_cmd)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/sql_database.py", line 176, in run
cursor = connection.execute(text(command))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1380, in execute
return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/sql/elements.py", line 334, in _execute_on_connection
return connection._execute_clauseelement(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1572, in _execute_clauseelement
ret = self._execute_context(
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1943, in _execute_context
self._handle_dbapi_exception(
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 2124, in _handle_dbapi_exception
util.raise_(
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/util/compat.py", line 211, in raise_
raise exception
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1900, in _execute_context
self.dialect.do_execute(
File "/opt/homebrew/lib/python3.11/site-packages/sqlalchemy/engine/default.py", line 736, in do_execute
cursor.execute(statement, parameters)
sqlalchemy.exc.ProgrammingError: (psycopg2.errors.SyntaxError) syntax error at or near ""SELECT data FROM table LIMIT 5""
LINE 1: "SELECT data FROM table LIMIT 5"
``` | SQLDatabaseChain malformed queries | https://api.github.com/repos/langchain-ai/langchain/issues/2027/comments | 11 | 2023-03-27T10:15:28Z | 2024-01-24T10:46:33Z | https://github.com/langchain-ai/langchain/issues/2027 | 1,641,823,337 | 2,027 |
[
"hwchase17",
"langchain"
]
| I am using `RecursiveCharacterTextSplitter` to split my documents for ingestion into a vector db. What is the intuition for selecting optimal chunk parameters?
It seems to me that chunk_size influences the size of documents being retrieved. Does that mean I should select the largest possible chunk_size to ensure maximum context retrieved?
What about chunk_overlap? This seems like a parameter that can be arbitrarily set i.e. select something that's not too big, not too small. Is that the right understanding? | Intuition for selecting optimal chunk_size and chunk_overlap for RecursiveCharacterTextSplitter | https://api.github.com/repos/langchain-ai/langchain/issues/2026/comments | 13 | 2023-03-27T09:43:31Z | 2024-04-04T16:06:31Z | https://github.com/langchain-ai/langchain/issues/2026 | 1,641,765,150 | 2,026 |
[
"hwchase17",
"langchain"
]
| openapi.json is so large for prompt token limit. and how to handle authorizations in the openai.json? | AIPluginTool submit long openapi.json in prompt exceeds the token limit | https://api.github.com/repos/langchain-ai/langchain/issues/2025/comments | 1 | 2023-03-27T08:16:49Z | 2023-08-21T16:07:49Z | https://github.com/langchain-ai/langchain/issues/2025 | 1,641,622,042 | 2,025 |
[
"hwchase17",
"langchain"
]
| II'm using some variable data in my prompt, and sending that prompt to ConversationChain and there I'm getting some validation error.
what changes we can make it to work as expected?
| ConversationChain Validation Error | https://api.github.com/repos/langchain-ai/langchain/issues/2024/comments | 6 | 2023-03-27T05:44:59Z | 2023-09-26T16:13:24Z | https://github.com/langchain-ai/langchain/issues/2024 | 1,641,417,241 | 2,024 |
[
"hwchase17",
"langchain"
]
| I am building an agent which answer user questions and I have plugged it with two tool, the regular llm and search tool like this
llm = OpenAI(temperature=0)
search = GoogleSerperAPIWrapper()
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events."
),
Tool(
name="LLM",
func=llm_chain.run,
description="useful for when you need to answer questions about anything in general."
)
]
mrkl = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
Now when I ask it a general question like "Who is Sachin", it still is trying to invoke the search action although the LLM can answer that question as it is not based on a recent event. How to make sure LLM answers any questions except questions on recent events to reduce the SERP usage ? | How to use Serp only for current events related searches and use GPT for regular info | https://api.github.com/repos/langchain-ai/langchain/issues/2023/comments | 9 | 2023-03-27T05:38:09Z | 2023-09-28T16:10:27Z | https://github.com/langchain-ai/langchain/issues/2023 | 1,641,412,258 | 2,023 |
[
"hwchase17",
"langchain"
]
| Trying to use `persist_directory` to have Chroma persist to disk:
```python
index = VectorstoreIndexCreator(vectorstore_kwargs={"persist_directory": "db"})
```
and it displays this warning message that implies it won't be persisted:
```
Using embedded DuckDB without persistence: data will be transient
```
However, it does create files to the `db` directory. The warning appears to originate from https://github.com/chroma-core/chroma/pull/204#issuecomment-1458243316. Do I have the setting correct? | Setting Chroma persist_directory says "Using embedded DuckDB without persistence" | https://api.github.com/repos/langchain-ai/langchain/issues/2022/comments | 18 | 2023-03-27T05:29:14Z | 2024-03-30T20:14:14Z | https://github.com/langchain-ai/langchain/issues/2022 | 1,641,406,148 | 2,022 |
[
"hwchase17",
"langchain"
]
| sqlalchemy in version 1.3.x, need to use select([]) way to query tables:
related stackoverflow question: https://stackoverflow.com/questions/62565993/sqlalchemy-exc-argumenterror-columns-argument-to-select-must-be-a-python-list
I will submit a PR to fix it | [bug]sqlalchemy 1.3.x compatibility issue | https://api.github.com/repos/langchain-ai/langchain/issues/2020/comments | 0 | 2023-03-27T05:22:30Z | 2023-03-28T06:45:52Z | https://github.com/langchain-ai/langchain/issues/2020 | 1,641,400,793 | 2,020 |
[
"hwchase17",
"langchain"
]
| If you want to create a Qdrant index and keep adding documents (i.e. adding new documents to existing qdrant), when your embedding is sentence transformers `qdrant_index.add_documents(docs)` gives error.
Because the `embedding_function` which is `model.encode()` is returning either `numpy` or `tensor` array and has to be converted to `list[float]`. Therefore, this line
`vectors=[self.embedding_function(text) for text in texts]`
needs to updated to:
`vectors=[self.embedding_function(text).tolist() for text in texts]`
To allow updating the collection.
https://github.com/hwchase17/langchain/blob/b83e8265102514d1722b2fb1aad29763c5cad62a/langchain/vectorstores/qdrant.py#L83
| Updating the Qdrant index with new documents raises error | https://api.github.com/repos/langchain-ai/langchain/issues/2016/comments | 5 | 2023-03-27T03:16:18Z | 2023-09-18T17:19:58Z | https://github.com/langchain-ai/langchain/issues/2016 | 1,641,294,710 | 2,016 |
[
"hwchase17",
"langchain"
]
| It would be great to see integration with [OpenAssistant](https://github.com/LAION-AI/Open-Assistant) by @LAION-AI, which aims to become the [largest and most open alternative to ChatGPT](https://projects.laion.ai/Open-Assistant/blog/we-need-your-help#:~:text=largest%20and%20most%20open%20alternative%20to%20ChatGPT).
> OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so
Here's the [Open Assistant architecture](https://projects.laion.ai/Open-Assistant/blog/2023-02-11-architecture):
<img width="504" alt="screen" src="https://user-images.githubusercontent.com/6625584/227813941-3c77f348-8874-409a-9e8f-2ee1604a8611.png">
| Open Assistant by LAION-AI | https://api.github.com/repos/langchain-ai/langchain/issues/2015/comments | 2 | 2023-03-27T00:18:02Z | 2023-09-28T16:10:32Z | https://github.com/langchain-ai/langchain/issues/2015 | 1,641,160,728 | 2,015 |
[
"hwchase17",
"langchain"
]
| When verbose=True and the agent executor is run, then in get_prompt_input_key() an error is raised:
```python
raise ValueError(f"One input key expected got {prompt_input_keys}")
```
This is because there are two entries [verbose, input] not just one [input]. It may be that setting verbose=True is not relevant. However, if it is, then a possible solution is to change utils.py line 10:
```python
if len(prompt_input_keys) != 1:
```
to
```python
if ~len(prompt_input_keys) in [1,2]:
``` | Error raised in utils.py get_prompt_input_key when AgentExecutor.run verbose=True | https://api.github.com/repos/langchain-ai/langchain/issues/2013/comments | 3 | 2023-03-26T19:57:11Z | 2023-10-21T16:10:16Z | https://github.com/langchain-ai/langchain/issues/2013 | 1,641,060,799 | 2,013 |
[
"hwchase17",
"langchain"
]
| In [here](https://python.langchain.com/en/latest/modules/indexes/vectorstore_examples/pinecone.html) - Pinecone.from_documents doesn't exist.
In the code [here](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/pinecone.py) it is from_texts | Pinecone in docs is outdated | https://api.github.com/repos/langchain-ai/langchain/issues/2009/comments | 4 | 2023-03-26T18:02:47Z | 2023-12-09T14:17:37Z | https://github.com/langchain-ai/langchain/issues/2009 | 1,641,013,216 | 2,009 |
[
"hwchase17",
"langchain"
]
| All "smart websites" will have AI-friendly API schemas, not just **robots.txt**. These websites will have JSON manifest files for LLMs (ex: **openai.yaml**) and LLM plugins (ex: **ai-plugin.json**). These JSON manifest files will allow LLM agents like ChatGPT to interact with websites without data scraping or parsing API docs.
OpenAI uses JSON manifest files as part of its plugin system for ChatGPT[[2]](https://www.mlq.ai/introducing-chatgpt-plugins/)[[4]](https://openai.com/blog/chatgpt-plugins). A plugin consists of an API, an API schema (in OpenAPI JSON or YAML format), and a manifest that describes what the plugin can do for both humans and models, as well as other metadata[[2]](https://www.mlq.ai/introducing-chatgpt-plugins/). The manifest file links to the OpenAPI specification and includes plugin-specific metadata such as the name, description, and logo[[2]](https://www.mlq.ai/introducing-chatgpt-plugins/)[[4]](https://openai.com/blog/chatgpt-plugins). To create a plugin, the first step is to build an API[[1]](https://platform.openai.com/docs/plugins/getting-started/plugin-manifest). The API is then documented in the OpenAPI JSON or YAML format[[1]](https://platform.openai.com/docs/plugins/getting-started/plugin-manifest)[[2]](https://www.mlq.ai/introducing-chatgpt-plugins/). Finally, a JSON manifest file is created that defines relevant metadata for the plugin[[1]](https://platform.openai.com/docs/plugins/getting-started/plugin-manifest). The manifest file includes information such as the name, description, and API URL[[2]](https://www.mlq.ai/introducing-chatgpt-plugins/).
