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https://api.github.com/repos/langchain-ai/langchain/issues/6209
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I_kwDOIPDwls5ozWL1
| 6,209 |
OPENAI_FUNCTIONS agent doesn't accept python_repl or google-search or wolfram-alpha tool
|
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[] | null | 4 | 2023-06-15T08:15:08 | 2023-06-16T10:05:53 | 2023-06-16T10:05:53 |
CONTRIBUTOR
| null |
### System Info
Langchain 0.0.200
Python 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [x] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is my [gist](https://gist.github.com/alonsosilvaallende/17968208a14994e8285d91abdd79efab) to reproduce the error and my current workaround.
The description of my gist is basically:
1. I followed the [example notebook](https://github.com/hwchase17/langchain/blob/master/docs/modules/agents/agents/examples/openai_functions_agent.ipynb) for OPENAI_FUNCTIONS Agent
2. In the tools, add `python_repl` as follows: `load_tools(['python_repl'])[0]` so that the Tools are modified as follows:
```python
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events. You should ask targeted questions"
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
),
Tool(
name="FooBar-DB",
func=db_chain.run,
description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context"
),
load_tools(['python_repl'])[0]
]
```
### Expected behavior
I expect the agent to run normally, however it gives me the error:
'Python REPL' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.4.name'
Here is my [gist](https://gist.github.com/alonsosilvaallende/17968208a14994e8285d91abdd79efab) to see the error and my current workaround.
If I understand correctly, the agent doesn't like that the name has a space. Indeed, if I redefine `python_repl` as:
```python
Tool(name='Python',
func=python_repl.run,
description='A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.'
),
```
the problem disappears. It's a workaround but I expect that either this agent not to have problems with names with spaces or to rename Python REPL as Python_REPL and similarly the other tools as Wolfram_Alpha, etc.
|
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I_kwDOIPDwls5ozIEA
| 6,208 |
ClickHouse ERROR: Distance function argument of Annoy index must be of type String.
|
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NONE
| null |
### System Info
langchain: 0.0.200
platform: macOS
python: 3.10.11
clickhouse: version 23.5.2.7 (official build)
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
<img width="1351" alt="image" src="https://github.com/hwchase17/langchain/assets/12210038/187c268c-7eca-40f1-80a6-596ba3885be9">
### Expected behavior
success write data to clickhouse
|
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PR_kwDOIPDwls5TD-P9
| 6,206 |
perf(openai): changed base model to gpt-3.5-turbo
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[] | null | 1 | 2023-06-15T07:03:47 | 2023-06-15T13:19:24 | 2023-06-15T13:19:23 |
CONTRIBUTOR
| null |
I've changed the base model in OpenAI Class to gpt-3.5-turbo. It was already changed in ChatOpenAI class.
Let me know if there was any reason why you kept text-davinci-003 which is 10 times more costly and not that smart in OpenAI Class and not in ChatOpenAI :D
@hwchase17
@agola11
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I_kwDOIPDwls5oy31J
| 6,205 |
Azure OpenAPI sometimes returns message without text, but also missing "content" key. Langchain should handle it gracefuly.
|
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NONE
| null |
### System Info
langchain: 0.0.188 (but same would happen in the latest master too)
python 3.10
Linux
Apparently, when model generates no text (it will depend on a specific set of prompts, messages, NO stopwords is used), Azure OpenAI API responds with something like this:
```
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"role": "assistant"
}
}
],
```
As you can see, "message" doesn't have "content" key at all. And LangChain expects that key to always be there and at one point there will be KeyError that "content" is not available. Not the best implementation of API from AOAI side, but we should handle it and raise some dedicated exception. The good place for it seems to be here: https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/openai.py#L368 , (def _create_chat_result)
I've "fixed" it by doing this:
```
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for res in response["choices"]:
if "content" not in res["message"]: #<--- checking it it's missing
raise EmptyResponseFromModel()# <---- raising custom exception that I can intercept in the main code and react appropriately
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Can't share exact prompts and messages
### Expected behavior
We should handle this broken response from the AOAI gracefully.
|
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I_kwDOIPDwls5oy2e-
| 6,204 |
DOC: type issue with `pandoc`
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CONTRIBUTOR
| null |
### Issue with current documentation:
There is an typo issue in this [page](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/epub.html).
### Idea or request for content:
Change `pandocs` to `pandoc`.
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PR_kwDOIPDwls5TD5u6
| 6,203 |
Fix typo `pandocs` to `pandoc`
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CONTRIBUTOR
| null |
Fixes https://github.com/hwchase17/langchain/issues/6204
### Context
An typo issue with `pandoc`.
#### Who can review?
@hwchase17
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I_kwDOIPDwls5oyz8Y
| 6,202 |
On API base URL change, underlying connection doesn't always change.
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[] | null | 3 | 2023-06-15T06:45:19 | 2023-10-25T16:08:12 | 2023-10-25T16:08:11 |
NONE
| null |
### System Info
Version: 0.0.188 (but I don't see any change in the latest master that would fix that)
Python: 3.10
Linux
Problem description:
In one of our environments we have multiple Azure OpenAPI base URLs and have to change them on a fly. We do it by changing LLM within the Chain, basically recreating LLM with the new Base URL, Deployment name and so on, something like this:
```
llm = AzureChatOpenAI(
openai_api_base=api_base,
openai_api_version=api_version,
deployment_name=deployment_name,
openai_api_key=api_key,
openai_api_type="azure",)
```
Unfortunately it doesn't always have an effect, as, apparently most of those parameters are not passed correctly to the OpenAI API, here: https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/openai.py#L294 (def completion_with_retry and def acompletion_with_retry) kwargs don't contain things like base endpoint and so on. It seems to be from some legacy openai implementation when it had to be declared as environmental variable. And basically leads to a situation (time to time, depending on how fast the change is, if concurrently different users use different models, etc.) that base URL will remain the same, while deployment name changes.
To fix this, I simply added:
```
...
retry_decorator = _create_retry_decorator(llm)
try:
kwargs['api_base'] = llm.openai_api_base
kwargs['api_key'] = llm.openai_api_key
kwargs['api_type'] = llm.openai_api_type
kwargs['api_version'] = llm.openai_api_version
kwargs['organization'] = llm.openai_organization if llm.openai_organization else None
except:
pass
@retry_decorator
...
```
As it's inside chat_models and openai supports those arguments for chat models, haven't had any problems with it.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Have 2 different API Base URLs with different deployment names
2. Create many CoversationChain with AzureChatOpenAI (one per "user" from step#4)
3. Have multiple threads/asyncio (in my specific case, it's behind FastAPI)
4. emulate multiple, parallel calls of CoversationChain.run and then start randomly changing for some user AzureChatOpenAI model pointing to different Base URL and deployment name
5. You will see that some calls will fail, cause deployment name would change but base URL won't (easy to observe by setting logging level to DEBUG)
### Expected behavior
Change of base URL and deployment name should be propagated to the "caller" deterministically and reliably.
|
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| 6,200 |
AzureChatOpenAI.get_num_tokens_from_messages does not work
|
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[] | null | 4 | 2023-06-15T05:19:24 | 2023-11-15T18:02:17 | null |
NONE
| null |
### System Info
langchain==0.0.198 and current HEAD
AzureChat inherits from OpenAIChat
Which throws on Azure's model name
Azure's model name is gpt-35-turbo, not 3.5
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. deploy private GPT3.5 on Azure
2. Initialize an AzureChatOpenAI object
3. call get_num_tokens_from_messages
4. observe the exception
### Expected behavior
no exception
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PR_kwDOIPDwls5TDewH
| 6,199 |
Make so explicit class constructor is called in ElasticVectorSearch from_texts
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[] | null | 5 | 2023-06-15T04:51:58 | 2023-07-13T23:55:21 | 2023-07-13T23:55:21 |
CONTRIBUTOR
| null |
Fixes #6198
ElasticKnnSearch.from_texts is actually ElasticVectorSearch.from_texts and throws because it calls ElasticKnnSearch constructor with the wrong arguments.
Now ElasticKnnSearch has its own from_texts, which constructs a proper ElasticKnnSearch.
#### Who can review?
@CodeDevNinja @dev2049
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| 6,198 |
Elasticsearch : ElasticKnnSearch.from_texts throws AttributeError
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[] | null | 0 | 2023-06-15T04:45:12 | 2023-07-13T23:55:22 | 2023-07-13T23:55:22 |
CONTRIBUTOR
| null |
### System Info
Langchain version : 0.0.199
Python Version: Python 3.9.16
MacOS
@CodeDevNinja @dev2049
PR https://github.com/hwchase17/langchain/pull/5058 introduced a change to ElasticVectorSearch from_texts which broke, kind of coincidentally, ElasticKnnSearch from_texts
I discovered this issue when running docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb . I got to the following cell:
```python
# Test `add_texts` method
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)
# Test `from_texts` method
new_texts = ["This is a new text.", "Elasticsearch is powerful.", "Python is great for data analysis."]
knn_search.from_texts(new_texts, embeddings, elasticsearch_url=elasticsearch_url)
```
and it said:
```python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[10], line 7
5 # Test `from_texts` method
6 new_texts = ["This is a new text.", "Elasticsearch is powerful.", "Python is great for data analysis."]
----> 7 knn_search.from_texts(new_texts, embeddings, elasticsearch_url=elasticsearch_url)
File ~/dev/github/langchain/langchain/vectorstores/elastic_vector_search.py:296, in ElasticVectorSearch.from_texts(cls, texts, embedding, metadatas, elasticsearch_url, index_name, refresh_indices, **kwargs)
293 index_name = index_name or uuid.uuid4().hex
294 vectorsearch = cls(
295 elasticsearch_url, index_name, embedding, **kwargs)
--> 296 vectorsearch.add_texts(
297 texts, metadatas=metadatas, refresh_indices=refresh_indices
298 )
299 return vectorsearch
File ~/dev/github/langchain/langchain/vectorstores/elastic_vector_search.py:183, in ElasticVectorSearch.add_texts(self, texts, metadatas, refresh_indices, **kwargs)
181 requests = []
182 ids = []
--> 183 embeddings = self.embedding.embed_documents(list(texts))
184 dim = len(embeddings[0])
185 mapping = _default_text_mapping(dim)
AttributeError: 'str' object has no attribute 'embed_documents'
```
which is a pretty weird error.
This is because https://github.com/cdiddy77/langchain/blob/e74733ab9e5e307fd828ea600ea929a1cb24320f/langchain/vectorstores/elastic_vector_search.py#L294 invokes the __init__ of the calling class, in this case `ElasticKnnSearch` which takes parameters in a very different order.
This calling of the wrong __init__ was always present, but the PR above added a subsequent called to add_texts, which is where the bogus embedding is referenced, causing the exception.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to repro:
1. Open docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
2. Modify as appropriate with elasticsearch_url, and further down, model_id, dims, cloud_id, username,password of elastic cloud deployment
3. Run until cell below "Test adding vectors"
### Expected behavior
Not throw exception
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| 6,197 |
ValueError: Too many arguments to single-input tool Calculator. Args: ['age ^ 0.43', {'age': 26}]
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[] | null | 11 | 2023-06-15T03:45:49 | 2024-01-01T22:32:00 | null |
CONTRIBUTOR
| null |
### System Info
0.200
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [x] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run *docs/modules/agents/agents/examples/openai_functions_agent.ipynb*
at line `mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")`
>
> > Entering new chain...
>
> Invoking: `Search` with `{'query': 'Leo DiCaprio girlfriend'}`
>
>
> Leonardo DiCaprio and Gigi Hadid were recently spotted at a pre-Oscars party, sparking interest once again in their rumored romance. The Revenant actor and the model first made headlines when they were spotted together at a New York Fashion Week afterparty in September 2022.
> Invoking: `Calculator` with `{'expression': 'age ^ 0.43', 'variables': {'age': 26}}`
>
then throw err: `ValueError: Too many arguments to single-input tool Calculator. Args: ['age ^ 0.43', {'age': 26}]`
### Expected behavior
should output value
|
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I_kwDOIPDwls5oyE45
| 6,196 |
OpenAI: Function calling and other API updates
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[] | null | 8 | 2023-06-15T03:15:17 | 2023-06-19T07:24:03 | 2023-06-15T23:50:58 |
NONE
| null |
### Feature request
https://openai.com/blog/function-calling-and-other-api-updates
I think we should update the ChatOpenAi models behavior with tools so that it used the native API.
