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PR_kwDOIPDwls5S6Z5K
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convert tools to openai
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PR_kwDOIPDwls5S6ZD1
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support functions
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I_kwDOIPDwls5ooCvH
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This new feature return_intermediate_steps=True is not working as intended, can you please update
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NONE
| null |
https://github.com/hwchase17/langchain/blame/ee3d0513addae0680a30afa384431a032244e76b/langchain/chains/graph_qa/cypher.py#L40
@tomasonjo This new feature **return_intermediate_steps=True** is not working as intended, can you please update
There is some issue in line number 258 of "langchain\chains\base.py"
**return self(args[0], callbacks=callbacks)[self.output_keys[0]]**
the above line always makes it to return only the result and not the intermediate steps.
As a temporary solution I modified line no: 130 of "langchain\chains\graph_qa\cypher.py" to return **{"result":chain_result}** which works.
Thanks in advance!
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I_kwDOIPDwls5ooBnf
| 6,097
|
OpenAI Cost calculation could have a bug!
|
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| 2023-06-13T17:02:34
| 2023-09-19T16:08:11
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NONE
| null |
### System Info
I've run a prompt that said `1 + 1 = ?` with my agent, I've used `get_openai_callback` to show some metric (see the image):
Used LLM model is `GPT-3.5-turbo`
On OpenAI website we have for `GPT-3.5-turbo` model a `0.002/1k tokens`, that means my test/prompt should cost:
2206 ÷ 1000 x 0.002 = 0.004412
The weird thing based on my test is the cost, it shows `$0.04412` and not `$0.004412`
It could be a bug, Any ideas?
Here is the code:
```python
with get_openai_callback() as cb:
response = agent.run(prompt)
# Show OpenAI Cost
print(f"Total Tokens: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost (USD): ${cb.total_cost}")
```
Can anyone please explain what's going on?
Thanks in advance.
### 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
- [X] Callbacks/Tracing
- [ ] Async
@agola11
@hwchase17
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I_kwDOIPDwls5ontfN
| 6,096
|
A change in the output format of documents loaded with CSVLoader: (+ weird redundancy in the output)
|
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| 2023-06-13T16:08:21
| 2023-09-26T16:05:53
| 2023-09-26T16:05:52
|
NONE
| null |
### System Info
from this format (example):
[Document(page_content='Team: **Athletics**'', metadata={'source': '**my source1**', 'row': **0**}, lookup_index=0), Document(page_content='Team: **Rangers**', lookup_str='', metadata={'source': '**my source2**', 'row': **1**}, lookup_index=0),
Document(page_content='Team: **Yankees**', lookup_str='', metadata={'source': '**my source3**', 'row': **2**},lookup_index=0)]
To this:
[Document(**lc_kwargs**={page_content='Team: **Athletics**'', metadata={'source': '**my source1**', 'row': **0**}, lookup_index=0),
page_content='Team: **Athletics**'', metadata={'source': '**my source1**', 'row': **0**}, lookup_index=0),
Document(**lc_kwargs**={page_content='Team: **Rangers**', lookup_str='', metadata={'source': '**my source2**', 'row': **1**}, lookup_index=0),page_content='Team: **Rangers**', lookup_str='', metadata={'source': '**my source2**', 'row': **1**}, lookup_index=0),
Document(**lc_kwargs**={page_content='Team: **Yankees**', lookup_str='', metadata={'source': '**my source3**', 'row': **2**},lookup_index=0),
page_content='Team: **Yankees**', lookup_str='', metadata={'source': '**my source3**', 'row': **2**},lookup_index=0)]
### Who can help?
@hwchase17 @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Check out my colab notebook for better understanding:
https://colab.research.google.com/drive/1w8ZTAkapRev8KHI9w4IAk37T9GfFGWW2?usp=drive_link#scrollTo=tl502z9-RRJC
### Expected behavior
expected output (as you can see in the official doc: [https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html] ) :
[Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0),
Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0)]
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PR_kwDOIPDwls5S55dR
| 6,095
|
Run event handler inline if it's not an async function
|
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CONTRIBUTOR
| null |
When we try to run a callback as part of an async call, we can potentially get an async function.
- If that's the case, we should call await on it to make sure it's finished before we move on.
- If it's not an async function, is a plain old function, and we should just call it.
This makes the setup slightly cleaner because it's not creating a new context several times for one callback.
@hwchase17, @agola11. Twitter username is @mariokostelac.
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PR_kwDOIPDwls5S5kbk
| 6,094
|
Firestore supported matchingengine
|
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[] | null | 7
| 2023-06-13T15:08:52
| 2023-09-15T15:30:54
| null |
NONE
| null |
So far the matching engine vector store module only supported GCS as backend storage. I have added support for firestore as it is a nicer option for document storage. The corresponding matching engine notebook example is adjusted accordingly.
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
|
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PR_kwDOIPDwls5S4-lf
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Fix little typo in summarize.ipynb
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| 2023-06-13T13:49:47
| 2023-06-17T18:07:08
| 2023-06-17T18:07:08
|
NONE
| null |
#### Description
Hello, I found a little typo while reading your documentation. This PR is just fixing a typo in the documentation at `Chains > How-To guides > Summarization`.
|
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I_kwDOIPDwls5ol_CI
| 6,092
|
when comparison.value is int the "valueText" should change to "valueInt"
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[] | null | 1
| 2023-06-13T12:36:27
| 2023-09-19T16:08:03
| 2023-09-19T16:08:03
|
NONE
| null |
### Feature request
when comparison.value is int the "valueText" should change to "valueInt"
### Motivation
when comparison.value is int the "valueText" should change to "valueInt"
### Your contribution
self_query.weaviate
```python
def visit_comparison(self, comparison: Comparison) -> Dict:
if isinstance(comparison.value,(int,float)):
return {
"path": [comparison.attribute],
"operator": self._format_func(comparison.comparator),
"valueInt": comparison.value,
}
else:
return {
"path": [comparison.attribute],
"operator": self._format_func(comparison.comparator),
"valueText": comparison.value,
}
``
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I_kwDOIPDwls5olQb4
| 6,091
|
'ValueError: "SQLiteEntityStore" object has no field "conn"' error for tutorial "Entity Memory with SQLite storage"
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[] | null | 8
| 2023-06-13T10:50:00
| 2023-12-29T14:52:29
| null |
NONE
| null |
### System Info
LangChain version: 0.0.198
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
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Reproduce tutorial ["Entity Memory with SQLite storage"](https://python.langchain.com/en/latest/modules/memory/examples/entity_memory_with_sqlite.html)
2. While executing the following code: ```entity_store = SQLiteEntityStore()```,
get the error: ValueError: "SQLiteEntityStore" object has no field "conn"
### Expected behavior
SQLiteEntityStore() must be executed correctly.
|
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| 1,754,499,202
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I_kwDOIPDwls5ok4iC
| 6,090
|
Non English Language Support in GoogleSearchAPIWrapper
|
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| 2023-06-13T09:55:50
| 2023-09-20T16:08:20
| 2023-09-20T16:08:20
|
NONE
| null |
### Feature request
While, I could pass gl and hl parameters in SerperAPI, one cannot do it for the GoogleSearchAPIWrapper even though the CSE API supports it.
### Motivation
This should be a priority addition to make the library more inclusive. I have tried passing it with serper and it does a great job at language likes German, Hindi, French, Spanish.
### Your contribution
I tried to see the base utility, but couldn't figure out a way to add the gl, hl parameters in the base utility file.
|
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PR_kwDOIPDwls5S3Xd0
| 6,089
|
Guardrails output parser: Pass LLM api for reasking
|
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| 2023-06-13T09:45:35
| 2023-06-18T17:50:21
| 2023-06-18T17:50:21
|
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 https://github.com/ShreyaR/guardrails/issues/155
Enables guardrails reasking by specifying an LLM api in the output parser.
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I_kwDOIPDwls5okyGn
| 6,088
|
Issue: If DynamoDB table does not exist conversation message history fails with "local variable 'response' referenced before assignment"
|
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| 2023-06-13T09:41:41
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NONE
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### System Info
If the dyanamodb table does not exist when retrieving conversation history then a generic "local variable 'response' referenced before assignment" error is returned. This is due to the exception handling at https://github.com/hwchase17/langchain/blob/master/langchain/memory/chat_message_histories/dynamodb.py#L50 does not determine if the table does not exist
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a dynamodb chat message history, initialised with a DynamoDB table name that doesn't exist
2. Interact with the message history
### Expected behavior
An error should be logged stating that the dynamodb table was not found
|
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| 1,754,459,647
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I_kwDOIPDwls5oku3_
| 6,087
|
Conversational Retriever Chain - condense_question_prompt parameter is not being considered.
|
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| 2023-06-13T09:35:40
| 2023-09-22T16:08:04
| 2023-09-22T16:08:03
|
NONE
| null |
### System Info
Langchain 0.0.198,
Python 3.10
AWS Sagemaker environment
### Who can help?
@agola11, @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`from langchain.chains import ConversationalRetrievalChain
import json
from langchain.prompts.prompt import PromptTemplate
prompt_template = """Answer based on context
Context: {context}
Question: {question}"""
TEST_PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
question = 'How do I bake cake?'
chain = ConversationalRetrievalChain.from_llm(llm=llm, condense_question_prompt=TEST_PROMPT, retriever=retriever, return_source_documents=True, verbose=True)
chat_history = []
chain({ "chat_history": chat_history, "question": question})
`
### Expected behavior
Expected behavior is that it should take the given TEST_PROMPT, while sending the PROMPT to LLM, which is not doing in the original behavior
|
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| 1,754,453,579
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PR_kwDOIPDwls5S3Ru5
| 6,086
|
Improve AnalyticDB Vector Store implementation without affecting user
|
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| 2023-06-13T09:32:35
| 2023-06-17T16:36:32
| 2023-06-17T16:36:31
|
CONTRIBUTOR
| null |
Hi there:
As I implement the AnalyticDB VectorStore use two table to store the document before. It seems just use one table is a better way. So this commit is try to improve AnalyticDB VectorStore implementation without affecting user behavior:
**1. Streamline the `post_init `behavior by creating a single table with vector indexing.
2. Update the `add_texts` API for document insertion.
3. Optimize `similarity_search_with_score_by_vector` to retrieve results directly from the table.
4. Implement `_similarity_search_with_relevance_scores`.
5. Add `embedding_dimension` parameter to support different dimension embedding functions.**
Users can continue using the API as before.
Test cases added before is enough to meet this commit.