Here's an example of [.well-known/ai-plugin.json](https://github.com/openai/chatgpt-retrieval-plugin/tree/main/.well-known)
```
{
"schema_version": "v1",
"name_for_model": "retrieval",
"name_for_human": "Retrieval Plugin",
"description_for_model": "Plugin for searching through the user's documents (such as files, emails, and more) to find answers to questions and retrieve relevant information. Use it whenever a user asks something that might be found in their personal information.",
"description_for_human": "Search through your documents.",
"auth": {
"type": "user_http",
"authorization_type": "bearer"
},
"api": {
"type": "openapi",
"url": "https://your-app-url.com/.well-known/openapi.yaml",
"has_user_authentication": false
},
"logo_url": "https://your-app-url.com/.well-known/logo.png",
"contact_email": "[email protected]",
"legal_info_url": "[email protected]"
}
```
Here's an example [.well-known/openai.yaml](https://github.com/openai/chatgpt-retrieval-plugin/blob/main/.well-known/openapi.yaml)
```
openapi: 3.0.2
info:
title: Retrieval Plugin API
description: A retrieval API for querying and filtering documents based on natural language queries and metadata
version: 1.0.0
servers:
- url: https://your-app-url.com
paths:
/query:
post:
summary: Query
description: Accepts search query objects array each with query and optional filter. Break down complex questions into sub-questions. Refine results by criteria, e.g. time / source, don't do this often. Split queries if ResponseTooLargeError occurs.
operationId: query_query_post
requestBody:
content:
application/json:
schema:
$ref: "#/components/schemas/QueryRequest"
required: true
responses:
"200":
description: Successful Response
content:
application/json:
schema:
$ref: "#/components/schemas/QueryResponse"
"422":
description: Validation Error
content:
application/json:
schema:
$ref: "#/components/schemas/HTTPValidationError"
security:
- HTTPBearer: []
components:
schemas:
DocumentChunkMetadata:
title: DocumentChunkMetadata
type: object
properties:
source:
$ref: "#/components/schemas/Source"
source_id:
title: Source Id
type: string
url:
title: Url
type: string
created_at:
title: Created At
type: string
author:
title: Author
type: string
document_id:
title: Document Id
type: string
DocumentChunkWithScore:
title: DocumentChunkWithScore
required:
- text
- metadata
- score
type: object
properties:
id:
title: Id
type: string
text:
title: Text
type: string
metadata:
$ref: "#/components/schemas/DocumentChunkMetadata"
embedding:
title: Embedding
type: array
items:
type: number
score:
title: Score
type: number
DocumentMetadataFilter:
title: DocumentMetadataFilter
type: object
properties:
document_id:
title: Document Id
type: string
source:
$ref: "#/components/schemas/Source"
source_id:
title: Source Id
type: string
author:
title: Author
type: string
start_date:
title: Start Date
type: string
end_date:
title: End Date
type: string
HTTPValidationError:
title: HTTPValidationError
type: object
properties:
detail:
title: Detail
type: array
items:
$ref: "#/components/schemas/ValidationError"
Query:
title: Query
required:
- query
type: object
properties:
query:
title: Query
type: string
filter:
$ref: "#/components/schemas/DocumentMetadataFilter"
top_k:
title: Top K
type: integer
default: 3
QueryRequest:
title: QueryRequest
required:
- queries
type: object
properties:
queries:
title: Queries
type: array
items:
$ref: "#/components/schemas/Query"
QueryResponse:
title: QueryResponse
required:
- results
type: object
properties:
results:
title: Results
type: array
items:
$ref: "#/components/schemas/QueryResult"
QueryResult:
title: QueryResult
required:
- query
- results
type: object
properties:
query:
title: Query
type: string
results:
title: Results
type: array
items:
$ref: "#/components/schemas/DocumentChunkWithScore"
Source:
title: Source
enum:
- email
- file
- chat
type: string
description: An enumeration.
ValidationError:
title: ValidationError
required:
- loc
- msg
- type
type: object
properties:
loc:
title: Location
type: array
items:
anyOf:
- type: string
- type: integer
msg:
title: Message
type: string
type:
title: Error Type
type: string
securitySchemes:
HTTPBearer:
type: http
scheme: bearer
```
Here are five ways to implement JSON manifest files inside LangChain:
1. **Plugin chaining**: Allow users to enable and chain multiple ChatGPT plugins in LangChain using OpenAI's JSON manifest files for powerful, customized agents.
2. **Unified plugin management**: Develop a plugin management interface in LangChain that can parse OpenAI JSON manifest files, simplifying plugin integration.
3. **Context enrichment**: Enhance LangChain's context management by incorporating plugin information from OpenAI JSON manifest files, leading to context-aware responses.
4. **Dynamic plugin invocation**: Implement a mechanism for LangChain agents to call appropriate OpenAI plugin APIs based on user input and conversation context.
5. **Cross-platform plugin development**: Encourage creating cross-platform plugins compatible with both OpenAI's ChatGPT ecosystem and LangChain applications by standardizing JSON manifest files and OpenAPI specifications. | JSON Manifest Files | https://api.github.com/repos/langchain-ai/langchain/issues/2008/comments | 1 | 2023-03-26T17:48:06Z | 2023-09-10T16:40:23Z | https://github.com/langchain-ai/langchain/issues/2008 | 1,641,007,401 | 2,008 |
[
"hwchase17",
"langchain"
]
| I'd like to run query against `parquet` files with `duckdb`. I see some `duckdb` stuffs on the docs when I search it up, also there's this PR: https://github.com/hwchase17/langchain/pull/1991, which seems to be a nice addition. What are the missing pieces to make this work?
One workaround I've found is turning the `parquet` files into a SQLite db, make an agent out of it, then proceed from there. | feat: parquet file support for SQL agent | https://api.github.com/repos/langchain-ai/langchain/issues/2002/comments | 3 | 2023-03-26T05:28:21Z | 2023-09-18T16:22:24Z | https://github.com/langchain-ai/langchain/issues/2002 | 1,640,778,468 | 2,002 |
[
"hwchase17",
"langchain"
]
| Hi! Thanks for creating langchain! I wanted to give its "agents" feature a try and quickly found an example of its shortcomings:
```
> Entering new AgentExecutor chain...
I need to find out who the author of Moore's law is and if they are alive.
Action: Search
Action Input: "author of Moore's law"
Observation: Gordon Moore
Thought: I need to find out if Gordon Moore is alive.
Action: Search
Action Input: "Gordon Moore alive"
Observation: March 24, 2023
Thought: I now know the final answer.
Final Answer: Yes, Gordon Moore is alive.
> Finished chain.
```
Not sure if it's something that can be fixed within langchain, but I figured I'd report it just in case. | Agent misbehaving: "is the author of Moore's law alive?" | https://api.github.com/repos/langchain-ai/langchain/issues/1994/comments | 2 | 2023-03-25T18:46:50Z | 2023-09-18T16:22:29Z | https://github.com/langchain-ai/langchain/issues/1994 | 1,640,621,419 | 1,994 |
[
"hwchase17",
"langchain"
]
| Am refereing to this documentaion [here](https://langchain.readthedocs.io/en/latest/modules/chat/examples/chat_vector_db.html) created a file
test.py
```
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.document_loaders import TextLoader
loader = TextLoader('test.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
documents = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
system_template="""Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}")
]
prompt = ChatPromptTemplate.from_messages(messages)
qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), vectorstore, qa_prompt=prompt)
chat_history = []
query = "Who is the CEO?"
result = qa({"question": query, "chat_history": chat_history})
print(result)
```
Which throws
Traceback (most recent call last):
File "test.py", line 44, in <module>
qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), vectorstore, qa_prompt=prompt)
File "/home/prajin/works/ults/gpt/lamda-openai-chat-python/venv/lib/python3.8/site-packages/langchain/chains/conversational_retrieval/base.py", line 140, in from_llm
return cls(
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ConversationalRetrievalChain
retriever
instance of BaseRetriever expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseRetriever)
| pydantic.error_wrappers.ValidationError: 1 validation error | https://api.github.com/repos/langchain-ai/langchain/issues/1986/comments | 4 | 2023-03-25T08:38:05Z | 2023-10-31T08:02:38Z | https://github.com/langchain-ai/langchain/issues/1986 | 1,640,432,940 | 1,986 |
[
"hwchase17",
"langchain"
]
| Pls. add support for Kagi Summarizer
https://blog.kagi.com/universal-summarizer#api | Add support for Kagi Summarizer API in Chains | https://api.github.com/repos/langchain-ai/langchain/issues/1983/comments | 1 | 2023-03-25T05:01:52Z | 2023-08-21T16:07:54Z | https://github.com/langchain-ai/langchain/issues/1983 | 1,640,380,648 | 1,983 |
[
"hwchase17",
"langchain"
]
| ### Summary
The QueryCheckerTool function currently creates an LLMChain object internally but does not provide a way to specify the `openai_api_key` manually via supplied arguments. This can cause issues for users who do not want to place their API key in environment variables.
### Steps to Reproduce
Run this code
```python
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain.agents import AgentExecutor
import sqlite3
with sqlite3.connect("data.db") as conn:
pass
db = SQLDatabase.from_uri("sqlite:///data.db")
toolkit = SQLDatabaseToolkit(db=db)
llm = OpenAI(temperature=0, openai_api_key="your key here")
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True
)
while True:
prompt = input("> ")
response = agent_executor.run(prompt)
print(response)
```
### Expected Behavior
`create_sql_agent` should not require me to supply an OpenAI API key, nor should it require me to have this stored in an environment variable.
### Actual Behavior
`create_sql_agent` generates an error when I call it without first setting the `OPENAI_API_KEY` environment variable.
### Obvious Solution
`create_sql_agent` should supply its argument `llm` to functions it calls, obviously. In particular, this line https://github.com/hwchase17/langchain/blob/b83e8265102514d1722b2fb1aad29763c5cad62a/langchain/tools/sql_database/tool.py#L85 is the source of the error.