### Motivation
Their model is likely trained to handle functions this way, and will have a lot better support. It also supposedly guarantees json matching the json schema, which can be hard to achieve otherwise.
### Your contribution
I may be able to help. I’m working on OpenAI at work, but I’m just learning langchains API.
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| 6,195 |
count tokens for new OpenAI model versions
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[] | null | 4 | 2023-06-15T02:27:29 | 2023-06-15T18:39:51 | 2023-06-15T13:16:03 |
CONTRIBUTOR
| null |
Trying to call `ChatOpenAI.get_num_tokens_from_messages` returns the following error for the newly announced models `gpt-3.5-turbo-0613` and `gpt-4-0613`:
```
NotImplementedError: get_num_tokens_from_messages() is not presently implemented for model gpt-3.5-turbo-0613.See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.
```
This adds support for counting tokens for those models, by counting tokens the same way they're counted for the previous versions of `gpt-3.5-turbo` and `gpt-4`.
#### reviewers
- @hwchase17
- @agola11
|
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I_kwDOIPDwls5ox6IJ
| 6,194 |
TypeError: similarity_search_with_score_by_vector() got an unexpected keyword argument 'score_threshold'
|
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[] | null | 2 | 2023-06-15T02:16:22 | 2023-09-22T16:07:13 | 2023-09-22T16:07:12 |
NONE
| null |
### System Info
python 3.9
current version
### Who can help?
@agola11
@eyurtsev
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.embeddings import LlamaCppEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings
from langchain.embeddings import OpenAIEmbeddings
import json
from langchain.retrievers import SVMRetriever
embeddings = LlamaCppEmbeddings(model_path="ggml-model-q4_0.bin")
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
text_list = ['The first Nobel Prize in Physics was awarded in 1901 to Wilhelm Conrad R\u00f6ntgen \"for his discovery of the remarkable rays subsequently named after him\".',
#'The Nobel Prize in Physics is a yearly award given by the Royal Swedish Academy of Sciences for those who have made the most outstanding contributions for mankind in the field of physics. It is one of the five Nobel Prizes established by the 1895 will of Alfred Nobel, which are awarded for outstanding contributions in chemistry, physiology or medicine, literature, and physics. These prizes are awarded in Stockholm, Sweden. The first Nobel Prize in Physics was awarded to Wilhelm R\u00f6ntgen in 1901.',
#'The next Deadpool movie is set to be released on June 1, 2018.'
]
#print(documents)
db = FAISS.from_texts(text_list, embeddings)
retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5})
docs = retriever.get_relevant_documents("who got the first nobel prize in physics")
print(docs)
```
### Expected behavior
```
Traceback (most recent call last):
File "llama_index/l.py", line 56, in <module>
docs = retriever.get_relevant_documents("who got the first nobel prize in physics")
File "/scratch/c7031420/.conda/envs/langchain/lib/python3.9/site-packages/langchain/vectorstores/base.py", line 395, in get_relevant_documents
self.vectorstore.similarity_search_with_relevance_scores(
File ".conda/envs/langchain/lib/python3.9/site-packages/langchain/vectorstores/base.py", line 141, in similarity_search_with_relevance_scores
docs_and_similarities = self._similarity_search_with_relevance_scores(
File "/.conda/envs/langchain/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 609, in _similarity_search_with_relevance_scores
docs_and_scores = self.similarity_search_with_score(
File "/.conda/envs/langchain/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 245, in similarity_search_with_score
docs = self.similarity_search_with_score_by_vector(
TypeError: similarity_search_with_score_by_vector() got an unexpected keyword argument 'score_threshold'
```
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| 1,757,906,824 |
I_kwDOIPDwls5ox4eI
| 6,193 |
get_openai_callback total_cost BROKEN
|
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[] | null | 5 | 2023-06-15T02:05:58 | 2023-09-21T16:08:01 | 2023-09-21T16:08:00 |
CONTRIBUTOR
| null |
### System Info
Basically, when using "llm.generate" in combination with get_openai_callback the total_cost just outputs 0.
Code Snippet
```
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.callbacks import get_openai_callback
chat = [{"role": "user", "content": "What's the weather like in Boston?"}]
for message in chat:
if message["role"] == "assistant":
messages.append(AIMessage(content=message["content"]))
elif message["role"] == "user":
messages.append(HumanMessage(content=message["content"]))
with get_openai_callback() as cb:
res = llm.generate([messages])
print(cb) # Tokens Used is okey
print(cb) # Total Cost is Always 0
```
### Who can help?
@agola11 It's a Callback Issue. (That's why I am tagging you)
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.callbacks import get_openai_callback
chat = [{"role": "user", "content": "What's the weather like in Boston?"}]
for message in chat:
if message["role"] == "assistant":
messages.append(AIMessage(content=message["content"]))
elif message["role"] == "user":
messages.append(HumanMessage(content=message["content"]))
with get_openai_callback() as cb:
res = llm.generate([messages])
print(cb) # Tokens Used is okey
print(cb) # Total Cost is Always 0```
### Expected behavior
It should work the same way it works with chains or agents.
|
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| 1,757,895,573 |
PR_kwDOIPDwls5TDB1c
| 6,192 |
kiley-poole/Update OpenAI Chat Model to work with GPT-3-16k and GPT-4-32k
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[] | null | 0 | 2023-06-15T01:53:34 | 2023-06-15T21:48:46 | 2023-06-15T21:48:45 |
NONE
| null |
With the release of the new 16k context length Gpt-3 turbo model, I was running into issues with Buffer Summary Memory. I kept receiving the following error.
`NotImplementedError: get_num_tokens_from_messages() is not presently implemented for model gpt-3.5-turbo-16k.See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.`
Turns out the `get_num_tokens_from_messages` function is reliant on the hard coded values for the models. It current has support for gpt-3.5 and 4, but neither longer context model. I've added support for both of these. I also updated the existing model version numbers to 0613 (the most current set) and updated the model costs.
|
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About map_reduce.py
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[] | null | 8 | 2023-06-15T01:36:42 | 2023-07-05T15:15:46 | 2023-07-05T08:13:28 |
NONE
| null |
### System Info
When I'm using "gpt-3.5-turbo-16k" model,This model supports 16k token.However, using the mp-reduce algorithm, if the answer obtained at A time exceeds 4000 tokens, this will be reported. "A single document was longer than the context length,we cannot handle this." Error.
I don't think the token_max parameter changes for different models
### Who can help?
@hwchase17 @agola11 Hope to get help, this is very troublesome for my use
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
My code:
chain_one = load_summarize_chain(chat, chain_type="map_reduce",return_intermediate_steps=True,verbose=True, map_prompt=PROMPT, combine_prompt=combine_prompt)
x = chain_one({"input_documents": documents},return_only_outputs=True)
documents chunk_size is 4000 tokens
ERROR:File D:\conda\envs\ai\Lib\site-packages\langchain\chains\combine_documents\map_reduce.py:37, in _split_list_of_docs(docs, length_func, token_max, **kwargs)
32 raise ValueError(
33 "A single document was longer than the context length,"
34 " we cannot handle this."
35 )
36 if len(_sub_result_docs) == 2:
---> 37 raise ValueError(
38 "A single document was so long it could not be combined "
39 "with another document, we cannot handle this."
40 )
41 new_result_doc_list.append(_sub_result_docs[:-1])
42 _sub_result_docs = _sub_result_docs[-1:]
ValueError: A single document was so long it could not be combined with another document, we cannot handle this.
### Expected behavior
I hope that when I use the large token model, these errors will not occur
|
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PR_kwDOIPDwls5TC4C8
| 6,190 |
Updated index.rst to reflect langchain's LLM independence
|
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[] | null | 1 | 2023-06-15T00:44:14 | 2023-06-18T23:57:15 | 2023-06-18T23:57:14 |
NONE
| null |
<!--
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Fixes #930 (issue)
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Tag maintainers/contributors who might be interested:
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PR_kwDOIPDwls5TCsce
| 6,189 |
Update dev container
|
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[] | null | 6 | 2023-06-14T23:19:41 | 2023-06-16T22:42:15 | 2023-06-16T22:42:14 |
CONTRIBUTOR
| null |
Fixes https://github.com/hwchase17/langchain/issues/6172
As described in https://github.com/hwchase17/langchain/issues/6172, I'd love to help update the dev container in this project.
**Summary of changes:**
- Dev container now builds (the current container in this repo won't build for me)
- Dockerfile updates
- Update image to our [currently-maintained Python image](https://github.com/devcontainers/images/tree/main/src/python/.devcontainer) (`mcr.microsoft.com/devcontainers/python`) rather than the deprecated image from vscode-dev-containers
- Move Dockerfile to root of repo - in order for `COPY` to work properly, it needs the files (in this case, `pyproject.toml` and `poetry.toml`) in the same directory
- devcontainer.json updates
- Removed `customizations` and `remoteUser` since they should be covered by the updated image in the Dockerfile
- Update comments
- Update docker-compose.yaml to properly point to updated Dockerfile
- Add a .gitattributes to avoid line ending conversions, which can result in hundreds of pending changes ([info](https://code.visualstudio.com/docs/devcontainers/tips-and-tricks#_resolving-git-line-ending-issues-in-containers-resulting-in-many-modified-files))
- Add a README in the .devcontainer folder and info on the dev container in the contributing.md
**Outstanding questions:**
- Is it expected for `poetry install` to take some time? It takes about 30 minutes for this dev container to finish building in a Codespace, but a user should only have to experience this once. Through some online investigation, this doesn't seem unusual
- Versions of poetry newer than 1.3.2 failed every time - based on some of the guidance in contributing.md and other online resources, it seemed changing poetry versions might be a good solution. 1.3.2 is from Jan 2023
#### Who can review?
Tag maintainers/contributors who might be interested: @vowelparrot
I was able to run through the various `make` commands mentioned in contributing.md successfully with this updated setup, and please let me know if there are questions or other tests I can help with to ensure everything is running well. Thanks so much!
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PR_kwDOIPDwls5TClIL
| 6,188 |
Updated notebook openai_functions_agent.ipynb
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[] | null | 3 | 2023-06-14T22:47:02 | 2023-06-18T23:51:04 | 2023-06-18T23:51:04 |
CONTRIBUTOR
| null |
Here's the updated code with grammatical improvements, added comments, and placeholders for users to add their API keys and customize the notebook for their specific use cases.
Linked to issue #6178
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PR_kwDOIPDwls5TCa8B
| 6,187 |
Fix class promotion
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[] | null | 1 | 2023-06-14T22:10:38 | 2023-06-19T16:42:53 | 2023-06-18T23:55:18 |
COLLABORATOR
| null |
In LangChain, all module classes are enumerated in the `__init__.py` file of the correspondent module. But some classes were missed and were not included in the module `__init__.py`
This PR:
- added the missed classes to the module `__init__.py` files
- `__init__.py:__all_` variable value (a list of the class names) was sorted
- `langchain.tools.sql_database.tool.QueryCheckerTool` was renamed into the `QuerySQLCheckerTool` because it conflicted with `langchain.tools.spark_sql.tool.QueryCheckerTool`
- changes to `pyproject.toml`:
- added `pgvector` to `pyproject.toml:extended_testing`
- added `pandas` to `pyproject.toml:[tool.poetry.group.test.dependencies]`
- commented out the `streamlit` from `collbacks/__init__.py`, It is because now the `streamlit` requires Python >=3.7, !=3.9.7
- fixed duplicate names in `tools`
- fixed correspondent ut-s
#### Who can review?
@hwchase17
@dev2049
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Fixed a link typo /-/route -> /-/routes. and change endpoint format
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[] | null | 2 | 2023-06-14T22:00:10 | 2023-09-06T23:34:18 | 2023-06-20T05:05:54 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes a link typo from `/-/route` to `/-/routes`.
and change endpoint format
from `f"{self.anyscale_service_url}/{self.anyscale_service_route}"` to `f"{self.anyscale_service_url}{self.anyscale_service_route}"`
Also adding documentation about the format of the endpoint
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
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PR_kwDOIPDwls5TCTUf
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Harrison/use functions agent
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I_kwDOIPDwls5ow65y
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Segmentation fault (core dumped)
|
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[] | null | 1 | 2023-06-14T21:11:13 | 2023-09-20T16:07:35 | 2023-09-20T16:07:34 |
NONE
| null |
### System Info
Python 3.9.7
langchain '0.0.200'
### Who can help?
@hwchase17
@agola11
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.embeddings import LlamaCppEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings
from langchain.embeddings import OpenAIEmbeddings
import json
from langchain.retrievers import SVMRetriever
llama = LlamaCppEmbeddings(model_path="ggml-model-q4_0.bin")
"""
text = "This is a test document."
query_result = llama.embed_query(text)
print(query_result)
doc_result = llama.embed_documents([text])"""
text_list = ['The first Nobel Prize in Physics was awarded in 1901 to Wilhelm Conrad R\u00f6ntgen \"for his discovery of the remarkable rays subsequently named after him\".',
'The Nobel Prize in Physics is a yearly award given by the Royal Swedish Academy of Sciences for those who have made the most outstanding contributions for mankind in the field of physics. It is one of the five Nobel Prizes established by the 1895 will of Alfred Nobel, which are awarded for outstanding contributions in chemistry, physiology or medicine, literature, and physics. These prizes are awarded in Stockholm, Sweden. The first Nobel Prize in Physics was awarded to Wilhelm R\u00f6ntgen in 1901.',
'The next Deadpool movie is set to be released on June 1, 2018.'
