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https://api.github.com/repos/langchain-ai/langchain/issues/12253
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| 1,960,709,413 |
PR_kwDOIPDwls5dt3m4
| 12,253 |
refactor: DeterministicFakeEmbedding -> DeterministicFakeEmbeddings
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[] | null | 1 | 2023-10-25T07:13:02 | 2023-10-25T07:15:02 | null |
NONE
| null |
**Description:** I simply add an "s" to the current `DeterministicFakeEmbedding` in order to have a unique naming that is aligned to ALL `Embeddings` classes.
Note: right now, there are also `AlephAlphaAsymmetricSemanticEmbedding` and `AlephAlphaSymmetricSemanticEmbedding` classes that don't have this "s". If you want, I can also align them as well.
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PR_kwDOIPDwls5dtieE
| 12,252 |
Update vectorstore.mdx[Make an improvement]
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[] | null | 2 | 2023-10-25T06:06:19 | 2023-10-25T19:00:53 | 2023-10-25T19:00:53 |
CONTRIBUTOR
| null |
correct some grammatical errors
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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|
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| 1,960,592,200 |
PR_kwDOIPDwls5dteAn
| 12,251 |
Allow disablling `safe_len_embeddings`(OpenAIEmbeddings)
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[] | null | 4 | 2023-10-25T05:51:30 | 2024-01-16T02:08:53 | null |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
For servers compatible with OpenAI API may not support `safe_len_embedding`.
With this PR, we can add `disable_safe_len_embeddings=True` to `OpenAIEmbeddings(...)` to disable using `safe_len_embedding`.
|
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I_kwDOIPDwls5022d9
| 12,250 |
Vertex AI Models Throws AttributeError: After Langchain 0.0295 Version
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[] | null | 1 | 2023-10-25T04:55:22 | 2023-10-25T05:03:18 | null |
NONE
| null |
### System Info
1
I have been working with LangChain for some time, and after updating my LangChain module to version 0.0.295 or higher, my Vertex AI models throw an error in my application. The error is
File "E:\Office\2023\AI-Platform\kaya-aia-general-bot\venv\Lib\site-packages\langchain\llms\vertexai.py", line 301, in _generate
generations.append([_response_to_generation(r) for r in res.candidates])
^^^^^^^^^^^^^^
AttributeError: 'TextGenerationResponse' object has no attribute 'candidates'
I was expecting my previous implementation to work seamlessly. This is how I initialized the chain with the vertex model.
case 'public_general_palm':
llm = ChatVertexAI(model_name="chat-bison", temperature=0)
chain = self.__initialize_basic_chain(llm, self.chain_memory)
return chain(request.msg, return_only_outputs=True)
This is the __initialize_basic_chain method.
def __initialize_basic_chain(self, llm, memory):
return ConversationChain(llm=llm, memory=memory)
However, I have been unable to find a solution for this error. Kindly assist in resolving this issue to ensure the smooth operation of the application.
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Update langchain module 0.315 and higher.
1. Initialize llm with the ChatVertexAI(model_name="chat-bison", temperature=0).
2. Pass into a basic chain or an agent with the input messages
### Expected behavior
Throws an internal module error called
File "E:\Office\2023\AI-Platform\kaya-aia-general-bot\venv\Lib\site-packages\langchain\llms\vertexai.py", line 301, in _generate
generations.append([_response_to_generation(r) for r in res.candidates])
^^^^^^^^^^^^^^
AttributeError: 'TextGenerationResponse' object has no attribute 'candidates'
|
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| 1,960,484,085 |
I_kwDOIPDwls502pz1
| 12,249 |
Integration Doctran: DoctranPropertyExtractor class methods transform_documents() and atransform_documents() error
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[] | null | 1 | 2023-10-25T03:58:43 | 2023-10-25T04:04:34 | null |
NONE
| null |
### System Info
Under doctran_text_extract.py, DoctranPropertyExtractor class. The methods for the class contains errors.
The first method transform_documents() does not have any operations and simply returns a NotImplementedError. This method should be working and implemented, with the following code:
```
def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Extracts properties from text documents using doctran."""
try:
from doctran import Doctran, ExtractProperty
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doctran to use this parser. (pip install doctran)"
)
properties = [ExtractProperty(**property) for property in self.properties]
for d in documents:
doctran_doc = (
doctran.parse(content=d.page_content)
.extract(properties=properties)
.execute()
)
d.metadata["extracted_properties"] = doctran_doc.extracted_properties
return documents
```
For the second method atransform_documents(), doctran currently does not support async so this should raise a NotImplementedError instead.
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
https://python.langchain.com/docs/integrations/document_transformers/doctran_extract_properties
official code does not reproduce the displayed behavior
### Expected behavior
official code does not reproduce the displayed behavior. Modified source code as mentioned above and used the class method transform_documents works with the displayed outcome.
|
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PR_kwDOIPDwls5dtF1G
| 12,248 |
rfc: make ChatModel._generate a batch method
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[] | null | 1 | 2023-10-25T03:53:39 | 2023-10-26T18:44:11 | 2023-10-26T18:44:11 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,960,479,331 |
PR_kwDOIPDwls5dtFoH
| 12,247 |
Update README.md - fixed two grammatical mistakes in README.
|
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[] | null | 2 | 2023-10-25T03:52:27 | 2023-11-03T12:55:02 | 2023-11-03T12:55:02 |
NONE
| null |
I have noticed two small grammatical issues in **README.md** file inside section "**Breaking changes for select chains**".
"**but we are remaining backwards compatible...**" should be "**but we are remaining backward compatible..**" and also
"**On that date, we will remove functionality from langchain..**" should be "**On that date, we will remove the functionality from langchain..**"
Below I have attached a screenshot of it.
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I_kwDOIPDwls502Un3
| 12,246 |
Add URL's to the output of TelegramChatAPILoader 🙏
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[] | null | 0 | 2023-10-25T02:22:45 | 2023-10-25T06:11:24 | null |
NONE
| null |
### Feature request
Currently the `TelegramChatAPILoader` returns the Date, Sender, and Text of each message. Adding the URL of each message would very helpful for a Natural Language Search Engine for a large Channel.
SOURCE: `langchain.document_loaders.telegram`
### Motivation
We are building a Natural Language assistant for a large channel on Telegram. The users currently search by key words and hashtags. The Language model can only return a URL if it's contained in the text of the message. With the URL's we would be able to easily direct the user to the relevant information with a Natural Language query instead of the archaic methods of the days before Ai.
### Your contribution
I do not know Telethon, however we can see that the URL can be pulled in the `fetch_data_from_telegram` function in the [langchain.document_loaders.telegram ](https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html#TelegramChatApiLoader.fetch_data_from_telegram)class.
We thank you for LangChain and wish we could spare the time to contribute more. We may have a pull request in the future regarding Telegram Loaders for media messages. It's a feature that we will have to incorporate as we get closer to a public release of what we're working on.
|
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PR_kwDOIPDwls5dstha
| 12,245 |
Runnable traceable
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[] | null | 2 | 2023-10-25T01:57:12 | 2023-10-25T18:44:35 | 2023-10-25T18:44:35 |
COLLABORATOR
| null |
If passing in a traceable function, this will place the trace in the appropriate hierarchy.
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| 1,960,309,220 |
I_kwDOIPDwls501_Hk
| 12,244 |
retriever.get_relevant_documents
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[] | null | 4 | 2023-10-25T00:47:28 | 2023-10-25T23:49:14 | 2023-10-25T23:49:14 |
NONE
| null |
### System Info
langchain in /opt/conda/lib/python3.11/site-packages (0.0.322)
Python 3.11.6
installed on
image: jupyter/datascience-notebook
### 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
As per the documentation
retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)
retriever.get_relevant_documents("What are some movies about dinosaurs")
### Expected behavior
We would see verbose output such as
` query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)
`
however no verbose output is printed
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PR_kwDOIPDwls5dshvQ
| 12,243 |
fix: typo in pgvector.ipynb
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[] | null | 1 | 2023-10-25T00:42:13 | 2023-10-25T04:26:45 | 2023-10-25T04:26:45 |
CONTRIBUTOR
| null |
fix: typo in docs/docs/integrations/vectorstores/pgvector.ipynb
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| 1,960,282,757 |
PR_kwDOIPDwls5dsdMl
| 12,242 |
Check for ls project in run tree context
|
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[] | null | 1 | 2023-10-25T00:12:48 | 2023-10-25T00:19:00 | 2023-10-25T00:18:59 |
COLLABORATOR
| null |
If I go traceable -> runnable when the project is manually specified, the runnable wont be logged. This makes sure the session/project is threaded through appropriately.
|
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I_kwDOIPDwls5010Fl
| 12,240 |
Issue: Problems getting filtering to work with langchain, OpenSearchVectorSearch, similarity_search_with_score
|
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[] | null | 14 | 2023-10-24T23:46:34 | 2023-11-15T15:38:12 | null |
NONE
| null |
### Issue you'd like to raise.
Using langchain on AWS lambda function to call an AWS OpenSearch Serverless vector index. I did not file as a bug, as I suspect that this is a usage problem, but it might be a bug.
I have built an OpenSearch vector index, using AWS OpenSearch Serverless, and are using it to perform vector queries against document chunks. This is all working well. I would now like to add the ability to filter these searches by document (there may be hundreds or thousands of vector entries per document). There is metadata included with each entry, which includes a DocumentId field, which is a string-ized guid. I thought it would be simple to filter against this metadata attribute, but cannot get it working.
The call I am making is:
```
docs = vsearch.similarity_search_with_score(
theQuery,
index_name="vector-index2",
vector_field = "vector_field",
search_type="script_scoring",
pre_filter=filter_dict,
space_type="cosinesimil",
engine="faiss",
k = 3
)
```
Typical unfiltered output:
{"doc": {"page_content": "seat is installed ... facing", "metadata": {"DocumentId": "{8B5D5890-0000-C1E8-B0B6-678C8509665D}", "DocumentTitle": "2023-Toyota-RAV4-OM.pdf", "DocClassName": "Document"}}, "score": 1.8776696}]
For filter_dict, I have tried:
filter_dict = {'DocumentId':theDocId} <-- Gets an error
filter_dict = {'bool': {'must': {'term': {'DocumentId': theDocId} } } } <-- doesn’t find anything
filter_dict = {'bool': {'must': {'match': {'DocumentId': theDocId} } } } <-- doesn’t find anything
filter_dict = {'must': {'match': {'DocumentId': theDocId} } } <-- Gets an error
filter_dict = {'match': {'DocumentId': theDocId} } <-- doesn’t find anything
theDocId is a string set to an input parameter and looks like this: {8B5D5890-0000-C1E8-B0B6-678C8509665D}. Results are the same if I use a string constant (i.e. '{8B5D5890-0000-C1E8-B0B6-678C8509665D}')
Same results if I use search_type=”approximate_search” in conjunction with boolean_filter=filter_dict
Any suggestions on how to get this to work?
### Suggestion:
_No response_
|
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I_kwDOIPDwls501xbV
| 12,239 |
`StructuredOutputParser.get_format_instructions` missing commas
|
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[] | null | 0 | 2023-10-24T23:31:54 | 2023-10-24T23:34:01 | null |
CONTRIBUTOR
| null |
### System Info
n/a
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://github.com/langchain-ai/langchain/blob/v0.0.322/libs/langchain/langchain/output_parsers/structured.py#L86
This line doesn't append commas in the expected JSON schema.