### Environment
- LangChain version: 0.0.123
- Python version: 3.9.16
- Operating System: Windows 10 | QueryCheckerTool creates LLMChain object, does not allow manual specification of openai_api_key | https://api.github.com/repos/langchain-ai/langchain/issues/1982/comments | 2 | 2023-03-25T03:45:48Z | 2023-03-26T04:37:50Z | https://github.com/langchain-ai/langchain/issues/1982 | 1,640,354,701 | 1,982 |
[
"hwchase17",
"langchain"
]
| Here's what i'm using atm...
data:
```
{
"num_tweets": 199,
"total_impressions": 154586,
"total_engagements": 0,
"total_retweets": 4621,
"total_likes": 4249,
"average_impressions": 776.8140703517588,
"average_engagements": 0.0,
"average_retweets": 23.22110552763819,
"average_likes": 21.35175879396985,
"tweets": [
{
"timestamp": "2023-03-24T02:02:49+00:00",
"text": "RT : No show tonight sorry y\u2019all, business dinner went way long \ud83d\ude4f\ud83e\uddd1\u200d\ud83d\ude80\ud83e\udee1",
"replies": 0,
"impressions": 0,
"retweets": 2,
"quote_retweets": 0,
"likes": 0
},
{
"timestamp": "2023-03-23T09:59:45+00:00",
"text": "",
"replies": 0,
"impressions": 296,
"retweets": 0,
"quote_retweets": 0,
"likes": 1
},
.....
```
```
question = """You are a Solana NFT Expert, and a web3 social media guru, Analyze the data, and based on that, provide a detailed report, the report contains datapoints about the project's professionalism, social impact and reach, and the project's overall engagement with the community., with numbers to support the case,
Number of followers
Follower growth rate
Number of retweets and likes per tweet
Engagement rate per tweet
Hashtag usage
Quality of content
Frequency of posting
Tone and sentiment of tweets
Provide a thorough analysis of each metric and support your findings with specific numbers and examples. Additionally, provide any insights or recommendations based on your analysis that could help improve the project's social media presence and impact. Please provide the report in Markdown format.
"""
model_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=model_name, temperature=0.7)
recursive_character_text_splitter = (
RecursiveCharacterTextSplitter.from_tiktoken_encoder(
encoding_name="cl100k_base" if model_name == "gpt-3.5-turbo" else "p50k_base",
chunk_size=4097
if model_name == "gpt-3.5-turbo"
else llm.modelname_to_contextsize(model_name),
chunk_overlap=0,
)
)
text_chunks = recursive_character_text_splitter.split_text(open("twitter_profile.json").read())
documents = [Document(page_content=question + text_chunk) for text_chunk in text_chunks]
# Summarize the document by summarizing each document chunk and then summarizing the combined summary
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(documents)
```
response
```
"The report analyzes the social media presence of a Solana NFT project, examining metrics such as follower count, engagement rate, hashtag usage, content quality, posting frequency, tone/sentiment, and growth rate. Specific numbers and examples are provided, and recommendations are given for improving the project's impact. The data includes tweets related to partnerships, revenue sharing, and a recent airdrop, with engagement metrics and impressions listed."
```
takes about 27 seconds
I'm new to langchain, is there a better way to go about this? the responses seem undetailed and dull at the moment. | How to run gpt-3.5-turbo against my own data while using map_reduce? | https://api.github.com/repos/langchain-ai/langchain/issues/1980/comments | 4 | 2023-03-25T00:34:20Z | 2023-08-21T20:15:50Z | https://github.com/langchain-ai/langchain/issues/1980 | 1,640,254,185 | 1,980 |
[
"hwchase17",
"langchain"
]
| I am trying to use a Custom Prompt Template as an example_prompt to the FewShotPromptTemplate. Although, I am getting a 'key error' template issue. Does FewShotPromptTemplate support using Custom Prompt Template ?
A snippet example for reference
```
from langchain import PromptTemplate, FewShotPromptTemplate
class CustomPromptTemplate(StringPromptTemplate, BaseModel):
@validator("input_variables")
def validate_input_variables(cls, v):
""" Validate that the input variables are correct. """
if len(v) != 1 or "function_name" not in v:
raise ValueError("function_name must be the only input_variable.")
return v
def format(self, **kwargs) -> str:
# Get the source code of the function
source_code = get_source_code(kwargs["function_name"])
# Generate the prompt to be sent to the language model
prompt = f"""
Given the function name and source code, generate an English language explanation of the function.
Function Name: {kwargs["function_name"].__name__}
Source Code:
{source_code}
Explanation:
"""
return prompt
def _prompt_type(self):
return "function-explainer"
fn_explainer = CustomPromptTemplate(input_variables=["function_name"])
few_shot_prompt = FewShotPromptTemplate(
# These are the examples we want to insert into the prompt.
examples=examples,
# This is how we want to format the examples when we insert them into the prompt.
example_prompt= fn_explainer,
# The prefix is some text that goes before the examples in the prompt.
# Usually, this consists of intructions.
prefix="Give the antonym of every input",
# The suffix is some text that goes after the examples in the prompt.
# Usually, this is where the user input will go
suffix="Word: {input}\nAntonym:",
# The input variables are the variables that the overall prompt expects.
input_variables=["input"],
# The example_separator is the string we will use to join the prefix, examples, and suffix together with.
example_separator="\n\n",
)
``` | Using Custom Prompt with FewShotPromptTemplate | https://api.github.com/repos/langchain-ai/langchain/issues/1977/comments | 5 | 2023-03-24T21:57:37Z | 2024-02-14T16:14:33Z | https://github.com/langchain-ai/langchain/issues/1977 | 1,640,095,874 | 1,977 |
[
"hwchase17",
"langchain"
]
| * Allow users to choose the type in the schema (string | List[string])
* Allow users to get multiple json objects (get JSON array) in the response. I achieved it by replacing the {{ }} -> [[ ]] as follows: `prompt.replace("""{{
"ID": string // IDs which refers to the sentences.
"Text": string // Sentences that contains the answer to the question.
}}""", """[[
"ID": string // IDs which refers to the sentences.
"Text": string // Sentences that contains the answer to the question.
]]""")` And got a list of json objects with this method. | StructuredOutputParser - Allow users to get multiple items from response. | https://api.github.com/repos/langchain-ai/langchain/issues/1976/comments | 5 | 2023-03-24T20:27:06Z | 2023-12-27T12:00:23Z | https://github.com/langchain-ai/langchain/issues/1976 | 1,639,991,330 | 1,976 |
[
"hwchase17",
"langchain"
]
| I think the sqlalchemy dependency in pyproject.toml still needs to be bumped.
_Originally posted by @sliedes in https://github.com/hwchase17/langchain/issues/1272#issuecomment-1473683519_
| sqlalchemy dependency in pyproject.toml still needs to be bumped to * | https://api.github.com/repos/langchain-ai/langchain/issues/1975/comments | 5 | 2023-03-24T19:52:44Z | 2023-09-27T16:12:00Z | https://github.com/langchain-ai/langchain/issues/1975 | 1,639,955,235 | 1,975 |
[
"hwchase17",
"langchain"
]
| The history part for summay of past conversation is always in English. How to change it please? | How to set the 'history' of ConversationSummaryBufferMemory in other language | https://api.github.com/repos/langchain-ai/langchain/issues/1973/comments | 2 | 2023-03-24T17:24:27Z | 2023-09-18T16:22:34Z | https://github.com/langchain-ai/langchain/issues/1973 | 1,639,777,702 | 1,973 |
[
"hwchase17",
"langchain"
]
| A couple problems:
- `ChatOpenAI.get_num_tokens_from_messages()` takes a `model` parameter that is not included in the [base class method signature](https://github.com/hwchase17/langchain/blob/master/langchain/schema.py#L208). Instead, it should use `self.model_name`, similar to how the `BaseOpenAI.get_num_tokens()` does.
- `ChatOpenAI.get_num_tokens_from_messages()` does not support GPT-4. See here for the updated formula: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb | ChatOpenAI.get_num_tokens_from_messages: use self.model_name and support GPT-4 | https://api.github.com/repos/langchain-ai/langchain/issues/1972/comments | 4 | 2023-03-24T16:47:58Z | 2023-09-18T16:22:40Z | https://github.com/langchain-ai/langchain/issues/1972 | 1,639,724,669 | 1,972 |
[
"hwchase17",
"langchain"
]
| I try to set the "system" role maessage when using ConversationChain with ConversationSummaryBufferMemory(CSBM), but it is failed. When I change the ConversationSummaryBufferMemory to the ConversationBufferMemory, it become worked. But I'd like to use the auto summarize utilities when exceeding the maxLength by CSBM.
Below is the error message:
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, MessagesPlaceholder(variable_name="history"),human_message_prompt])
from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
llm=chat,
memory=ConversationSummaryBufferMemory(llm=chat, max_token_limit=10),
prompt=chat_prompt,
verbose=True
)
conversation_with_summary.predict(input="hello")
*******************************************************************************
> Entering new ConversationChain chain...
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[125], line 34
24 conversation_with_summary = ConversationChain(
25 #llm=llm,
26 llm=chat,
(...)
31 verbose=True
32 )
33 #conversation_with_summary.predict(identity="ไฝ็ฅ",text="ไฝ ๅฅฝ")
---> 34 conversation_with_summary.predict(input="ไฝ ๅฅฝ")
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\llm.py:151, in LLMChain.predict(self, **kwargs)
137 def predict(self, **kwargs: Any) -> str:
138 """Format prompt with kwargs and pass to LLM.
139
140 Args:
(...)
149 completion = llm.predict(adjective="funny")
150 """
--> 151 return self(kwargs)[self.output_key]
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\llm.py:57, in LLMChain._call(self, inputs)
56 def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
---> 57 return self.apply([inputs])[0]
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\llm.py:118, in LLMChain.apply(self, input_list)
116 def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
117 """Utilize the LLM generate method for speed gains."""
--> 118 response = self.generate(input_list)
119 return self.create_outputs(response)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\llm.py:61, in LLMChain.generate(self, input_list)
59 def generate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
60 """Generate LLM result from inputs."""
---> 61 prompts, stop = self.prep_prompts(input_list)
62 return self.llm.generate_prompt(prompts, stop)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\llm.py:79, in LLMChain.prep_prompts(self, input_list)
77 for inputs in input_list:
78 selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
---> 79 prompt = self.prompt.format_prompt(**selected_inputs)
80 _colored_text = get_colored_text(prompt.to_string(), "green")
81 _text = "Prompt after formatting:\n" + _colored_text
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\prompts\chat.py:173, in ChatPromptTemplate.format_prompt(self, **kwargs)
167 elif isinstance(message_template, BaseMessagePromptTemplate):
168 rel_params = {
169 k: v
170 for k, v in kwargs.items()
171 if k in message_template.input_variables
172 }
--> 173 message = message_template.format_messages(**rel_params)
174 result.extend(message)
175 else:
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\prompts\chat.py:43, in MessagesPlaceholder.format_messages(self, **kwargs)
41 value = kwargs[self.variable_name]
42 if not isinstance(value, list):
---> 43 raise ValueError(
44 f"variable {self.variable_name} should be a list of base messages, "
45 f"got {value}"
46 )
47 for v in value:
48 if not isinstance(v, BaseMessage):
ValueError: variable history should be a list of base messages, got | how to use SystemMessagePromptTemplate with ConversationSummaryBufferMemory please? | https://api.github.com/repos/langchain-ai/langchain/issues/1971/comments | 4 | 2023-03-24T16:39:01Z | 2023-09-28T16:10:42Z | https://github.com/langchain-ai/langchain/issues/1971 | 1,639,712,764 | 1,971 |
[
"hwchase17",
"langchain"
]
| I am reaching out to inquire about the possibility of implementing a `caching system` for popular prompts as vectors. As you may know, constantly re-embedding questions can be `costly` and `time-consuming`, especially for larger datasets.