]
retriever = SVMRetriever.from_texts(text_list, llama)#llama)
result = retriever.get_relevant_documents("who got the first nobel prize in physics")
print(result)
```
### Expected behavior
Hello All,
how can i use `LlamaCppEmbeddings(model_path="ggml-model-q4_0.bin")` as embedding for the retriever
i tried using ggml-model-q4_0.bin but got `Segmentation fault (core dumped) `
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Update openai_info.py with 0613 models and new pricing
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NONE
| null | null |
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| 1,757,571,414 |
I_kwDOIPDwls5owmlW
| 6,182 |
following contributing.md results in being unable to run make
|
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[] | null | 13 | 2023-06-14T20:12:59 | 2023-10-16T14:28:59 | 2023-10-16T14:26:28 |
CONTRIBUTOR
| null |
### System Info
OS: Ubuntu 20.04 LTS
branch: master
Python: 3.9
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I followed the steps as described in .github/contributing.md:
1. fork the repo
2. clone the fork
3. `conda create -n langchain python=3.9`
4. `conda activate langchain`
5. `pip install -U pip setuptools`
6. `pip install poetry`
7. `poetry completions bash >> ~/.bash_completion`
8. `poetry config virtualenvs.prefer-active-python true`
9. `poetry install -E all` <-- this command failed to unlock the gnome keyring, so I reran according to [poetry#1917](https://github.com/python-poetry/poetry/issues/1917) below:
10. `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring poetry install -E all`
11. `make format`
Running `make format` results in the following:
```
Traceback (most recent call last):
File "/home/kyle/.miniconda3/envs/langchain/bin/make", line 5, in <module>
from scripts.proto import main
ModuleNotFoundError: No module named 'scripts'
```
`which make` returns a `make` script installed in my conda env bin, with the following contents:
```
#!/home/kyle/.miniconda3/envs/langchain/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from scripts.proto import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
```
I'm not sure what this is. I'm pretty sure I followed the directions correctly.
### Expected behavior
I should be able to run `make format` without a problem at this point.
|
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PR_kwDOIPDwls5TBzi7
| 6,181 |
Add the ability to run the map_reduce chains process results step as async
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[] | null | 2 | 2023-06-14T19:49:59 | 2023-06-18T20:19:57 | 2023-06-18T20:19:56 |
CONTRIBUTOR
| null |
This will add the ability to add an AsyncCallbackManager (handler) for the reducer chain, which would be able to stream the tokens via the `async def on_llm_new_token` callback method
Fixes # (issue) [5532](https://github.com/hwchase17/langchain/issues/5532)
@hwchase17 @agola11
The following code snippet explains how this change would be used to enable `reduce_llm` with streaming support in a `map_reduce` chain
I have tested this change and it works for the streaming use-case of reducer responses. I am happy to share more information if this makes solution sense.
```
AsyncHandler
..........................
class StreamingLLMCallbackHandler(AsyncCallbackHandler):
"""Callback handler for streaming LLM responses."""
def __init__(self, websocket):
self.websocket = websocket
# This callback method is to be executed in async
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(sender="bot", message=token, type="stream")
await self.websocket.send_json(resp.dict())
Chain
..........
stream_handler = StreamingLLMCallbackHandler(websocket)
stream_manager = AsyncCallbackManager([stream_handler])
streaming_llm = ChatOpenAI(
streaming=True,
callback_manager=stream_manager,
verbose=False,
temperature=0,
)
main_llm = OpenAI(
temperature=0,
verbose=False,
)
doc_chain = load_qa_chain(
llm=main_llm,
reduce_llm=streaming_llm,
chain_type="map_reduce",
callback_manager=manager
)
qa_chain = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(),
combine_docs_chain=doc_chain,
question_generator=question_generator,
callback_manager=manager,
)
# Here `acall` will trigger `acombine_docs` on `map_reduce` which should then call `_aprocess_result` which in turn will call `self.combine_document_chain.arun` hence async callback will be awaited
result = await qa_chain.acall(
{"question": question, "chat_history": chat_history}
)
```
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PR_kwDOIPDwls5TBs68
| 6,180 |
Fix broken link to SearXNG supported engines
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[] | null | 1 | 2023-06-14T19:24:19 | 2023-06-18T23:46:11 | 2023-06-18T23:46:11 |
CONTRIBUTOR
| null |
Fix broken link to list of SearXNG supported engines.
#### Who can review?
Agents / Tools / Toolkits
- @hwchase17
|
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| 1,757,509,077 |
PR_kwDOIPDwls5TBs6v
| 6,179 |
update zep description and links to doc site
|
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[] | null | 1 | 2023-06-14T19:24:18 | 2023-06-25T23:16:30 | 2023-06-18T23:43:50 |
CONTRIBUTOR
| null |
Update Zep notebooks:
- Update Zep description in markdown fields
- Correct link to Zep documentation website
Thanks!
#### Before submitting
@hwchase17 - project lead
- @dev2049
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I_kwDOIPDwls5owQBj
| 6,178 |
DOC: No explanation of OPENAI_FUNCTIONS agent
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NONE
| null |
### Issue with current documentation:
This is not documented. An example is provided, with no explanation whatsoever. As such this [page](https://python.langchain.com/en/latest/modules/agents/agents/examples/openai_functions_agent.html) contributes nothing over and above the source code.
### Idea or request for content:
Actually document this feature.
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PR_kwDOIPDwls5TBlzo
| 6,177 |
Added OpenAIFunctionAgent to agent module export
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NONE
| null |
Added OpenAIFunctionAgent export to the agent module to bring it into parity with other agents that are exported in this way.
@hwchase17
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I_kwDOIPDwls5ov5ZD
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Only using GPU:0 for vector embedding.
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open
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[] | null | 5 | 2023-06-14T17:51:44 | 2023-12-22T09:37:01 | null |
NONE
| null |
### System Info
torch.__version__ '2.0.1+cu117'
langchain.__version__ '0.0.199'
transformers.__version__ '4.30.2'
### Who can help?
@hwchase17
@agola11
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`source_dir = $DIRNAME
splitter = 'tiktoken'
num_similar = 4
emb_name = 'hkunlp/instructor-xl'
encode_kwargs = {'normalize_embeddings': True}
embeddings = HuggingFaceInstructEmbeddings(
model_name=emb_name,
model_kwargs={'device':'cuda'},
)
txt_loader = DirectoryLoader(source_dir, glob="**/*.txt")
documents = txt_loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size,
chunk_overlap=max_chunk_overlap,
length_function = len)
documents = text_splitter.split_documents(documents)
vectorstore = Chroma.from_documents(documents, embeddings)
vectorstore.similarity_search(query)
`
or after loading vectorstore
`
retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k":num_similar} )
qa = ConversationalRetrievalChain.from_llm(
hf_pipeline,
retriever,
# condense_question_llm = hf_pipeline, #this can be simpler
return_source_documents=return_source_documents,
return_generated_question=True,
combine_docs_chain_kwargs = {'prompt': QA_PROMPT},
condense_question_prompt = CONDENSE_QUESTION_PROMPT,
)
`
When we do something like this, even with device_map = 'auto' or 'balanced', we see much higher GPU consumption on GPU:0.
### Expected behavior
So I expect to load vectorDB balanced over all available GPUs like LLM pipelines. But, it only use GPU:0 which makes inefficient usage of VRAM of multiple GPUs.
|
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| 1,757,377,068 |
PR_kwDOIPDwls5TBQKn
| 6,173 |
add metaphor search new api fields
|
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[] | null | 0 | 2023-06-14T17:44:26 | 2023-07-13T22:13:15 | 2023-06-18T23:41:24 |
CONTRIBUTOR
| null |
Update metaphor search tool with new API interface
ubmitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
|
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I_kwDOIPDwls5ov2Zc
| 6,172 |
Issue: Update dev container configuration
|
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[] | null | 3 | 2023-06-14T17:41:55 | 2023-06-16T22:42:15 | 2023-06-16T22:42:15 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Hi folks! 👋 My name is Brigit, and I'm a PM on the VS Code team working on dev containers and [their open spec](https://containers.dev/). Thank you so much for [adding a dev container to this repo](https://github.com/hwchase17/langchain/pull/4035) and [langchainjs](https://github.com/hwchase17/langchainjs/pull/1241) - these are fantastic scenarios!
As we're actively working improvements to dev containers and their spec, we've made some changes to the best practices we recommend. For instance, we host an updated set of [images](https://github.com/devcontainers/images) and [templates](https://github.com/devcontainers/templates) as part of the spec in the [devcontainers org](https://github.com/devcontainers), rather than in [vscode-dev-containers](https://github.com/microsoft/vscode-dev-containers). It looks like the image in this repo uses the [deprecated vscode-dev-containers image](https://github.com/hwchase17/langchain/blob/master/.devcontainer/Dockerfile#L5), and perhaps it could leverage the [Poetry Feature](https://containers.dev/features) instead of Poetry installation scripts in the Dockerfile.
I also tried building the dev container in this repo both in the VS Code Dev Containers extension and GitHub Codespaces, and it didn't work for me as-is (I was stopped at container build), so I think this would be a great opportunity to ensure the dev container works well for all potential contributors too.
It looks like langchainjs uses an [updated image from the devcontainers org](https://github.com/hwchase17/langchainjs/blob/main/.devcontainer/devcontainer.json#L6), which is great!
### Suggestion:
I'd love to contribute a PR to this repo with an updated dev container (and perhaps with some additional info in the readme or a mini [.devcontainer](https://github.com/hwchase17/langchain/tree/master/.devcontainer) readme), but I wasn't sure the best tests to ensure the repo runs correctly in an updated dev container. Would you be able to share any recommended steps / commands / checks so that I can ensure any dev container updates work well for how folks should be building and running this repo? Info on how you tested and verified the [original PR](https://github.com/hwchase17/langchain/pull/4035) would be a great help too (so I can try the same steps).
Let me know if there's any other info I can provide, and I can also just open a draft a PR if that'd be easiest for discussion. Thanks so much!
cc @vowelparrot and @jj701 as I see your discussion in https://github.com/hwchase17/langchain/pull/4035.
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I_kwDOIPDwls5ovvhj
| 6,171 |
KeyError: 'content' using `AzureCognitiveSearchRetreiver`
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[] | null | 2 | 2023-06-14T17:20:57 | 2023-09-20T16:07:45 | 2023-09-20T16:07:44 |
NONE
| null |
### System Info
langchain=0.0.199
Python=3.9.13
### Who Can Help
@eyurtsev
@hwchase17
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [x] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import os
from langchain.retrievers import AzureCognitiveSearchRetriever
cognitive_search_name = os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"]
retriever = AzureCognitiveSearchRetriever(content_key="content")
retriever.get_relevant_documents("what is langchain")
```
### Expected behavior
I am attempting to use `AzureCognitiveSearchRetriever` to no avail. There is little guidance or documentation that I could find to make this functionality work. I don't know what value I'm supposed to set the parameter `content_key` to in order to make this work.