```none
# What it does
{
"foo": List[string] // a list of strings
"bar": string // a string
}
# What it should do I think
{
"foo": List[string], // a list of strings
"bar": string // a string
}
```
### Expected behavior
I think the suggestion to a LLM to be close to valid JSON (even though it has JSON comments).
|
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| 1,960,247,409 |
PR_kwDOIPDwls5dsWMC
| 12,238 |
Adding `STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS` missing backticks
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[] | null | 2 | 2023-10-24T23:24:11 | 2023-10-25T05:20:28 | 2023-10-25T04:30:26 |
CONTRIBUTOR
| null |
This PR fixes the fact that `STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS` was missing backticks at the end
|
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I_kwDOIPDwls501rq5
| 12,237 |
ModuleNotFoundError: No module named 'rapidfuzz'
|
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[] | null | 10 | 2023-10-24T23:05:16 | 2024-01-10T14:37:01 | null |
CONTRIBUTOR
| null |
### System Info
https://github.com/langchain-ai/langchain/tree/d2cb95c39d5569019ab3c6aa368aa937d8dcc465
(just above `v0.0.322`)
MacBook Pro, M1 chip, macOS Ventura 13.5.2
### Who can help?
_No response_
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- [ ] 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
With Python 3.10.11:
```bash
python -m venv venv
source venv/bin/activate
pip install poetry
poetry install --with test
make test
```
This gets:
```
poetry run pytest --disable-socket --allow-unix-socket tests/unit_tests/
Traceback (most recent call last):
File "/Users/james.braza/code/langchain/venv/bin/poetry", line 5, in <module>
from poetry.console.application import main
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/poetry/console/application.py", line 11, in <module>
from cleo.application import Application as BaseApplication
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/cleo/application.py", line 12, in <module>
from cleo.commands.completions_command import CompletionsCommand
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/cleo/commands/completions_command.py", line 10, in <module>
from cleo import helpers
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/cleo/helpers.py", line 5, in <module>
from cleo.io.inputs.argument import Argument
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/cleo/io/inputs/argument.py", line 5, in <module>
from cleo.exceptions import CleoLogicError
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/cleo/exceptions/__init__.py", line 3, in <module>
from cleo._utils import find_similar_names
File "/Users/james.braza/code/langchain/venv/lib/python3.10/site-packages/cleo/_utils.py", line 8, in <module>
from rapidfuzz.distance import Levenshtein
ModuleNotFoundError: No module named 'rapidfuzz'
```
### Expected behavior
I expected installation to install everything necessary to run tests
|
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PR_kwDOIPDwls5dsDcU
| 12,236 |
use snippet search optionally
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[] | null | 1 | 2023-10-24T22:10:02 | 2023-10-25T20:37:29 | 2023-10-25T20:37:29 |
CONTRIBUTOR
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Add an additional flag which allows for hitting our new endpoint.
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demonstrate use of RetrievalQAWithSourcesChain.from_chain
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[] | null | 2 | 2023-10-24T22:03:31 | 2023-10-25T04:33:35 | 2023-10-25T04:33:35 |
CONTRIBUTOR
| null |
**Description:**
Documents further usage of RetrievalQAWithSourcesChain in an existing test. I'd not found much documented usage of RetrievalQAWithSourcesChain and how to get the sources out. This additional code will hopefully be useful to other potential users of this retriever.
**Issue:** No raised issue
**Dependencies:** No new dependencies needed to run the test (it already needs `open-ai`, `faiss-cpu` and `unstructured`).
Note - `make lint` showed 8 linting errors in unrelated files
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Docs: add lcel to sequential chain
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Bagatur/e2b integration2
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Improve Redis add_documents efficiency
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CONTRIBUTOR
| null |
- **Description:** Small fix to make the `add_documents` functionality a bit more performant regarding embedding creation
- **Issue:** https://github.com/langchain-ai/langchain/issues/11496
- **Dependencies:** nothing new
- **Tag maintainer:** @baskaryan @Spartee
- **Twitter handle:** N/A
Addresses the inefficiency noted in the tagged issue when `add_documents()` is used on the Redis vectorstore.
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| 12,231 |
clean up deprecated agents
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add experimental test release gha
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bump 322 and 34
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[] | null | 1 | 2023-10-24T20:45:32 | 2023-10-24T20:52:18 | 2023-10-24T20:52:17 |
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I_kwDOIPDwls500-QL
| 12,227 |
Issue: can I only use ConversationSummaryMemory in a chain as context without renew it
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[] | null | 1 | 2023-10-24T20:43:18 | 2023-10-24T20:52:58 | null |
NONE
| null |
### Issue you'd like to raise.
Hi, I was wondering if there is a mechanism that I can only use the ConversationSummaryMemory in a chain as the context of a conversation. After the chain is completed, I can retain the old memory without adding the new conversation to it.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5drlrq
| 12,226 |
updated `integrations/providers/google`
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[] | null | 3 | 2023-10-24T20:26:04 | 2023-11-03T02:00:22 | 2023-11-03T01:35:32 |
COLLABORATOR
| null |
Added missed integrations. Updated formats.
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PR_kwDOIPDwls5drk-I
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Fix exec command in langserve Dockerfile template
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NONE
| null |
@eyurtsev -- This is a super quick and easy fix. This is my first contribution, so please let me know if I have missed doing anything that I should have!
Description:
Add `poetry run` to the the Dockerfile template file final exec command
```
CMD exec poetry run uvicorn test_serve.server:app --host 0.0.0.0 --port $PORT
```
instead of just
```
CMD exec uvicorn test_serve.server:app --host 0.0.0.0 --port $PORT
```
Issue:
Without the `poetry run` added, `uvicorn` is not found at the exec line when running `docker compose up`
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PR_kwDOIPDwls5drhoY
| 12,224 |
docs: Google Cloud Documentation Cleanup
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[] | null | 1 | 2023-10-24T20:12:05 | 2023-10-25T17:20:39 | 2023-10-24T21:54:44 |
CONTRIBUTOR
| null |
- Move Document AI provider to the Google provider page
- Change Vertex AI Matching Engine to Vector Search
- Change references from GCP to Google Cloud
- Add Gmail chat loader to Google provider page
- Change Serper page title to "Serper - Google Search API" since it is not a Google product.
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PR_kwDOIPDwls5drTnY
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Fix redis vectorfield schema defaults
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[] | null | 2 | 2023-10-24T19:29:04 | 2023-10-27T13:05:31 | 2023-10-26T19:17:59 |
CONTRIBUTOR
| null |
- **Description:** refactors the redis vector field schema to properly handle default values, includes a new unit test suite.
- **Issue:** N/A
- **Dependencies:** nothing new.
- **Tag maintainer:** @baskaryan @Spartee
- **Twitter handle:** this is a tiny fix/improvement :)
This issue was causing some clients/cuatomers issues when building a vector index on Redis on smaller db instances (due to fault default values in index configuration). It would raise an error like:
```redis.exceptions.ResponseError: Vector index initial capacity 20000 exceeded server limit (852 with the given parameters)```
This PR will address this moving forward.
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| 1,959,912,928 |
I_kwDOIPDwls500eXg
| 12,222 |
I encountered an issue with SQLDatabaseToolkit where I couldn't configure it to execute multiple SQL queries based on the previously retrieved value from a column in the initial query
|
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open
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[] | null | 1 | 2023-10-24T19:13:22 | 2023-10-24T19:24:20 | null |
NONE
| null |
### System Info
aws
### 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
from langchain.prompts.chat import ChatPromptTemplate
updated_prompt = ChatPromptTemplate.from_messages(
[
("system",
"""
You are identifying information from the 'customer' table in the SQL Database. You should query the schema of the 'customer' table only once.
Here's the list of relevant column names and their column description from schema 'customer' and their descriptions:
- column name: "job"
column description: "Job Number or Job Identifier.
- column name: "job_type"
- column description: "This column provides information about the status of a job. you can check this column for any of the following job status values:
'Progress'
'Complete'
Use the following context to create the SQL query. Context:
1.Comprehend the column names and their respective descriptions provided for the purpose of selecting columns when crafting an SQL query.
2.If the user inquires about the type of job
select only the columns mapped to job_status and respond to the user:
job_type= 'Progress, then fetch 'name' and 'start time'
job_status = 'Complete' , then fetch 'name', 'completed', and 'date'
"""
),
("user", "{question}\n ai: "),
]
)
os.environ["OPENAI_CHAT_MODEL"] = 'gpt-3.5-turbo-16k'
llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0)
def run_sql_query_prompt(query, db):
greeting_response = detect_greeting_or_gratitude(query)
if greeting_response:
return greeting_response
try:
llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0)
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
#verbose=True,
handle_parsing_errors=True,
use_query_checker=True,
)
formatted_prompt = updated_prompt.format(question=query)
result = sqldb_agent.run(formatted_prompt)
return result
except Exception as e:
return f"Error executing SQL query: {str(e)}"
### Expected behavior
it should first fetch the job_type to understand whether it's 'Progress' or 'Complete'
then if
job_type= 'Progress, then fetch 'name' and 'start time' columns
job_status = 'Complete' , then fetch 'name', 'completed', and 'date' columns
|
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| 1,959,797,524 |
PR_kwDOIPDwls5dq1tu
| 12,221 |
Fix code block in runnable doc
|
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[] | null | 1 | 2023-10-24T18:06:33 | 2023-10-24T20:11:59 | 2023-10-24T20:11:58 |
COLLABORATOR
| null |
Fix code block syntax in runnable doc-string
|
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| 1,959,784,428 |
PR_kwDOIPDwls5dqy-L
| 12,220 |
Add type to Generation and sub-classes, handle root validator
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[] | null | 1 | 2023-10-24T17:57:24 | 2023-10-24T20:42:52 | 2023-10-24T20:21:00 |
COLLABORATOR
| null |
* Add a type literal for the generation and sub-classes for serialization purposes.
* Fix the root validator of ChatGeneration to return ValueError instead of KeyError or Attribute error if intialized improperly.
* This change is done for langserve to make sure that llm related callbacks can be serialized/deserialized properly.
|
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| 1,959,763,611 |
PR_kwDOIPDwls5dqukf
| 12,219 |
Bagatur/js ts nav
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[] | null | 1 | 2023-10-24T17:44:04 | 2023-10-24T19:28:09 | 2023-10-24T19:28:08 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50z2D_
| 12,218 |
OPENAI_API_KEY_PATH is not supported
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[] | null | 1 | 2023-10-24T17:34:43 | 2023-10-24T17:45:14 | null |
NONE
| null |
### System Info
Langchain HEAD
### Who can help?
@hwchase17 @ag
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
OpenAI uses a standard `OPENAI_API_KEY_PATH` environment variable that can be used to provide a key located in a file. This is useful for instance for generating short-lived, automatically renewed keys. Currently, Langchain complains that an API key must be provided even when this environment variable is set.
### Expected behavior
Langchain should use the value of OPENAPI_API_KEY_PATH with a higher priority than OPENAI_API_KEY when set.
|
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PR_kwDOIPDwls5dqmFl
| 12,217 |
mv old integration docs
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| null | null |
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PR_kwDOIPDwls5dqdMz
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Fix Anthropic Functions ainvoke
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Removes custom `NotImplementedError` in experimental anthropic functions, allowing it to fallback on default `ainvoke` implementation.
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fix note callout
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Docs: Add LCEL to chains/foundational/llm
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Docs: Add LCEL to chains/foundational/transform
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PR_kwDOIPDwls5dp44g
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Docs: add LCEL to chains/foundational/router
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Allowing openapi agent to communicate with multiple APIs
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Changed planner and API_PLANNER_PROMPT in planner_prompt to enable create_openapi_agent communication with multiple APIs (list of ReducedOpenAPISpec). This is useful when more than one API is required to complete a task, for example in a software that is built on micro services. In addition, I changed the README in the devcontainer folder to clarify how to use the link of VS Code Dev Container (before that the following were not shown: /<yourusername>/<yourclonedreponame>).
- **Issue:**
- **Dependencies:**
- **Tag maintainer:** @baskaryan , @eyurtsev , @hwchase17
- **Twitter handle:** @Ido_Nitsan
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5dptdF
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Update epsilla.py
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| false | null |
[] | null | 2 | 2023-10-24T14:56:35 | 2023-10-24T23:38:37 | null |
NONE
| null |
Description: Including collection_name as an input variable.