Therefore, I was wondering if there are any plans to create a sub package that would allow users to store and reuse embeddings of commonly used questions. This would not only decrease the cost of re-embedding but also improve the overall efficiency of the system.
I would greatly appreciate it if you could let me know if this is something that may be considered in the future. Thank you for your time and consideration. | Proposal to Implement Caching of Popular Prompts as Vectors | https://api.github.com/repos/langchain-ai/langchain/issues/1968/comments | 7 | 2023-03-24T14:22:18Z | 2023-09-18T16:22:44Z | https://github.com/langchain-ai/langchain/issues/1968 | 1,639,490,018 | 1,968 |
[
"hwchase17",
"langchain"
]
| I am reaching out to inquire about the possibility of implementing a `caching system` for popular prompts as vectors. As you may know, constantly re-embedding questions can be `costly` and `time-consuming`, especially for larger datasets.
Therefore, I was wondering if there are any plans to create a sub package that would allow users to store and reuse embeddings of commonly used questions. This would not only decrease the cost of re-embedding but also improve the overall efficiency of the system.
I would greatly appreciate it if you could let me know if this is something that may be considered in the future. Thank you for your time and consideration. | Proposal to Implement Caching of Popular Prompts as Vectors | https://api.github.com/repos/langchain-ai/langchain/issues/1967/comments | 3 | 2023-03-24T14:21:57Z | 2023-10-23T02:26:40Z | https://github.com/langchain-ai/langchain/issues/1967 | 1,639,489,619 | 1,967 |
[
"hwchase17",
"langchain"
]
| I am reaching out to inquire about the possibility of implementing a `caching system` for popular prompts as vectors. As you may know, constantly re-embedding questions can be `costly` and `time-consuming`, especially for larger datasets.
Therefore, I was wondering if there are any plans to create a sub package that would allow users to store and reuse embeddings of commonly used questions. This would not only decrease the cost of re-embedding but also improve the overall efficiency of the system.
I would greatly appreciate it if you could let me know if this is something that may be considered in the future. Thank you for your time and consideration. | Proposal to Implement Caching of Popular Prompts as Vectors | https://api.github.com/repos/langchain-ai/langchain/issues/1966/comments | 1 | 2023-03-24T14:21:38Z | 2023-08-21T16:07:59Z | https://github.com/langchain-ai/langchain/issues/1966 | 1,639,489,505 | 1,966 |
[
"hwchase17",
"langchain"
]
| Say I query my vector database with a minimum distance threshold and no documents ("sources") are returned, how can I stop ChatVectorDBChain from answering the question without using prompts?
I observe that even though it finds no sources, it will answer based on chat history or overwrite the prompt instructions. I would like to have a deterministic switch like:
```python
if no_documents:
raise ValueError()
```
and then catch this error to print some message and continue. | Is there a non-prompt way to stop ChatVectorDBChain from answering when no vectors are found? | https://api.github.com/repos/langchain-ai/langchain/issues/1963/comments | 3 | 2023-03-24T10:23:54Z | 2023-09-25T16:15:24Z | https://github.com/langchain-ai/langchain/issues/1963 | 1,639,119,073 | 1,963 |
[
"hwchase17",
"langchain"
]
| I am trying to load load video and came across below issue.
I am using langchain version 0.0.121
| AttributeError: type object 'YoutubeLoader' has no attribute 'from_youtube_url' | https://api.github.com/repos/langchain-ai/langchain/issues/1962/comments | 5 | 2023-03-24T10:08:17Z | 2023-04-12T04:13:00Z | https://github.com/langchain-ai/langchain/issues/1962 | 1,639,095,508 | 1,962 |
[
"hwchase17",
"langchain"
]
| The score is useful for a lot of things. It'd be great if VectorStore exported the abstract methods so a VectorStore dependency can be injected.
| `*_with_score` methods should be part of VectorStore | https://api.github.com/repos/langchain-ai/langchain/issues/1959/comments | 1 | 2023-03-24T08:17:09Z | 2023-09-10T16:40:38Z | https://github.com/langchain-ai/langchain/issues/1959 | 1,638,930,794 | 1,959 |
[
"hwchase17",
"langchain"
]
| null | How to work with multiple csv files in the same agent session ? is there any option to call agent with multiple csv files, so that the model can interact multiple files and answer us. | https://api.github.com/repos/langchain-ai/langchain/issues/1958/comments | 12 | 2023-03-24T07:46:39Z | 2023-05-25T21:23:12Z | https://github.com/langchain-ai/langchain/issues/1958 | 1,638,890,881 | 1,958 |
[
"hwchase17",
"langchain"
]
| ## Summary
I'm seeing this `ImportError`ย on my Mac M1 when trying to use Chroma
```
(mach-o file, but is an incompatible architecture (have (x86_64), need (arm64e)))
```
Any ideas?
## Traceback
```
Traceback (most recent call last):
File "/Users/homanp/Library/Caches/com.vercel.fun/runtimes/python3/../python/bootstrap.py", line 147, in <module>
lambda_runtime_main()
File "/Users/homanp/Library/Caches/com.vercel.fun/runtimes/python3/../python/bootstrap.py", line 127, in lambda_runtime_main
fn = lambda_runtime_get_handler()
File "/Users/homanp/Library/Caches/com.vercel.fun/runtimes/python3/../python/bootstrap.py", line 113, in lambda_runtime_get_handler
mod = importlib.import_module(module_name)
File "/opt/homebrew/Cellar/[email protected]/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 855, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "/var/folders/b5/g5rvd57j2wq3nysn1n8h66yr0000gn/T/zeit-fun-6d31861b417ef/vc__handler__python.py", line 12, in <module>
__vc_spec.loader.exec_module(__vc_module)
File "<frozen importlib._bootstrap_external>", line 855, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "/private/var/folders/b5/g5rvd57j2wq3nysn1n8h66yr0000gn/T/zeit-fun-6d31861b417ef/./api/index.py", line 13, in <module>
index = VectorstoreIndexCreator().from_loaders([loader])
File "/var/folders/b5/g5rvd57j2wq3nysn1n8h66yr0000gn/T/zeit-fun-6d31861b417ef/langchain/indexes/vectorstore.py", line 71, in from_loaders
vectorstore = self.vectorstore_cls.from_documents(
File "/var/folders/b5/g5rvd57j2wq3nysn1n8h66yr0000gn/T/zeit-fun-6d31861b417ef/langchain/vectorstores/chroma.py", line 268, in from_documents
return cls.from_texts(
File "/var/folders/b5/g5rvd57j2wq3nysn1n8h66yr0000gn/T/zeit-fun-6d31861b417ef/langchain/vectorstores/chroma.py", line 231, in from_texts
chroma_collection = cls(
File "/var/folders/b5/g5rvd57j2wq3nysn1n8h66yr0000gn/T/zeit-fun-6d31861b417ef/langchain/vectorstores/chroma.py", line 78, in __init__
self._client = chromadb.Client(self._client_settings)
File "/Users/homanp/Projects/VERCEL_LANGCHAIN_ENV/lib/python3.9/site-packages/chromadb/__init__.py", line 68, in Client
return chromadb.api.local.LocalAPI(settings, get_db(settings))
File "/Users/homanp/Projects/VERCEL_LANGCHAIN_ENV/lib/python3.9/site-packages/chromadb/__init__.py", line 41, in get_db
import chromadb.db.duckdb
File "/Users/homanp/Projects/VERCEL_LANGCHAIN_ENV/lib/python3.9/site-packages/chromadb/db/duckdb.py", line 3, in <module>
from chromadb.db.index.hnswlib import Hnswlib
File "/Users/homanp/Projects/VERCEL_LANGCHAIN_ENV/lib/python3.9/site-packages/chromadb/db/index/hnswlib.py", line 8, in <module>
import hnswlib
```
| `ImportError` ChromaDB | https://api.github.com/repos/langchain-ai/langchain/issues/1957/comments | 16 | 2023-03-24T07:42:33Z | 2023-11-10T16:10:52Z | https://github.com/langchain-ai/langchain/issues/1957 | 1,638,886,467 | 1,957 |
[
"hwchase17",
"langchain"
]
| I worked on this version:
git rev-parse HEAD: 9555bbd5bb3397e66d279d802576b4c65123b484
I plan to add a ParaChain class in the Chains module. At present, it seems that only serial calls are supported, but in some scenarios, we need it work in parallel, and support the combination of parallel chains and serial chains to achieve a better effect, and I hope that in the future, I can introduce FSM into it.
I was about to start this part of the work, when I was reading the source code of the SequentialChain class, I found that there was a section in the validate_chains function that identified the intersection variable of memory and input, but only from the perspective of looking at the code, it seemed that there was a bug, but I did It has not been verified by test_case(because there are too many homework at present), I want to start a discussion here first, whether it is a known bug or my understanding is wrong.
the code section:
```
@root_validator(pre=True)
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that the correct inputs exist for all chains."""
chains = values["chains"]
input_variables = values["input_variables"]
memory_keys = list()
if "memory" in values and values["memory"] is not None:
"""Validate that prompt input variables are consistent."""
memory_keys = values["memory"].memory_variables
if any(input_variables) in memory_keys:
overlapping_keys = input_variables & memory_keys
raise ValueError(
f"The the input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
)
```
At first: ** if any(input_variables) in memory_keys:** any() function return a bool value. what's mean of bool in a List[str]?