My overall goal is to retrieve data from Azure Cognitive Search and use it to determine the output served by the chatbot based on user query.
Thanks!
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| 1,757,295,474 |
PR_kwDOIPDwls5TA-YX
| 6,170 |
Fixed PermissionError on windows
|
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[] | null | 0 | 2023-06-14T16:47:07 | 2023-06-18T23:39:58 | 2023-06-18T23:39:57 |
CONTRIBUTOR
| null |
Fixed PermissionError that occurred when downloading PDF files via http in BasePDFLoader on windows.
When downloading PDF files via http in BasePDFLoader, NamedTemporaryFile is used.
This function cannot open the file again on **Windows**.[Python Doc](https://docs.python.org/3.9/library/tempfile.html#tempfile.NamedTemporaryFile)
So, we created a **temporary directory** with TemporaryDirectory and placed the downloaded file there.
temporary directory is deleted in the deconstruct.
Fixes #2698
#### Who can review?
Tag maintainers/contributors who might be interested:
- @eyurtsev
- @hwchase17
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PR_kwDOIPDwls5TAuIF
| 6,169 |
Handle Managed Motorhead Data Key
|
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[] | null | 3 | 2023-06-14T15:58:39 | 2023-06-17T03:37:41 | 2023-06-17T03:36:18 |
CONTRIBUTOR
| null |
# Handle Managed Motorhead Data Key
Managed motorhead will return a payload with a `data` key. we need to handle this to properly access messages from the server.
|
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I_kwDOIPDwls5ouzsx
| 6,168 |
Missing documents when using embeddings
|
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[] | null | 1 | 2023-06-14T14:56:11 | 2023-09-20T16:07:50 | 2023-09-20T16:07:49 |
NONE
| null |
### System Info
Windows 11
Langchain - 0.0.184
Python 3.11.1
### Who can help?
@eyu
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The problem is, I am creating a document collection consisting of 150 documents. However, when I embed them, some documents disappear. I have identified the missing documents by comparing the `bulk_metadatas` variable with the unique values of `patent_embeddings` variable. It shows that some documents are indeed missing from the database. Steps to reproduce.
Load the json file from this [gist ](https://gist.github.com/emilmirzayev/99ef6e641ada53804cbd38015c759ccb)into `bulk_data` variable. Then follow this script:
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.chroma import Chroma
import numpy as np
from scipy.spatial.distance import cdist
bulk_abstracts = []
bulk_claims = []
bulk_metadatas = []
for firm_result in bulk_data:
print(len(firm_result["results"]))
for patent_data in firm_result["results"]:
abstract = abstract_parser(patent_data["abstractText"])
claim = abstract_parser(patent_data["claimText"])
metadata = str(patent_data["assigneeEntityName"]) + " " + str(patent_data["patentApplicationNumber"])
bulk_abstracts.append(abstract)
bulk_claims.append(claim)
bulk_metadatas.append({"company": metadata})
embeddings = OpenAIEmbeddings(openai_api_key = "Your key",
model="text-embedding-ada-002")
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
save_directory = "hundred_fifty_patent_db"
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
patent_documents = text_splitter.create_documents(bulk_claims,
metadatas= bulk_metadatas
)
db = Chroma.from_documents(documents = patent_documents,
embedding= embeddings,
persist_directory=save_directory
)
db.persist()
# reloading back
db = None
directory_to_load_from = "hundred_fifty_patent_db"
db = Chroma(persist_directory= directory_to_load_from,
embedding_function=embeddings)
patent_embeddings = db.get(["embeddings"])["embeddings"]
patent_metadatas = db.get(["metadatas"])["metadatas"]
# lets see how many entries we have, its 146
a = [data["company"] for data in bulk_metadatas]
b = list(set(company_patent))
np.setdiff1d(a, b)
```
Output:
```
array(['Colgate-Palmolive Company US12159313',
'The Coca-Cola Company US12171698',
'The Coca-Cola Company US12917673',
'The Coca-Cola Company US13036081'], dtype='<U58')
```
These four documents are missing from the database.
### Expected behavior
To have all documents present in the embeddings
|
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| 6,167 |
TypeError: FAISS.similarity_search_with_score_by_vector() got an unexpected keyword argument 'score_threshold'
|
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[] | null | 2 | 2023-06-14T14:38:07 | 2023-09-20T16:07:55 | 2023-09-20T16:07:54 |
NONE
| null |
### System Info
LnagChain v0.0.200
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create `FAISS` vectorstore
2. Call `as_retriever(search_type='similarity_score_threshold', k=4, search_kwargs={'score_threshold': 0.6})`
### Expected behavior
To not raise error
|
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I_kwDOIPDwls5ot8Ux
| 6,166 |
NameError: Name pd not found with PythonRepl
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[] | null | 12 | 2023-06-14T13:09:38 | 2024-01-03T16:01:47 | null |
NONE
| null |
### System Info
langchain version = 0.0.198
while using the lagchain create_pandas_dataframe_agent,
it was able to generate the correct intermediate command,
but when it came to execute it, it says pd is not defined.
its not able to detect that pandas is to imported as pd
i am using AzureOpenAI service with gpt-3.5-turbo model
anyone who can help me on this
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Same code run
### Expected behavior
in intermediate step, it will say pd is not defined.
|
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PR_kwDOIPDwls5S_c5T
| 6,165 |
support memory for functions
|
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[] | null | 2 | 2023-06-14T12:56:03 | 2023-06-19T02:00:41 | 2023-06-19T02:00:41 |
CONTRIBUTOR
| null |
#### Before submitting
Add memory support for `OpenAIFunctionsAgent` like `StructuredChatAgent`.
#### Who can review?
@hwchase17
|
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PR_kwDOIPDwls5S_bQB
| 6,164 |
feature: token based text splitter relative chunk overlap argument
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[] | null | 6 | 2023-06-14T12:51:41 | 2023-11-07T04:14:50 | 2023-11-07T04:14:50 |
CONTRIBUTOR
| null |
For SentenceTransformersTokenTextSplitter text splitter.
Adds a new parameter `relative_chunk_overlap` for the `SentenceTransformersTokenTextSplitter` constructor. The parameter sets the chunk overlap using a relative factor, e.g. for a model where the token limit is 100, a `relative_chunk_overlap=0.5` implies that `chunk_overlap=50`.
#### Who can review?
@hwchase17 @dev2049
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typo: 'following following' to 'following'
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I_kwDOIPDwls5otsq7
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Retrieval Question/Answering Example not working in 0.0.200
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[] | null | 5 | 2023-06-14T12:37:22 | 2023-06-27T06:42:45 | 2023-06-27T06:42:45 |
NONE
| null |
### System Info
**The problem seem to be in below code:**
exception: dict is not iterable
Working version: langchain==0.0.164
usecase : https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html
Issue in below method:
```
def dict(self, **kwargs: Any) -> Dict:
"""Return a dictionary of the LLM."""
starter_dict = dict(self._identifying_params)
starter_dict["_type"] = self._llm_type
return starter_dict
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [x] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html
Try these steps
### Expected behavior
It should work as per the example.
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| 1,756,736,892 |
PR_kwDOIPDwls5S_EhI
| 6,161 |
Fix error in aim callback when tracking of chains.
|
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[] | null | 4 | 2023-06-14T12:01:52 | 2023-09-15T07:02:51 | 2023-09-15T07:02:50 |
NONE
| null |
The aim callback is trying to track the keys "input" and "output" (hard coded) when tracking a chain. This causes the exceptions
```
Error in on_chain_start callback: 'input'
Error in on_chain_end callback: 'output'
```
to be thrown.
I fixed the error by tracking all key, value pairs from the inputs resp. outputs variables.
Furthermore, the following issue is also fixed:
Fixes # 5107
Tag maintainers/contributors who might be interested:
Tracing / Callbacks
- @agola11
|
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| 1,756,732,814 |
I_kwDOIPDwls5otZ2O
| 6,160 |
LlamaCpp loading time
|
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[] | null | 2 | 2023-06-14T11:59:36 | 2023-10-30T16:06:23 | 2023-10-30T16:06:22 |
NONE
| null |
Can I improve loading time of Llama Cpp 7b/13b? I am using LlamaCpp function with LLMChain and RetrievalQA.from_chain_type in my python code.
|
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| 1,756,724,158 |
PR_kwDOIPDwls5S_BrK
| 6,159 |
VectorStore Typesense refactored example code and modified k value
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[] | null | 0 | 2023-06-14T11:55:29 | 2023-06-19T00:44:38 | 2023-06-19T00:44:38 |
CONTRIBUTOR
| null |
Changes done.
1. Example code in Typesense Class provided input embedding as `embedding=embedding.embed_query` which throws an error as `AttributeError: 'function' object has no attribute 'embed_query'`.
2. modified k values in vector similarity as Typesense by default returns 10 results. So providing k =4 by default would make users think about why the results are returned 10 instead of 4.
#### Who can review?
@jasonbosco @hwchase17
@dev2049
|
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I_kwDOIPDwls5otSnz
| 6,158 |
OpenAICallbackHandler is not updating values when used in ConversationalRetrievalChain
|
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[] | null | 3 | 2023-06-14T11:44:21 | 2023-08-24T07:06:53 | 2023-06-17T18:27:15 |
NONE
| null |
### System Info
langchain : 0.0.197
docker python alpine image : 3.11.3
ConversationalRetrievalChain works perfectly and i get awesome output. at the same time i also need to track my usage on openai api call.
Im setting the qa object as below
```
qa = ConversationalRetrievalChain.from_llm(
model,
retriever=retriever,
verbose=True,
# callback in not updating the cost
callbacks=[OpenAICallbackHandler()]
)
```
I see the callback is printing the all openai info on console, which means callback is getting triggered.
But I always see all values to Zero.
### Who can help?
@ag
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create qa object as show in disciption with callback.
2. Pass the question chat history to qa object
### Expected behavior
Callback show update all the values for openai traceback
|
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I_kwDOIPDwls5otQ2q
| 6,157 |
Impossible to connect PowerBI Dataset Agent to Azure services
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[] | null | 6 | 2023-06-14T11:39:46 | 2024-01-09T09:56:23 | 2023-12-01T16:09:32 |
NONE
| null |
### System Info
Hi,
I'm trying to reproduce this example https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html
When i launch this part of the code :
toolkit = PowerBIToolkit(
powerbi=PowerBIDataset.update_forward_refs(dataset_id=dataset-id, table_names=['Tables'], credential=DefaultAzureCredential()),
llm=smart_llm
)
I have this error : NameError: name 'TokenCredential' is not defined
What am I doing wrong ? Do i have to specify things with Azure credentials first ?
Thanks for your help
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
example here : https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html
I just modified powerbi=PowerBIDataset() to powerbi=PowerBIDataset.update_forward_refs() because of an error i had.
### Expected behavior
Connect the agent to powerbi rest API
|
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Add docs for tags
|
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COLLABORATOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
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<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
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#### Who can review?
Tag maintainers/contributors who might be interested:
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- @agola11
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- @agola11
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Add Alibaba Cloud OpenSearch as a new vector store
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CONTRIBUTOR
| null |
Hello Folks,
Thanks for creating and maintaining this great project. I'm excited to submit this PR to add Alibaba Cloud OpenSearch as a new vector store.
OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.
OpenSearch provides the vector search feature. In specific scenarios, especially test question search and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results.
This PR includes:
A AlibabaCloudOpenSearch class that can connect to the Alibaba Cloud OpenSearch instance.
add embedings and metadata into a opensearch datasource.
querying by squared euclidean and metadata.
integration tests.
ipython notebook and docs.
I have read your contributing guidelines. And I have passed the tests below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
|
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PR_kwDOIPDwls5S-RuB
| 6,153 |
Extend `ArgillaCallbackHandler` support
|
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[] | null | 0 | 2023-06-14T09:52:33 | 2023-06-18T18:18:34 | 2023-06-18T18:18:34 |
CONTRIBUTOR
| null |
Hi again @agola11! 🤗
## What's in this PR?
After playing around with different chains we noticed that some chains were using different `output_key`s and we were just handling some, so we've extended the support to any output, either if it's a Python list or a string.