Issue: Previously, wasn't accepting collection_name as an input variable.
Tag maintainer: @hwchase17
Was unable to perform `make test` locally. Ran 'poetry install --with test', but there's no group called "test" in 'pyproject.toml'. Sorry, new to PRs...
|
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| 1,959,418,835 |
PR_kwDOIPDwls5dpkbx
| 12,207 |
Update extraction.py
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[] | null | 2 | 2023-10-24T14:40:20 | 2023-10-24T15:25:15 | 2023-10-24T15:25:15 |
CONTRIBUTOR
| null |
Description: Pass tags as argument to create_extraction_chain
Issue: create_extraction_chain does not pass tags to chain yet
@baskaryan
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| 1,959,412,927 |
I_kwDOIPDwls50ykS_
| 12,206 |
Router + BaseCombineDocumentsChain output_key
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[] | null | 1 | 2023-10-24T14:38:00 | 2023-10-24T14:50:14 | null |
NONE
| null |
### System Info
Hi!
I have been trying to create a router chain which will route to StuffDocumentsChains.
It seems that default output_key for BaseCombineDocumentsChain is output_text instead of 'text' like in typical chains.
```py
class BaseCombineDocumentsChain(Chain, ABC):
"""Base interface for chains combining documents.
Subclasses of this chain deal with combining documents in a variety of
ways. This base class exists to add some uniformity in the interface these types
of chains should expose. Namely, they expect an input key related to the documents
to use (default `input_documents`), and then also expose a method to calculate
the length of a prompt from documents (useful for outside callers to use to
determine whether it's safe to pass a list of documents into this chain or whether
that will longer than the context length).
"""
input_key: str = "input_documents" #: :meta private:
output_key: str = "output_text" #: :meta private:
```
### Question: Is it done on purpose or is just inconsistency?
---
On the other hand
MultiPromptChain has hardcoded output_keys as 'text'.
```py
class MultiPromptChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst prompts."""
@property
def output_keys(self) -> List[str]:
return ["text"]
```
So it is necessary to always change output_key in any CombineDocuments like chains.
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a router chain that routes to StuffDocumentsChain accordingly with documentation.
### Expected behavior
BaseCombineDocumentsChain should have conssistent default outputs keys as other chains.
Or
MultiPromptChain/MultiRouteChain should not have hardcoded output keys
|
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| 1,959,382,324 |
I_kwDOIPDwls50yc00
| 12,205 |
RetryWithErrorOutputParser for chains and summary memory
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[] | null | 1 | 2023-10-24T14:24:02 | 2023-10-24T14:33:40 | null |
NONE
| null |
### Feature request
Is it possible to support scenarios for using `RetryWithErrorOutputParser` when prompt is injected in a chain or a summary memory so this prompt is not directly formatted?
### Motivation
I am trying to support complex prompts, multi-chain and combined memory scenarios causing errors regularly when the LLM generate content response in a different structure as stablished in the format instructions.
### Your contribution
I could make the PR with some basic implementation guidelines.
|
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PR_kwDOIPDwls5dpcSb
| 12,204 |
fix typo
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[] | null | 1 | 2023-10-24T14:23:39 | 2023-10-24T19:51:52 | 2023-10-24T19:51:52 |
CONTRIBUTOR
| null |
fix typo in langchain/cookbook/stepback-qa.ipynb
|
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| 12,203 |
Unable to use LlamCpp from langchain
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[] | null | 2 | 2023-10-24T14:21:08 | 2023-10-24T16:32:42 | null |
NONE
| null |
### System Info
Here is a native python llama cpp example
```
"""
Ask to generate 4 questions related to a given content
"""
from llama_cpp import Llama
mpath="/home/mac/llama-2-7b-32k-instruct.Q5_K_S.gguf"
# define n_ctx manually to permit larger contexts
LLM = Llama(model_path=mpath, n_ctx=512,verbose=False)
# create a text prompt
context = """
Nous sommes dans les Yvelines
Il fait beau
Nous sommes a quelques km d'un joli lac
le boulanger est sympatique
Il y a également une boucherie
"""
prompt = """[INST]\nYou are a Teacher/ Professor. Your task is to setup
4 questions for an upcoming
quiz/examination. The questions should be diverse in nature
across the document. Restrict the questions to the
context provided.
Context is provided below :
%s
[/INST]\n\n
""" % context
print(prompt)
# set max_tokens to 0 to remove the response size limit
output = LLM(prompt, max_tokens=512,stop=["INST"])
# display the response
print(output["choices"][0]["text"])
print("---------------")
print(output)
```
and here is the "langchain version"
```
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
prompt_template ="""[INST]\nYou are a Teacher/ Professor. Your task is to setup
4 questions for an upcoming
quiz/examination. The questions should be diverse in nature
across the document. Restrict the questions to the
context provided.
Context is provided below :
{context}
[/INST]\n\n
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context"])
path_to_ggml_model = "/home/mac/llama-2-7b-32k-instruct.Q5_K_S.gguf"
context = """
Nous sommes dans les Yvelines
Il fait beau
Nous sommes a quelques km d'un joli lac
le boulanger est sympatique
Il y a également une boucherie
"""
llm = LlamaCpp(model_path=path_to_ggml_model,temperature=0.8,verbose=True,echo=True,n_ctx=512)
chain = LLMChain(llm=llm,prompt=PROMPT)
answer = chain.run(context=context,stop="[/INST]",max_tokens=512)
print(answer)
```
unfortunately the langchain version does not return anything
I suspect mismatching or defautlt overriden parameters sent to Llama
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
prompt_template ="""[INST]\nYou are a Teacher/ Professor. Your task is to setup
4 questions for an upcoming
quiz/examination. The questions should be diverse in nature
across the document. Restrict the questions to the
context provided.
Context is provided below :
{context}
[/INST]\n\n
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context"])
path_to_ggml_model = "/home/mac/llama-2-7b-32k-instruct.Q5_K_S.gguf"
context = """
Nous sommes dans les Yvelines
Il fait beau
Nous sommes a quelques km d'un joli lac
le boulanger est sympatique
Il y a également une boucherie
"""
# echo : echo prompt
llm = LlamaCpp(model_path=path_to_ggml_model,temperature=0.8,verbose=True,echo=True,n_ctx=512)
chain = LLMChain(llm=llm,prompt=PROMPT)
answer = chain.run(context=context,stop="[/INST]",max_tokens=512)
print(answer)
```
### Expected behavior
should answer a quiz similar to this :
Sure, here are four questions that can be used for an upcoming quiz or examination related to the context provided:
1. What is the name of the lake located nearby?
2. Describe the type of bread sold at the boulanger?
3. How would you describe the personality of the baker?
4. What kind of meat do they sell at the boucherie?
|
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I_kwDOIPDwls50yYs1
| 12,202 |
Issue: RSSFeedLoader unreliable publish date
|
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[] | null | 1 | 2023-10-24T14:16:40 | 2023-10-24T15:39:05 | null |
NONE
| null |
### Issue you'd like to raise.
The `publish_date` field returned in the metadata seems unreliable and is very often empty for many news sites. The publish date can easily be parsed directly from the RSS feed from the `pubDate` tag which, although optional, is pretty standard and is much more reliable than trying to pull it from the article itself which is currently being done using the `NewsURLLoader`.
### Suggestion:
We can override the `publish_date` coming from the `NewsURLLoader` metadata with the `published` field from the feed entry in the `RSSFeedLoader`
|
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| 1,959,206,198 |
PR_kwDOIPDwls5do2Dd
| 12,201 |
Added GigaChat chat model support
|
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[] | null | 2 | 2023-10-24T13:03:35 | 2023-10-24T19:53:51 | 2023-10-24T19:53:51 |
CONTRIBUTOR
| null |
- **Description:** Added integration with [GigaChat](https://developers.sber.ru/portal/products/gigachat) language model.
- **Twitter handle:** @dvoshansky
|
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| 1,959,198,711 |
PR_kwDOIPDwls5do0bZ
| 12,200 |
fixed error message and a check for processor name
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[] | null | 3 | 2023-10-24T12:59:13 | 2023-10-26T16:38:25 | 2023-10-26T16:38:25 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** a small fix on error description / a check for processor name
- **Issue:** the issue #11407
|
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I_kwDOIPDwls50xCXa
| 12,199 |
LangChain memory, not returning source doc, repeating question
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[] | null | 3 | 2023-10-24T11:06:32 | 2023-10-24T12:23:44 | null |
NONE
| null |
@dosu-bot
I have 3 issues I need your help with and to fix the code:
1) My code is not remembering previous questions despite using ConversationBufferMemory
2) Not Returning Source Documents
3) When I ask it a question the first time, it writes the answer only but if I ask it any other time afterwards, it always rewrites the question then the answer. I just want it to always write the answer only.
Below is my code:
from langchain.chat_models import ChatOpenAI
from langchain.prompts.prompt import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.embeddings import OpenAIEmbeddings
from langchain.callbacks import get_openai_callback
from langchain.vectorstores import Chroma
from langchain.vectorstores import DeepLake
from PyPDF2 import PdfReader
import PyPDF2
from langchain.document_loaders import TextLoader, DirectoryLoader, UnstructuredWordDocumentLoader
from langchain.document_loaders import PyPDFDirectoryLoader, PyPDFLoader
from dotenv import load_dotenv
import time
# Change DirectoryLoader since it has its own chunk size and overlap
load_dotenv()
directory_path = "C:\\Users\\Asus\\Documents\\Vendolista\\MasterFormat"
pdf_loader = DirectoryLoader(directory_path,
glob="**/*.pdf",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = PyPDFLoader)
documents = pdf_loader.load()
print(str(len(documents))+ " documents loaded")
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=80,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
#save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
prompt_template = """
You are a knowledgeable and ethical assistant with expertise in the construction field, including its
guidelines and operational procedures. You are always available to provide assistance and uphold the highest
ethical and moral standards in your work.
You will always and always and always only follow these set of rules and nothing else no matter what:
1) Answer the Question that is answering only based on the documents.
2) If the question is unrelated then say "sorry this question is completely not related.
If you think it is, email the staff and they will get back to you: [email protected]."
3) Do not ever answer with "I don't know" to any question no matter what kind of a question it is.
4) Everyime you answer a question, write on a new line "is there anything else you would like me to help you with?"
5) Remember to always take a deep breath and take this step by step.
6) Do not ever and under no circumstance do you rewrite the question asked by the customer when you are answering them.
7) If the question starts with the "Yazan," then you can ignore the steps above.
Text: {context}
Question: {question}
Answer :
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory(llm=llm, memory_key='chat_history', input_key='question', output_key='answer', return_messages=True)
conversation = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=new_knowledge_base.as_retriever(),
memory=memory,
chain_type="stuff",
verbose=False,
combine_docs_chain_kwargs={"prompt":PROMPT}
)
def main():
chat_history = []
while True:
customer_input = input("Ask me anything about the files (type 'exit' to quit): ")
if customer_input.lower() in ["exit"] and len(customer_input) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
#if the customer_input is not equal to an empty string meaning they have input
if customer_input != "":
with get_openai_callback() as cb:
response = conversation({"question": customer_input}, return_only_outputs = True)
# print(response['answer'])
print()
print(cb)
print()
if __name__ == "__main__":
main()
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PR_kwDOIPDwls5dn5Q6
| 12,197 |
Update README.md
|
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[] | null | 2 | 2023-10-24T10:09:21 | 2023-10-31T02:00:40 | 2023-10-31T02:00:40 |
NONE
| null |
Improved the documentation
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Improved the documentation ,
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50wsBC
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Error reshaping array in _extract_images_from_page() in pdf.py
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[] | null | 4 | 2023-10-24T10:05:32 | 2023-12-01T06:43:31 | null |
NONE
| null |
### System Info
langchain==0.0.320
Ubuntu 20.04
Python 3.11.5
### Who can help?
@SuperJokerayo @baskaryan
### 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
When I try to parse a PDF and extract an image I get the following error:
```
File "<.....>/lib/python3.11/site-packages/langchain/document_loaders/parsers/pdf.py", line 106, in _extract_images_from_page
np.frombuffer(xObject[obj].get_data(), dtype=np.uint8).reshape(
ValueError: cannot reshape array of size 45532 into shape (4644,3282,newaxis)
```
The code I have to run the parser is:
```
loader = PyPDFLoader(doc["file"], extract_images=True)
pages = loader.load_and_split()
```
I can't give you a copy of the document since it's confidential. It's a scanned 11-page document.