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I_kwDOIPDwls5oksqn
| 6,085
|
Issue: <search tool only uses its snippets to answer the question, that is not sufficient>
|
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| 2023-06-13T09:31:24
| 2023-07-17T10:25:04
| 2023-07-17T10:25:03
|
NONE
| null |
### search tool only uses its snippets to answer the question, that is not sufficient
I found that when calling search tools like 'bing search', 'google search' in an agent, these original api simply returns several urls and their snippets(main topic of the whole webpage).
However, LangChain directly use these snippets to answer the question, like in bing search:
```python
def run(self, query: str) -> str:
"""Run query through BingSearch and parse result."""
snippets = []
results = self._bing_search_results(query, count=self.k)
if len(results) == 0:
return "No good Bing Search Result was found"
for result in results:
snippets.append(result["snippet"])
return " ".join(snippets)
```
This concise message passed to the large language model is not sufficient to answer detailed questions, thus leading to bad answers.
Does anyone has a solution to this?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5okpCo
| 6,084
|
Weird: Same prompt works with LLMChain but not with load_qa_with_sources Chain
|
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| 2023-06-13T09:24:12
| 2023-09-19T16:08:19
| 2023-09-19T16:08:18
|
NONE
| null |
### Issue you'd like to raise.
Custom Prompt formatted and used by `load_qa_with_sources` chain is not giving the answer and saying "Sorry, I am unable to answer" (message to be given in the prompt) but the same prompt when used with simple `LLMChain` or `LLM` directly gives back the expected answer.
1) custom prompt + retrieved documents provided to `load_qa_with_sources` (stuff) chain:
2) Same prompt when used directly with `LLMChain`:
Why the same prompt is causing two different results if the underlying LLM is the same?
### Suggestion:
Following are the versions of the packages used for testing purposes:
- OpenAI: 0.27.8 and 0.27.2
- LangChain: 0.0.198 and 0.0.142
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I_kwDOIPDwls5okjKk
| 6,083
|
"Error in on_chain_start callback: 'name'" warning for tutorial "How to add Memory to an LLMChain"
|
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| 2023-06-13T09:12:55
| 2023-06-29T15:34:59
| 2023-06-15T20:01:42
|
NONE
| null |
### System Info
LangChain version: 0.0.198
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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Reproduce tutorial [How to add Memory to an LLMChain](https://python.langchain.com/en/latest/modules/memory/examples/adding_memory.html)
2. Get the same warning for each invocation of llm_chain.predict():
```Error in on_chain_start callback: 'name'```
3. The same warning will be for the following different configurations (only difference from original code is specified):
3.1.```llm = ChatOpenAI(temperature=0)```
3.2 ```chain = ConversationChain(
memory=memory,
verbose=True,
prompt=prompt_template,
llm=llm,
input_key='human_input'
)```
3.3 ```memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)```
### Expected behavior
There will be no such warnings (or the actions which resolved it must be specified in warning and/or in documentation).
|
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I_kwDOIPDwls5oj1h7
| 6,082
|
Enhancement: Multiple calls of MapReduceDocumentsChain should run asynchronously
|
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| 2023-06-13T07:28:16
| 2023-07-06T07:30:03
| 2023-06-23T19:21:29
|
CONTRIBUTOR
| null |
### Issue you'd like to raise.
When executing `arun()` of the `MapReduceDocumentsChain` multiple times in an async loop, the requests are not run concurrently.
Given the example:
```python
import asyncio
from langchain.llms import OpenAI
from langchain.chains.summarize import load_summarize_chain
from langchain.schema import Document
summary_chain = load_summarize_chain(
OpenAI(temperature=0),
chain_type="map_reduce",
)
docs = [
Document(page_content="A doc"),
Document(page_content="Another doc"),
]
tasks = [
summary_chain.arun({"input_documents": docs}),
summary_chain.arun({"input_documents": docs})
]
await asyncio.gather(*tasks)
```
While the mapping step is run asynchronously, the combine step is not. We can see that that the last two requests run sequentially:
<img width="817" alt="image" src="https://github.com/hwchase17/langchain/assets/872712/0472a000-4eb2-43bd-8f97-2e66869c857c">
This is due to the `async acombine()` method also calling the synchronous _process_results() method.
```python
async def acombine_docs(…):
…
return self._process_results(...)
def _process_results(…):
...
```
### Suggestion:
## How I can contribute
Will provide a PR!
The fact that an redundant mapping call is made in case the `input_documents` array contains a single document, is addressed in https://github.com/hwchase17/langchain/pull/5942
@agola11
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I_kwDOIPDwls5ojTGC
| 6,081
|
For web page loaders
|
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| 2023-09-12T16:17:01
| 2023-09-12T16:17:01
|
CONTRIBUTOR
| null |
### Feature request
It will be really cool if we can specify a website like : www.example.edu/* and it would get all the possible accessible pages from it and load it using WebBaseLoader.
### Motivation
I'm always frustrated when I have to make a list of websites and pass it in the loader. Instead, using this way we can query the loader and we can get all the relevant web pages.
### Your contribution
Not sure how I can contribute.
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I_kwDOIPDwls5ojSZh
| 6,080
|
Issue: HuggingFaceEmbeddings can not take trust_remote_code argument
|
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| 2023-06-13T05:42:23
| 2023-12-17T07:33:06
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CONTRIBUTOR
| null |
### HuggingFaceEmbeddings can not take trust_remote_code argument
### Suggestion:
_No response_
|
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| 1,754,079,026
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I_kwDOIPDwls5ojR8y
| 6,079
|
Issue: Can't load a public webpage
|
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| 2023-06-13T05:40:52
| 2023-06-17T18:10:50
| 2023-06-17T18:10:50
|
CONTRIBUTOR
| null |
### I want to load in the webpage below.
Hi,
Trying to extract some webpage using webbaseloader:
"""
loader = WebBaseLoader("https://researchadmin.asu.edu/)
data = loader.load()
"""
But gives the following error:
SSLError: HTTPSConnectionPool(host='researchadmin.asu.edu', port=443): Max retries exceeded with url: / (Caused by
SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get
local issuer certificate (_ssl.c:1002)')))
It is a public web page. Can anyone help?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5S2AIV
| 6,078
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turn off repr
|
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| 1,754,059,636
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PR_kwDOIPDwls5S18iu
| 6,077
|
Fix for ModuleNotFoundError while running langchain-server. Issue #5833
|
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| 2023-06-13T05:19:29
| 2023-06-13T15:37:08
| 2023-06-13T15:37:08
|
CONTRIBUTOR
| null |
This PR fixes the error
`ModuleNotFoundError: No module named 'langchain.cli'`
Fixes https://github.com/hwchase17/langchain/issues/5833 (issue)
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PR_kwDOIPDwls5S18RX
| 6,076
|
propogate kwargs fully
|
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I_kwDOIPDwls5oixPA
| 6,075
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unknown command `MODULE`, with args beginning with: `LIST`,
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| 2023-06-13T03:03:19
| 2023-12-20T16:07:41
| 2023-12-20T16:07:40
|
NONE
| null |
### System Info
when trying to connect to azure redis I get the following error:
unknown command `MODULE`, with args beginning with: `LIST`,
Here is the code:
fileName = "somefile.pdf"
loader = PyPDFLoader(fileName)
docs = loader.load_and_split()
redis_conn = f"rediss://:{utils.REDIS_PWD}@{utils.REDIS_HOST}:{utils.REDIS_PORT}"
rds = Redis.from_documents(docs, embeddings, redis_url=redis_conn, index_name='link')
Important:
Connecting to redis like this, works!
r = redis.StrictRedis(host=utils.REDIS_HOST,port=utils.REDIS_PORT, db=0, password=utils.REDIS_PWD, ssl=True)
r.set('foo', 'bar')
r.get('foo')
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This works:
import redis
r = redis.StrictRedis(host=utils.REDIS_HOST,port=utils.REDIS_PORT, db=0, password=utils.REDIS_PWD, ssl=True)
r.set('foo', 'bar')
r.get('foo')
This does not:
from langchain.document_loaders import PyPDFLoader # for loading the pdf
from langchain.vectorstores.redis import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model=OPENAI_EMBEDDING_MODEL_NAME, chunk_size=1)
fileName = "somefile.pdf"
loader = PyPDFLoader(fileName)
docs = loader.load_and_split()
redis_conn = f"rediss://:{utils.REDIS_PWD}@{utils.REDIS_HOST}:{utils.REDIS_PORT}"
rds = Redis.from_documents(docs, embeddings, redis_url=redis_conn, index_name='link')
### Expected behavior
save embeddings into REdis
|
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PR_kwDOIPDwls5S1icm
| 6,074
|
Improve AnalyticDB VectorStore implementation without affecting user behavior
|
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| 2023-06-13T02:57:15
| 2023-06-13T09:16:03
| 2023-06-13T09:16:03
|
CONTRIBUTOR
| null |
Hi there:
As I implement the AnalyticDB VectorStore use two table to store the document before. It seems just use one table is a better way after our user try to use it. So this commit is try to improve AnalyticDB VectorStore implementation without affecting user behavior behavior:
1) Streamline the __post_init__ behavior by creating a single table with automatic indexing
2) Update the add_texts API for document insertion
3) Optimize similarity_search_with_score_by_vector to retrieve results directly from the table.
4) Add an embedding_dimension parameter to support different dimension embedding functions.
Users can continue using the API as before. And the test cases added before is enough to meet this commit.
|
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I_kwDOIPDwls5oitl4
| 6,073
|
Issue: chroma where_document parameter passed in search_kwargs is invalid
|
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[] | null | 2
| 2023-06-13T02:46:14
| 2023-12-08T16:06:50
| 2023-12-08T16:06:49
|
NONE
| null |
### Issue you'd like to raise.
I want to use the chroma where_document parameter to do precise filtering of the content during data retrieval, but I found that the where_document parameter is not passed after the similarity_search_by_vector method is finally called in the source code
### Suggestion:
I hope that I can generate where_document in the retrieval parameter of the retriever, just like the existing filter will be passed to where, the code is as follows：
```py
search_kwargs = {
"k": k,
"filter": filter,
"where_document": {"$contains": "1000001"}
}
retriever = vectordb.as_retriever(
search_kwargs=search_kwargs
)
```
|
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PR_kwDOIPDwls5S1FPl
| 6,072
|
Minor: add StreamlitCallbackHandler to import and __all__
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NONE
| null |
I just wanted to add in the import for this callback handler. Thanks for making it!