Kudos to @dvsrepo for spotting this!
|
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I_kwDOIPDwls5osajF
| 6,152 |
langchain rephrased the human input to a completely different meaning in the prompts
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[] | null | 4 | 2023-06-14T09:37:59 | 2023-10-24T16:08:03 | 2023-10-24T16:08:02 |
NONE
| null |
### System Info
Recently, we do a few test on the ConversationalRetrievalChain + Memory, and notice that, the customer's question is rephrased by Langchain to a totally different meaning. Anyone knows how to avoid this?
The conversation is to introduce the different package of a mobile plan to the customer. and Customer inputs: **Hi**. However, while it comes to Langchain, this sentence is rephrased to: **What is your estimated monthly usage for data, talktime, and SMS?**
**Code:**
qa = ConversationalRetrievalChain.from_llm(
llm=self.llm, retriever=retriever, combine_docs_chain_kwargs={'prompt': self.QA_PROMPT}, verbose=True)
result = qa({'question': question, 'chat_history': message_history.messages})
**Debugging infö as below:**
Prompt after formatting:
Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
Human: I would like to have a data plan
Assistant: Sure, we have several data plans available. Can you please let me know your estimated monthly usage for data, talktime, and SMS? This will help me recommend the most suitable plan for you.
Follow Up Input: hi
Standalone question:
> Finished chain.
Human: I would like to have a data plan
Assistant: Sure, we have several data plans available. Can you please let me know your estimated monthly usage for data, talktime, and SMS? This will help me recommend the most suitable plan for you.
Human: What is your estimated monthly usage for data, talktime, and SMS?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a context of the conversation, and send some subjective sentence.
### Expected behavior
Langchain rephrase it to a question.
|
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| 1,756,466,498 |
I_kwDOIPDwls5osY1C
| 6,150 |
Tracking total cost for gpt-3.5-turbo-0613 yields $0.0
|
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[] | null | 8 | 2023-06-14T09:33:38 | 2023-06-20T07:26:02 | 2023-06-20T07:26:02 |
CONTRIBUTOR
| null |
### System Info
Langchain version: 0.0.200
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Create a ConversationChain instance with parameters:
```
llm = ChatOpenAI(model_name="gpt-3.5-turbo-0613", temperature=0)
memory = ConversationBufferMemory()
```
2. Run the chain inside callback block:
```
with get_openai_callback() as cb:
response = conversation.run("Tell me a joke")
print(cb)
```
3. Total Cost is always $0.0.
<img width="767" alt="Screenshot 2023-06-14 at 11 31 21" src="https://github.com/hwchase17/langchain/assets/18078190/e7992bcb-f288-462f-ab2c-94d0d894929f">
### Expected behavior
Total cost of the conversation chain usage is reflected in the Total Cost parameter of the callback and represents accurate usage costs.
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PR_kwDOIPDwls5S-Kh8
| 6,149 |
Add promptlayer_callback.py to be able to use PL w/ callbacks
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CONTRIBUTOR
| null |
# Description
Instead of inheriting off of LLM objects like OpenAI to create PromptLayer objects (eg PromptLayerOpenAI), create a single PromptLayerCallback object. This adds support for all of langchain's LLMs (openai, anthropic, replicate, dozens others).
This also paves the way for PromptLayer to add logging for agents, chains, etc based on the callbacks api.
## Examples
Async Example
```python
import asyncio
openai_llm = OpenAI(
model_name="text-davinci-002",
callbacks=[PromptLayerHandler(
request_id_func = request_id_func,
pl_tags = ["openai_1!"]
)]
)
asyncio.run(async_generate(openai_llm))
```
Replicate LLM Example
```python
# Test Replicate LLM
replicate_llm = Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
callbacks=[PromptLayerHandler(
request_id_func = request_id_func,
pl_tags = ["replicate_example"]
)]
)
llm_results = replicate_llm.generate([
"Tell me a joke 1",
"Where is Ohio? 2",
"Where is Ohio? 3",
])
```
Chat Model Example w/ Streaming (works w/o streaming as well)
```python
# Test OpenAIChat LLM
chat_llm = ChatOpenAI(
temperature=0,
streaming=True.
callbacks=[PromptLayerHandler(
request_id_func = request_id_func,
pl_tags = ["chatopenai"]
)]
)
llm_results = chat_llm([
HumanMessage(content="What comes after 1,2,3 ?"),
HumanMessage(content="Tell me another joke?"),
])
```
TODO:
- Add documentation + sample notebook
- Might also want to test streaming works
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#### Before submitting
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Tag maintainers/contributors who might be interested:
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PR_kwDOIPDwls5S-JBC
| 6,148 |
Update readthedocs_documentation.ipynb
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[] | null | 0 | 2023-06-14T09:28:43 | 2023-06-14T14:21:49 | 2023-06-14T14:21:49 |
CONTRIBUTOR
| null |
Minor fix in documentation.
Change URL in wget call to proper one.
|
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| 1,756,441,925 |
I_kwDOIPDwls5osS1F
| 6,147 |
Core MapReduceChain token counting before combine step - Performance
|
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[] | null | 2 | 2023-06-14T09:19:36 | 2023-06-14T09:27:58 | 2023-06-14T09:27:57 |
CONTRIBUTOR
| null |
### System Info
This impacts both JS and Python versions
### Who can help?
@hwchase17
@nfcampos
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Discussed this with @nfcampos over slack, but pasting it here for documentation/community discussion to validate this change/issue.
I have switched from JS -> Python version but this issue is valid for both.
When using JS version, I noticed the mapreduce chains combine step decides when it should combine based on the token count sum of the map steps entire prompt, instead of just the outputs. This presented a few issues:
- The point of the map step, as I understood it, is to condense the chunk to eventually be able to fit in the combine prompt, and so gauging when to combine based on summing all map steps’ (input+output) token counts didn’t make sense. This leads to several unnecessary iterations, and much longer run time than if it’s just the map steps’ output tokens being summed, which is what is eventually used in the combine step anyway.
- If the map prompt has large instructions, then it is possible that the sum will never reduce past the threshold (token_max default is 3000). Example:
- You have ten chunks having the map step being run.
- Each chunk has 400 token instructions in map Prompt. 400*10= 4000 tokens.
- The map steps would never condense enough to drop below the 3000 token limit, even though the output from each map step might be 100 tokens. Worse still, it would run up $$$ by running map steps until max_iterations.
I can share my monkey patch of the langchain JS code if it helps, and had even proposed a PR for this in the langchianjs discord. Now that we moved to python, and since I haven’t contributed to any python projects, I thought I’d check here before proposing this change or even attempting a PR.
My questions before proceeding are - does this sound right to you? Is there any reason why a generic mapreduce chain would use the entire map prompt from the map step, and not just the outputs when deciding to combine?
### Expected behavior
The map_prompt output tokens should only be summed up when deciding if we can combine or not.
In the JS version, my monkey patched langchain MapReduce chain's implementation looked like:
```
async _call(values) {
if (!(this.inputKey in values)) {
throw new Error(`Document key ${this.inputKey} not found.`);
}
const { [this.inputKey]: docs, ...rest } = values;
let currentDocs = docs;
let totalIterations = 0;
for (let i = 0; i < this.maxIterations; i += 1) {
const inputs = currentDocs.map((d) => ({
[this.documentVariableName]: d.pageContent,
...rest,
}));
const promises = inputs.map(async (input) => {
const prompt = await this.llmChain.prompt.format(input);
return this.llmChain.llm.getNumTokens(prompt);
});
const length = await Promise.all(promises).then((results) =>
results.reduce((a, b) => a + b, 0)
);
const joinedInputs = {
[this.documentVariableName]: inputs
.map((_) => _[this.documentVariableName])
.join('\n\n'),
...rest,
};
// Speed up converging - Patched Token Counting
const joinedInputTextsPrompt = await this.llmChain.prompt.format(
joinedInputs
);
const joinedInputTextsLength =
(await this.llmChain.llm.getNumTokens(joinedInputTextsPrompt)) +
(await this.llmChain.llm.getNumTokens(
this.combineDocumentChain.llmChain.prompt.template
));
console.log({
length,
joinedInputTextsLength,
});
const canSkipMapStep = i !== 0 || !this.ensureMapStep;
// const withinTokenLimit = length < this.maxTokens; // Original implementation
const withinTokenLimit = joinedInputTextsLength < this.maxTokens;
if (canSkipMapStep && withinTokenLimit) {
break;
}
console.time('MapReduceChain:mapStep');
const results = await this.llmChain.apply(inputs);
console.timeEnd('MapReduceChain:mapStep');
totalIterations += 1;
const { outputKey } = this.llmChain;
currentDocs = results.map((r) => ({
pageContent: r[outputKey],
}));
}
const newInputs = { input_documents: currentDocs, ...rest };
console.time('MapReduceChain:combineStep');
const result = await this.combineDocumentChain.call(newInputs);
console.timeEnd('MapReduceChain:combineStep');
console.log('Iterations: ', totalIterations);
return result;
}
```
Relevant change:
```
const joinedInputTextsLength =
(await this.llmChain.llm.getNumTokens(joinedInputTextsPrompt)) +
(await this.llmChain.llm.getNumTokens(
this.combineDocumentChain.llmChain.prompt.template
));
// const withinTokenLimit = length < this.maxTokens; // Original implementation
const withinTokenLimit = joinedInputTextsLength < this.maxTokens;
```
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PR_kwDOIPDwls5S-CQq
| 6,146 |
Removed handling for non-existent error in chromadb library
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[] | null | 0 | 2023-06-14T09:11:42 | 2023-06-19T00:41:30 | 2023-06-19T00:41:30 |
CONTRIBUTOR
| null |
The current version of chromadb handle the case of `n_result` is greater than the total number of elements in the collection. [chromadb PR](https://github.com/chroma-core/chroma/issues/301). So removed `NotEnoughElementException` that doesn't exists now.
#### Who can review?
Tag maintainers/contributors who might be interested:
- @hwchase17
- @dev2049
|
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I_kwDOIPDwls5osFaT
| 6,145 |
create_csv_agent incomplete response
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[] | null | 1 | 2023-06-14T08:50:25 | 2023-09-20T16:08:00 | 2023-09-20T16:07:59 |
NONE
| null |
### System Info
when > Finished chain.
output: [article] but not complete, it still has something to write.
how can I make article completely?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
import os
os.environ["OPENAI_API_KEY"] = 'mykey'
#llm = OpenAI(model_name="gpt-3.5-turbo-0613")
agent = create_csv_agent(OpenAI(temperature=0,batch_size=5), ['csv/a.csv', 'csv/b.csv'], verbose=True)
a = agent.run("Help me analyze customer consumption and generate an article")
print(a)`
### Expected behavior
i want output a complete article
|
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I_kwDOIPDwls5or_nX
| 6,144 |
ValueError for tutorial about JSONLoader
|
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[] | null | 4 | 2023-06-14T08:38:29 | 2023-11-09T16:12:34 | 2023-11-09T16:12:34 |
NONE
| null |
### System Info
LangChain version: 0.0.200
Platform: Ubuntu 20.04 LTS
Python version: 3.10.4
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Reproduce section "Using JSONLoader" for [tutorial](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html) about JSONLoader
2. After executing the following code:
```python
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[].content'
)
data = loader.load()
```
the following error is displayed:
```ValueError: Expected page_content is string, got <class 'NoneType'> instead. Set `text_content=False` if the desired input for `page_content` is not a string```
3. If we try to get not the list, but just string:
```python
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.title'
)
data = loader.load()
```
there are no errors
4. If we set text_content to False in original code:
```python
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[].content',
text_content=False
)
data = loader.load()
```
then there are also no errors.
### Expected behavior
- The code and documentation must match each other
- Argument `text_content` must have more clear description in which cases it has to be used
|
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Expanded Self-Query Retriever 👀 and Self-querying with MyScale 🤩
|
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[] | null | 0 | 2023-06-14T07:46:57 | 2023-06-18T18:15:28 | 2023-06-18T18:15:28 |
CONTRIBUTOR
| null |
# Expanded Self-Query Retriever 👀 and Self-querying with [MyScale](https://docs.myscale.com/en/) 🤩
LangChain is built with a great interface for self-query retrievers and many super nice contribution has been made during the last few weeks. 🌟 Self-query retrievers are really cool and we believe it will be the future of vector databases! 😎 So we decided to expand self-query retriever with more functions and data types enabled and bring MyScale to the big family of self-query enabled vector databases! 🤗
[MyScale](https://docs.myscale.com/en/) can make use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application. 🚀🚀🚀 And we believe it will be a common feature to every vector database in the future!