Second: **overlapping_keys = input_variables & memory_keys** maybe **overlapping_keys = set(input_variables) & set(memory_keys)**
I love LangChain. Awesome & impressive library | Bug:: SequentialChain root_validator::validate_chain. judgment logic of overlapping_keys | https://api.github.com/repos/langchain-ai/langchain/issues/1953/comments | 2 | 2023-03-24T04:52:12Z | 2023-09-10T16:40:43Z | https://github.com/langchain-ai/langchain/issues/1953 | 1,638,714,871 | 1,953 |
[
"hwchase17",
"langchain"
]
| Summary: the Chroma vectorstore search does not return top-scored embeds.
The issue appears only when the number of documents in the vector store exceeds a certain threshold (I have ~4000 chunks).
I could not determine when it breaks exactly.
I loaded my documents, chunked them, and then indexed into a vectorstore:
```
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(all_docs, embeddings)
```
Then I tried to search this vector store:
```
text = "my search text"
list(score for doc, score in docsearch.similarity_search_with_score(text))
```
Output:
```
[0.3361772298812866,
0.3575538694858551,
0.360953152179718,
0.36677438020706177]
```
Search **did not return the expected document** from the embed (within a list of 4 items returned by default).
Then I performed another test forcing to return all scores and **got the expected result**.
`list(score for doc, score in docsearch.similarity_search_with_score(text, len(all_docs))[:4])`
Output:
```
[0.31715911626815796,
0.3361772298812866,
0.3575538694858551,
0.360953152179718]
```
You can clearly see that the top scores are different.
Any help is appreciated.
| Chroma vectorstore search does not return top-scored embeds | https://api.github.com/repos/langchain-ai/langchain/issues/1946/comments | 17 | 2023-03-24T00:47:14Z | 2024-07-12T14:58:40Z | https://github.com/langchain-ai/langchain/issues/1946 | 1,638,542,872 | 1,946 |
[
"hwchase17",
"langchain"
]
| Currently, there is no way to pass in headers to UnstructuredURLLoader, e.g. for `"User-Agent"`. There should be.
Upstream issue: https://github.com/Unstructured-IO/unstructured/issues/396 | Ability to pass in headers to UnstructuredURLLoader | https://api.github.com/repos/langchain-ai/langchain/issues/1944/comments | 0 | 2023-03-23T23:08:43Z | 2023-03-29T04:32:13Z | https://github.com/langchain-ai/langchain/issues/1944 | 1,638,464,544 | 1,944 |
[
"hwchase17",
"langchain"
]
| Problem since update 0.0.120, when using a AzureChatOpenAI model instance of gpt-35-turbo you get a "Resource not found error" tried with both load_qa_with_sources_chain and MapReduceChain.from_params
Here is a sample error trace from right after I call the chain() instance:
```
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/base.py", line 213, in run
return self(args[0])[self.output_keys[0]]
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/base.py", line 116, in __call__
raise e
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/base.py", line 113, in __call__
outputs = self._call(inputs)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/mapreduce.py", line 73, in _call
outputs, _ = self.combine_documents_chain.combine_docs(docs)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/combine_documents/map_reduce.py", line 139, in combine_docs
results = self.llm_chain.apply(
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/llm.py", line 118, in apply
response = self.generate(input_list)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chains/llm.py", line 62, in generate
return self.llm.generate_prompt(prompts, stop)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 79, in generate_prompt
raise e
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 76, in generate_prompt
output = self.generate(prompt_messages, stop=stop)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 53, in generate
results = [self._generate(m, stop=stop) for m in messages]
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 53, in <listcomp>
results = [self._generate(m, stop=stop) for m in messages]
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 264, in _generate
response = self.completion_with_retry(messages=message_dicts, **params)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 226, in completion_with_retry
return _completion_with_retry(**kwargs)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "/anaconda/envs/exp_env/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/anaconda/envs/exp_env/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 224, in _completion_with_retry
return self.client.create(**kwargs)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/openai/api_resources/chat_completion.py", line 25, in create
return super().create(*args, **kwargs)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request
resp, got_stream = self._interpret_response(result, stream)
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response
self._interpret_response_line(
File "/anaconda/envs/exp_env/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: Resource not found
``` | Resource not found error when using AzureChatOpenAI gpt-35-turbo | https://api.github.com/repos/langchain-ai/langchain/issues/1942/comments | 1 | 2023-03-23T22:17:32Z | 2023-03-23T22:34:53Z | https://github.com/langchain-ai/langchain/issues/1942 | 1,638,418,561 | 1,942 |
[
"hwchase17",
"langchain"
]
| When presented with intalling chatgpt-wrapper, I have the issue "C:\Users\BigB\Documents\Yeet>pip install git+https://github.com/mmabrouk/chatgpt-wrapper
Collecting git+https://github.com/mmabrouk/chatgpt-wrapper
Cloning https://github.com/mmabrouk/chatgpt-wrapper to c:\users\refddfea~1\appdata\local\temp\pip-req-build-h89ta3sh
Running command git clone --filter=blob:none -q https://github.com/mmabrouk/chatgpt-wrapper 'C:\Users\RCfDfEA~1\AppData\Local\Temp\pip-req-build-h89ta3sh'
Resolved https://github.com/mmabrouk/chatgpt-wrapper to commit df1725d7fbbff27e36bc22e4eb73cf5fea887dc1
Preparing metadata (setup.py) ... done
Requirement already satisfied: email-validator in c:\users\redfdeaffdspc\appdata\local\programs\python\python36\lib\site-packages (from chatGPT==0.6.5) (1.3.1)
Requirement already satisfied: Flask in c:\users\refdfdeafdspc\appdata\local\programs\python\python36\lib\site-packages (from chatGPT==0.6.5) (2.0.3)
Requirement already satisfied: Jinja2 in c:\users\reddefadspc\appdata\local\programs\python\python36\lib\site-packages (from chatGPT==0.6.5) (3.0.3)
ERROR: Could not find a version that satisfies the requirement langchain>=0.0.115 (from chatgpt) (from versions: 0.0.1, 0.0.2, 0.0.3, 0.0.4, 0.0.5, 0.0.6, 0.0.7, 0.0.8, 0.0.9, 0.0.10, 0.0.11, 0.0.12, 0.0.13, 0.0.14, 0.0.15, 0.0.16, 0.0.17, 0.0.18, 0.0.19, 0.0.20, 0.0.21, 0.0.22, 0.0.23, 0.0.24, 0.0.25, 0.0.26, 0.0.27)
ERROR: No matching distribution found for langchain>=0.0.115". | Issue when trying to install chatgpt-wrapper with this module. | https://api.github.com/repos/langchain-ai/langchain/issues/1941/comments | 2 | 2023-03-23T21:08:08Z | 2023-09-18T16:22:50Z | https://github.com/langchain-ai/langchain/issues/1941 | 1,638,329,189 | 1,941 |
[
"hwchase17",
"langchain"
]
| I'm wondering how does [ChatGPT plugin](https://platform.openai.com/docs/plugins/) work under-the-hood? Are they using the same techique as Langchain agent? Basically sending prompt and manually parse out the result -> and feed result back to another plugin in subsequential steps if necessary.
Or is it using a superior technique than LangChain agent? I can't find a mention of LangChain in their doc.
Any thoughts are welcome! | ChatGPT Plugins vs. LangChain agent | https://api.github.com/repos/langchain-ai/langchain/issues/1940/comments | 17 | 2023-03-23T20:57:52Z | 2023-09-29T16:09:47Z | https://github.com/langchain-ai/langchain/issues/1940 | 1,638,313,085 | 1,940 |
[
"hwchase17",
"langchain"
]
| As reported by Kranos in Discord, there is no a way to robustly iterate through a list of URL's with UnstructuredURLLoader. The workaround for now is to create a UnstructuredURLLoader object per url and do the following:
```
Yep, exactly my problem - I had a load of URLs loaded into a pandas dataframe I was iterating through. I basically added the following at the end of the loop to keep things ticking over and ignoring any errors:
# Manage any errors
except (NameError, ValueError, KeyError, OSError, TypeError):
# Pass the error
pass
```
UnstructuredURLLoader should likely do this by default, or provide a `strict` option to exit on any failures. | UnstructuredURLLoader does not gracefully handle failures given a list of URL's | https://api.github.com/repos/langchain-ai/langchain/issues/1939/comments | 0 | 2023-03-23T20:15:27Z | 2023-03-28T05:26:22Z | https://github.com/langchain-ai/langchain/issues/1939 | 1,638,238,132 | 1,939 |
[
"hwchase17",
"langchain"
]
| When feeding the map_reduce summarization chain with a single document, the doc is run through an unnecessary map step before running a combine prompt on it.
The combine prompt would imo be sufficient to avoid summarizing a single summary which then is very lossy. Great project, hope this thrives! | Improvement: MapReduce summarization chains executes a map step on a single document | https://api.github.com/repos/langchain-ai/langchain/issues/1937/comments | 7 | 2023-03-23T18:46:21Z | 2023-09-18T16:28:44Z | https://github.com/langchain-ai/langchain/issues/1937 | 1,638,109,379 | 1,937 |
[
"hwchase17",
"langchain"
]
| UJsing py 3.11 (works in py 3.10) with latest (0.4.36) version.
Traceback:
```
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 565, in _run_script
exec(code, module.__dict__)
File "D:\dev\openai-pr-summarization\main.py", line 9, in <module>
from engine.llama import LlamaEngine
File "D:\dev\openai-pr-summarization\engine\llama.py", line 3, in <module>
from llama_index import GPTSimpleVectorIndex, Document
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\__init__.py", line 18, in <module>
from llama_index.indices.common.struct_store.base import SQLDocumentContextBuilder
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\indices\__init__.py", line 4, in <module>
from llama_index.indices.keyword_table.base import GPTKeywordTableIndex
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\indices\keyword_table\__init__.py", line 4, in <module>
from llama_index.indices.keyword_table.base import GPTKeywordTableIndex
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\indices\keyword_table\base.py", line 16, in <module>
from llama_index.indices.base import DOCUMENTS_INPUT, BaseGPTIndex
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\indices\base.py", line 23, in <module>
from llama_index.indices.prompt_helper import PromptHelper
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\indices\prompt_helper.py", line 12, in <module>
from llama_index.langchain_helpers.chain_wrapper import LLMPredictor
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\langchain_helpers\chain_wrapper.py", line 6, in <module>
from llama_index.llm_predictor.base import ( # noqa: F401
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\llm_predictor\__init__.py", line 4, in <module>
from llama_index.llm_predictor.base import LLMPredictor
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\llm_predictor\base.py", line 15, in <module>
from llama_index.prompts.base import Prompt
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\prompts\__init__.py", line 3, in <module>
from llama_index.prompts.base import Prompt
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\prompts\base.py", line 11, in <module>
from llama_index.output_parsers.base import BaseOutputParser
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\output_parsers\__init__.py", line 4, in <module>
from llama_index.output_parsers.langchain import LangchainOutputParser
File "C:\Users\diman\mambaforge\envs\py311\Lib\site-packages\llama_index\output_parsers\langchain.py", line 6, in <module>
from langchain.schema import BaseOutputParser as LCOutputParser
``` | cannot import name 'BaseOutputParser' from 'langchain.schema' | https://api.github.com/repos/langchain-ai/langchain/issues/1936/comments | 8 | 2023-03-23T17:59:56Z | 2023-09-28T16:10:53Z | https://github.com/langchain-ai/langchain/issues/1936 | 1,638,029,215 | 1,936 |
[
"hwchase17",
"langchain"
]
| I tried creating a pandas dataframe agent (using create_dataframe_agent) with ChatOpenAI from promptlayer or otherwise just plain version (ChatOpenAI) as the LLM! But langchain isn't able to parse the LLM's output code. If I modify some regular expression manually, it works (but again fails if the code is a single line, etc.). Basically the regex and parsing logic for figuring out Action, Action_input for chat models needs to be changed. This probably means lot of other existing chains with ChatOpenAI as LLM are up for a toss!