#### Who can review?
@hwchase17
@agola11
|
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I_kwDOIPDwls5oiIK7
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|
Langchain rate limit error while invoking OpenAI API
|
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| 2023-09-20T16:08:31
| 2023-09-20T16:08:30
|
NONE
| null |
### System Info
## Description
While using the Langchain application, I am frequently encountering an error that relates to rate limiting when invoking OpenAI's API. This tends to occur when I try to perform multiple translations consecutively or concurrently, causing a significant interruption to the user experience.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
## Steps to Reproduce
1. Open the Langchain application.
2. Select a source language and enter some text for translation.
3. Choose the target language and submit for translation.
4. Repeat steps 2-3 multiple times in quick succession or concurrently.
### Expected behavior
## Expected Behavior
The application should be able to handle multiple translation requests without any disruptions, including but not limited to rate limit errors from OpenAI's API.
## Actual Behavior
When submitting multiple translation requests quickly or at the same time, a rate limit error is produced and no translations are returned. The error message is as follows:
`Error: OpenAI API rate limit exceeded`
How to implement retry logic with LangChain?
|
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PR_kwDOIPDwls5S08hK
| 6,070
|
Allow FAISS score_threshold
|
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| 2023-06-12T23:52:24
| 2023-06-19T08:53:41
| 2023-06-17T16:45:13
|
CONTRIBUTOR
| null |
Allow FAISS's similarity_search_with_score_by_vector to accept kwargs, specifically: `score_threshold`.
Fixes error for document compression users in cases where there are no relevant docs - for instance:
```
human_message: "hi" (<-- will return no relevant docs depending on score_threshold)
> IndexError: index 0 is out of bounds for axis 0 with size 0
```
Combined with the new merger_retriever, FAISS users can now perform more granular vectorstore retrieving based on score_threshold:
```
store_1 = st[0][1].as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .8, "k": 3})
store_2 = st[1][1].as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .3, "k": 3})
store_3 = st[2][1].as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5, "k": 3})
merged = MergerRetriever(retrievers=[store_1, store_2, store_3])
pipeline_compressor = prep_compressor()
compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=merged)
chain = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), chain_type="stuff", retriever=compression_retriever)
```
@dev2049 or @hwchase17 would appreciate a check if available. Thanks.
|
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PR_kwDOIPDwls5S070C
| 6,069
|
Add tests and update notebook for MarkdownHeaderTextSplitter
|
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| 2023-06-12T23:45:52
| 2023-06-13T16:07:52
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|
COLLABORATOR
| null |
Add test and update notebook for `MarkdownHeaderTextSplitter`.
|
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PR_kwDOIPDwls5S01vL
| 6,067
|
Use Run object from SDK
|
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|
CONTRIBUTOR
| null |
Update the Run object in the tracer to extend that in the SDK to include the parameters necessary for tracking/tracing
|
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PR_kwDOIPDwls5S0nsN
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|
Log tracer errors
|
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| 2023-06-12T22:03:38
| 2023-06-13T00:13:50
| 2023-06-13T00:13:49
|
CONTRIBUTOR
| null |
Example (would log several times if not for the helper fn. Would emit no logs due to mulithreading previously)
|
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PR_kwDOIPDwls5S0fht
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|
Fix Confluence loader doc headers
|
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| 2023-06-17T18:09:32
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NONE
| null |
SInce the page had two level 1 headers, it was showing two entries for the Confluence loader in the loader docs.
This PR adjusts the header levels.
|
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| 2023-06-22T13:21:21
| 2023-06-22T07:58:28
|
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 -->
🎉 OpenLLM 🤝 LangChain
[OpenLLM](https://github.com/bentoml/OpenLLM) is a new open platform for operating large language models(LLMs) in
production. Serve, deploy, and monitor any LLMs with ease.
OpenLLM lets developers and researchers to easily run inference with any
open-source LLMs, deploy to the cloud or on-premises, build powerful AI apps,
and fine tune your own LLM (coming soon...)
It currently supports ChatGLM, Dolly-v2, Flan-T5, Falcon, Starcoder, and more to
come. One can also easily start either a REST/gRPC server, which is powered by
BentoML.
Now that's out of the way, lets dive in!
The current depenedencies for this integration: `openllm`
This integrations brings a `OpenLLM` llms to LangChain, that can be used for
both running LLMs locally as well as interacting with a remote OpenLLM server.
To quickly start a local LLM, simply do the following:
```python
from langchain.llms import OpenLLM
llm = OpenLLM(model_name="dolly-v2", model_id='databricks/dolly-v2-7b', device_map='auto')
llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?")
```
`langchain.llms.OpenLLM`, as mentioned above also have the capabilities to
interact with remote OpenLLM Server. Given there is a running OpenLLM server at
http://44.23.123.1, you can do the following:
```python
from langchain.llms import OpenLLM
llm = OpenLLM(server_url='http://44.23.123.1:3000', server_type='grpc')
llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?")
```
Features soon to be open-sourced:
- OpenAI compatible API, allowing users to easily use LangChain's OpenAI llm.
- SSE support for OpenLLM server, allowing users to stream inference results.
- Last but not least, easily fine-tune your own LLMs with `LLM.tuning()`
Last but not least, I would love to hear feedback and response from the
community about the project, and feel free to reach out to me via Twitter
@aarnphm_.
Feel free to join our [Discord](https://l.bentoml.com/join-openllm-discord) to get the latest updates and developments.
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
#### 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
-->
I have added tests for this integration.
#### 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
-->
cc @hwchase17 @agola11
|
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PR_kwDOIPDwls5S0eJU
| 6,063
|
ArxivAPIWrapper - doc_content_chars_max
|
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| 2023-06-16T05:16:42
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|
CONTRIBUTOR
| null |
This PR refactors the ArxivAPIWrapper class making `doc_content_chars_max` parameter optional. Additionally, tests have been added to ensure the functionality of the doc_content_chars_max parameter.
Fixes #6027 (issue)
|
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PR_kwDOIPDwls5S0UKd
| 6,062
|
improve tools
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| 2023-06-12T21:01:18
| 2023-06-13T05:19:04
| 2023-06-13T05:19:04
|
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Sz1v0
| 6,060
|
Update output format of MosaicML endpoint to be more flexible
|
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|
CONTRIBUTOR
| null |
There will likely be another change or two coming over the next couple weeks as we stabilize the API, but putting this one in now which just makes the integration a bit more flexible with the response output format.
```
(langchain) danielking@MML-1B940F4333E2 langchain % pytest tests/integration_tests/llms/test_mosaicml.py tests/integration_tests/embeddings/test_mosaicml.py
=================================================================================== test session starts ===================================================================================
platform darwin -- Python 3.10.11, pytest-7.3.1, pluggy-1.0.0
rootdir: /Users/danielking/github/langchain
configfile: pyproject.toml
plugins: asyncio-0.20.3, mock-3.10.0, dotenv-0.5.2, cov-4.0.0, anyio-3.6.2
asyncio: mode=strict
collected 12 items
tests/integration_tests/llms/test_mosaicml.py ...... [ 50%]
tests/integration_tests/embeddings/test_mosaicml.py ...... [100%]
=================================================================================== slowest 5 durations ===================================================================================
4.76s call tests/integration_tests/llms/test_mosaicml.py::test_retry_logic
4.74s call tests/integration_tests/llms/test_mosaicml.py::test_mosaicml_llm_call
4.13s call tests/integration_tests/llms/test_mosaicml.py::test_instruct_prompt
0.91s call tests/integration_tests/llms/test_mosaicml.py::test_short_retry_does_not_loop
0.66s call tests/integration_tests/llms/test_mosaicml.py::test_mosaicml_extra_kwargs
=================================================================================== 12 passed in 19.70s ===================================================================================
```
#### Who can review?
@hwchase17
@dev2049
|
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I_kwDOIPDwls5og1x2
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|
Issue: LangChain Plus Api Key
|
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| 2023-06-12T19:35:50
| 2023-09-23T16:05:18
| 2023-09-23T16:05:17
|
NONE
| null |
### Issue you'd like to raise.
Started a local server just to learn more about with `langchain plus start`. It says that I can generate a new api as it appears to have an endpoint in port 1984. But when I click the menu there's no **Settings** option there.
---
<img width="1787" alt="Screenshot 2023-06-12 at 16 32 31" src="https://github.com/hwchase17/langchain/assets/3484029/e42ef803-f216-486a-84f3-6c4026a37a8f">
### Suggestion:
_No response_
|
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I_kwDOIPDwls5ogbQQ
| 6,058
|
Issue: ModuleNotFoundError: No module named 'langchain.callbacks.shared'
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| 2023-06-12T18:33:04
| 2023-09-27T16:06:14
| 2023-09-27T16:06:14
|
NONE
| null |
### Issue you'd like to raise.
Hello,
I am having trouble running my code due to an error that I encountered. The error message that I received is "ModuleNotFoundError: No module named 'langchain.callbacks.shared'". I am not sure how to resolve this issue and would appreciate any help or guidance.
This line of code is using the `pickle` module to load data from a file. The `pickle.load(file)` function reads the pickled representation of an object from the open file object `file` and returns the reconstituted object hierarchy specified therein.
In this case, the returned object is expected to be a tuple with two elements, which are assigned to the variables `self.chain` and `self.vectorstore`, respectively. This means that the first element of the tuple is assigned to `self.chain`, and the second element is assigned to `self.vectorstore`.
Here is some information about my environment that may be helpful:
- OS: Ubuntu
- Python version: 3.10
- Langchain Version: 0.0.198
Issue Screen Shot:
<br />
Thank you in advance for your help and support in resolving this issue.
### Suggestion:
_No response_
|
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| 1,753,321,160
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PR_kwDOIPDwls5Sza-z
| 6,057
|
Replace self-built devcontainer with default python image
|
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| 2023-09-18T16:27:05
| 2023-09-18T16:18:52
|
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.
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This replaces the custom docker image for devcontainer with the official python one with the `poetry` feature and `black-formatter` extension added, because:
- The current one is broken during building when opening in codespaces. I tried a few times and it errors out during poetry install colored for some reasons.
- It's faster to build and load when opening in codespaces.
- It's easier to maintain as it's composed from existing things pre-built by others.