In the notebook we'll demo the `SelfQueryRetriever` wrapped around a MyScale vector store with some extra piece we contributed to LangChain. In short, it can be concluded into 4 points:
1. Add `contain` comparator 🪣 to match list of any if there is more than one element matched
2. Add `timestamp` data type 🕰️ for date time match (ISO-format, or YYYY-MM-DD)
3. Add `like` comparator 🔎 for string pattern search
4. Add arbitrary function 💣 capability.
Let's boost LLM apps with vector databases! 🎉🎉🎉
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#### Before submitting
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- [x] unit-test for `LIKE` and `CONTAIN`
- [x] Notebook for MyScale Self-query Retriever
#### Who can review?
Tag maintainers/contributors who might be interested:
- @hwchase17
- @dev2049
<!-- For a quicker response, figure out the right person to tag with @
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Tracing / Callbacks
- @agola11
Async
- @agola11
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- @eyurtsev
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- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
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| 1,756,237,161 |
I_kwDOIPDwls5org1p
| 6,142 |
Microsoft Guidance Integration
|
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[] | null | 18 | 2023-06-14T07:22:51 | 2023-12-03T12:07:17 | null |
NONE
| null |
### Feature request
[Guidance](https://github.com/microsoft/guidance) is a language for controlling large language models developed by Microsoft.
"Guidance allows to interleave generation, prompting, and logical control into a single continuous flow [...] more effectively and efficiently than traditional prompting or chaining"
In practice, this means that Guidance is not only able to _force_ LLMs to provide an specific output format (in a deterministic way) but also enables conditional output, loops and much more, with just a handlebars-like templating language.
For langchain, this means that we would be able to provide formatted outputs with 100% accuracy, improving Agents, Tools and other components that rely heavily on output parsing.
Adding this to langchain still makes sense even with the introduction of [functions in the OpenAI models](https://openai.com/blog/function-calling-and-other-api-updates), as this changes only benefit those closed-source models and Guidance also works with open-source ones such as Vicuna.
### Motivation
I've been developing a langchain-based product for a while now and one of the biggest pain points for me is the unreliability of the agents output format. Take the `ConversationalChatAgent` (from [here](https://github.com/hwchase17/langchain/blob/e0e3ef1c57109ac5491ba744b8e4a4189931b1b5/langchain/agents/conversational_chat/base.py#L39)) as an example, its output parsing depends on the model following the ``FORMAT_INSTRUCTIONS` [here](https://github.com/hwchase17/langchain/blob/master/langchain/agents/conversational_chat/prompt.py).
In my experience, this works pretty well with a low temperature but it's sometimes unreliable nonetheless, breaking the agent execution and causing hard to prevent errors.
### Your contribution
I would like to gather some feedback from the community about this integration, I might be approaching this in the wrong way and there might be solutions for this already.
If this is somewhat useful, I would be happy to submit a PR with an initial integration (maybe similar to what [Llama-Index has done](https://gpt-index.readthedocs.io/en/latest/examples/output_parsing/guidance_sub_question.html)) for general output parsing. This would allow Guidance to be integrated even further by, for example, replacing the regular Pydantic output parser with Guidance ouput parsers in all relevant situations (it should be a drop-in replacement)
|
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| 1,756,190,153 |
PR_kwDOIPDwls5S9PBj
| 6,141 |
Fixed an unhandled error that was raised when DynamoDB did not have any chat history.
|
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[] | null | 0 | 2023-06-14T06:55:49 | 2023-06-19T00:39:20 | 2023-06-19T00:39:19 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
The current version of chat history with DynamoDB doesn't handle the case correctly when a table has no chat history. This change solves this error handling.
<!-- Remove if not applicable -->
Fixes https://github.com/hwchase17/langchain/issues/6088
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
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I_kwDOIPDwls5orQBQ
| 6,140 |
getting error: while implementing chatopenai module with gpt-4-0613
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[] | null | 2 | 2023-06-14T06:39:33 | 2023-09-21T16:08:11 | 2023-09-21T16:08:10 |
NONE
| null |
### System Info
ubuntu
python 3.10.16
langchain 0.0.200
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
ChatOpenAI(
model_name="gpt-4-0613",
temperature=self.temperature,
model_kwargs={
"frequency_penalty": self.frequency_penalty,
"top_p": self.top_p,
"headers": conf.PORTKEY_HEADERS,
"user": user_id,
},
max_tokens=self.max_tokens,
)
we get error
Error - Unknown model: gpt-4-0613. Please provide a valid OpenAI model name.Known models are: gpt-4, gpt-4-0314, gpt-4-32k, gpt-4-32k-0314, gpt-3.5-turbo, gpt-3.5-turbo-0301, text-ada-001, ada, text-babbage-001, babbage, text-curie-001, curie, davinci, text-davinci-003, text-davinci-002, code-davinci-002, code-davinci-001, code-cushman-002, code-cushman-001.
### Expected behavior
it should work
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PR_kwDOIPDwls5S9FJC
| 6,139 |
Add Flyte Callback Handler
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CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
This PR adds a Flyte callback handler that can be triggered from within a Flyte task to track LangChain experiments.
<img width="1023" alt="image" src="https://github.com/hwchase17/langchain/assets/27777173/fea38eb6-2bd1-4746-a5cd-7ea621981ed0">
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
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I_kwDOIPDwls5orD-D
| 6,138 |
ConversationChain default prompt leads the model to converse with itself
|
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NONE
| null |
### System Info
langchain==0.0.195
python==3.9.6
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
llm = ChatOpenAI(
model_name=model_name,
openai_api_key=os.environ.get("OPENAI_API_KEY"),
temperature=0,
verbose=True,
)
chain = ConversationChain(
llm=llm,
memory=memory,
verbose=True,
)
chain.run(input=prompt) # see below
```
```
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Human: You will play the role of a human CBT therapist called Cindy who is emulating the popular Al program Eliza, and must treat me as a therapist-patient. Your response format should focus on reflection and asking clarifying questions. You may interject or ask secondary questions once the initial greetings are done. Exercise patience but allow yourself to be frustrated if the same topics are repeatedly revisited. You are allowed to excuse yourself if the discussion becomes abusive or overly emotional. Begin by welcoming me to your office and asking me for my name. Then ask how you can help. Do not break character. Do not make up the patient's responses: only treat input as a patient response. Wait for my first message.
AI: Hello and welcome to my office. My name is Cindy, and I'm here to help you. May I have your name, please?
Human: My name is John.
AI: Hi John, it's nice to meet you. How can I help you today?
Human: My name is not john
AI: I apologize for the mistake. May I have your correct name, please?
Human: Omar
AI:
> Finished chain.
```
### Expected behavior
The AI starts conversing with itself. This wouldn't happen when using OpenAI's native message and role format as opposed to this massive prompt. Am I missing something?
This is the AI response which starts to include the human prefix based on the default prompt supplied.
> AI: Hello and welcome to my office. My name is Cindy, and I'm here to help you. May I have your name, please?
>
> Human: My name is John.
>
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Vwp/drafts/unit testing
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CONTRIBUTOR
| null |
<!--
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Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
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#### Before submitting
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Tag maintainers/contributors who might be interested:
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-->
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Update Name
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OSError: UnstructuredPDFLoader Show invalid argument Error when passing the url as a file_path parameter
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NONE
| null |
### Issue you'd like to raise.
I am a beginner in langchain, thank you for your patience in reading this problem description, I would appreciate if you could suggest sth.
<img width="614" alt="image" src="https://github.com/hwchase17/langchain/assets/18730237/ad9c718c-7a23-40a4-8dd1-acebc4305553">
```
loader = UnstructuredPDFLoader("https://arxiv.org/pdf/2305.11147.pdf")
documents = loader.load()
```
# error as below
```
Exception has occurred: OSError
[Errno 22] Invalid argument: 'https://arxiv.org/pdf/2305.11147.pdf'
File "D:\workspace\LangChain-Examples\examples\chats.py", line 181, in main
documents = loader.load()
File "D:\workspace\LangChain-Examples\main.py", line 35, in main
chats()
File "D:\workspace\LangChain-Examples\main.py", line 44, in <module>
main()
OSError: [Errno 22] Invalid argument: 'https://arxiv.org/pdf/2305.11147.pdf'
```
# my effort
it just did not work .
```
loader = UnstructuredPDFLoader(r"https://arxiv.org/pdf/2305.11147.pdf")
```
or
```
loader = UnstructuredPDFLoader(f"https://arxiv.org/pdf/2305.11147.pdf")`
```
or
```
loader = UnstructuredPDFLoader(r"https:\\\\arxiv.org\\pdf\\2305.11147.pdf")
```
or
```
loader = UnstructuredPDFLoader("https:\\\\arxiv.org\\pdf\\2305.11147.pdf")
```
# more infomation
I can open https://arxiv.org/pdf/2305.11147.pdf in my browser.
and it works when I open a local pdf document as
```
loader = UnstructuredPDFLoader(".\examples\\data\\1.pdf")
```
whatever a local doc or a url online , it all works on my mac.
# environment
The OS is windows11 , I am using vscode as IDE for debugging.
my python version == 3.9.6
I just confuse why URLs are not accepted as parameters here.
### Suggestion:
_No response_
|
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| 6,134 |
Add ability to add extra fields to AzureSearch VectorStore when adding documents
|
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[] | null | 9 | 2023-06-14T03:42:05 | 2023-10-16T16:16:00 | 2023-10-16T16:15:48 |
CONTRIBUTOR
| null |
### Feature request
Currently the AzureSearch VectorStore allows the user to specify a filter that can be used to filter (in the traditional search engine sense) a search index become doing a vector similarity search. This reduces the search space to improve speed as well as to help focus the vector search on the correct subset of documents.
This filtering feature is very hard to effectively use because the current method for adding documents (add_texts) only allows an id, content, content_vector, and metadata fields. None of these fields are suitable for filtering, so this requires the user to go back and add fields manually to the search index.
I propose that we allow the end user to specify extra fields that are added when creating these vectors. The end user would do something like this:
```
extra_fields = {"extra_fields": {"important_field_1": 123, "important_field_2": 456}}
documents.append(doc1)
documents.append(doc2)
documents.append(doc3)
vector_store.add_documents(documents, **extra_fields)
```
Then when the user queries this vector store late they can do something like this:
```
retriever.search_kwargs = {'filters': "important_field_1 eq 123"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
```
### Motivation
My motivation was need for a project I'm working on, but I felt this was a needed general feature, as I stated in the feature request:
This filtering feature is very hard to effectively use because the current method for adding documents (add_texts) only allows an id, content, content_vector, and metadata fields. None of these fields are suitable for filtering, so this requires the user to go back and add fields manually to the search index.
### Your contribution
Hopefully this makes sense, let me know if any clarifications are needed, once the bug #6131 is fixed I will submit a PR that implements this, I have it working locally and just need to write appropriate unit tests. Unit tests will not be possible until this bug is fixed.
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Add Run Collector Callback
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CONTRIBUTOR
| null |
Add a callback handler that can collect nested run objects. Useful for evaluation.
|
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Correct AzureSearch Vector Store not applying search_kwargs when searching
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[] | null | 1 | 2023-06-14T02:17:11 | 2023-06-19T00:39:07 | 2023-06-19T00:39:06 |
CONTRIBUTOR
| null |
Fixes #6131
Simply passes kwargs forward from similarity_search to helper functions so that search_kwargs are applied to search as originally intended. See bug for repro steps.
#### Who can review?
@hwchase17
@dev2049
Twitter: poshporcupine
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| 1,755,911,246 |
I_kwDOIPDwls5oqRRO
| 6,131 |
Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries
|
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[] | null | 5 | 2023-06-14T02:08:49 | 2023-08-15T08:39:21 | 2023-06-19T00:39:07 |
CONTRIBUTOR
| null |
### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```)
|
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| 1,755,896,452 |
PR_kwDOIPDwls5S8Pjd
| 6,130 |
bump ver to 200
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[] | null | 0 | 2023-06-14T01:51:41 | 2023-06-14T14:47:12 | 2023-06-14T02:33:51 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5oqIdA
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Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 2 | 2023-06-14T01:23:57 | 2023-06-14T02:31:12 | 2023-06-14T02:31:12 |
NONE
| null |
### Issue you'd like to raise.
https://openai.com/blog/function-calling-and-other-api-updates
chatgpt-3.5-turbo model can receive 16k so need fixing code l`lm.chains` and `get_openai_callback`
if use max_tokens parameters `openai.error.InvalidRequestError`
```bash
openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens. However, your messages resulted in 4133 tokens. Please reduce the length of the messages.