Code:
```
chat = PromptLayerChatOpenAI(pl_tags=["langchain"])
df_agent = create_pandas_dataframe_agent(chat, haplo_insuf, verbose=True)
df_agent.run("find the maximum number of employees working in any company based on the range. a-b means minimum a, maximum b!")
````
Output:
```
Entering new AgentExecutor chain...
Thought: We need to extract the minimum and maximum number of employees from the "df" dataframe and find the maximum value.
Action: python_repl_ast
Action Input:```max_emp = df['# Employees'].str.split('-', expand=True).astype(int).max(axis=1)max_emp.max()```
Observation: invalid syntax (<unknown>, line 2)
Thought:There seems to be a syntax error in the code. Let me fix it.
Action: python_repl_ast
Action Input:```max_emp = df['# Employees'].str.split('-', expand=True).astype(int).max(axis=1)max_emp.max()```
Observation: invalid syntax (<unknown>, line 2)
Thought:...
``` | ChatOpenAI isn't compatible with create_pandas_dataframe_agent | https://api.github.com/repos/langchain-ai/langchain/issues/1931/comments | 10 | 2023-03-23T15:52:32Z | 2023-07-06T19:34:35Z | https://github.com/langchain-ai/langchain/issues/1931 | 1,637,808,698 | 1,931 |
[
"hwchase17",
"langchain"
]
| After sending several requests to OpenAI, it always encounter request timeouts, accompanied by long periods of waiting.
Env:
OS: Ubuntu 22
Python: 3.10
langchain: 0.0.117
## Request time out
```shell
WARNING:/home/soda/.local/lib/python3.10/site-packages/langchain/chat_models/openai.py:Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised Timeout: Request timed out.
```
## TimeoutError from None
```shell
answer, _ = await self.combine_documents_chain.acombine_docs(docs, question=question)
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 97, in acombine_docs
return await self.llm_chain.apredict(**inputs), {}
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 167, in apredict
return (await self.acall(kwargs))[self.output_key]
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 154, in acall
raise e
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 148, in acall
outputs = await self._acall(inputs)
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 135, in _acall
return (await self.aapply([inputs]))[0]
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 123, in aapply
response = await self.agenerate(input_list)
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 67, in agenerate
return await self.llm.agenerate_prompt(prompts, stop)
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 103, in agenerate_prompt
raise e
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 97, in agenerate_prompt
output = await self.agenerate(prompt_messages, stop=stop)
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 62, in agenerate
results = [await self._agenerate(m, stop=stop) for m in messages]
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 62, in <listcomp>
results = [await self._agenerate(m, stop=stop) for m in messages]
File "/home/soda/.local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 286, in _agenerate
async for stream_resp in await acompletion_with_retry(
File "/home/soda/.local/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 230, in <genexpr>
return (
File "/home/soda/.local/lib/python3.10/site-packages/openai/api_requestor.py", line 319, in wrap_resp
async for r in resp:
File "/home/soda/.local/lib/python3.10/site-packages/openai/api_requestor.py", line 633, in <genexpr>
return (
File "/home/soda/.local/lib/python3.10/site-packages/openai/api_requestor.py", line 114, in parse_stream_async
async for line in rbody:
File "/home/soda/.local/lib/python3.10/site-packages/aiohttp/streams.py", line 35, in __anext__
rv = await self.read_func()
File "/home/soda/.local/lib/python3.10/site-packages/aiohttp/streams.py", line 311, in readline
return await self.readuntil()
File "/home/soda/.local/lib/python3.10/site-packages/aiohttp/streams.py", line 343, in readuntil
await self._wait("readuntil")
File "/home/soda/.local/lib/python3.10/site-packages/aiohttp/streams.py", line 303, in _wait
with self._timer:
File "/home/soda/.local/lib/python3.10/site-packages/aiohttp/helpers.py", line 721, in __exit__
raise asyncio.TimeoutError from None
```
Have other people encountered similar errors?
| Frequent Request timed out | https://api.github.com/repos/langchain-ai/langchain/issues/1929/comments | 5 | 2023-03-23T14:44:31Z | 2023-06-07T03:02:27Z | https://github.com/langchain-ai/langchain/issues/1929 | 1,637,686,943 | 1,929 |
[
"hwchase17",
"langchain"
]
| Right now to create an agent that has memory, we do
`memory = ConversationBufferMemory(memory_key=str('chat_history'))`
Then pass it to the agent
`agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)`
However, the memory will be gone if the server restarts. I learned that memory can be dumped as json, but to avoid losing messages, we have to dump to json every time a message comes in, which is not a good solution.
Can we add support for storing conversation buffer memory in redis and when constructing the agent pass the redis instance to the agent executor?
Let me know if there is an existing solution for this. Appreciate the help!
| Support Save and Retrieve Memory from Redis | https://api.github.com/repos/langchain-ai/langchain/issues/1927/comments | 2 | 2023-03-23T13:47:03Z | 2023-04-07T19:55:27Z | https://github.com/langchain-ai/langchain/issues/1927 | 1,637,579,089 | 1,927 |
[
"hwchase17",
"langchain"
]
| HI,
I am getting an error "InvalidRequestError: Resource not found" while using Chat model using VectorDBQA chain.
This issue is coming up after v0.0.120 release and I was not getting this in the previous version which I used which was v0.0.118.
I am using Chatmodel from Azure through AzureChatOpenAI and embeddings through Azure`s model.
| Getting an error InvalidRequestError: Resource not found in VectorDBQA | https://api.github.com/repos/langchain-ai/langchain/issues/1923/comments | 10 | 2023-03-23T11:37:04Z | 2023-11-03T07:22:09Z | https://github.com/langchain-ai/langchain/issues/1923 | 1,637,360,666 | 1,923 |
[
"hwchase17",
"langchain"
]
| Have we considered using Ruff in this repo instead of flake8? Ruff is a lot faster and used by many popular Python repos.
https://github.com/charliermarsh/ruff | Ruff instead of flake8? | https://api.github.com/repos/langchain-ai/langchain/issues/1919/comments | 2 | 2023-03-23T06:36:12Z | 2023-03-23T06:51:45Z | https://github.com/langchain-ai/langchain/issues/1919 | 1,636,937,677 | 1,919 |
[
"hwchase17",
"langchain"
]
| Add optional parameters (like a `siterestrict`) to GoogleSearchAPIWrapper.
https://developers.google.com/custom-search/v1/site_restricted_api
| Support for Google's Custom Search Site Restricted JSON API | https://api.github.com/repos/langchain-ai/langchain/issues/1915/comments | 0 | 2023-03-23T04:53:09Z | 2023-03-28T04:24:27Z | https://github.com/langchain-ai/langchain/issues/1915 | 1,636,847,361 | 1,915 |
[
"hwchase17",
"langchain"
]
| How to add a json example into the prompt template. Currently, it errors.
Here is one example prompt
human_template = """Summarize user's order into the json format keys:"name","size", "topping", "ice", "sugar", "special_instruction".
Here are two examples of the order JSON object:
{
"name": "Jasmine Green Tea/Milk Tea",
"quantity": 2,
"size": "Large",
"ice": "25%",
"sugar": "100%",
"toppings": "Pearl, Green Bean, Grass Jelly"
},
{
"name": "Popcorn Chicken",
"quantity": 1,
"special_instruction": "Make it spicy"
}
{extra_store_instruction}
"""
I will get this error:
File "/home/ubuntu/.local/lib/python3.10/site-packages/langchain/prompts/chat.py", line 67, in from_template
prompt = PromptTemplate.from_template(template)
File "/home/ubuntu/.local/lib/python3.10/site-packages/langchain/prompts/prompt.py", line 130, in from_template
return cls(input_variables=list(sorted(input_variables)), template=template)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for PromptTemplate
__root__
Invalid format specifier (type=value_error)
| how to escape {} for the PromptTemplate | https://api.github.com/repos/langchain-ai/langchain/issues/1914/comments | 9 | 2023-03-23T04:16:24Z | 2024-05-10T06:08:49Z | https://github.com/langchain-ai/langchain/issues/1914 | 1,636,824,214 | 1,914 |
[
"hwchase17",
"langchain"
]
| I just upgraded to the most recent version of langchain (0.0.119) and I get the following error when I try to import langchain in python.
Here's the error message:
>
--> 111 class SelfHostedHuggingFaceLLM(SelfHostedPipeline, BaseModel):
112 Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
113
114 Supported hardware includes auto-launched instances on AWS, GCP, Azure,
149 model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
150 152 model_id: str = DEFAULT_MODEL_ID
File ~/.local/lib/python3.9/site-packages/pydantic/main.py:254, in pydantic.main.ModelMetaclass.__new__()
File ~/.local/lib/python3.9/site-packages/pydantic/utils.py:144, in pydantic.utils.validate_field_name()
NameError: Field name inference_fn shadows a BaseModel attribute; use a different field name with alias=inference_fn
>
Any help will be appreciated.