#### Who can review?
Tag maintainers/contributors who might be interested: - @vowelparrot
<!-- 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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| 1,753,301,223
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PR_kwDOIPDwls5SzWpI
| 6,056
|
Implement `max_marginal_relevance_search` in `VectorStore` of Pinecone
|
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| 2023-06-13T17:46:46
| 2023-06-13T17:46:46
|
CONTRIBUTOR
| null |
This adds implementation of MMR search in pinecone; and I have two semi-related observations about this vector store class:
- Maybe we should also have a `similarity_search_by_vector_returning_embeddings` like in supabase, but it's not in the base `VectorStore` class so I didn't implement
- Talking about the base class, there's `similarity_search_with_relevance_scores`, but in pinecone it is called `similarity_search_with_score`; maybe we should consider renaming it to align with other `VectorStore` base and sub classes (or add that as an alias for backward compatibility)
#### Who can review?
Tag maintainers/contributors who might be interested:
- VectorStores / Retrievers / Memory - @dev2049
|
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I_kwDOIPDwls5ogLb8
| 6,055
|
When using MongoDBAtlasVectorSearch i get KeyError: 'text' despite having the collection populated
|
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| 2023-06-12T17:58:39
| 2023-10-14T20:12:22
| 2023-10-14T20:12:21
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NONE
| null |
### System Info
Python version: 3.10.11 (main, May 17 2023, 14:30:36) [Clang 14.0.6 ]
pymongo 4.3.3
langchain 0.0.190
### Who can help?
@eyurtsev @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [x] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I followed religiously the following tutorial:
https://python.langchain.com/en/stable/modules/indexes/vectorstores/examples/mongodb_atlas_vector_search.html
Despite that I get the following error.
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[14], line 12
10 # perform a similarity search between the embedding of the query and the embeddings of the documents
11 query = "my query?"
---> 12 docs = vectorStore.similarity_search(query)
File ~/opt/miniconda3/envs/LLM_langchain_exploration/lib/python3.10/site-packages/langchain/vectorstores/mongodb_atlas.py:222, in MongoDBAtlasVectorSearch.similarity_search(self, query, k, pre_filter, post_filter_pipeline, **kwargs)
194 def similarity_search(
195 self,
196 query: str,
(...)
200 **kwargs: Any,
201 ) -> List[Document]:
202 """Return MongoDB documents most similar to query.
203
204 Use the knnBeta Operator available in MongoDB Atlas Search
(...)
220 List of Documents most similar to the query and score for each
221 """
--> 222 docs_and_scores = self.similarity_search_with_score(
223 query,
224 k=k,
225 pre_filter=pre_filter,
226 post_filter_pipeline=post_filter_pipeline,
227 )
228 return [doc for doc, _ in docs_and_scores]
File ~/opt/miniconda3/envs/LLM_langchain_exploration/lib/python3.10/site-packages/langchain/vectorstores/mongodb_atlas.py:189, in MongoDBAtlasVectorSearch.similarity_search_with_score(self, query, k, pre_filter, post_filter_pipeline)
187 docs = []
188 for res in cursor:
--> 189 text = res.pop(self._text_key)
190 score = res.pop("score")
191 docs.append((Document(page_content=text, metadata=res), score))
KeyError: 'text'
```
### Expected behavior
should get the docs back
|
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PR_kwDOIPDwls5Sy-Cx
| 6,054
|
feat: Add support for the Solidity language
|
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| 2023-06-14T21:25:09
| 2023-06-14T21:25:03
|
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!
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## Add Solidity programming language support for code splitter.
Twitter: @0xjord4n_
<!-- 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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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_kwDOIPDwls5ofkoQ
| 6,053
|
Progress bar for LLMChain
|
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| 2023-06-12T16:21:29
| 2023-10-31T12:20:58
| 2023-09-22T16:07:45
|
CONTRIBUTOR
| null |
Hello,
Is there a way to track progress when giving a list of inputs to a LLMChain object using tqdm for example?
I didn't see any parameter that would allow me to use tqdm.
I also checked if I could write a Callback for this. But the hooks doesn't seem to allow for that.
Anyone managed to use some progress bar?
|
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| 1,753,074,942
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I_kwDOIPDwls5ofcz-
| 6,052
|
tables names are not case insensitive in the Snowflake Client
|
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| 2023-06-12T16:01:50
| 2023-06-12T17:44:27
| 2023-06-12T17:44:27
|
NONE
| null |
### System Info
langchain==0.0.195
python 3.9
the client doesn't recognize 'MY_TABLE' and 'my_table' as the same table.
### 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
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
when initializing the sql client, add the param `include_tables` and set the strings in upper case fashion.
`sql_database = SQLDatabase(engine,view_support=True, include_tables=["MY_TRADE"])`
you should see this error:
`ValueError: include_tables {'MY_TRADE'} not found in database`
However, everything should go through with:
`sql_database = SQLDatabase(engine,view_support=True, include_tables=["my_trade"])`
### Expected behavior
`sql_database = SQLDatabase(..., include_tables=["MY_TRADE"])`
to be equal to
`sql_database = SQLDatabase(..., include_tables=["my_trade"])`
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PR_kwDOIPDwls5SyKl5
| 6,051
|
Mitigate issue #5923 (Prompt injection -> SQL injection in SQLChain)
|
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| 2023-06-12T14:58:10
| 2023-08-28T17:27:34
| 2023-08-28T17:27:34
|
CONTRIBUTOR
| null |
Add validation controls to the SQL chain to mitigate SQL injection issues.
Using sqlfluff to perform static analysis:
1. Disallow non select statement (INSERT, DROP)
2. Disallow wildcard select statement
Some dialects that are supported by langchain are not supported by sqlfluff. It is possible to disallow usage of such dialects as well.
Also fixed the SQL integration tests which were not working as expected
Fixes #5923
BTW, looks like there is a huge diff on poetry.lock which doesn't look OK to me, would appreciate any advice on how to resovle it (I was following the instructions found here https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md)
#### Who can review?
@hwchase17
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PR_kwDOIPDwls5SyFvF
| 6,050
|
Implement RDF Graph and RDF QA Chain
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| 2023-06-12T14:46:05
| 2023-07-13T11:28:51
| 2023-07-13T11:28:51
|
NONE
| null |
This pull request adds support for RDF Graph-based QA Chain. It uses the same signature and provides similar functionalities as the current Neo4j and Nebula implementations.
In theory, any RDF stores that support the W3C SPARQL Query Language 1.1 standard can be used, although only a few were tested during the development.
https://github.com/lifan0127/langchain/blob/rdf-graph/docs/modules/chains/examples/graph_rdf_qa.ipynb
This approach will not scale well for large, public RDF databases such as Wikidata, but that is a more general problem that requires more research.
cc @hwchase17
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PR_kwDOIPDwls5SyB_7
| 6,049
|
Enable serialization for anthropic
|
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| 2023-06-12T14:37:04
| 2023-06-13T05:39:11
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5SyBFe
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|
Add embaas document extraction api endpoints
|
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| 2023-06-12T14:34:35
| 2023-06-13T02:13:52
| 2023-06-13T02:13:52
|
CONTRIBUTOR
| null |
# Introduces embaas document extraction api endpoints
In this PR, we add support for embaas document extraction endpoints to Text Embedding Models (with LLMs, in different PRs coming). We currently offer the MTEB leaderboard top performers, will continue to add top embedding models and soon add support for customers to deploy thier own models. Additional Documentation + Infomation can be found [here](https://embaas.io).
While developing this integration, I closely followed the patterns established by other langchain integrations. Nonetheless, if there are any aspects that require adjustments or if there's a better way to present a new integration, let me know! :)
Additionally, I fixed some docs in the embeddings integration.
Related PR: #5976
#### Who can review?
DataLoaders
- @eyurtsev
|
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| 1,752,825,122
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I_kwDOIPDwls5oef0i
| 6,046
|
similarity_search_with_score witn Chroma DB keeps higher score for less relevant documents.
|
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| 2023-06-12T13:57:49
| 2023-12-30T08:59:06
| 2023-06-12T15:26:36
|
NONE
| null |
### System Info
Python 3.10.6
chromadb==0.3.22
langchain==0.0.194
### Who can help?
similarity_search_with_score witn Chroma DB keeps higher score for less relevant documents.
```
embeddings = OpenAIEmbeddings(
model="text-embedding-ada-002",
openai_api_key = openai.api_key,
chunk_size=1
)
db = Chroma(collection_name="docs", embedding_function=embeddings, persist_directory=vector_db_path)
question = """What is the rate of arthralgia in the combined RCP and OLP inebilizumab population?"""
[d[1] for d in db.similarity_search_with_score(question, k=5 )]
```
```
[0.3035728335380554,
0.3159480392932892,
0.3345768451690674,
0.3543674945831299,
0.36075425148010254]
```
```
[d[1] for d in db.similarity_search_with_score(question, k=10 )]
[0.3035728335380554,
0.3159480392932892,
0.3345768451690674,
0.3543674945831299,
0.36075425148010254,
0.36337000131607056,
0.3656774163246155,
0.36993658542633057,
0.37518084049224854,
0.3755079507827759]
```
Seems more like i should be doing (1-score) , to filter more relevant documents. So i tried to test with similarity threshold of .35 , but then it returns the least similar docs ( as .30 was more similar )
### 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
question = """What is the rate of arthralgia in the combined RCP and OLP inebilizumab population?"""
[d[1] for d in db.similarity_search_with_score(question, k=5 )]
[d[1] for d in db.similarity_search_with_score(question, k=10 )]
### Expected behavior
I would expect higher similarity score for the documents that are earlier in the retruned list ( which the document is more related but has a lower score )
|
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I_kwDOIPDwls5oeTQa
| 6,045
|
Issue: langchain + pgvector fail to create new tables in postgresql db
|
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| 2023-06-12T13:29:06
| 2023-07-29T04:11:48
| 2023-06-13T07:21:36
|
NONE
| null |
### Issue you'd like to raise.
I am trying to load llama-ccp embeddings into a postgresql with the vector extension. The db has the vector extension installed. I am using a user that has the correct privileges to create a new table and insert the embeddings. The code runs smoothly without any errors, but the collection is not being created as a new table in the db. The db name is `embeddings` and the schema name is `vector_store`.