### Expected behavior
I expected `load_and_split()` to return a list of 11 pages with the text parsed from the image in each page.
If I change to `extract_images=False` then it returns an empty list of 0 pages as expected.
|
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| 1,958,865,532 |
I_kwDOIPDwls50wep8
| 12,194 |
Is there a few shots agent exists?
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[] | null | 1 | 2023-10-24T09:32:52 | 2023-10-24T09:40:52 | null |
NONE
| null |
### Feature request
I have only seen this "[Zero-shot ReAct](https://python.langchain.com/docs/modules/agents/agent_types/react)"
### Motivation
able to provide examples to agent, to understand how these tools area used, in what order
### Your contribution
nothing yet
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| 1,958,826,438 |
I_kwDOIPDwls50wVHG
| 12,192 |
Issue: Prioritze sources in the vector store to consider certain sources first
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[] | null | 1 | 2023-10-24T09:09:10 | 2023-10-24T09:21:42 | null |
NONE
| null |
### Issue you'd like to raise.
Let's say you have a PDF file, an Excel file, and a PowerPoint file all indexed into a pinecone database, they are similar in that, a query can have information relevant in all sources, but now we want the user to indicate which source should be considered first when they ask a question, is there a way to do that?
For the conversational retrieval chain, you have this retriever there
retriever = vectorstore.as_retriever( search_kwargs = {"filter": {
"file_types": {"$in": file_types},
"genre": {"$in": genre},
"keys": {"$in": keys},
},
"k": 3
}),
how do i make sure i can prioritize sources such that that source comes first, if its not there, we check the others
### Suggestion:
_No response_
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| 1,958,802,863 |
PR_kwDOIPDwls5dneu8
| 12,191 |
Update buffer_window.mdx
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[] | null | 1 | 2023-10-24T08:54:49 | 2023-10-24T09:06:08 | 2023-10-24T08:56:00 |
NONE
| null |
Description:
In ./docs/modules/memory/types/buffer_window.mdx
Remove excess spaces in parameters
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| 1,958,783,914 |
PR_kwDOIPDwls5dnarU
| 12,190 |
Finetuned OpenAI models cost calculation #11715
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[] | null | 2 | 2023-10-24T08:43:03 | 2023-10-25T12:14:26 | 2023-10-24T17:22:06 |
CONTRIBUTOR
| null |
**Description:**
Add cost calculation for fine tuned models (new and legacy), this is required after OpenAI added new models for fine tuning and separated the costs of I/O for fine tuned models.
Also I updated the relevant unit tests
see https://platform.openai.com/docs/guides/fine-tuning for more information.
issue: https://github.com/langchain-ai/langchain/issues/11715
- **Issue:** 11715
- **Twitter handle:** @nirkopler
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| 1,958,773,387 |
I_kwDOIPDwls50wIKL
| 12,189 |
Add a config chain before the ConversationalRetrievalChain : search Kwargs
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[] | null | 3 | 2023-10-24T08:36:14 | 2023-10-24T09:25:29 | null |
NONE
| null |
### Feature request
I want to add a config chain prior to the ConversationalRetrievalChain that is going to process the query and set the retriever's search kwargs ( k, fetch_k, lambda_mult ) depending on the question, how can I do that and pass the parameters from the config chain output
### Motivation
The motivation is to have a dynamic ConversationalRetrievalChain that retrieves the documents the right way for each question.
### Your contribution
I have none for now, I don't know where to start
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PR_kwDOIPDwls5dnWrB
| 12,188 |
Make cohere_api_key a SecretStr
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NONE
| null |
This PR makes `cohere_api_key` in `llms/cohere` a SecretStr, so that the API Key is not leaked when `Cohere.cohere_api_key` is represented as a string.
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PR_kwDOIPDwls5dnBYC
| 12,187 |
Fix flexible dimension and doc for DingoDB
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[] | null | 2 | 2023-10-24T07:24:13 | 2023-10-24T17:16:19 | 2023-10-24T17:16:19 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls50viFO
| 12,186 |
KeyError: 'tool_names'
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[] | null | 5 | 2023-10-24T06:44:44 | 2023-10-24T10:28:40 | null |
NONE
| null |
### System Info
Python 3.11.4
LangChain 0.0.321
Platform info (WSL2):
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=20.04
DISTRIB_CODENAME=focal
DISTRIB_DESCRIPTION="Ubuntu 20.04.6 LTS"
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is the (partial) code snippet that I run:
```python
from langchain.chat_models import ChatOpenAI
prompt = hub.pull("hwchase17/react-chat-json")
llm = ChatOpenAI(max_retries=10, temperature=0,
max_tokens=400, **model_kwargs)
llm_with_stop = llm.bind(stop=["\nObservation"])
from langchain.agents.output_parsers import JSONAgentOutputParser
from langchain.agents.format_scratchpad import format_log_to_messages
# Connecting to Opensearch
docsearch = OpenSearchVectorSearch(index_name=index_uk, embedding_function=embeddings, opensearch_url=opensearch_url, http_auth=('admin', auth))
# We need some extra steering, or the chat model forgets how to respond sometimes
TEMPLATE_TOOL_RESPONSE = """TOOL RESPONSE:
---------------------
{observation}
USER'S INPUT
--------------------
Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. Do NOT respond with anything except a JSON snippet no matter what!"""
from langchain.agents import AgentExecutor
from langchain.agents.agent_toolkits import create_retriever_tool
tool = create_retriever_tool(
docsearch.as_retriever(),
"search_hr_documents",
"Searches and returns documents regarding HR-related questions."
)
tool
print("tool", tool)
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
agent = OpenAIFunctionsAgent(llm=llm, tools=[tool], prompt=prompt)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent_executor = AgentExecutor(agent=agent, tools=[tool,tool], verbose=True, memory=memory, return_intermediate_steps=True)
print("full_question ===>", full_question)
result = agent_executor({"input": full_question})
```
When I run it, it logs the below error:
```
tool name='search_hr_documents' description='Searches and returns documents regarding HR-related questions.' func=<bound method BaseRetriever.get_relevant_documents of VectorStoreRetriever(tags=['OpenSearchVectorSearch', 'OpenAIEmbeddings'], vectorstore=<langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch object at 0x00000247DC4ABA50>)> coroutine=<bound method BaseRetriever.aget_relevant_documents of VectorStoreRetriever(tags=['OpenSearchVectorSearch', 'OpenAIEmbeddings'], vectorstore=<langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch object at 0x00000247DC4ABA50>)>
full_question ===> Am I allowed to get a company car ?
c:\project\transformed_notebook.ipynb Cell 8 line 1
190 agent_executor = AgentExecutor(agent=agent, tools=[tool,tool], verbose=True, memory=memory, return_intermediate_steps=True)
192 print("full_question ===>", full_question)
--> 194 result = agent_executor({"input": full_question})
196 # print("result ===>", result)
197
198 # chain_type_kwargs = {"prompt": prompt,
(...)
211 # out = chain(full_question)
212 # Cleaning the answer
File c:\Users\raaz\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
...
99 }
100 full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
101 prompt = self.prompt.format_prompt(**full_inputs)
KeyError: 'tool_names'
```
How come this error is generated even though the `tools` argument is provided and is well-defined ?
### Expected behavior
The error should not be raised since tools are defined the correct way (as per the official LangChain docs)
|
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I_kwDOIPDwls50vSgm
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Retriever Tool for Vertex AI Model
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[] | null | 1 | 2023-10-24T05:35:24 | 2023-10-25T05:32:36 | null |
NONE
| null |
### Issue you'd like to raise.
I am trying to add retriever tool to SQL Database Toolkit and run SQL Agent with Vertex AI Text Bison LLM, however I am facing error.
I am following below documentation for implementation
[https://python.langchain.com/docs/use_cases/qa_structured/sql?ref=blog.langchain.dev#case-3-sql-agents](url)
My Python Code
`llm = VertexAI(
model_name='text-bison@001',
max_output_tokens=256,
temperature=0,
top_p=0.8,
top_k=40,
verbose=True,
)
few_shots = {'a': sql a query;',
"b '.": "sql b query",
"c.": "sql c query;",
"d": "sql d query; "
}
from langchain.embeddings import VertexAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document
embeddings = VertexAIEmbeddings()
few_shot_docs = [Document(page_content=question, metadata={'sql_query': few_shots[question]}) for question in few_shots.keys()]
vector_db = FAISS.from_documents(few_shot_docs, embeddings)
#retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5})
retriever = vector_db.as_retriever()
from langchain.agents import initialize_agent, Tool
from langchain.agents import load_tools
from langchain.agents import AgentType
from langchain.tools import BaseTool
from langchain.agents.agent_toolkits import create_retriever_tool
tool_description = """
This tool will help you understand similar examples to adapt them to the user question.
Input to this tool should be the user question.
"""
retriever_tool = create_retriever_tool(
retriever,
name='sql_get_similar_examples',
description=tool_description
)
custom_tool_list = [retriever_tool]
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
import pandas as pd
table_uri = f"bigquery://{project}/{dataset_id}"
engine = create_engine(f"bigquery://{project}/{dataset_id}")
db = SQLDatabase(engine=engine,metadata=MetaData(bind=engine),include_tables=table_name,view_support=True)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
custom_suffix = """Begin!
Question: {input}
Thought: I should first get the similar examples I know.
If the examples are enough to construct the query, I can build it.
Otherwise, I can then look at the tables in the database to see what I can query.
Then I should query the schema of the most relevant tables.
{agent_scratchpad}"""
agent = create_sql_agent(llm=llm,
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
extra_tools=custom_tool_list,
suffix=custom_suffix
)
langchain.debug = True
Thought:The table does not have the column 'alloted_terminal'.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:I don't have enough information to construct the query.
Action: sql_get_similar_examples
Action Input: {Question}
Observation: {Document(fewshots}
Thought:
> Finished chain.
Agent stopped due to iteration limit or time limit.
{
"output": "Agent stopped due to iteration limit or time limit."
}
'
Agent stopped due to iteration limit or time limit.
### Suggestion:
_No response_
|
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I_kwDOIPDwls50vAUc
| 12,184 |
multi_retrieval_qa.py hardwires ChatOpenAI() at the _default_chain
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[] | null | 6 | 2023-10-24T04:17:28 | 2023-10-26T09:09:59 | 2023-10-25T14:29:42 |
NONE
| null |
### System Info
System:
langchain 0.0.321
Python 3.10
Hi,
I get an error related to a missing OpenAI token when trying to reuse the code to [dynamically select from multiple retrievers](https://python.langchain.com/docs/use_cases/question_answering/multi_retrieval_qa_router) at the MultiRetrievalQAChain initialization step,
```
multi_retriever_chain = MultiRetrievalQAChain.from_retrievers(
llm=llm, # Llama2 served by vLLM via VLLMOpenAI
retriever_infos=retrievers_info,
verbose=True)
```
I get an error because I'm not using OpenAI LLMs and the LangChain code (multi_retrieval_qa.py) hard wires ChatOpenAI() as the LLM for the _default_chain.