Thanks in advance
| import langchain (0.0.119) does not work | https://api.github.com/repos/langchain-ai/langchain/issues/1913/comments | 4 | 2023-03-23T03:40:53Z | 2023-09-25T16:15:39Z | https://github.com/langchain-ai/langchain/issues/1913 | 1,636,797,392 | 1,913 |
[
"hwchase17",
"langchain"
]
| Code to create a ConstitutionalChain from an LLM:
```
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
chain: LLMChain,
critique_prompt: BasePromptTemplate = CRITIQUE_PROMPT,
revision_prompt: BasePromptTemplate = REVISION_PROMPT,
**kwargs: Any,
) -> "ConstitutionalChain":
"""Create a chain from an LLM."""
critique_chain = LLMChain(llm=llm, prompt=critique_prompt)
revision_chain = LLMChain(llm=llm, prompt=revision_prompt)
return cls(
chain=chain,
critique_chain=critique_chain,
revision_chain=revision_chain,
**kwargs,
)
```
It requires an LLMChain.
SimpleSequentialChain doesn't inherit from LLMChain. It inherits from Chain.
```
class SimpleSequentialChain(Chain, BaseModel):
"""Simple chain where the outputs of one step feed directly into next."""
chains: List[Chain]
strip_outputs: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
```
Therefore we cannot pass a SimpleSequentialChain to the ConstitutionalChain.
This disrupts the workflow where we create a pipeline and want to pass it to a constitutional chain for checks.
I would be happy to submit a PR to fix this : )
Presumably there should be no issue as a ConstitutionalChain shouldn't require anything more than a run method from the input chain, as it contains it's own LLM and prompts (in the form of principles). | ConstitutionalChain cannot be composed with SimpleSequentialChain | https://api.github.com/repos/langchain-ai/langchain/issues/1904/comments | 4 | 2023-03-23T00:35:35Z | 2024-01-10T19:17:00Z | https://github.com/langchain-ai/langchain/issues/1904 | 1,636,674,948 | 1,904 |
[
"hwchase17",
"langchain"
]
| When we use `OpenSearchVectorSearch` to index documents into Opensearch, there is no way to decide the index name.
There is `index_name` argument in `OpenSearchVectorSearch` however this is ignored when we call `OpenSearchVectorSearch.from_documents()` method. In the method index name is set randomly with uuid [see here](https://github.com/hwchase17/langchain/blob/f155d9d3ec194146c28710f43c5cd9da3f709447/langchain/vectorstores/opensearch_vector_search.py#L366)
| OpenSearch index_name is not deterministic | https://api.github.com/repos/langchain-ai/langchain/issues/1900/comments | 1 | 2023-03-22T22:59:59Z | 2023-03-23T10:10:01Z | https://github.com/langchain-ai/langchain/issues/1900 | 1,636,610,281 | 1,900 |
[
"hwchase17",
"langchain"
]
| It seems this can already calculate costs, can it throw errors/warnings if usage exceeds a certain pre-configured amount? That way people won't accidentally spend $500 if they miscalculate, etc. | Add a way to configure soft/hard caps on $ spent on APIs | https://api.github.com/repos/langchain-ai/langchain/issues/1899/comments | 2 | 2023-03-22T22:13:30Z | 2023-09-10T16:40:53Z | https://github.com/langchain-ai/langchain/issues/1899 | 1,636,565,917 | 1,899 |
[
"hwchase17",
"langchain"
]
| I'm on Windows 10 and one of the examples from the Documentation [Load a prompt template from LangChainHub](https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html#:~:text=such%20as%20Mako.-,Load%20a%20prompt%20template%20from%20LangChainHub,-%23) has the following code:
```python
from langchain.prompts import load_prompt
prompt = load_prompt("lc://prompts/conversation/prompt.json")
prompt.format(history="", input="What is 1 + 1?")
```
Which in Windows produces the following error:
```console
(venv) PS D:\Usuarios\Dev01\Documents\GitHub\almohada\chat> python prompts.py
Traceback (most recent call last):
File "D:\Usuarios\Dev01\Documents\GitHub\almohada\chat\prompts.py", line 58, in <module>
prompt = load_prompt("lc://prompts/conversation/prompt.json")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Usuarios\Dev01\Documents\GitHub\almohada\chat\venv\Lib\site-packages\langchain\prompts\loading.py", line 120, in load_prompt
if hub_result := try_load_from_hub(
^^^^^^^^^^^^^^^^^^
File "D:\Usuarios\Dev01\Documents\GitHub\almohada\chat\venv\Lib\site-packages\langchain\utilities\loading.py", line 44, in try_load_from_hub
raise ValueError(f"Could not find file at {full_url}")
ValueError: Could not find file at https://raw.githubusercontent.com/hwchase17/langchain-hub/master/prompts\conversation\prompt.json
```
This is caused by line 41 in `utilities/loading.py`
```python
full_url = urljoin(URL_BASE.format(ref=ref), str(remote_path))
```
I have not tested this on another OS but in my mind the fix it's the following:
```python
def try_load_from_hub(
path: Union[str, Path],
loader: Callable[[str], T],
valid_prefix: str,
valid_suffixes: Set[str],
**kwargs: Any,
) -> Optional[T]:
"""Load configuration from hub. Returns None if path is not a hub path."""
if not isinstance(path, str) or not (match := HUB_PATH_RE.match(path)):
return None
ref, remote_path_str = match.groups()
ref = ref[1:] if ref else DEFAULT_REF
remote_path = Path(remote_path_str)
if remote_path.parts[0] != valid_prefix:
return None
if remote_path.suffix[1:] not in valid_suffixes:
raise ValueError("Unsupported file type.")
full_url = urljoin(URL_BASE.format(ref=ref), remote_path_str) # here, instead of stringifying the remote_path just use the one extracted in the line above
r = requests.get(full_url, timeout=5)
if r.status_code != 200:
raise ValueError(f"Could not find file at {full_url}")
with tempfile.TemporaryDirectory() as tmpdirname:
file = Path(tmpdirname) / remote_path.name
with open(file, "wb") as f:
f.write(r.content)
return loader(str(file), **kwargs)
```
I'd feedback on the solution. Since I can't test on all possible environments I'm not 100% sure about it | Error try_load_from_hub could not find file | https://api.github.com/repos/langchain-ai/langchain/issues/1897/comments | 4 | 2023-03-22T20:51:53Z | 2023-08-14T16:58:59Z | https://github.com/langchain-ai/langchain/issues/1897 | 1,636,480,289 | 1,897 |
[
"hwchase17",
"langchain"
]
| ImportError: cannot import name 'BaseOutputParser' from 'langchain.output_parsers' | Import Error | https://api.github.com/repos/langchain-ai/langchain/issues/1896/comments | 1 | 2023-03-22T18:58:13Z | 2023-03-23T14:24:41Z | https://github.com/langchain-ai/langchain/issues/1896 | 1,636,338,302 | 1,896 |
[
"hwchase17",
"langchain"
]
| With a Postgres database, I've found I need to add the schema to [line 69](https://github.com/hwchase17/langchain/blob/ce5d97bcb3e263f6aa69da6c334e35e20bf4db11/langchain/sql_database.py#L69):
`self._metadata.reflect(bind=self._engine, schema=self._schema)` | SQLDatabase does not handle schema correctly | https://api.github.com/repos/langchain-ai/langchain/issues/1894/comments | 1 | 2023-03-22T17:34:41Z | 2023-08-21T16:08:15Z | https://github.com/langchain-ai/langchain/issues/1894 | 1,636,218,153 | 1,894 |
[
"hwchase17",
"langchain"
]
| This is the message
| pip install -U langchain is the best thing that you can do before you start your day | https://api.github.com/repos/langchain-ai/langchain/issues/1892/comments | 7 | 2023-03-22T16:30:44Z | 2024-03-17T09:26:09Z | https://github.com/langchain-ai/langchain/issues/1892 | 1,636,122,407 | 1,892 |
[
"hwchase17",
"langchain"
]
| null | Support for Google BARD API? | https://api.github.com/repos/langchain-ai/langchain/issues/1889/comments | 17 | 2023-03-22T15:46:42Z | 2024-08-09T09:57:05Z | https://github.com/langchain-ai/langchain/issues/1889 | 1,636,046,362 | 1,889 |
[
"hwchase17",
"langchain"
]
| It looks like Llama uses an unsupported embedding scheme:
https://nn.labml.ai/transformers/rope/index.html
I'm opening this thread so we can have a conversation about how to support these embeddings within langchain. I'm happy to help, but my knowledge is limited. | Support for Rotary Embeddings for Llama | https://api.github.com/repos/langchain-ai/langchain/issues/1885/comments | 3 | 2023-03-22T15:04:00Z | 2023-09-18T16:22:55Z | https://github.com/langchain-ai/langchain/issues/1885 | 1,635,952,857 | 1,885 |
[
"hwchase17",
"langchain"
]
| ### Describe the bug
in `WandbCallbackHandler.on_chain_start` (row 533 in wandb_callback.py), the name of the input key is hardcoded set to "input" and the name of the output key is hard-coded "output", so if the chain uses any other name for the input key, this results in `KyeError`.
<!--- A minimal code snippet between the quotes below -->
```python
```
<!--- A full traceback of the exception in the quotes below -->
```shell
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevd.py", line 1448, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "use_wandb.py", line 21, in <module>
res = intenter.get_intent(IntenterInput(query="hi there"))
File "intenter.py", line 183, in get_intent
result = self.full_chain(inputs.dict())
File ".venv/lib/python3.9/site-packages/langchain/chains/base.py", line 107, in __call__
self.callback_manager.on_chain_start(
File "venv/lib/python3.9/site-packages/langchain/callbacks/base.py", line 184, in on_chain_start
handler.on_chain_start(serialized, inputs, **kwargs)
File ".venv/lib/python3.9/site-packages/langchain/callbacks/wandb_callback.py", line 533, in on_chain_start
chain_input = inputs["input"]
KeyError: 'input'
python-BaseException
```
### Additional Files
_No response_
### Environment
WandB version: wandb==0.14.0
OS: macOS big sur
Python version: 3.9.9
Versions of relevant libraries: langchain==0.0.118
### Additional Context
_No response_ | Bug in WandbCallbackHandler | https://api.github.com/repos/langchain-ai/langchain/issues/1884/comments | 3 | 2023-03-22T13:15:40Z | 2023-09-10T16:40:58Z | https://github.com/langchain-ai/langchain/issues/1884 | 1,635,746,539 | 1,884 |
[
"hwchase17",
"langchain"
]
| Hi,
I am a bit confused as to what is the best approach to implement the "chatting with a document store". There seem to be two approaches to do this:
- ChatVectorDBChain -- https://github.com/mayooear/gpt4-pdf-chatbot-langchain
- agent + ConversationBufferMemory -- https://github.com/jerryjliu/llama_index/blob/main/examples/chatbot/Chatbot_SEC.ipynb
Is there an advantage to using ChatVectorDBChain?