From pgadmin I can see that the there is the following query, which means the embeddings are created and are ready to be loaded:
`INSERT INTO langchain_pg_embedding (collection_id, embedding, document, cmetadata, custom_id, uuid) VALUES ('d10c0cd8-300f-4592-aa2a-42f827...`
However, it seems that no new table is created and the table names do not correspond to the collection name I am setting - `vector_store.test_table`. Any help will be appreciated.
packages:
pgvector 0.1.8
psycopg2-binary 2.9.6
langchain 0.0.149
This is my code:
```
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.document_loaders import TextLoader
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import DirectoryLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.embeddings import LlamaCppEmbeddings
from langchain.vectorstores.faiss import FAISS
import os
import datetime
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.pgvector import PGVector
from langchain.docstore.document import Document
gpt4all_path = '../models/gpt4all-converted.bin'
llama_path = '../models/ggml-model-q4_0.bin'
embeddings = LlamaCppEmbeddings(model_path=llama_path)
loader = TextLoader('../data/test.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
docs = text_splitter.split_documents(documents)
import os
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host=os.environ.get("PGVECTOR_HOST", "xxxxxxxxxxx"),
port=int(os.environ.get("PGVECTOR_PORT", "5432")),
database=os.environ.get("PGVECTOR_DATABASE", "embeddings"),
user=os.environ.get("PGVECTOR_USER", "xxxxxxxx"),
password=os.environ.get("PGVECTOR_PASSWORD", "xxxxxxxxxxx"),
)
db = PGVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name="vector_store.test_table",
connection_string=CONNECTION_STRING,
pre_delete_collection=True
)
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5SxeVc
| 6,044
|
chore: spedd up integration test by using smaller model
|
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| 2023-06-12T13:16:56
| 2023-06-12T20:27:10
| 2023-06-12T20:27:10
|
CONTRIBUTOR
| null |
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`
Tag maintainers/contributors who might be interested:
@hwchase17, @dev2049
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PR_kwDOIPDwls5Sxdpl
| 6,043
|
qdrant: search by vector
|
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| 2023-06-12T13:15:19
| 2023-06-17T16:44:29
| 2023-06-17T16:44:29
|
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 -->
Added support to `search_by_vector` to Qdrant Vector store.
<!-- 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
VectorStores / Retrievers / Memory
- @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
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PR_kwDOIPDwls5SxOkP
| 6,042
|
Mongo db doc fix
|
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| 2023-06-12T12:39:15
| 2023-06-12T14:29:28
| 2023-06-12T14:29:28
|
CONTRIBUTOR
| null |
I missed a few errors in my initial fix @hwchase1. Thanks!
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I_kwDOIPDwls5odpnl
| 6,041
|
Emiting events during processing of intermediate steps
|
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| 2023-06-12T12:00:43
| 2023-09-18T16:07:58
| 2023-09-18T16:07:57
|
NONE
| null |
### Feature request
Langchain agent performs multiple intermediate steps before returning a response. We want to emit an event for every intermediate step during processing, so that the client-side can be updated about what's being processed at any given time.
For example, if an intermediate steps involves searching Google, we can then emit an event that informs the client about the search taking place.
This could massively improve UX.
### Motivation
The motivation comes from wanting to improve UX. We're developing a langchain-based app and this feature is sorely missed.
### Your contribution
We could definitely submit a PR if you give a little bit of guidance on how to go about with it.
|
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PR_kwDOIPDwls5SwVFW
| 6,040
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make modelname_to_contextsize as a staticmethod
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| 2023-06-17T16:13:09
| 2023-06-17T16:13:09
|
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!
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<!-- Remove if not applicable -->
Fixes ##6039
#### 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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etc:
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#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17 @agola11
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@hwchase17 - project lead
Tracing / Callbacks
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- @eyurtsev
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- @hwchase17
- @agola11
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- @hwchase17
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- @dev2049
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I_kwDOIPDwls5oc7-D
| 6,039
|
Make modelname_to_contextsize as a staticmethod to use it without create an object
|
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| 2023-06-12T10:23:07
| 2023-06-23T11:58:44
| 2023-06-23T11:58:44
|
CONTRIBUTOR
| null |
### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040
|
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I_kwDOIPDwls5ocp6X
| 6,038
|
Validation error for gpt4all
|
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| 2023-06-12T09:44:27
| 2023-09-20T16:08:46
| 2023-09-20T16:08:45
|
NONE
| null |
### System Info
I followed the steps to install gpt4all based on this repo
https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python
I have the latest version of langchain
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
def mainllm():
template = """Question: {question}
prompt = PromptTemplate(template=template, input_variables=["question"])
local_path = './models/gpt4all-converted.bin'
llm = gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy.bin")
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)
if __name__ == '__main__':
mainllm()
```
This is the error I get related to this lines on code:
```
Traceback (most recent call last):
File "gpt4all.py", line 174, in <module>
mainllm()
File "gpt4all.py", line 166, in mainllm
llm = gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy.bin")
TypeError: Serializable.__init__() takes 1 positional argument but 2 were given
```
### Expected behavior
Could you help me understand what is wrong?
Thanks
|
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| 6,037
|
Report:Discussion about the bug mentioned in the documentation
|
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| 2023-06-12T09:43:17
| 2023-09-15T22:13:04
| 2023-09-13T22:58:49
|
NONE
| null |
### Issue with current documentation:
Report
I am currently reading the documentation for version 0.0.197 and testing step by step. When I ran into the "agents-with-chat-models" section, I encountered a type error in the code. The specific situation is as follows:
[doc link](https://python.langchain.com/en/latest/getting_started/getting_started.html#agents-with-chat-models)
```python
# this is my code
llm = AzureOpenAI(
model_name = os.environ["GPT_ENGINE"],
deployment_name = os.environ["GPT_ENGINE"],
openai_api_key = os.environ["API_KEY"],
temperature = 0,
max_tokens = 1000,
top_p = 0.95,
frequency_penalty = 0,
presence_penalty = 0
)
chat = AzureChatOpenAI(
model_name = os.environ["GPT_ENGINE"],
deployment_name = os.environ["GPT_ENGINE"],
openai_api_key = os.environ["API_KEY"],
openai_api_base = os.environ["API_BASE"],
openai_api_version= os.environ["API_VERSION"],
temperature = 0,
max_tokens = 1000
)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?").format
```
```log-output
> Entering new AgentExecutor chain...
Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
"action": "Search",
"action_input": "Olivia Wilde boyfriend"
}
Observation: Looks like Olivia Wilde and Jason Sudeikis are starting 2023 on good terms. Amid their highly publicized custody battle – and the actress' ...
Thought:Now I need to use a calculator to raise Jason Sudeikis' age to the 0.23 power.
Action:
{
"action": "Calculator",
"action_input": "pow(47, 0.23)"
}
```
```error-output
ValueError: LLMMathChain._evaluate("
pow(47, 0.23)
") raised error: 'VariableNode' object is not callable. Please try again with a valid numerical expression
```
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5Sv96b
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Resolve the issue with the asynchronous function generate_concurrentl…
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| 2023-06-12T09:26:10
| 2023-11-01T23:49:36
| 2023-11-01T23:49:36
|
NONE
| null |
…y in async_chain.ipynb
<!--
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)
Doc code err!
#### Before submitting
```
async def generate_concurrently():
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
tasks = [async_generate(chain) for _ in range(5)]
await asyncio.gather(*tasks)
s = time.perf_counter()
# If running this outside of Jupyter, use asyncio.run(generate_concurrently())
await generate_concurrently()
```
#### After submitting
```
async def generate_concurrently():
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
tasks = [async_generate(chain) for _ in range(5)]
await asyncio.gather(*tasks)
s = time.perf_counter()
# If running this outside of Jupyter, use asyncio.run(generate_concurrently())
asyncio.run(generate_concurrently())
```
<!-- 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_kwDOIPDwls5Sv631
| 6,035
|
Allow GoogleDrive to authenticate via application default credentials on Cloud Run/GCE etc without service key
|
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[] | null | 1
| 2023-06-12T09:17:10
| 2023-06-17T16:44:18
| 2023-06-17T16:44:18
|
CONTRIBUTOR
| null |
@eyurtsev
The existing GoogleDrive implementation always needs a service account to be available at the credentials location. When running on GCP services such as Cloud Run, a service account already exists in the metadata of the service, so no physical key is necessary. This change adds a check to see if it is running in such an environment, and uses that authentication instead.
|
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I_kwDOIPDwls5ocMzc
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TypeError("Requests.apost() missing 1 required positional argument: 'data'")
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[] | null | 3
| 2023-06-12T08:37:26
| 2023-09-25T09:49:03
| 2023-09-25T09:49:02
|
NONE
| null |
### System Info
langchain = "^0.0.197"
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
``` python
import asyncio
from langchain.utilities import TextRequestsWrapper
requests = TextRequestsWrapper()
async def fun():
ret = await requests.apost("http://127.0.0.1:8080", data={"data": 123})
if __name__ == '__main__':
asyncio.run(fun())
```
### Expected behavior
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I_kwDOIPDwls5ocGex
| 6,033
|
Add clearer API for defining `k` (number of documents retrieved) in VectorStore/retriever defining functions
|
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[] | null | 2
| 2023-06-12T08:24:14
| 2023-10-25T16:08:23
| 2023-10-25T16:08:21
|
NONE
| null |
### Feature request
Let users easily define the value of `k`, which is the number of documents retrieved, when specifying a VectorStore.
### Motivation
When working with documents, users typically need to specify VectorStore, and users may often want to define the specific number of documents being retrieved, i.e., the `k`, when similarity search runs behind the scene. For example, I am doing
```
docsearch = Chroma.from_documents(texts1 + texts3, embeddings)
retriever=docsearch.as_retriever(search_kwargs = {'k':1})
```
However, there is no easy way or clear documentation for this. I have to go through the source code all the ways to find that I can do it with adding `search_kwargs = {'k':1}` when I specify the retriever. This is not user-friendly, especially noticing that this is a common feature that users need.
### Your contribution
I would love to open a PR for this. I am willing to either change the code or writing a clearer documentation for this feature.
|
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I_kwDOIPDwls5ob2dK
| 6,032
|
KeyError: 'langchain' (circular import error)
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| 2023-06-12T07:52:33
| 2023-12-24T16:01:24
| null |
NONE
| null |
### System Info
@hwchase17
@agola11
Hi. From time to time, I am getting the following error:
```
2023-06-12 02:27:55.993 Uncaught app exception
Traceback (most recent call last):
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 565, in run_script
exec(code, module.dict)
File "C:\Users\v-alakubov\OneDrive\Desktop\app_v2\src\pages\AskData.py", line 7, in <module>
from modules.table_tool import PandasAgent
File "C:\Users\v-alakubov\OneDrive\Desktop\Listens\app_v2.\src\modules\table_tool.py", line 6, in <module>
from langchain.callbacks import get_openai_callback
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain_init.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\agents_init_.py", line 2, in <module>
from langchain.agents.agent import (
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\agents\agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\agents\tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\tools_init_.py", line 46, in <module>
from langchain.tools.powerbi.tool import (
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\tools\powerbi\tool.py", line 11, in <module>
from langchain.chains.llm import LLMChain
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\chains_init_.py", line 7, in <module>
from langchain.chains.conversational_retrieval.base import (
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 22, in <module>
from langchain.chains.question_answering import load_qa_chain
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\chains\question_answering_init_.py", line 13, in <module>
from langchain.chains.question_answering import (
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\chains\question_answering\map_reduce_prompt.py", line 2, in <module>
from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\chains\prompt_selector.py", line 7, in <module>
from langchain.chat_models.base import BaseChatModel
ImportError: cannot import name 'BaseChatModel' from partially initialized module 'langchain.chat_models.base' (most likely due to a circular import) (C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\langchain\chat_models\base.py)
2023-06-12 02:27:56.013 Uncaught app exception
Traceback (most recent call last):
File "C:\Users\v-alakubov\AppData\Local\anaconda3\envs\py311\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 565, in _run_script
exec(code, module.dict)
File "C:\Users\v-alakubov\OneDrive\Desktop\Listens\app_v2\src\pages\AI-Chat.py", line 8, in <module>
from modules.utils import Utilities
File "C:\Users\v-alakubov\OneDrive\Desktop\Listens\app_v2.\src\modules\utils.py", line 6, in <module>
from modules.chatbot import Chatbot
File "C:\Users\v-alakubov\OneDrive\Desktop\Listens\app_v2.\src\modules\chatbot.py", line 3, in <module>
from langchain.chat_models import AzureChatOpenAI
File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
File "<frozen importlib._bootstrap>", line 1155, in _find_and_load_unlocked
KeyError: 'langchain'
```
any ideas why?