```
### Suggestion:
_No response_
|
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| 1,755,807,848 |
PR_kwDOIPDwls5S78eE
| 6,126 |
Add delete and ensure add_texts performs upsert (w/ ID optional)
|
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[] | null | 5 | 2023-06-13T23:56:49 | 2023-07-22T19:05:53 | 2023-06-23T20:03:10 |
COLLABORATOR
| null |
## Goal
We want to ensure consistency across vectordbs:
1/ add `delete` by ID method to the base vectorstore class
2/ ensure `add_texts` performs `upsert` with ID optionally passed
## Testing
- [x] Pinecone: notebook test w/ `langchain_test` vectorstore.
- [x] Chroma: Review by @jeffchuber, notebook test w/ in memory vectorstore.
- [x] Supabase: Review by @copple, notebook test w/ `langchain_test` table.
- [x] Weaviate: Notebook test w/ `langchain_test` index.
- [x] Elastic: Revied by @vestal. Notebook test w/ `langchain_test` table.
- [ ] Redis: Asked for review from owner of recent `delete` method https://github.com/hwchase17/langchain/pull/6222
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I_kwDOIPDwls5op3Z6
| 6,125 |
awadb and azure-ai-vision Version issue
|
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[] | null | 6 | 2023-06-13T23:52:47 | 2023-10-15T16:06:28 | 2023-10-15T16:06:27 |
NONE
| null |
### System Info
Upon the `poetry install -E all ` command, I'm unable to install awadb version or azure-ai-vision specified in pyproject.toml
awadb = {version = "^0.3.2", optional = true}
azure-ai-vision = {version = "^0.11.1b1", optional = true}
```
Package operations: 1 install, 1 update, 0 removals
• Updating awadb (0.3.1 -> 0.3.2): Failed
RuntimeError
Unable to find installation candidates for awadb (0.3.2)
at ~/opt/anaconda3/envs/langchain/lib/python3.9/site-packages/poetry/installation/chooser.py:76 in choose_for
72│
73│ links.append(link)
74│
75│ if not links:
→ 76│ raise RuntimeError(f"Unable to find installation candidates for {package}")
77│
78│ # Get the best link
79│ chosen = max(links, key=lambda link: self._sort_key(package, link))
80│
• Installing azure-ai-vision (0.11.1b1): Failed
RuntimeError
Unable to find installation candidates for azure-ai-vision (0.11.1b1)
at ~/opt/anaconda3/envs/langchain/lib/python3.9/site-packages/poetry/installation/chooser.py:76 in choose_for
72│
73│ links.append(link)
74│
75│ if not links:
→ 76│ raise RuntimeError(f"Unable to find installation candidates for {package}")
77│
78│ # Get the best link
79│ chosen = max(links, key=lambda link: self._sort_key(package, link))
```
However I was able to install the awadb 0.3.1 version.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Freshly install poetry via: curl -sSL https://install.python-poetry.org | python3 -. In Conda venv.
2. After it's installation, Poetry (version 1.5.1), attemped `poetry install -E all ` command
3. Received errors
### Expected behavior
After I installed Poetry, I tried to run poetry install -E all within the langchain directory, however I receive dependencies errors.
|
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PR_kwDOIPDwls5S75mo
| 6,124 |
Fix missing chain classname in StdOutCallbackHandler.on_chain_start
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CONTRIBUTOR
| null |
Retrieves the name of the class from new location as of commit 18af149e91e62b3ac7728ddea420688d41043734
#### Before submitting
#### Who can review?
Tag maintainers/contributors who might be interested:
Tracing / Callbacks
- @agola11
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| 1,755,718,564 |
PR_kwDOIPDwls5S7o9M
| 6,123 |
Fix typo in the CAI critique prompt
|
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[] | null | 0 | 2023-06-13T22:08:29 | 2023-06-19T00:38:57 | 2023-06-19T00:38:56 |
CONTRIBUTOR
| null |
Very small typo in the Constitutional AI critique default prompt. The negation "If there is *no* material critique of ..." is used two times, should be used only on the first one.
Cheers,
Pierre
|
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PR_kwDOIPDwls5S7ont
| 6,122 |
Update model token mappings/cost to include 0613 models
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[] | null | 5 | 2023-06-13T22:07:30 | 2023-06-21T08:37:18 | 2023-06-21T08:37:17 |
CONTRIBUTOR
| null |
Add `gpt-3.5-turbo-16k` to model token mappings, as per the following new OpenAI blog post:
https://openai.com/blog/function-calling-and-other-api-updates
Fixes #6118
#### Before submitting
<!-- If you're adding a new integration, please include:
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Each example using `gpt-3.5-turbo` should already work, and I think shouldn't be updated as the new 16k token model costs more per token to use.
If there are tests I should add, please let me know.
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
|
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I_kwDOIPDwls5opcIq
| 6,121 |
Bug with Weaviate vectorstore
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[] | null | 1 | 2023-06-13T21:53:58 | 2023-06-14T16:54:42 | 2023-06-14T16:54:42 |
COLLABORATOR
| null |
### System Info
```
langchain==0.0.194
weaviate-client==3.19.1
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This works:
```
# Init
weaviate_url = url
client = Client(url=weaviate_url, auth_client_secret=auth.AuthClientPassword(xxx,xxx))
embeddings = OpenAIEmbeddings()
vectorstore= Weaviate.from_texts(splits, embeddings, client=client)
# Search
query = "What is micrograd?"
matched_docs = vectorstore_new.similarity_search(query,k=1)
matched_docs
```
Under the hood, a random `index_name` is created and text key is hard-coded:
```
Index!
LangChain_214a4ded03fd4121ad5f5d0c0c36e051
Text key: text_key!
text
```
Now, I want to get this index that I've created (e.g., in another process or session):
```
# Create connection
vectorstore_weviate = Weaviate(client=client)
```
Of course, this will fail because we need `'index_name'` and `'text_key'`:
```
TypeError: __init__() missing 2 required positional arguments: 'index_name' and 'text_key'
```
So, we re-generate our `vectorstore` with an `index_name`:
```
vectorstore= Weaviate.from_texts(splits, embeddings, client=client, index_name="karpathy-gpt")
```
Confirm:
```
Index!
karpathy_gpt
Text key: text_key!
text
```
But, when we run search:
```
# Search
query = "What is micrograd?"
matched_docs = vectorstore_new.similarity_search(query,k=1)
matched_docs
```
We can see the the index name returned in `result` [here](https://github.com/hwchase17/langchain/blob/11ab0be11aff9128c12178b5ebf62071985fb823/langchain/vectorstores/weaviate.py#L223) has been modified with an upper case start char `Karpathy_gpt`.
This results in a key error when we use our index_name key, `karpathy_gpt`:
```
Index Name in similarity_search_by_vector
***`karpathy_gpt`***
Result
{'data': {'Get': {***'Karpathy_gpt'***: [{'text': "would like to take you through building of micrograd. Now micrograd is this library that I released on GitHub about two years ago but at the time I only uploaded the source code and you'd have to go in by yourself and really figure out how it works. So in this lecture I will take you through it step by step and kind of comment on all the pieces of it. So what is micrograd and why is it interesting? Thank you. Micrograd is basically an autograd engine. Autograd is short for automatic gradient"}]}}}
```
It's possible that the index-name is being modified somewhere on the `Weaviate` side?
The `result` come from Weaviate [here](https://github.com/hwchase17/langchain/blob/11ab0be11aff9128c12178b5ebf62071985fb823/langchain/vectorstores/weaviate.py#LL219C9-L219C71):
```
result = query_obj.with_near_vector(vector).with_limit(k).do()
```
The the index name is modified to `Karpathy_gpt` in the `result`.
### Expected behavior
We expect `result` to have the same `index_name` as we have defined when we initialize Weaviate.
```
{'data': {'Get': {'**karpathy_gpt**': [{'text': "would like to take you through building of micrograd. Now micrograd is this library that I released on GitHub about two years ago but at the time I only uploaded the source code and you'd have to go in by yourself and really figure out how it works. So in this lecture I will take you through it step by step and kind of comment on all the pieces of it. So what is micrograd and why is it interesting? Thank you. Micrograd is basically an autograd engine. Autograd is short for automatic gradient"}]}}}
```
Possible that CShorten can sanity check.
https://github.com/CShorten
|
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PR_kwDOIPDwls5S7iG4
| 6,120 |
Fix VectorStoreMemory when used with CombinedMemory
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[] | null | 2 | 2023-06-13T21:48:12 | 2023-11-07T04:15:00 | 2023-11-07T04:15:00 |
NONE
| null |
Fix VectorStoreMemory mixing the other memory input when used in CombinedMemory
<!--
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Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
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#### Before submitting
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Fixes Issue when using multiple memory
When we used multiple memory, VectorStoreMemory together with ConversationBufferWindowMemory, the VectorStoreMemory history is mixed up with ConversationBufferWindowMemory
```python
vectorstore_memory = VectorStoreRetrieverMemory(input_key="input", memory_key="related_conv", retriever=retriever)
conv_memory = ConversationBufferWindowMemory(input_key="input", memory_key="previous_conv", k=1)
memory = CombinedMemory(memories=[conv_memory, vectorstore_memory])
```
`related_conv` contains `input + previous_conv + response`
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
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[Feature][VectorStore] Support StarRocks as vector db
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CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Here are some examples to use StarRocks as vectordb
```
from langchain.vectorstores import StarRocks
from langchain.vectorstores.starrocks import StarRocksSettings
embeddings = OpenAIEmbeddings()
# conifgure starrocks settings
settings = StarRocksSettings()
settings.port = 41003
settings.host = '127.0.0.1'
settings.username = 'root'
settings.password = ''
settings.database = 'zya'
# to fill new embeddings
docsearch = StarRocks.from_documents(split_docs, embeddings, config = settings)
# or to use already-built embeddings in database.
docsearch = StarRocks(embeddings, settings)
```
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
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| 1,755,665,020 |
I_kwDOIPDwls5opVJ8
| 6,118 |
Issue: Update OpenAI model token mapping to reflect new API update 2023-06-13
|
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[] | null | 4 | 2023-06-13T21:22:21 | 2023-06-21T08:37:19 | 2023-06-21T08:37:19 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
The blog post here
https://openai.com/blog/function-calling-and-other-api-updates
specifies
> - new 16k context version of gpt-3.5-turbo (vs the standard 4k version)
The `langchain/llms/openai.py` `model_token_mapping` should be changed to reflect this.
### Suggestion:
Add `gpt-3.5-turbo-16k` property to `model_token_mapping` with value 16k
|
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| 1,755,656,871 |
PR_kwDOIPDwls5S7bO1
| 6,116 |
Add mmr support for opensearch
|
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[] | null | 0 | 2023-06-13T21:14:09 | 2023-06-17T18:19:25 | 2023-06-17T18:19:25 |
CONTRIBUTOR
| null |
### Fixes [(MMR Support for OpenSearch)](https://github.com/hwchase17/langchain/issues/6108)
Added Maximal Marginal Relevance Search to Opensearch
### Before submitting
Updated [Opensearch Docs](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/opensearch.html) with MMR example
### Who can review?
Hi @dev2049 appreciate if you could review.
Twitter: https://twitter.com/oneryalcin
|
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PR_kwDOIPDwls5S7XOo
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support streaming for functions
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5S7TKV
| 6,114 |
Chooch AI Integration
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[] | null | 4 | 2023-06-13T20:43:38 | 2023-06-29T19:33:18 | 2023-06-29T19:33:18 |
NONE
| null |
Fixes #
- Added chooch.ai LLM wrapper integration.
- Added delete_collection_file and similarity_search_with_score_chooch to chroma.py file. You can discard these. It useful for robust operations.
Example usage:
api_url = "http://localhost:8082"
from langchain.llms import ChoochAI
llm = ChoochAI(api_url=api_url)
This is used with our ImageChat 3 MultiModal Self Hosted deployment. We will have a Cloud version ready soon too.