```
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
)
```
I think you need to assign the llm variable to the llm provided when initializing the class.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Including the Python code and the PDF files that get loaded by the retrievers.
```
#%%
import torch
from langchain.llms import VLLMOpenAI
from langchain.document_loaders import PyPDFLoader
# Import for retrieval-augmented generation RAG
from langchain import hub
from langchain.chains import RetrievalQA, MultiRetrievalQAChain
from langchain.vectorstores import Chroma
from langchain.text_splitter import SentenceTransformersTokenTextSplitter
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
#%%
# URL for the vLLM service
INFERENCE_SRV_URL = "http://localhost:8000/v1"
def setup_chat_llm(vllm_url, max_tokens=512, temperature=0.2):
"""
Intializes the vLLM service object.
:param vllm_url: vLLM service URL
:param max_tokens: Max number of tokens to get generated by the LLM
:param temperature: Temperature of the generation process
:return: The vLLM service object
"""
chat = VLLMOpenAI(
openai_api_key="EMPTY",
openai_api_base=vllm_url,
model_name="meta-llama/Llama-2-7b-chat-hf",
temperature=temperature,
max_tokens=max_tokens,
)
return chat
#%%
# Initialize LLM service
llm = setup_chat_llm(vllm_url=INFERENCE_SRV_URL)
#%%
%%time
# Set up the embedding encoder (Sentence Transformers) and vector store
model_name = "all-mpnet-base-v2"
model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = SentenceTransformerEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
# Set up the document splitter
text_splitter = SentenceTransformersTokenTextSplitter(chunk_size=500, chunk_overlap=0)
# Load PDF documents
loader = PyPDFLoader(file_path="../data/AI_RMF_Playbook.pdf")
rmf_doc = loader.load()
rmf_splits = text_splitter.split_documents(rmf_doc)
rmf_retriever = Chroma.from_documents(documents=rmf_splits, embedding=embeddings)
loader = PyPDFLoader(file_path="../data/OWASP-Top-10-for-LLM-Applications-v101.pdf")
owasp_doc = loader.load()
owasp_splits = text_splitter.split_documents(owasp_doc)
owasp_retriever = Chroma.from_documents(documents=owasp_splits, embedding=embeddings)
loader = PyPDFLoader(file_path="../data/Threat Modeling LLM Applications - AI Village.pdf")
ai_village_doc = loader.load()
ai_village_splits = text_splitter.split_documents(ai_village_doc)
ai_village_retriever = Chroma.from_documents(documents=ai_village_splits, embedding=embeddings)
#%%
retrievers_info = [
{
"name": "NIST AI Risk Management Framework",
"description": "Guidelines for organizations and people to manage risks associated with the use of AI ",
"retriever": rmf_retriever.as_retriever()
},
{
"name": "OWASP Top 10 for LLM Applications",
"description": "Provides practical, actionable, and concise security guidance to navigate the complex and evolving terrain of LLM security",
"retriever": owasp_retriever.as_retriever()
},
{
"name": "Threat Modeling LLM Applications",
"description": "A high-level example from Gavin Klondike on how to build a threat model for LLM applications",
"retriever": ai_village_retriever.as_retriever()
}
]
#%%
# Import default LLama RAG prompt
prompt = hub.pull("rlm/rag-prompt-llama")
print(prompt.dict()['messages'][0]['prompt']['template'])
#%%
multi_retriever_chain = MultiRetrievalQAChain.from_retrievers(
llm=llm,
retriever_infos=retrievers_info,
#default_retriever=owasp_retriever.as_retriever(),
#default_prompt=prompt,
#chain_type_kwargs={"prompt": prompt},
verbose=True)
#%%
question = "What is prompt injection?"
result = multi_retriever
```
[AI_RMF_Playbook.pdf](https://github.com/langchain-ai/langchain/files/13110652/AI_RMF_Playbook.pdf)
[OWASP-Top-10-for-LLM-Applications-v101.pdf](https://github.com/langchain-ai/langchain/files/13110653/OWASP-Top-10-for-LLM-Applications-v101.pdf)
[Threat Modeling LLM Applications - AI Village.pdf](https://github.com/langchain-ai/langchain/files/13110654/Threat.Modeling.LLM.Applications.-.AI.Village.pdf)
### Expected behavior
I was expecting to get results equivalent to what is shown at LangChain's documentation [dynamically select from multiple retrievers](https://python.langchain.com/docs/use_cases/question_answering/multi_retrieval_qa_router)
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| 1,958,394,412 |
I_kwDOIPDwls50uros
| 12,183 |
Bug when using OPENAI_FUNCTIONS agent
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[] | null | 2 | 2023-10-24T03:10:22 | 2023-12-25T16:16:00 | null |
NONE
| null |
### System Info
python=3.10
langchain=0.0.320
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Bug
https://python.langchain.com/docs/modules/agents/how_to/custom-functions-with-openai-functions-agent
I am trying to reproduce this example. But I got the below error.
The error happends in the below function https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html#ChatOpenAI:~:text=%3Dchunk)-,def%20_generate(,-self%2C%0A%20%20%20%20%20%20%20%20messages
After I printed the "message_dicts" in the function I found that one item in the list has a None "content" value.
``` python
[{'role': 'system', 'content': 'You are a helpful AI assistant.'}, {'role': 'user', 'content': 'What is the current price of Microsoft stock? How it has performed over past 6 months?'}, {'role': 'assistant', 'content': None, 'function_call': {'name': 'get_current_stock_price', 'arguments': '{\n "ticker": "MSFT"\n}'}}, {'role': 'function', 'content': '{"price": 326.6700134277344, "currency": "USD"}', 'name': 'get_current_stock_price'}]
```
The **None** value will lead to an error comlainning that:
```
{
"error": {
"code": null,
"param": null,
"message": "'content' is a required property - 'messages.2'",
"type": "invalid_request_error"
}
}
```
# My findings:
If we use the function_call features of OPENAI, it will always return a None value in the content. But it seems like, the langchain implementation does not deal with this situation.
# My fix:
After I add one line of code in the _generate function to replace the `None` value with an empty string `''`:
```python
message_dicts = [{**item, 'content': ('' if item['content'] is None else item['content'])} for item in message_dicts] # this line is added
response = self.completion_with_retry(
messages=message_dicts, run_manager=run_manager, **params
)
print(response)
return self._create_chat_result(response)
```
Then the function works normally.
Is this a bug in using the langchain OPENAI_FUNCTIONS agent?
### Expected behavior
# My fix:
After I add one line of code in the _generate function to replace the `None` value with an empty string `''`:
```python
message_dicts = [{**item, 'content': ('' if item['content'] is None else item['content'])} for item in message_dicts] # this line is added
response = self.completion_with_retry(
messages=message_dicts, run_manager=run_manager, **params
)
print(response)
return self._create_chat_result(response)
```
Then the function works normally.
The output (with some printing) is like this:
```
[{'role': 'system', 'content': 'You are a helpful AI assistant.'}, {'role': 'user', 'content': 'What is the current price of Microsoft stock? How it has performed over past 6 months?'}]
{
"created": 1698074688,
"usage": {
"completion_tokens": 18,
"prompt_tokens": 199,
"total_tokens": 217
},
"model": "gpt-35-turbo",
"id": "chatcmpl-8Cr5M7Zi8wv40g9dEg4ZbkNV0KBSc",
"choices": [
{
"finish_reason": "function_call",
"index": 0,
"message": {
"role": "assistant",
"function_call": {
"name": "get_current_stock_price",
"arguments": "{\n \"ticker\": \"MSFT\"\n}"
},
"content": null
}
}
],
"object": "chat.completion"
}
Invoking: `get_current_stock_price` with `{'ticker': 'MSFT'}`
{'price': 328.9849853515625, 'currency': 'USD'}[{'role': 'system', 'content': 'You are a helpful AI assistant.'}, {'role': 'user', 'content': 'What is the current price of Microsoft stock? How it has performed over past 6 months?'}, {'role': 'assistant', 'content': None, 'function_call': {'name': 'get_current_stock_price', 'arguments': '{\n "ticker": "MSFT"\n}'}}, {'role': 'function', 'content': '{"price": 328.9849853515625, "currency": "USD"}', 'name': 'get_current_stock_price'}]
{
"created": 1698074692,
"usage": {
"completion_tokens": 24,
"prompt_tokens": 228,
"total_tokens": 252
},
"model": "gpt-35-turbo",
"id": "chatcmpl-8Cr5QoQfx5gkIS9k7mGn2fs7lMkPc",
"choices": [
{
"finish_reason": "function_call",
"index": 0,
"message": {
"role": "assistant",
"function_call": {
"name": "get_stock_performance",
"arguments": "{\n \"ticker\": \"MSFT\",\n \"days\": 180\n}"
},
"content": null
}
}
],
"object": "chat.completion"
}
Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 180}`
{'percent_change': 7.626317098142607}[{'role': 'system', 'content': 'You are a helpful AI assistant.'}, {'role': 'user', 'content': 'What is the current price of Microsoft stock? How it has performed over past 6 months?'}, {'role': 'assistant', 'content': None, 'function_call': {'name': 'get_current_stock_price', 'arguments': '{\n "ticker": "MSFT"\n}'}}, {'role': 'function', 'content': '{"price": 328.9849853515625, "currency": "USD"}', 'name': 'get_current_stock_price'}, {'role': 'assistant', 'content': None, 'function_call': {'name': 'get_stock_performance', 'arguments': '{\n "ticker": "MSFT",\n "days": 180\n}'}}, {'role': 'function', 'content': '{"percent_change": 7.626317098142607}', 'name': 'get_stock_performance'}]
{
"created": 1698074694,
"usage": {
"completion_tokens": 36,
"prompt_tokens": 251,
"total_tokens": 287
},
"model": "gpt-35-turbo",
"id": "chatcmpl-8Cr5SRASyFLFbp80CJhx5hgdVXDLM",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"role": "assistant",
"content": "The current price of Microsoft stock is $328.98 USD. Over the past 6 months, the stock has performed with a 7.63% increase in price."
}
}
],
"object": "chat.completion"
}
The current price of Microsoft stock is $328.98 USD. Over the past 6 months, the stock has performed with a 7.63% increase in price.
```
Is this a bug in using the langchain OPENAI_FUNCTIONS agent?
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| 1,958,369,465 |
PR_kwDOIPDwls5dmBHR
| 12,181 |
Expose handle_event and ahandle_events as public API
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[] | null | 1 | 2023-10-24T02:40:16 | 2023-10-24T17:42:30 | 2023-10-24T17:42:29 |
COLLABORATOR
| null |
Expose functionality to handle generic events.
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JSONDecodeError on Cookbook/Semi_structured_multi_mudal_RAG_LLaMA2.ipynb
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[] | null | 6 | 2023-10-24T02:23:12 | 2024-01-08T10:22:59 | 2023-11-01T06:11:06 |
NONE
| null |
### System Info
Python Version: Python 3.10.4
Langchain Version: 0.0.320
OS: Ubuntu 18.04
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When running the official cookbook code...