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Here structure of project:
```
Home.py
pages
modules
embeddings
```
Where pages, modules and embeddings are folders.
"pages" folder has two files:
AI-Chat.py
AskData.py
modules folder has 7 files:
chatbot.py
embedder.py
history.py
layout.py
sidebar.py
table_tool.py
utils.py
Home.py (the file I run as ` streamlit run .\src\Home.py`)
AI-Chat.py:
```
import os
import streamlit as st
from io import StringIO
import re
import sys
from modules.history import ChatHistory
from modules.layout import Layout
from modules.utils import Utilities
from modules.sidebar import Sidebar
....
```
AskData.py:
```
import os
import importlib
import sys
import pandas as pd
import streamlit as st
from io import BytesIO
from modules.table_tool import PandasAgent
from modules.layout import Layout
from modules.utils import Utilities
from modules.sidebar import Sidebar
....
```
chatbot.py:
```
import streamlit as st
# from langchain.chat_models import ChatOpenAI
from langchain.chat_models import AzureChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
import os
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verbose = False
import traceback
.....
```
utils.py:
```
import os
import pandas as pd
import streamlit as st
import pdfplumber
from modules.chatbot import Chatbot
from modules.embedder import Embedder
```
embedder.py:
```
import os
import pickle
import tempfile
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
....
```
table_tool.py:
```
import re
import sys
from io import StringIO, BytesIO
import matplotlib.pyplot as plt
import streamlit as st
from langchain.callbacks import get_openai_callback
from streamlit_chat import message
import os
from pandasai import PandasAI
# from pandasai.llm.openai import OpenAI
from pandasai.llm.azure_openai import AzureOpenAI
....
```
history.py:
```
import os
import streamlit as st
from streamlit_chat import message
......
```
layout.py:
```
import streamlit as st
....
```
sidebar.py:
```
import streamlit as st
.......
```
### Expected behavior
no error should rise
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PR_kwDOIPDwls5SvOqV
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|
DocArray as a Retriever
|
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| 2023-06-12T07:28:29
| 2023-06-17T16:09:35
| 2023-06-17T16:09:34
|
CONTRIBUTOR
| null |
## DocArray as a Retriever
[DocArray](https://github.com/docarray/docarray) is an open-source tool for managing your multi-modal data. It offers flexibility to store and search through your data using various document index backends. This PR introduces `DocArrayRetriever` - which works with any available backend and serves as a retriever for Langchain apps.
Also, I added 2 notebooks:
DocArray Backends - intro to all 5 currently supported backends, how to initialize, index, and use them as a retriever
DocArray Usage - showcasing what additional search parameters you can pass to create versatile retrievers
Example:
```python
from docarray.index import InMemoryExactNNIndex
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import DocArrayRetriever
# define document schema
class MyDoc(BaseDoc):
description: str
description_embedding: NdArray[1536]
embeddings = OpenAIEmbeddings()
# create documents
descriptions = ["description 1", "description 2"]
desc_embeddings = embeddings.embed_documents(texts=descriptions)
docs = DocList[MyDoc](
[
MyDoc(description=desc, description_embedding=embedding)
for desc, embedding in zip(descriptions, desc_embeddings)
]
)
# initialize document index with data
db = InMemoryExactNNIndex[MyDoc](docs)
# create a retriever
retriever = DocArrayRetriever(
index=db,
embeddings=embeddings,
search_field="description_embedding",
content_field="description",
)
# find the relevant document
doc = retriever.get_relevant_documents("action movies")
print(doc)
```
#### Who can review?
@dev2049
|
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Add OctoAI LLM endpoint wrapper and embedding endpoint wrapper
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| 2023-06-29T05:36:44
| 2023-06-29T05:36:43
|
CONTRIBUTOR
| null |
This pull request introduces support for [OctoAI's Compute Service](https://octoai.cloud/) embedding endpoints and large language model endpoints into the LangChain project. The changes encompass additions to various files to accommodate OctoAI's API integration.
**Details of the changes:**
**langchain/__init__.py** and **langchain/llms/__init__.py:** Updates to these initialization files allow OctoAI's services to be utilized in the LangChain's workflow. The changes add 'OctoAIEndpoint' as an additional language model option.
**langchain/embeddings/octoai_embeddings.py:** This new file creates a wrapper for OctoAI embedding endpoints. This addition allows users to compute embeddings of documents and queries using OctoAI's service.
**langchain/llms/octoai_endpoint.py**: This file introduces a new class, 'OctoAIEndpoint', which serves as a wrapper for OctoAI's large language model endpoints. It includes methods for interacting with OctoAI's API and getting responses from it.
**tests/integration_tests/embeddings/test_octoai_embeddings.py**: This file introduces tests to ensure the correct functioning of the OctoAI embedding endpoint using the Instructor-Large model endpoint. It includes tests for document and query embedding functionalities.
**tests/integration_tests/llms/test_octoai_endpoint.py**: This file includes tests for OctoAI's LLM wrapper for MPT-7B model endpoint. It covers various scenarios including valid API calls, calls that result in errors, and the saving/loading of language model endpoints.
**docs/modules/models/llms/integrations/octoai.ipynb**: This notebook shows how to use LangChain to interact with `OctoAI` [LLM endpoints](https://octoai.cloud/templates)
Please note that to use the newly added OctoAI's services, users should have the octoai python package installed `pip install octoai`, and the environment variables OCTOAI_API_TOKEN set with the API token and ENDPOINT_URL set with endpoint_url. The API token and endpoint URL can be obtained by cloning a template and creating an API token at https://octoai.cloud.
Thanks in advance for your review!
@hwchase17 @agola11
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I_kwDOIPDwls5obJMr
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ValueError: not enough values to unpack (expected 2, got 1)
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| 2023-06-12T05:50:34
| 2023-09-19T16:08:44
| 2023-09-19T16:08:44
|
NONE
| null |
### Issue you'd like to raise.
I keep getting this error when generating Chroma vectors. Here's my code:
`from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.document_loaders import GoogleDriveLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
folder_id = ''
loader = GoogleDriveLoader(folder_id = folder_id,
recursive=False)
docs = loader.load(encoding='utf8')
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=4000, chunk_overlap=0, separators=["", "\n", ","]
)
texts = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings(openai_api_key="")
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
llm = ChatOpenAI(temperature=0, model_name="davinci")
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
while True:
question = input("> ")
answer = qa.run(question)
print(answer)`
### Suggestion:
_No response_
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I_kwDOIPDwls5obHbn
| 6,028
|
LLMMathChain 'VariableNode' object has no attribute 'factorial'. Please try again with a valid numerical expression
|
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| 2023-06-12T05:42:29
| 2023-09-18T16:08:14
| 2023-09-18T16:08:13
|
CONTRIBUTOR
| null |
### System Info
langchain version 0.0.196
windows, wsl2
python 3.11.3
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow instructions on this page:
https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html
Then run the following prompt:
```
agent.run("Who is Elon Musk in a relationship with? What is their current age factorial?")
```
### Expected behavior
It should plan out how to solve the problem (search significant other, search age, find factorial, respond) then execute each action and respond to the user. Instead, when it gets to the factorial part, it fails, claiming that there's no factorial function. Stacktrace:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File [~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chains/llm_math/base.py:80](https://vscode-remote+wsl-002bubuntu-002d20-002e04.vscode-resource.vscode-cdn.net/home/oriont/dev/langchain-phx/~/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/chains/llm_math/base.py:80), in LLMMathChain._evaluate_expression(self, expression)
78 local_dict = {"pi": math.pi, "e": math.e}
79 output = str(
---> 80 numexpr.evaluate(
81 expression.strip(),
82 global_dict={}, # restrict access to globals
83 local_dict=local_dict, # add common mathematical functions
84 )
85 )
86 except Exception as e:
File [~/miniconda3/envs/langchain/lib/python3.11/site-packages/numexpr/necompiler.py:817](https://vscode-remote+wsl-002bubuntu-002d20-002e04.vscode-resource.vscode-cdn.net/home/oriont/dev/langchain-phx/~/miniconda3/envs/langchain/lib/python3.11/site-packages/numexpr/necompiler.py:817), in evaluate(ex, local_dict, global_dict, out, order, casting, **kwargs)
816 if expr_key not in _names_cache:
--> 817 _names_cache[expr_key] = getExprNames(ex, context)
818 names, ex_uses_vml = _names_cache[expr_key]
File [~/miniconda3/envs/langchain/lib/python3.11/site-packages/numexpr/necompiler.py:704](https://vscode-remote+wsl-002bubuntu-002d20-002e04.vscode-resource.vscode-cdn.net/home/oriont/dev/langchain-phx/~/miniconda3/envs/langchain/lib/python3.11/site-packages/numexpr/necompiler.py:704), in getExprNames(text, context)
703 def getExprNames(text, context):
--> 704 ex = stringToExpression(text, {}, context)
705 ast = expressionToAST(ex)
File [~/miniconda3/envs/langchain/lib/python3.11/site-packages/numexpr/necompiler.py:289](https://vscode-remote+wsl-002bubuntu-002d20-002e04.vscode-resource.vscode-cdn.net/home/oriont/dev/langchain-phx/~/miniconda3/envs/langchain/lib/python3.11/site-packages/numexpr/necompiler.py:289), in stringToExpression(s, types, context)
288 # now build the expression
...