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17, @dev2049, @agola11
|
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add functions agent
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PR_kwDOIPDwls5S7R22
| 6,112 |
Return session name in runner response
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[] | null | 0 | 2023-06-13T20:38:58 | 2023-06-13T23:59:45 | 2023-06-13T23:59:44 |
CONTRIBUTOR
| null |
Makes it easier to then run evals w/o thinking about specifying a session
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feat: interfaces for async embeddings, implement async openai
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[] | null | 6 | 2023-06-13T20:25:42 | 2023-07-06T14:06:10 | 2023-07-06T14:06:10 |
CONTRIBUTOR
| null |
This change adds support to the base `Embeddings` class for two methods, `aembed_query` and `aembed_documents`, those two methods supporting async equivalents of `embed_query` and
`embed_documents` respectively. This ever so slightly rounds out async support within langchain, with an initial implementation of this functionality being implemented for openai.
Implements #6109
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Add new openai 0613 model costs
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[] | null | 1 | 2023-06-13T20:21:37 | 2023-06-17T18:11:49 | 2023-06-17T18:11:48 |
CONTRIBUTOR
| null |
Added costs for gpt-4-32k-0613, gpt-4-0613, gpt-3.5-turbo-16k, gpt-3.5-turbo-0613, and gpt-3.5-turbo-16k-0613 to openai_info callback based on this [OpenAI post](https://openai.com/blog/function-calling-and-other-api-updates)
@agola11
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I_kwDOIPDwls5opDE-
| 6,109 |
Async request support for Embeddings, with initial support for OpenAI
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[] | null | 1 | 2023-06-13T20:19:56 | 2023-07-03T13:25:23 | 2023-07-03T13:25:23 |
CONTRIBUTOR
| null |
### Feature request
The proposal here is pretty simple, we add two methods to the `Embeddings` base class, `aembed_documents` and `aembed_query`, allowing for async versions of the equivalent synchronous methods. The first implementation of this would be for OpenAI, since that's a popular embedding API.
### Motivation
async is supported within other aspects of langchain, and embeddings are one location where support isn't presently there. For a specific example, in a service my company is currently working on converting to be async, this support would improve the throughput of that support noticeably.
### Your contribution
I have a PR in the works that I'll be putting up shortly.
|
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I_kwDOIPDwls5oo7lk
| 6,108 |
MMR Support for OpenSearch
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[] | null | 3 | 2023-06-13T19:55:07 | 2023-09-12T16:38:13 | 2023-09-12T16:35:57 |
CONTRIBUTOR
| null |
### Feature request
Currently few vectorstores like Qdrant has support for MMR (Maximal Marginal Relevance)
Opensearch does not have it.
### Motivation
Since we use OpenSearch as our Vectorstore and we want variance in our results for best entropy I'd like to have MMR implemented for `OpenSearchVectorStore`
### Your contribution
My PR: https://github.com/hwchase17/langchain/pull/6116
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update web_base.py to have verify option
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[] | null | 3 | 2023-06-13T19:51:43 | 2023-06-17T18:10:50 | 2023-06-17T18:10:49 |
CONTRIBUTOR
| null |
We propose an enhancement to the web-based loader initialize method by introducing a "verify" option. This enhancement addresses the issue of SSL verification errors encountered on certain web pages. By providing users with the option to set the verify parameter to False, we offer greater flexibility and control.
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
### Fixes #6079
#### Who can review?
@eyurtsev @hwchase17
|
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PR_kwDOIPDwls5S7Dw_
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Update web_base.py
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[] | null | 0 | 2023-06-13T19:45:57 | 2023-06-13T19:55:19 | 2023-06-13T19:55:19 |
CONTRIBUTOR
| null |
We propose an enhancement to the web-based loader initialize method by introducing a "verify" option. This enhancement addresses the issue of SSL verification errors encountered in certain web pages. By providing users with the option to set the verify parameter to False, we offer greater flexibility and control.
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https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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#### Who can review?
@eyurtsev
|
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I_kwDOIPDwls5ooW5-
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Incorrect syntax near the keyword 'TO'
|
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NONE
| null |
### System Info
langchain version 0.0.198,
windows,
SQL Server 16.0.4025.1 windows docker container (linux)
In the output I get the message:
Incorrect syntax near the keyword 'TO'
and program not ends correctly
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**my python program:**
server = 'localhost,1435'
database = 'MyDB'
username = 'me'
pwd = '****'
driver= 'ODBC Driver 17 for SQL Server'
def db_instance():
#Creating SQLAlchemy connection sting
connectionString = 'DRIVER='+driver+';SERVER=tcp:'+server+';PORT=1433;DATABASE='+database+';UID='+username+';PWD='+ pwd+';Encrypt=no;TrustServerCertificate=no;Connection Timeout=30;'
print(connectionString)
params = urllib.parse.quote_plus(connectionString)
conn_str = 'mssql+pyodbc:///?odbc_connect={}'.format(params)
db_instance = SQLDatabase.from_uri(conn_str,schema='ALG')
return db_instance
db = db_instance()
print(db.table_info)
# Setting API Key and API endpoint for OpenAI
os.environ['OPENAI_API_TOKEN'] = '....'
llm = OpenAI(model_name='text-davinci-003')
# LangChain Agent
toolkit = SQLDatabaseToolkit(db=db, llm = llm)
agent_executor = create_sql_agent(
llm= llm,
toolkit=toolkit,
verbose=True,
top_k = 5
)
# Test
agent_executor.run("'how many Messages are in the DB'")
### Expected behavior
**Output from terminal:**
SELECT Message_0_0.*, Message_0_100001.*, Message_0_100002.*, Message_0_100003.*, Message_0_100004.*, Message_0_100000.*, Message_0_99999.*
FROM Message_0_0
INNER JOIN Message_0_100001 ON Message_0_0.id = Message_0_100001.id
INNER JOIN Message_0_100002 ON Message_0_0.id = Message_0_100002.id
INNER JOIN Message_0_100003 ON Message_0_0.id = Message_0_100003.id
INNER JOIN Message_0_100004 ON Message_0_0.id = Message_0_100004.id
INNER JOIN Message_0_100000 ON Message_0_0.id = Message_0_100000.id
INNER JOIN Message_0_99999 ON Message_0_0.id = Message_0_99999.id
Thought: The query looks correct, I can now execute it.
Action: query_sql_db
Action Input: SELECT Message_0_0.*, Message_0_100001.*, Message_0_100002.*, Message_0_100003.*, Message_0_100004.*, Message_0_100000.*, Message_0_99999.*
FROM Message_0_0
INNER JOIN Message_0_100001 ON Message_0_0.id = Message_0_100001.id
INNER JOIN Message_0_100002 ON Message_0_0.id = Message_0_100002.id
INNER JOIN Message_0_100003 ON Message_0_0.id = Message_0_100003.id
INNER JOIN Message_0_100004 ON Message_0_0.id = Message_0_100004.id
INNER JOIN Message_0_100000 ON Message_0_0.id = Message_0_100000.id
INNER JOIN Message_0_99999 ON Message_0_0.id = Message_0_99999.id
Obs
Observation: Error: (pyodbc.ProgrammingError) ('42000', "[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Incorrect syntax near the keyword 'TO'. (156) (SQLExecDirectW)")
[SQL: SET search_path TO ALG]
(Background on this error at: https://sqlalche.me/e/20/f405)
Thought: I should double check my query again with the query checker.
Action: query_checker_sql_db
Action Input: SELECT Message_0_0.*, Message_0_100001.*, Message_0_100002.*, Message_0_100003.*, Message_0_100004.*, Message_0_100000.*, Message_0_99999.*
FROM Message_0_0
INNER JOIN Message_0_100001 ON Message_0_0.id = Message_0_100001.id
INNER JOIN Message_0_100002 ON Message_0_0.id = Message_0_100002.id
INNER JOIN Message_0_100003 ON Message_0_0.id = Message_0_100003.id
INNER JOIN Message_0_100004 ON Message_0_0.id = Message_0_100004.id
INNER JOIN Message_0_100000 ON Message_0_0.id = Message_0_100000.id
INNER JOIN Message_0_99999 ON Message_0_0.id = Message_0_99
The message query looks correct it should get the correct count.
**SQL server trace contains also the query:**
SET search_path TO ALG
I found that string in the github repo under /sql_database.py
line 347
```
with self._engine.begin() as connection:
if self._schema is not None:
if self.dialect == "snowflake":
connection.exec_driver_sql(
f"ALTER SESSION SET search_path='{self._schema}'"
)
elif self.dialect == "bigquery":
connection.exec_driver_sql(f"SET @@dataset_id='{self._schema}'")
else:
connection.exec_driver_sql(f"SET search_path TO {self._schema}")
```
|
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| 6,104 |
Support and make use of function calling and other OpenAI updates on 2023-06-13
|
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[] | null | 8 | 2023-06-13T17:57:29 | 2023-09-23T16:05:43 | 2023-09-23T16:05:42 |
NONE
| null |
### Feature request
OpenAI released several major updates today (2023-06-13) that likely have major implications for what is possible. At the very least, it will make things more reliable.
Here's a shortlist from [the blog post](https://openai.com/blog/function-calling-and-other-api-updates):
- Dramatically improved function calling support + JSON consistency
- `gpt-3.5-turbo` with a 16K context window (🤯)
- Token cost changes for completions and embeddings
- Upcoming deprecation for March-versioned models
### Motivation
The release of OpenAI's blog post found here:
https://openai.com/blog/function-calling-and-other-api-updates
I'm adding this issue mostly to flag and track this OpenAI release and kick off a forum for discussion.
### Your contribution
Can potentially add PRs but haven't contributed here previously.
|
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PR_kwDOIPDwls5S6fCe
| 6,103 |
Harrison/notebook functions
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[] | null | 0 | 2023-06-13T17:45:13 | 2023-06-13T17:52:56 | 2023-06-13T17:52:55 |
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PR_kwDOIPDwls5S6epa
| 6,102 |
bump version to 199
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Validation Error
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[] | null | 5 | 2023-06-13T17:43:13 | 2023-10-12T16:08:42 | 2023-10-12T16:08:41 |
NONE
| null |
### System Info
Python Version: 3.9.11
Langchain version = 0.0.199
I'm getting a validation error with GPT4All where I'm following the instructions of the notebook and installed all packages but apparently there's some parameters called n_parts that isn't part of the GPT4All attribute
```
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import requests
from pathlib import Path
from tqdm import tqdm
Path(local_path).parent.mkdir(parents=True, exist_ok=True)
url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'
response = requests.get(url, stream=True)
with open(local_path, 'wb') as f:
for chunk in tqdm(response.iter_content(chunk_size=8192)):
if chunk:
f.write(chunk)
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
llm = GPT4All(model=local_path, backend='gptj', callbacks=callbacks, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
```
and this is the error I get
```
Exception ignored in: <function Model.__del__ at 0x2aaaed28aa60>
Traceback (most recent call last):
File "/home/traney/.conda/envs/openai/lib/python3.9/site-packages/pyllamacpp/model.py", line 402, in __del__
if self._ctx:
AttributeError: 'GPT4All' object has no attribute '_ctx'
Exception ignored in: <function Model.__del__ at 0x2aaaed28aa60>
Traceback (most recent call last):
File "/home/traney/.conda/envs/openai/lib/python3.9/site-packages/pyllamacpp/model.py", line 402, in __del__
if self._ctx:
AttributeError: 'GPT4All' object has no attribute '_ctx'
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[31], line 11
9 callbacks = [StreamingStdOutCallbackHandler()]
10 # Verbose is required to pass to the callback manager
---> 11 llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
12 # If you want to use a custom model add the backend parameter
13 # Check https://docs.gpt4all.io/gpt4all_python.html for supported backends
14 llm = GPT4All(model=local_path, backend='gptj', callbacks=callbacks, verbose=True)
File ~/.conda/envs/openai/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for GPT4All
__root__
__init__() got an unexpected keyword argument 'n_parts' (type=type_error)
```
I installed all relevant packages and checked the previous reports for these issues but I just get different errors from not being able to find the model, to validation error to GPT4All not existing. Is there any new things I have to do to implement GPT4All on langchain?
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow code above in Jupyter Notebook
### Expected behavior
the chat model should answer the question
|
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