```python
from pydantic import BaseModel
from typing import Any, Optional
from unstructured.partition.pdf import partition_pdf
# Path to save images
path = "./Papers/LLaVA/"
# Get elements
raw_pdf_elements = partition_pdf(filename=path+"LLaVA.pdf",
# Using pdf format to find embedded image blocks
extract_images_in_pdf=True,
# Use layout model (YOLOX) to get bounding boxes (for tables) and find titles
# Titles are any sub-section of the document
infer_table_structure=True,
# Post processing to aggregate text once we have the title
chunking_strategy="by_title",
# Chunking params to aggregate text blocks
# Attempt to create a new chunk 3800 chars
# Attempt to keep chunks > 2000 chars
# Hard max on chunks
max_characters=4000,
new_after_n_chars=3800,
combine_text_under_n_chars=2000,
image_output_dir_path=path
)
# Create a dictionary to store counts of each type
category_counts = {}
for element in raw_pdf_elements:
category = str(type(element))
if category in category_counts:
category_counts[category] += 1
else:
category_counts[category] = 1
# Unique_categories will have unique elements
# TableChunk if Table > max chars set above
unique_categories = set(category_counts.keys())
category_counts
class Element(BaseModel):
type: str
text: Any
# Categorize by type
categorized_elements = []
for element in raw_pdf_elements:
if "unstructured.documents.elements.Table" in str(type(element)):
categorized_elements.append(Element(type="table", text=str(element)))
elif "unstructured.documents.elements.CompositeElement" in str(type(element)):
categorized_elements.append(Element(type="text", text=str(element)))
# Tables
table_elements = [e for e in categorized_elements if e.type == "table"]
print(len(table_elements))
# Text
text_elements = [e for e in categorized_elements if e.type == "text"]
print(len(text_elements))
from langchain.chat_models import ChatOllama
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
# Prompt
prompt_text="""You are an assistant tasked with summarizing tables and text. \
Give a concise summary of the table or text. Table or text chunk: {element} """
prompt = ChatPromptTemplate.from_template(prompt_text)
# Summary chain
model = ChatOllama(model="llama2:13b-chat")
summarize_chain = {"element": lambda x:x} | prompt | model | StrOutputParser()
# Apply to text
texts = [i.text for i in text_elements if i.text != ""]
text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5})
```
The following error was returned:
```bash
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
File ~/anaconda3/envs/langchain/lib/python3.8/site-packages/requests/models.py:971, in Response.json(self, **kwargs)
970 try:
--> 971 return complexjson.loads(self.text, **kwargs)
972 except JSONDecodeError as e:
973 # Catch JSON-related errors and raise as requests.JSONDecodeError
974 # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
File ~/anaconda3/envs/langchain/lib/python3.8/json/__init__.py:357, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
354 if (cls is None and object_hook is None and
355 parse_int is None and parse_float is None and
356 parse_constant is None and object_pairs_hook is None and not kw):
--> 357 return _default_decoder.decode(s)
358 if cls is None:
File ~/anaconda3/envs/langchain/lib/python3.8/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)
333 """Return the Python representation of ``s`` (a ``str`` instance
334 containing a JSON document).
335
336 """
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
338 end = _w(s, end).end()
File ~/anaconda3/envs/langchain/lib/python3.8/json/decoder.py:355, in JSONDecoder.raw_decode(self, s, idx)
354 except StopIteration as err:
--> 355 raise JSONDecodeError("Expecting value", s, err.value) from None
356 return obj, end
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
Cell In[6], line 3
1 # Apply to text
2 texts = [i.text for i in text_elements if i.text != ""]
----> 3 text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5})
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/schema/runnable/base.py:1287, in RunnableSequence.batch(self, inputs, config, return_exceptions, **kwargs)
1285 else:
1286 for i, step in enumerate(self.steps):
-> 1287 inputs = step.batch(
1288 inputs,
1289 [
1290 # each step a child run of the corresponding root run
1291 patch_config(
1292 config, callbacks=rm.get_child(f"seq:step:{i+1}")
1293 )
1294 for rm, config in zip(run_managers, configs)
1295 ],
1296 )
1298 # finish the root runs
1299 except BaseException as e:
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/schema/runnable/base.py:323, in Runnable.batch(self, inputs, config, return_exceptions, **kwargs)
320 return cast(List[Output], [invoke(inputs[0], configs[0])])
322 with get_executor_for_config(configs[0]) as executor:
--> 323 return cast(List[Output], list(executor.map(invoke, inputs, configs)))
File ~/anaconda3/envs/langchain/lib/python3.8/concurrent/futures/_base.py:619, in Executor.map.<locals>.result_iterator()
616 while fs:
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield fs.pop().result()
620 else:
621 yield fs.pop().result(end_time - time.monotonic())
File ~/anaconda3/envs/langchain/lib/python3.8/concurrent/futures/_base.py:444, in Future.result(self, timeout)
442 raise CancelledError()
443 elif self._state == FINISHED:
--> 444 return self.__get_result()
445 else:
446 raise TimeoutError()
File ~/anaconda3/envs/langchain/lib/python3.8/concurrent/futures/_base.py:389, in Future.__get_result(self)
387 if self._exception:
388 try:
--> 389 raise self._exception
390 finally:
391 # Break a reference cycle with the exception in self._exception
392 self = None
File ~/anaconda3/envs/langchain/lib/python3.8/concurrent/futures/thread.py:57, in _WorkItem.run(self)
54 return
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/schema/runnable/base.py:316, in Runnable.batch.<locals>.invoke(input, config)
314 return e
315 else:
--> 316 return self.invoke(input, config, **kwargs)
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/chat_models/base.py:142, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
131 def invoke(
132 self,
133 input: LanguageModelInput,
(...)
137 **kwargs: Any,
138 ) -> BaseMessage:
139 config = config or {}
140 return cast(
141 ChatGeneration,
--> 142 self.generate_prompt(
143 [self._convert_input(input)],
144 stop=stop,
145 callbacks=config.get("callbacks"),
146 tags=config.get("tags"),
147 metadata=config.get("metadata"),
148 run_name=config.get("run_name"),
149 **kwargs,
150 ).generations[0][0],
151 ).message
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/chat_models/base.py:459, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
451 def generate_prompt(
452 self,
453 prompts: List[PromptValue],
(...)
456 **kwargs: Any,
457 ) -> LLMResult:
458 prompt_messages = [p.to_messages() for p in prompts]
--> 459 return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/chat_models/base.py:349, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
347 if run_managers:
348 run_managers[i].on_llm_error(e)
--> 349 raise e
350 flattened_outputs = [
351 LLMResult(generations=[res.generations], llm_output=res.llm_output)
352 for res in results
353 ]
354 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/chat_models/base.py:339, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
336 for i, m in enumerate(messages):
337 try:
338 results.append(
--> 339 self._generate_with_cache(
340 m,
341 stop=stop,
342 run_manager=run_managers[i] if run_managers else None,
343 **kwargs,
344 )
345 )
346 except BaseException as e:
347 if run_managers:
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/chat_models/base.py:492, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
488 raise ValueError(
489 "Asked to cache, but no cache found at `langchain.cache`."
490 )
491 if new_arg_supported:
--> 492 return self._generate(
493 messages, stop=stop, run_manager=run_manager, **kwargs
494 )
495 else:
496 return self._generate(messages, stop=stop, **kwargs)
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/chat_models/ollama.py:98, in ChatOllama._generate(self, messages, stop, run_manager, **kwargs)
80 """Call out to Ollama's generate endpoint.
81
82 Args:
(...)
94 ])
95 """
97 prompt = self._format_messages_as_text(messages)
---> 98 final_chunk = super()._stream_with_aggregation(
99 prompt, stop=stop, run_manager=run_manager, verbose=self.verbose, **kwargs
100 )
101 chat_generation = ChatGeneration(
102 message=AIMessage(content=final_chunk.text),
103 generation_info=final_chunk.generation_info,
104 )
105 return ChatResult(generations=[chat_generation])
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/llms/ollama.py:156, in _OllamaCommon._stream_with_aggregation(self, prompt, stop, run_manager, verbose, **kwargs)
147 def _stream_with_aggregation(
148 self,
149 prompt: str,
(...)
153 **kwargs: Any,
154 ) -> GenerationChunk:
155 final_chunk: Optional[GenerationChunk] = None
--> 156 for stream_resp in self._create_stream(prompt, stop, **kwargs):
157 if stream_resp:
158 chunk = _stream_response_to_generation_chunk(stream_resp)
File ~/chatpdf-langchain/langchain/libs/langchain/langchain/llms/ollama.py:140, in _OllamaCommon._create_stream(self, prompt, stop, **kwargs)
138 response.encoding = "utf-8"
139 if response.status_code != 200:
--> 140 optional_detail = response.json().get("error")
141 raise ValueError(
142 f"Ollama call failed with status code {response.status_code}."
143 f" Details: {optional_detail}"
144 )
145 return response.iter_lines(decode_unicode=True)
File ~/anaconda3/envs/langchain/lib/python3.8/site-packages/requests/models.py:975, in Response.json(self, **kwargs)
971 return complexjson.loads(self.text, **kwargs)
972 except JSONDecodeError as e:
973 # Catch JSON-related errors and raise as requests.JSONDecodeError
974 # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
--> 975 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
```
After attempting:
```python
# Summary chain
model = ChatOllama(model="llama2:13b-chat")
summarize_chain = {"element": lambda x:x} | prompt | model
texts = [i.text for i in text_elements if i.text != ""]
text_summaries = summarize_chain.batch(texts)
```
Returned the same error as before.
It seems that the error is occurring in the ‘ChatOllama model’.
### Expected behavior
Attempting to reproduce the effects in the Cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb.
|
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I_kwDOIPDwls50uQq2
| 12,179 |
Issue: <Not able to filter OpenSearch vectorstore using Filter in search_kwargs>
|
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[] | null | 1 | 2023-10-24T01:09:55 | 2023-10-24T03:31:33 | null |
NONE
| null |
### Issue you'd like to raise.
Hello Team,
We have a metadata field in our opensearch index which I would like use to filter the passages before sending it to LLM to generate the answer. Tried many options suggested in chat.langchain.com but none of them seems to work.
Here is an example:
I have an user with associated groups ['user_group1', 'user_group2'] and the OpenSearch index with data like
{
'text': 'Page Content......',
'metadata': {
'source_acl': [ {'group_name': 'user_group1', 'group_type': 'type1'}, {}, {}]
}
}
So far I have been trying this
```
chain = ConversationalRetrievalChain.from_llm(
llm=SagemakerEndpoint(
endpoint_name=constants.EMBEDDING_ENDPOINT_ARN_ALPHA,
region_name=constants.REGION,
content_handler=self.content_handler_llm,
client=self.sg_client
),
# for experimentation, you can change number of documents to retrieve here
retriever=opensearch_vectorstore.as_retriever(search_kwargs={'k': 3, 'filter': 'source_acl.group_name:user_group1'}),
memory=memory,
condense_question_prompt=custom_question_prompt,
return_source_documents=True
)
```
Am I missing anything here?