93 return re.sub(r"^\[|\]$", "", output)
ValueError: LLMMathChain._evaluate("
math.factorial(33)
") raised error: 'VariableNode' object has no attribute 'factorial'. Please try again with a valid numerical expression
```
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I_kwDOIPDwls5obE9Q
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ArxivAPIWrapper
|
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| 2023-06-12T05:30:46
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NONE
| null |
The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you!
|
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bump ver to 198
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I_kwDOIPDwls5oatJa
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Issue : Agent Executor stops due to iteration limit or time limit.
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NONE
| null |
### System Info
python 3.9.13
langchain (0.0.163)
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chat_models import AzureChatOpenAI
from langchain.agents import create_pandas_dataframe_agent
from langchain.agents import create_csv_agent
import os
import openai
os.environ[“OPENAI_API_BASE”] = os.environ[“AZURE_OPENAI_ENDPOINT”] = AZURE_OPENAI_ENDPOINT
os.environ[“OPENAI_API_KEY”] = os.environ[“AZURE_OPENAI_API_KEY”] = AZURE_OPENAI_API_KEY
os.environ[“OPENAI_API_VERSION”] = os.environ[“AZURE_OPENAI_API_VERSION”] = AZURE_OPENAI_API_VERSION
os.environ[“OPENAI_API_TYPE”] = “azure”
df=pd.read_csv(‘data.csv’).fillna(value = 0)
llm = AzureChatOpenAI(deployment_name=“gpt-35-turbo”, model_name=“gpt-35-turbo”,temperature=0)
agent_executor = create_pandas_dataframe_agent(llm=llm,df=df,verbose=True)
response = agent_executor.run(prompt + QUESTION)
print(response)
### Expected behavior
The current execution of agent results in errors like "Agent stopped due to iteration limit or time limit" and "Couldn't parse LLM output".
Also noticed that agent runs gets into multiple loops in identifying the valid tool but fails to identify tool , says as not valid tool and tries another one and ultimately stops with iteration limit or time limit.
Kindly suggest me on advice/suggestions to resolve the same.
Is there any particular tabular data structure that langchain works best with?
Do I need to add any particular kind of tool (As observed it tries multiple tools and says not valid tool)
Any modifications I need to make with the code?
|
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I_kwDOIPDwls5oapuy
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LLM技术和训练问题的微信交流群
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NONE
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### Issue you'd like to raise.
扫码进群，交流Chinese LLM 模型训练的问题和LLM的技术
|
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PR_kwDOIPDwls5SuJOe
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add from_documents interface in awadb vector store
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CONTRIBUTOR
| null |
added new interface from_documents in awadb vector store
@dev2049
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PR_kwDOIPDwls5SuGRa
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Update MongoDB Atlas support docs
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CONTRIBUTOR
| null |
Updating MongoDB Atlas support docs @hwchase17 let me know if you have any questions
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CONTRIBUTOR
| null |
added from_documents interface for the awadb vector store
@dev2049
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Update check
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CONTRIBUTOR
| null |
We were assigning the name as None in on_chat_model_start then not updating, resulting in a validation error. Huzzah for pydantic
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CONTRIBUTOR
| null | ERROR: type should be string, got "https://github.com/hwchase17/langchain/blob/18af149e91e62b3ac7728ddea420688d41043734/langchain/text_splitter.py#L420\r\n\r\nBecause it goes from top to bottom the last chunk has the potential to be of any size and is frequently too small to be useful.\r\n\r\nI wrote a new class that is more or less a copy of the original function, but adjusts the output if if the last chunk is too small (less than 75% of chunk_size). It does this by adjusting the chunk size upwards to chunk_size = chunk_sizer + (small sized chunk / (chunks - 1)). This allows the last chunks token count to be distributed across all chunks and the end result is that there are no longer bad (small) chunks.\r\n\r\nI'm hesitant to create a PR from this because it's such a large change. I believe the correct course would be to integrate it into the main function, but because it rewrites the merge_splits function it would impact all splitters. It's also quite a bit slower than the original class because it can take a few tries to get to the write size. There are optimizations to be had.\r\n@hwchase17 \r\nhttps://github.com/ShelbyJenkins/langchain/blob/master/langchain/text_splitter.py#L793"
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PR_kwDOIPDwls5Sttzt
| 6,018
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Remove from PR template
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CONTRIBUTOR
| null | null |
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PR_kwDOIPDwls5StsD0
| 6,017
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Langchain decorators
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| 2023-06-11T22:37:16
| 2023-06-12T02:32:25
| 2023-06-12T02:32:24
|
CONTRIBUTOR
| null |
Added description of LangChain Decorators ✨ into the integration section
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
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<!-- Remove if not applicable -->
#### Before submitting
<!-- If you're adding a new integration, please include:
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2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
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-->
#### 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
- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
-->
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I_kwDOIPDwls5oZ8R-
| 6,016
|
extra semicolon with SQLDatabaseChain when used with Oracle
|
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| 2023-06-11T22:24:31
| 2023-09-19T16:08:49
| 2023-09-19T16:08:49
|
NONE
| null |
### System Info
SQLDatabaseChain is appending a semicolon to created SQL. which is not liked by cx_oracle (or Oracle database by implication) - since it appends another semicolon in the end. making two semicolons in effect and causing error
sqlalchemy.exc.DatabaseError: (cx_Oracle.DatabaseError) ORA-00933: SQL command not properly ended
https://cx-oracle.readthedocs.io/en/latest/user_guide/sql_execution.html
Need a way to control this behavior.
https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html#
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps:
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
import cx_Oracle
db = SQLDatabase.from_uri("oracle://ora-user-name:ora-user-password@ora-host-name:1521/service")
llm = OpenAI(temperature=0, verbose=True)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("How many employees are there?")
sqlalchemy.exc.DatabaseError: (cx_Oracle.DatabaseError) ORA-00933: SQL command not properly ended
[SQL: SELECT COUNT(*) FROM YOUR_TABLE_NAME;]
(Background on this error at: https://sqlalche.me/e/20/4xp6)
### Expected behavior
Need a way to optionally suppress a semicolon being appended. or if it could be avoided in favor of cx_oracle - since it doesnt like a semicolon anyways in SQL
|
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PR_kwDOIPDwls5StrTz
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I before E
|
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| 2023-06-11T22:22:34
| 2023-06-11T22:45:13
| 2023-06-11T22:45:12
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CONTRIBUTOR
| null | null |
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| 1,751,628,136
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I_kwDOIPDwls5oZ7lo
| 6,014
|
PALChain In-context-learning won't scale to multiple problem types.
|
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| 2023-06-11T22:13:42
| 2023-09-17T17:13:12
| 2023-09-17T17:13:11
|
CONTRIBUTOR
| null |
### Feature request
Currently the PALChain does in-context learning by passing an in-context learning prompt with 8 maths problem solutions in a single large prompt.
If we want the power of PALChain to increase and solve different problem types, we can't keep on adding more and more problem solutions in a single prompt.
As an alternative, we should store more problem solutions in a list, and only include the most relevant to the query in the prompt.
### Motivation
I would like to enhance PAL to solve many more types of problems that can be solved in Python code, e.g. those described in leetcode.com, maths problems in the UK 11+, GCSE and even "A" level example, etc. etc.
### Your contribution
I am happy to work on a PR for this.
How I see it working:
- long list of maths problems solutions in the existing format (natural language question; python code to print the answer).
- the first time the PALChain is invoked, every one of these is embedded using an embedding model.
- every query that a user submits is also embedded, and the most similar questions are passed into the prompt.
There is a risk that "Most similar" gets distracted by proper nouns. E.g. if there is a problem solution saying "Jonathan has 5 gold balls, ..." and the user submits a question on a completely different maths topic, e.g. "Jonathan wants to express 3/5 as a percentage." Jonathan is irrelevant but the embedding similiarity algorithm may flag them as similar. For that reason I propose that as a first pass, both the problem solution and the user query is first passed through a named entity recognition pass to a LLM, which removed names entity related to people, geographical locations etc and replaces them with generic tokens.
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I_kwDOIPDwls5oZ0MU
| 6,013
|
How to use Output parser with LLM Chain
|
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| 2023-10-15T16:06:38
| 2023-10-15T16:06:37
|
NONE
| null |
###Use of Output parser with LLM Chain
I want to use the sequential chain of two LLm chains. The first chain is coded as below. I want to get the output of this chain as a Python list of aspects.
```
# This is an LLMChain for Aspects Extraction.
examples =[ {
"review": '''Improve the communication updates to families. Improve consistency, with housekeeping it was supposed to be weekly but now it is every 2 or 3 weeks. There is no consistency in the staff either due to the high turnover rate. Improve the services in the dining room along with meal options.''',
"aspects": '''Relevant Aspects are communication on updates, housekeeping consistency, staff turnover, and dining services.'''
},
{"review": '''On paper they do, but my wife has not been brought to them. I have not had a meeting to set up a plan for her. No one wheels her to partake in the activities. They need somebody there that could take them to activities if they wanted. They should bring them to activities where other people will watch over them. The people that are in charge, like the head nurse and activities director, are good about getting ahold of and answering your questions. Once you get down to the next level, they are overwhelmed. They could use another set of eyes and hands. In the memory care area, a lot of people need care at the same time.''',
"aspects": '''Relevant Aspects are Staff Attitude, Care Plan Setup, Staff Involvement in Activities, Oversight during Activities, Memory Care Area'''}
]
#Configure a formatter that will format the few shot examples into a string. This formatter should be a PromptTemplate object.
prompt_template='''
Review: {review}
{aspects}
'''
example_prompt = PromptTemplate(input_variables=["review", "aspects"], template= prompt_template, output_parser=output_parser )
final_prompt = FewShotPromptTemplate(
examples=examples,
example_prompt=example_prompt,
suffix="Review: {review}",
input_variables=["review"],
prefix="We are extracting the aspects from the review given by the residents of nursing homes. Take the Review as input and extract the different aspects about the the staff, food, \
building, activities, management, cost of the the nuursing home."
)
output=aspect_extraction_chain.predict_and_parse(review="The community has popcorn days, church, birthday celebrations, holiday parties, therapy dogs, and so much more. My mother is very happy here, and she is kept active. They do a great job of keeping the elderly minds active and involved. The dining program is great as well. My mother tends to eat slow, but the dining program always lets my mother stay to finish her food. Any residents that want to practice religion, this is also offered here! More outings have been added, they just went to Walmart recently.")
```
The current result is a string like
'Relevant Aspects are Activities, Elderly Minds Engagement, Dining Program, Religious Offerings, Outings.'
I want the result as: ['\nActivities', 'Elderly Minds Engagement', 'Dining Program', 'Religious Offerings', 'Outings.']