Thank you very much!
| ChatVectorDBChain vs. agent + ConversationBufferMemory for chat | https://api.github.com/repos/langchain-ai/langchain/issues/1883/comments | 5 | 2023-03-22T12:41:20Z | 2023-10-29T16:07:28Z | https://github.com/langchain-ai/langchain/issues/1883 | 1,635,694,115 | 1,883 |
[
"hwchase17",
"langchain"
]
| ```
Traceback (most recent call last):
File "/Users/xingfanxia/projects/notion-qa/qa.py", line 25, in <module>
result = chain({"question": args.question})
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/base.py", line 116, in __call__
raise e
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/base.py", line 113, in __call__
outputs = self._call(inputs)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/qa_with_sources/base.py", line 118, in _call
answer, _ = self.combine_documents_chain.combine_docs(docs, **inputs)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/map_reduce.py", line 143, in combine_docs
return self._process_results(results, docs, token_max, **kwargs)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/map_reduce.py", line 173, in _process_results
num_tokens = length_func(result_docs, **kwargs)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 83, in prompt_length
return self.llm_chain.llm.get_num_tokens(prompt)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 331, in get_num_tokens
enc = tiktoken.encoding_for_model(self.model_name)
File "/opt/homebrew/lib/python3.10/site-packages/tiktoken/model.py", line 51, in encoding_for_model
raise KeyError(
KeyError: 'Could not automatically map gpt-3.5-turbo to a tokeniser. Please use `tiktok.get_encoding` to explicitly get the tokeniser you expect.'
``` | Tiktoken version is too old for `gpt-3.5-turbo` | https://api.github.com/repos/langchain-ai/langchain/issues/1881/comments | 11 | 2023-03-22T09:23:39Z | 2023-10-18T05:27:58Z | https://github.com/langchain-ai/langchain/issues/1881 | 1,635,371,810 | 1,881 |
[
"hwchase17",
"langchain"
]
| As of the latest release, this is not accepted.
```
/opt/homebrew/lib/python3.10/site-packages/langchain/llms/openai.py:169: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
warnings.warn(
```
And will throw error when asked | Is it possible to use `gpt-3.5-turbo` for `VectorDBQAWithSourcesChain` | https://api.github.com/repos/langchain-ai/langchain/issues/1880/comments | 4 | 2023-03-22T09:01:34Z | 2023-03-24T04:55:20Z | https://github.com/langchain-ai/langchain/issues/1880 | 1,635,332,994 | 1,880 |
[
"hwchase17",
"langchain"
]
| Love Langchain library, so obsessed with it lately!
I've using ChatVectorDBChain which retrieves answers from Pinecone vectorstore and it's been working very well.
But one thing I noticed is that for normal `ConversationChain`, you can add `memory` argument, which provides nice user experience because it remembers the discussed entities.
Question: can we add `memory` argument to `ChatVectorDBChain`? If it is already existed, could you point me whether below code is the right way to use it?
Thanks again so much!!๐
```
from langchain.chains import ChatVectorDBChain
from langchain.memory import ConversationEntityMemory
chat_with_sources = ChatVectorDBChain.from_llm(
llm=llm,
chain_type="stuff",
vectorstore=vectorstore,
return_source_documents=True
#memory=ConversationEntityMemory(llm=llm, k=5)
)
``` | Entity memory + ChatVectorDB ? | https://api.github.com/repos/langchain-ai/langchain/issues/1876/comments | 6 | 2023-03-22T06:25:42Z | 2023-09-20T18:16:25Z | https://github.com/langchain-ai/langchain/issues/1876 | 1,635,138,622 | 1,876 |
[
"hwchase17",
"langchain"
]
| We should add human input as a tool. Human is AGI and can step in when the model is confused or lost, or need some help. | Use human input as a tool | https://api.github.com/repos/langchain-ai/langchain/issues/1871/comments | 7 | 2023-03-22T00:23:26Z | 2024-07-26T22:26:55Z | https://github.com/langchain-ai/langchain/issues/1871 | 1,634,874,206 | 1,871 |
[
"hwchase17",
"langchain"
]
| If I have a context (document) to answer questions from, is there a way to send multiple messages to openai using langchain API? I would like to get a list back where each item corresponds to answer for each question.
Thanks,
Ravi | Sending multiple questions in one API call and get responses for each | https://api.github.com/repos/langchain-ai/langchain/issues/1870/comments | 2 | 2023-03-22T00:08:09Z | 2023-09-18T16:22:59Z | https://github.com/langchain-ai/langchain/issues/1870 | 1,634,864,043 | 1,870 |
[
"hwchase17",
"langchain"
]
| Are you considering to add ability to use ensembling during document search? | Use ensemble of indicies on document search | https://api.github.com/repos/langchain-ai/langchain/issues/1868/comments | 1 | 2023-03-21T22:25:49Z | 2023-08-21T16:08:19Z | https://github.com/langchain-ai/langchain/issues/1868 | 1,634,779,399 | 1,868 |
[
"hwchase17",
"langchain"
]
| The current constructor for ElasticVectorSearch does not support connecting to Elastic Cloud.
```
>>> db = ElasticVectorSearch.from_documents(docs, embeddings,
... cloud_id = CLOUD_ID,
... basic_auth=(USER_NAME, PASSWORD))Traceback (most recent call last): File "<stdin>", line 1, in <module>
File "c:\Users\bcollingsworth\code\langchain_testing\.venv\lib\site-packages\langchain\vectorstores\base.py", line 113, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "c:\Users\bcollingsworth\code\langchain_testing\.venv\lib\site-packages\langchain\vectorstores\elastic_vector_search.py", line 161, in from_texts
elasticsearch_url = get_from_dict_or_env(
File "c:\Users\bcollingsworth\code\langchain_testing\.venv\lib\site-packages\langchain\utils.py", line 17, in get_from_dict_or_env
raise ValueError(
ValueError: Did not find elasticsearch_url, please add an environment variable `ELASTICSEARCH_URL` which contains it, or pass `elasticsearch_url` as a named parameter.
```
It looks like the current implementation would only support an instance of Elastic Search on localhost without security enabled, which is not recommended.
I can connect with the elasticsearch python client like this:
```
from elasticsearch import Elasticsearch
es = Elasticsearch(cloud_id=CLOUD_ID, http_auth=(USER_NAME, PASSWORD))
es.info()
```
See the documentation at [Connecting to Elastic Cloud](https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/connecting.html#connect-ec)
An alternative constructor to the ElasticVectorSearch that takes a elasticsearch client instance, index name and embedding object would allow the user to deal with all the possible authentication set ups before using langchain.
| ElasticVectorSearch constructor to support Elastic Cloud | https://api.github.com/repos/langchain-ai/langchain/issues/1865/comments | 6 | 2023-03-21T20:56:51Z | 2023-09-19T15:17:29Z | https://github.com/langchain-ai/langchain/issues/1865 | 1,634,676,623 | 1,865 |
[
"hwchase17",
"langchain"
]
| How can we load directly xlsx file in langchain just like CSV loader? I could not be able to find in the documentation | Xlsx loader | https://api.github.com/repos/langchain-ai/langchain/issues/1862/comments | 7 | 2023-03-21T17:25:01Z | 2023-12-02T16:10:07Z | https://github.com/langchain-ai/langchain/issues/1862 | 1,634,398,916 | 1,862 |
[
"hwchase17",
"langchain"
]
| Hey all, Gor from [Aim](http://github.com/aimhubio/aim) here ๐
First of all thanks for this awesome project!
After playing with LangChain, I found that Aim // LangChain integration can be extremely helpful with tracing and deep exploration of prompts... at scale... with just a few steps. โฌ๏ธ
## How can Aim trace and enhance prompts observability?
### Prompts comparison
It would be great to be able to compare different prompt templates.
Here is an example view where it is possible to explore and compare outputs of different prompts:
Querying, grouping, coloring included!
### Prompts exploration
We are also planning to integrate [ICE](https://github.com/oughtinc/ice) (or an alternative) for an upgraded experience to analyze an individual session:
---
The integration might be helpful both for prompt engineering and prompt monitoring in production.
Please consider this as an early iteration. Thoughts and feedbacks are highly appreciated. ๐
I believe this will be a great addition to both of the tools and will be beneficial for the community!
Would be super happy to tell more about our roadmap and learn if there is a space for integrations and collaboration. Happy to jump on a quick call or open a shared channel on slack/discord.
<details>
<summary>
Prompts tracking
</summary>
</br>
Aim provides a generic interface to track and store strings with an additional context.
And it is super easy to integrate with any python script or tool.
It is as simple as calling:
```py
aim_run.track(input_text, name="inputs", context={'function': ..., 'kwargs': ..., 'tags': ...})
```
...or both inputs and outputs:
```py
aim_run.track([input_text, output_text], name="inputs", context={'function': ..., 'kwargs': ..., 'tags': ...})
```
...or inputs, outputs and intermediate results:
```py
aim_run.track([input_text, intermediate_res_1, intermediate_res_2, output_text], name="inputs", context={'function': ..., 'kwargs': ..., 'tags': ...})
```
...or even arbitrary py objects:
```py
aim_run.track({
"prompt": input_text,
"intermediate_res_1": Image(intermediate_res_1), # tracking images
... # tracking any other python objects
}], name="inputs", context={'function': ..., 'kwargs': ..., 'tags': ...})
```
</details>
<details>
<summary>
More details
</summary>
</br>
Aim is fully open source, it tracks and stores data at a local .aim repository.
Aim has builtin tools - explorers, which enable in-depth exploration and comparison of any type of metadata.
With the upcoming releases it is also going to enable building fully customizable dashboards. Particularly targeting prompt engineering use-cases.
Aim repo: http://github.com/aimhubio/aim
Aim docs: http://aimstack.readthedocs.io
</details>
@hwchase17
| Prompts tracing and visualization with Aim (Aim // LangChain integration) | https://api.github.com/repos/langchain-ai/langchain/issues/1861/comments | 1 | 2023-03-21T17:11:07Z | 2023-09-18T16:23:05Z | https://github.com/langchain-ai/langchain/issues/1861 | 1,634,374,448 | 1,861 |
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