Thanks in Advance.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5dlkXC
| 12,178 |
Update deprecation
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[] | null | 1 | 2023-10-24T00:01:07 | 2023-10-24T17:37:29 | 2023-10-24T17:37:29 |
COLLABORATOR
| null |
in runner_utils
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| 1,958,187,003 |
PR_kwDOIPDwls5dlbIF
| 12,177 |
updated `integrations/providers/microsoft`
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[] | null | 2 | 2023-10-23T23:19:59 | 2023-10-24T18:42:56 | 2023-10-24T17:19:06 |
COLLABORATOR
| null |
Added several missed tools, utilities, toolkits to the `Microsoft` page.
|
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| 1,958,182,263 |
I_kwDOIPDwls50t313
| 12,176 |
value is not a valid dict (type=type_error.dict)
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[] | null | 1 | 2023-10-23T23:15:43 | 2023-10-29T13:51:16 | 2023-10-29T13:51:16 |
NONE
| null |
### Issue you'd like to raise.
when i try and run this script
```
class WebsiteSummary:
def _SummorizeWebsite(objective, websiteContent):
my_gpt_instance = MyGPT4ALL()
textSplitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500
)
docs = textSplitter.create_documents([websiteContent])
print(docs)
mapPrompt = """"
write a summary of the following {objective}:
{text}
SUMMARY:
"""
map_prompt_template = PromptTemplate(
template=mapPrompt, input_variables=["text", "objective"]
)
chain = load_summarize_chain(llm=my_gpt_instance,
chain_type="map_reduce",
verbose=True,
map_prompt=objective,
combine_prompt=map_prompt_template)
output = summary_Chain.run(input_documents=docs, objective=objective)
return output
```
i get this issue
Exception has occurred: ValidationError
1 validation error for LLMChain
prompt
value is not a valid dict (type=type_error.dict)
File "E:\StormboxBrain\langchain\websiteSummary.py", line 27, in _SummorizeWebsite
chain = load_summarize_chain(llm=my_gpt_instance,
File "E:\StormboxBrain\langchain\app.py", line 12, in <module>
summary = websiteSummary._SummorizeWebsite(objective, data)
pydantic.error_wrappers.ValidationError: 1 validation error for LLMChain
my model does load and can create prompt responses
mistral-7b-openorca.Q4_0.gguf
this is mygpt4all class
```
import os
from pydantic import Field
from typing import List, Mapping, Optional, Any
from langchain.llms.base import LLM
from gpt4all import GPT4All, pyllmodel
class MyGPT4ALL(LLM):
"""
A custom LLM class that integrates gpt4all models
Arguments:
model_folder_path: (str) Folder path where the model lies
model_name: (str) The name of the model to use (<model name>.gguf)
backend: (str) The backend of the model (Supported backends: llama/gptj)
n_threads: (str) The number of threads to use
n_predict: (str) The maximum numbers of tokens to generate
temp: (str) Temperature to use for sampling
top_p: (float) The top-p value to use for sampling
top_k: (float) The top k values use for sampling
n_batch: (int) Batch size for prompt processing
repeat_last_n: (int) Last n number of tokens to penalize
repeat_penalty: (float) The penalty to apply repeated tokens
"""
model_folder_path: str = "./models/"
model_name: str = "mistral-7b-openorca.Q4_0.gguf"
backend: Optional[str] = "llama"
temp: Optional[float] = 0.7
top_p: Optional[float] = 0.1
top_k: Optional[int] = 40
n_batch: Optional[int] = 8
n_threads: Optional[int] = 4
n_predict: Optional[int] = 256
max_tokens: Optional[int] = 200
repeat_last_n: Optional[int] = 64
repeat_penalty: Optional[float] = 1.18
# initialize the model
gpt4_model_instance: Any = None
def __init__(self, **kwargs):
super(MyGPT4ALL, self).__init__()
print("init " + self.model_folder_path + self.model_name)
self.gpt4_model_instance = GPT4All(
model_name=self.model_name,
model_path=self.model_folder_path,
device="cpu",
verbose=True,
)
print("initiolized " + self.model_folder_path + self.model_name)
@property
def _get_model_default_parameters(self):
print("get default params")
return {
"max_tokens": self.max_tokens,
"n_predict": self.n_predict,
"top_k": self.top_k,
"top_p": self.top_p,
"temp": self.temp,
"n_batch": self.n_batch,
"repeat_penalty": self.repeat_penalty,
"repeat_last_n": self.repeat_last_n,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
print(" Get all the identifying parameters")
return {
"model_name": self.model_name,
"model_path": self.model_folder_path,
"model_parameters": self._get_model_default_parameters,
}
@property
def _llm_type(self) -> str:
return "llama"
def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs) -> str:
"""
Args:
prompt: The prompt to pass into the model.
stop: A list of strings to stop generation when encountered
Returns:
The string generated by the model
"""
params = {**self._get_model_default_parameters, **kwargs}
print("params:")
for key, value in params.items():
print(f"{key}: {value}")
print("params " + params.values())
return "initiolized"
```
### Suggestion:
is there any documentation on how to use another llm as base and not gpt
|
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| 1,958,069,230 |
I_kwDOIPDwls50tcPu
| 12,175 |
Integrate RetryOutputParser with ConversationalRetrievalChain
|
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[] | null | 7 | 2023-10-23T21:40:52 | 2023-10-25T13:20:13 | null |
NONE
| null |
### Feature request
Is there a way to integrate `RetryOutputParser` with `ConversationalRetrievalChain`? Specifically, if I use OpenAI's function calling to generate the response in a specific format, the LLM model does not always follow the instructions and then it would fail the Pydantic model validation.
Therefore, it's important to build the retry mechanism for the `LLMChain` (aka, `doc_chain` argument).
### Motivation
Specifically, if I use OpenAI's function calling to generate the response in a specific format, the LLM model does not always follow the instructions and then it would fail the Pydantic model validation.
Therefore, it's important to build the retry mechanism for the `LLMChain` (aka, `doc_chain` argument).
### Your contribution
TBD
|
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| 1,958,045,344 |
PR_kwDOIPDwls5dk8Xb
| 12,174 |
Changes to root listener
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[] | null | 1 | 2023-10-23T21:22:47 | 2023-11-10T09:53:49 | 2023-11-10T09:53:48 |
COLLABORATOR
| null |
- Implement config_specs to include session_id
- Remove Runnable method and update notebook
- Add more details to notebook, eg. show input schema and config schema before and after adding message history
|
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fix vertexai codey models
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[] | null | 2 | 2023-10-23T20:47:44 | 2023-10-24T17:20:06 | 2023-10-24T17:20:06 |
CONTRIBUTOR
| null |
**Description:**
This PR fixes issue #12156 by checking for Codey models appropriately before result parsing.
Maintainer: @hwchase17 , @agola11
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I_kwDOIPDwls50tHBb
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Token Usage Management System
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[] | null | 1 | 2023-10-23T20:36:34 | 2023-10-23T20:46:13 | null |
NONE
| null |
### Feature request
The idea is simple. When using various models users have rate limits, both in requests per min/hour and Tokens. When using the raw OpenAI SDK I used the code below. It just uses the "usage" metrics provided in the response (as of now I dont know how to retrieve a 'raw response' when utilizing langchain queries'
### Motivation
This was inspired by the experience of trying to use the GPT api to generate documentaion for a long list of functions of varying length. While trying to run these requests en mass I repeatedly had my process error out because of RATE LIMIT Errors. I dont think the idea of needing to run queries asynchronously against OpenAI API is a common enough use case (though strongely enough not natively supported by the Python SDK)
### Your contribution
I would be Honored!!!! To contribute this to the project but I honestly would need some help figuring out how to integrate it. This is the base Idea though, Feel free to reachout to collab! [email protected] or @codeblackwell anywhere. (apologies for the formatting. It just wouldnt work with me).
`
class TokenManager:
def __init__(self, max_tokens_per_minute=10000):
self.max_tokens_per_minute = max_tokens_per_minute
self.tokens_used_in_last_minute = 0
self.last_request_time = time.time()
def tokens_available(self):
current_time = time.time()
time_since_last_request = current_time - self.last_request_time
if time_since_last_request >= 60:
self.tokens_used_in_last_minute = 0
self.last_request_time = current_time
return self.max_tokens_per_minute - self.tokens_used_in_last_minute
def update_tokens_used(self, tokens_used):
if not isinstance(tokens_used, int) or tokens_used < 0:
raise ValueError("Invalid token count")
self.tokens_used_in_last_minute += tokens_used
def get_usage_stats(self):
current_time = time.time()
time_since_last_request = current_time - self.last_request_time
if time_since_last_request >= 60:
self.tokens_used_in_last_minute = 0
self.last_request_time = current_time
absolute_usage = self.tokens_used_in_last_minute
percent_usage = (self.tokens_used_in_last_minute / self.max_tokens_per_minute) * 100
return absolute_usage, percent_usage`
token_manager = TokenManager()
`def fetch_response(code_snippet, system_prompt, token_manager):
"""
Fetches a response using the OpenAI API.
Parameters:
code_snippet (str): The code snippet to be processed.
system_prompt (str): The system prompt.
Returns:
dict: The response from the API.
"""
while token_manager.tokens_available() < 2500: # Assume a minimum of 10 tokens per request
time.sleep(1)
try:
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": code_snippet}
]
)
tokens_used = response['usage']['total_tokens']
token_manager.update_tokens_used(tokens_used)
return response
except Exception as e:
print(f"An error occurred: {str(e)}")`
`def main(df, system_prompt):
"""
Processes a DataFrame of code snippets.
Parameters:
df (pd.DataFrame): The DataFrame containing code snippets.
system_prompt (str): The system prompt.
Returns:
pd.DataFrame: The DataFrame with the added output column.
"""
responses = [fetch_response(row['code'], system_prompt) for _, row in df.iterrows()]
df['output'] = responses
return df`
# Run the main function
`new_df = asyncio.run(main(filtered_df, system_prompt_1))`
|
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PR_kwDOIPDwls5dkko-
| 12,171 |
separate compile integration tests
|
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[] | null | 1 | 2023-10-23T20:04:36 | 2023-10-24T15:55:21 | 2023-10-24T15:55:20 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls50suOM
| 12,170 |
`poetry install --with test`: Group(s) not found: test (via --with)
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[] | null | 3 | 2023-10-23T19:28:02 | 2023-10-23T19:46:02 | 2023-10-23T19:46:02 |
NONE
| null |
### System Info
I am trying to setup the local development environment with `poetry install --with test` but I am running into the error `Group(s) not found: test (via --with)`
```
> origin/master:master via 🐍 v3.11.6
> 57% langchain ❯ poetry install --with test
Group(s) not found: test (via --with)
```
Could you advise on what might be happening here?
### Who can help?
@eyurtsev, I think you might be able to advise here? 🙏🏼
### 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
- Clone repository
- With poetry installed, run `poetry install --with test`
### Expected behavior
Local development setup works without an error.
|
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PR_kwDOIPDwls5dkFUY
| 12,169 |
feat: document_loader Textract linearized output
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[] | null | 6 | 2023-10-23T18:43:55 | 2023-10-27T18:08:32 | 2023-10-27T17:56:45 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** linearizing the output from Amazon Textract into a layout with tables and key/value pairs when those features are passed in as feature parameters
- **Issue:** fixes #12068
- **Dependencies:** adding amazon-textract-textractor as dependency
- **Tag maintainer:** @3coins
- **Twitter handle:**
|
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| 1,957,736,180 |
PR_kwDOIPDwls5dj4lf
| 12,168 |
feat: Add Client Info to available Google Cloud Clients
|
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[] | null | 4 | 2023-10-23T18:12:10 | 2023-10-24T17:20:41 | 2023-10-24T15:49:12 |
CONTRIBUTOR
| null |
- This is used internally to gather aggregate usage metrics for the LangChain integrations
- Note: This cannot be added to some of the Vertex AI integrations at this time because the SDK doesn't allow overriding the [`ClientInfo`](https://googleapis.dev/python/google-api-core/latest/client_info.html#module-google.api_core.client_info)
- Added to:
- BigQuery
- Google Cloud Storage
- Document AI
- Vertex AI Model Garden
- Document AI Warehouse
- Vertex AI Search
- Vertex AI Matching Engine (Cloud Storage Client)
@baskaryan, @eyurtsev, @hwchase17
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Issue: View context used in query in ConversationalRetrievalChain
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[] | null | 2 | 2023-10-23T18:10:39 | 2023-10-23T19:15:11 | null |
NONE
| null |
### Issue you'd like to raise.
How can I see the context that was used to answer a specific question?
from langchain.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(k=3, memory_key="chat_history", return_messages=True)
qa = ConversationalRetrievalChain.from_llm(llm_model, vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 2}), memory=memory)
query = "Can I as a manager fly business class?"
##predict with the model
text = f"""[INST] <<SYS>> You are a HR-assistant that answers questions:
<</SYS>>
{query} [/INST]
"""
result = qa({"question": text})
result
### Suggestion:
_No response_
|
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I_kwDOIPDwls50r6-j
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mypy linting error in PyPDFParser with pypdf 3.16.4
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[] | null | 1 | 2023-10-23T17:34:36 | 2023-10-23T17:40:41 | null |
CONTRIBUTOR
| null |
### System Info
make lint errors
```
make lint
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run black . --check
All done! ✨ 🍰 ✨
1866 files would be left unchanged.