### Suggestion:
Kindly guide me on How to use langchain output parser for it.
|
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PR_kwDOIPDwls5StaRR
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[Bug fix] Fix the chat memory to save the result when output text is nested in json
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CONTRIBUTOR
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<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
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-->
I used the`RetrievalQA` chain with the argument `return_source_documents=True` as part of the tools in the agent framework with conversation buffer memory. It turns out at the last step, the output text is still nested in json like `{...,'output':{'result': ..., 'source_documents':...}}` and then it caused the error in the `self.chat_memory.add_ai_message(output_str)` which only expects the output to be only string type.
So I added another check to parse out the final output text if the `output`(original output key) is still the `Dict` type and apply some heuristics to retrieve the text with the new assumed output key based on the common output keys in common chains.
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
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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
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#### Who can review?
@vowelparrot @hwchase17
Tag maintainers/contributors who might be interested:
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PR_kwDOIPDwls5StYHs
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[APIChain] enhance the robustness or url
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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.
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!
-->
I used the APIChain sometimes it failed during the intermediate step when generating the api url and calling the `request` function. After some digging, I found the url sometimes includes the space at the beginning, like `%20https://...api.com` which causes the ` self.requests_wrapper.get` internal function to fail.
Including a little string preprocessing `.strip` to remove the space seems to improve the robustness of the APIchain to make sure it can send the request and retrieve the API result more reliably.
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
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See contribution guidelines for more information on how to write tests, lint
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https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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@vowelparrot
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@hwchase17 - project lead
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- @agola11
Async
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DataLoaders
- @eyurtsev
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PR_kwDOIPDwls5StUmm
| 6,007
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bump version to 197
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PR_kwDOIPDwls5StTF0
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Update databricks.md
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| 2023-06-11T16:49:53
| 2023-06-11T20:13:34
| 2023-06-11T20:13:33
|
CONTRIBUTOR
| null |
HuggingFace -> Hugging Face
#### 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
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PR_kwDOIPDwls5StQif
| 6,005
|
Raise an exception in MRKL if no tool is provided.
|
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| 2023-06-17T16:43:02
|
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
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Implemented validation for cases where no tools are provided.
This change allows for the handling of scenarios where certain MRKL tools, such as Zapier Natural Language Actions, may not support any tools. With this validation, we prevent unnecessary chaining of API usage.
This pull request is partially related to issue #5977.
#### Who can review?
@vowelparrot
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I_kwDOIPDwls5oZbzP
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|
Issue with retrieving the chunk number from similarity search in lang chain
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| 2023-11-16T16:07:26
|
NONE
| null |
### Issue you'd like to raise.
Description:
I am currently using the lang chain repository for my project, specifically the functionality related to similarity search. I have encountered an issue when attempting to retrieve the chunk number from the search results.
To provide some context, I have already performed the necessary steps to generate embeddings for each chunk using the provided functions in lang chain. Here is the relevant code snippet:
```
embeddings = OpenAIEmbeddings()
knowledge_base = FAISS.from_texts(chunks, embeddings)
```
After creating the knowledge base, I utilize the similarity_search function to find the most similar chunk to a given query:
`docs = knowledge_base.similarity_search(query)`
The docs object returned contains information about the search results, but I am struggling to access the specific chunk number associated with the most similar result.
My question is: Is there a method or property available in lang chain that allows me to retrieve the chunk number from the docs object?
I would greatly appreciate any assistance or guidance in resolving this issue. Thank you for your support!
### Suggestion:
I could not able to find any documentation, Once I know how to do it I can add it to the documentation
|
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PR_kwDOIPDwls5StI6L
| 6,003
|
Mitigate issue #5872 (Prompt injection -> RCE in PAL chain)
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| 2023-07-18T05:46:18
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|
CONTRIBUTOR
| null |
Adds some selective security controls to the PAL chain:
1. Prevent imports
2. Prevent arbitrary execution commands
3. Enforce execution time limit (prevents DOS and long sessions where the flow is hijacked like remote shell)
4. Enforce the existence of the solution expression in the code
This is done mostly by static analysis of the code using the ast library.
Also added tests to the pal chain.
Fixes #5872
@vowelparrot
|
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PR_kwDOIPDwls5Ss3ia
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|
Obey handler.raise_error in _ahandle_event_for_handler
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CONTRIBUTOR
| null |
Obey `handler.raise_error` in `_ahandle_event_for_handler`
Exceptions for async callbacks were only logged as warnings, also when `raise_error = True`
#### Who can review?
@hwchase17
@agola11
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I_kwDOIPDwls5oYyxu
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UnstructuredUrlLoader or SeleniumUrlLoader are not able to upload the pdf's consisting by urls.
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| 2023-09-28T16:06:24
| 2023-09-28T16:06:23
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NONE
| null |
### System Info
Hi,
I am using UnstructureUrlLoader to upload the urls into Chroma vector database but it is only uploading the html content of the url, not uploading the pdfs available on this url that's why it is not giving the answer for query related to pdf files while performing querying using RetrievalQA chain
I also tried with SeleniumUrlLoader but still no results.
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter , TokenTextSplitter
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains import VectorDBQA, RetrievalQA
from langchain.document_loaders import TextLoader, UnstructuredFileLoader, DirectoryLoader,UnstructuredURLLoader,SeleniumURLLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.prompts import PromptTemplate
import chromadb
import wx
import os
import requests
from bs4 import BeautifulSoup
openai_api_key = my_openai_key
persist_directory = "Zdb_directory"
collection_name = 'my collection'
temperature = 0
max_tokens = 200
llm=ChatOpenAI(openai_api_key=openai_api_key)
urls = ['url1,'url2','url3']
#loader = UnstructuredURLLoader(urls)
loader = SeleniumURLLoader(urls)
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory=persist_directory)
vectordb.persist()
vectordb = None
vectodb = Chroma(persist_directory=persist_directory, embedding_function=embeddings, collection_name='my_collection)
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(openai_api_key=openai_api_key), chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"), return_source_documents=True)
ques = 'my_question'
ans = qa(ques)
Can anyone please help me that how can I upload all the pdf files consisting by a url into the chroma vector database using UnstructuredUrlLoader or SeleniumUrlLoader, I will be thankful to you.
Thank You
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter , TokenTextSplitter
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains import VectorDBQA, RetrievalQA
from langchain.document_loaders import TextLoader, UnstructuredFileLoader, DirectoryLoader,UnstructuredURLLoader,SeleniumURLLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.prompts import PromptTemplate
import chromadb
import wx
import os
import requests
from bs4 import BeautifulSoup
openai_api_key = my_openai_key
persist_directory = "Zdb_directory"
collection_name = 'my collection'
temperature = 0
max_tokens = 200
llm=ChatOpenAI(openai_api_key=openai_api_key)
#all url are consisting pdf files inside it or in sub url
urls = ['url1,'url2','url3']
#loader = UnstructuredURLLoader(urls)
loader = SeleniumURLLoader(urls)
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory=persist_directory)
vectordb.persist()
vectordb = None
vectodb = Chroma(persist_directory=persist_directory, embedding_function=embeddings, collection_name='my_collection)
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(openai_api_key=openai_api_key), chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"), return_source_documents=True)
ques = 'my_question'
ans = qa(ques)
### Expected behavior
while I am uploading a url to chroma vector database using Unstructured or SeleniumUrlLoader so rather than just uploading the html website it should also upload the content of all sub urls and the content of all files which are available in this url.
Thank You
|
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|
AttributeError: choice while creating python agent
|
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closed
| false
| null |
[] | null | 1
| 2023-06-11T06:54:03
| 2023-09-17T17:13:16
| 2023-09-17T17:13:16
|
CONTRIBUTOR
| null |
### System Info
langchain version - 0.0.154
ubuntu - 18.04
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents.agent_toolkits import create_python_agent
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/practice_ml/lib/python3.9/site-packages/openai/openai_object.py:59, in OpenAIObject.__getattr__(self, k)
58 try:
---> 59 return self[k]
60 except KeyError as err:
**KeyError: 'choice'**
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
Cell In[11], line 1
----> 1 from langchain.agents.agent_toolkits import create_python_agent
File ~/practice/langchain.py:23
16 response = openai.ChatCompletion.create(
17 model = model,
18 messages = messages,
19 temperature=0
20 )
21 return response.choice[0].message['content']
---> 23 get_completion("what is 1 + 1")
24 customer_email = """
25 Arrr, I be fuming that me blender lid \
26 flew off and splattered me kitchen walls \
(...)
30 right now, matey!
31 """
34 from langchain.chat_models import ChatOpenAI
File ~/practice/langchain.py:21, in get_completion(prompt, model)
12 messages = [
13 {'role' : 'user',
14 "content" : prompt}
15 ]
16 response = openai.ChatCompletion.create(
17 model = model,
18 messages = messages,
19 temperature=0
20 )
---> 21 return response.choice[0].message['content']
File ~/practice_ml/lib/python3.9/site-packages/openai/openai_object.py:61, in OpenAIObject.__getattr__(self, k)
59 return self[k]
60 except KeyError as err:
---> 61 raise AttributeError(*err.args)
AttributeError: choice
### Expected behavior
no error .
|
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https://api.github.com/repos/langchain-ai/langchain/issues/5999/timeline
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not_planned
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https://api.github.com/repos/langchain-ai/langchain/issues/5998
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https://github.com/langchain-ai/langchain/issues/5998
| 1,751,271,273
|
I_kwDOIPDwls5oYkdp
| 5,998
|
TypeError: TextSplitter.__init__() got an unexpected keyword argument 'add_start_index'
|
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[] |
closed
| false
| null |
[] | null | 4
| 2023-06-11T06:33:16
| 2023-12-06T17:45:30
| 2023-12-06T17:45:29
|
CONTRIBUTOR
| null |
### System Info
version = "0.0.157"
### 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
just run
```
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=900,
chunk_overlap=0,
separators=separators,
add_start_index = True,
length_function=tiktoken_len,
)
```
### Expected behavior
no error
|
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https://github.com/langchain-ai/langchain/pull/5997
| 1,751,262,145
|
PR_kwDOIPDwls5SsfTO
| 5,997
|
Add oobabooga/text-generation-webui support as a llm
|
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closed
| false
| null |
[] | null | 4
| 2023-06-11T05:58:20
| 2023-06-17T16:42:16
| 2023-06-17T16:42:15
|
CONTRIBUTOR
| null |
Add oobabooga/text-generation-webui support as an LLM. Currently, supports using text-generation-webui's non-streaming API interface. Allows users who already have text-gen running to use the same models with langchain.
#### Before submitting
Simple usage, similar to existing LLM supported:
```
from langchain.llms import TextGen
llm = TextGen(model_url = "http://localhost:5000")
```
#### Who can review?
@hwchase17 - project lead
|
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