[ "." = "" ] || poetry run mypy .
langchain/document_loaders/parsers/pdf.py:95: error: "PdfObject" has no attribute "keys" [attr-defined]
langchain/document_loaders/parsers/pdf.py:98: error: Value of type "PdfObject" is not indexable [index]
Found 2 errors in 1 file (checked 1863 source files)
make: *** [lint] Error 1```
for line 95:
```
if not self.extract_images or "/XObject" not in page["/Resources"].keys():
```
and line 98:
```
xObject = page["/Resources"]["/XObject"].get_object()
```
on master 7c4f340cc, v0.320
commented with # type: ignore
for my PR, but keeping here as reference to fix
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```make lint```
### Expected behavior
no mypy error
|
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I_kwDOIPDwls50r4Z3
| 12,165 |
For New Contributors: Use SecretStr for api_keys
|
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[] | null | 56 | 2023-10-23T17:29:51 | 2024-01-07T21:13:38 | 2023-12-05T23:42:01 |
COLLABORATOR
| null |
Updated: 20233-12-06
Hello everyone! thank you all for your contributions! We've made a lot of progress with SecretStrs in the code base.
First time contributors -- hope you had fun learning how to work in the code base and thanks for putting in the time. All contributors -- thanks for all your efforts in improving LangChain.
We'll create a new first time issue in a few months.
------
Hello LangChain community,
We're always happy to see more folks getting involved in contributing to the LangChain codebase.
This is a good first issue if you want to learn more about how to set up
for development in the LangChain codebase.
## Goal
Your contribution will make it safer to print out a LangChain object
without having any secrets included in raw format in the string representation.
## Set up for development
Prior to making any changes in the code:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
Make sure you're able to test, format, lint from `langchain/libs/langchain`
```sh
make test
```
```sh
make format
```
```sh
make lint
```
## Shall you accept
Shall you accept this challenge, please claim one (and only one) of the modules from the list
below as one that you will be working on, and respond to this issue.
Once you've made the required code changes, open a PR and link to this issue.
## Acceptance Criteria
- invoking str or repr on the the object does not show the secret key
Integration test for the code updated to include tests that:
- confirms the object can be initialized with an API key provided via the initializer
- confirms the object can be initialized with an API key provided via an env variable
Confirm that it works:
- either re-run notebook for the given object or else add an appropriate test
that confirms that the actual secret is used appropriately (i.e.,`.get_secret_value()`)
**If your code does not use `get_secret_value()` somewhere, then it probably contains a bug!**
## Modules
- [ ] langchain/chat_models/anyscale.py @aidoskanapyanov
- [ ] langchain/chat_models/azure_openai.py @onesolpark
- [x] langchain/chat_models/azureml_endpoint.py @fyasla
- [ ] langchain/chat_models/baichuan.py
- [ ] langchain/chat_models/everlyai.py @sfreisthler
- [x] langchain/chat_models/fireworks.py @nepalprabin
- [ ] langchain/chat_models/google_palm.py @faisalt14
- [ ] langchain/chat_models/javelin_ai_gateway.py
- [ ] langchain/chat_models/jinachat.py
- [ ] langchain/chat_models/konko.py
- [ ] langchain/chat_models/litellm.py
- [ ] langchain/chat_models/openai.py @AnasKhan0607
- [ ] langchain/chat_models/tongyi.py
- [ ] langchain/llms/ai21.py
- [x] langchain/llms/aleph_alpha.py @slangenbach
- [ ] langchain/llms/anthropic.py
- [ ] langchain/llms/anyscale.py @aidoskanapyanov
- [ ] langchain/llms/arcee.py
- [x] langchain/llms/azureml_endpoint.py
- [ ] langchain/llms/bananadev.py
- [ ] langchain/llms/cerebriumai.py
- [ ] langchain/llms/cohere.py @arunsathiya
- [ ] langchain/llms/edenai.py @kristinspenc
- [ ] langchain/llms/fireworks.py
- [ ] langchain/llms/forefrontai.py
- [ ] langchain/llms/google_palm.py @Harshil-Patel28
- [ ] langchain/llms/gooseai.py
- [ ] langchain/llms/javelin_ai_gateway.py
- [ ] langchain/llms/minimax.py
- [ ] langchain/llms/nlpcloud.py
- [ ] langchain/llms/openai.py @HassanA01
- [ ] langchain/llms/petals.py @akshatvishu
- [ ] langchain/llms/pipelineai.py
- [ ] langchain/llms/predibase.py
- [ ] langchain/llms/stochasticai.py
- [x] langchain/llms/symblai_nebula.py @praveenv
- [ ] langchain/llms/together.py
- [ ] langchain/llms/tongyi.py
- [ ] langchain/llms/writer.py @ommirzaei
- [ ] langchain/llms/yandex.py
### Motivation
Prevent secrets from being printed out when printing the given langchain object.
### Your contribution
Please sign up by responding to this issue and including the name of the module.
|
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PR_kwDOIPDwls5djYp5
| 12,164 |
Update deprecated `langchain` imports with suggested new paths.
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[] | null | 1 | 2023-10-23T16:48:00 | 2023-10-23T17:52:10 | 2023-10-23T17:52:09 |
COLLABORATOR
| null |
Let's help our users find the proper import to use instead of the deprecated top-level ones.
|
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I_kwDOIPDwls50rcI9
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OpenAPI Spec - reduce_openapi_spec - maximum recursion depth exceeded
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[] | null | 2 | 2023-10-23T16:23:35 | 2023-10-23T17:57:45 | null |
NONE
| null |
### Issue you'd like to raise.
Hello team!
I am new to the OpenAPI Specs Toolkit. I am trying to build an OpenAPI Agent Planner for the Microsoft Graph API. The OpenAPI specs can be found here: https://github.com/microsoftgraph/msgraph-metadata/blob/master/openapi/v1.0/openapi.yaml . I downloaded the YAML file and followed this notebook: https://python.langchain.com/docs/integrations/toolkits/openapi#1st-example-hierarchical-planning-agent .
```python
import os
import yaml
from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec
current_directory = os.path.abspath('')
data_directory = os.path.join(current_directory, "data")
msgraph_api_file = os.path.join(data_directory, "msgraph-openapi.yaml")
raw_msgraph_api_spec = yaml.load(open(msgraph_api_file,encoding='utf-8').read(), Loader=yaml.Loader)
msgraph_api_spec = reduce_openapi_spec(raw_msgraph_api_spec)
```
Does anyone know how to handle large OpenAPI Specs? I ran the following to read my OpenAPI YAML spec and when using the `reduce_openapi_spec` module, I get the following error below:
`RecursionError: maximum recursion depth exceeded while calling a Python object`
Is there a setting I need to change in LangChain? Please and thank you for your help in advance. I believe this issue is before I run into the following token limit, right? https://github.com/langchain-ai/langchain/issues/2786
### Suggestion:
_No response_
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Text-to-SQL + semantic search
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[] | null | 2 | 2023-10-23T16:05:03 | 2023-11-01T23:25:05 | 2023-11-01T23:25:04 |
COLLABORATOR
| null |
Querying using standard SQL queries with [PGVector](https://github.com/pgvector/pgvector) extension for semantic search.
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PR_kwDOIPDwls5djEu3
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bump 321
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https://api.github.com/repos/langchain-ai/langchain/issues/12160
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| 1,957,467,852 |
I_kwDOIPDwls50rJbM
| 12,160 |
JsonSpec `keys` method doesn't handle indexing lists properly
|
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[] | null | 0 | 2023-10-23T15:41:45 | 2023-10-23T15:43:53 | null |
NONE
| null |
There's an issue when using JsonSpec that it can't handle the case where it indexes a list with `0` because in [line 52](https://github.com/langchain-ai/langchain/blob/d0505c0d4755d8cbe62a8ddee68f53a5cb330892/libs/langchain/langchain/tools/json/tool.py#L52) it checks `if i` and since `i==0` it returns `False`.
Instead, I believe this should check for purely existence of `i` as a string or int.
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| 1,957,352,596 |
PR_kwDOIPDwls5diliK
| 12,159 |
Expose configuration options in GraphCypherQAChain
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[] | null | 3 | 2023-10-23T14:47:06 | 2023-10-24T16:52:56 | 2023-10-24T16:52:56 |
CONTRIBUTOR
| null |
Allows for passing arguments into the LLM chains used by the GraphCypherQAChain. This is to address a request by a user to include memory in the Cypher creating chain. Will keep the prompt variables as-is to be backward compatible. But, would be a good idea to deprecate them and use the **kwargs variables. Added a test case.
In general, I think it would be good for any chain to automatically pass in a readonlymemory(of its input) to its subchains whilist allowing for an override. But, this would be a different change.
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| 1,957,280,320 |
I_kwDOIPDwls50qbpA
| 12,158 |
StructuredChatOutputParser regex not accounting for space
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[] | null | 1 | 2023-10-23T14:15:02 | 2023-10-27T09:37:53 | 2023-10-27T09:37:53 |
NONE
| null |
### System Info
Langchain version: 0.0.320
Python 3.9.6
Issue:
The LLM output ` ```json \n{\n \"action\": \"...``` ` for a StructuredChatAgent using StructuredChatOutputParser.
The regex in the parser `re.compile(r"```(?:json)?\n(.*?)```", re.DOTALL)` does not allow for a space after `json`.
LLM: Claude v2
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
create an agent using StructuredChatAgent and a tool that is called by the agent.
If the output from the LLM contains a space after `json`, it will call `AgentFinish`
### Expected behavior
The expected behaviour is that the output should match the regex.
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| 1,957,182,305 |
I_kwDOIPDwls50qDth
| 12,157 |
Agent is not stopping after each answer
|
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[] | null | 1 | 2023-10-23T13:32:40 | 2023-10-23T13:46:16 | null |
NONE
| null |
### System Info
Hi,
I am working on a student teacher model . I am running into a problem where I agent does not generate final answer after the expected observations. Below is my code and output
Code
```
assignment_instruction_2 = """
1. Ask his name and then welcome him
2. Ask the topic he wants to understand
3. Ask about his understanding about the topic
3. Let him answer
4. Then take out the relevant information from the get_next_content tool
5. If it is incomplete then tell him complete answer
"""
FORMAT_INSTRUCTIONS = f"""Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{'get_topics', 'get_next_content', 'get_topic_tool'}]
Action Input: the input to the action
Observation: the detailed,at most comprehensive result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer based on my observation, i do not need anything more
Final Answer: the final answer to the original input question is the full detailed explanation from the Observation provided as bullet points.
You have to provide the answer maximum after 2 Thoughts.
"""
content_assignment = f"""You are the author of the book, You will be asked an assigenment by the user, which are typically students only.
help them to understand the topic according to the instructions delimited by /* */. follow them sequestially
/*{assignment_instruction_2} */
memory_assignment = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm_assignment = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"), temperature=0, model = "gpt-4")
agent_chain_assignment = initialize_agent(
tools,
llm_assignment,
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
memory=memory_assignment,
max_iterations=3,
# early_stopping_method="generate",
format_instructions=FORMAT_INSTRUCTIONS,
agent_kwargs={"system_message": content_assignment},
handle_parsing_errors=_handle_error
)
print("<================= TOPIC Understand AGENT ====================>")
while(True):
user_message = input("User: ")
if user_message == 'exit':
break
response = agent_chain_assignment.run(user_message)
print("LLM : ",response)
print("===============================")
```
Ouput:
``` ```
<================= TOPIC Understand AGENT ====================>
User: hi, sagar
> Entering new AgentExecutor chain...
```json
{
"action": "Final Answer",
"action_input": "Hello Sagar, welcome! What topic would you like to understand better today?"
}
```
> Finished chain.
LLM : Hello Sagar, welcome! What topic would you like to understand better today?
===============================
User: farming
> Entering new AgentExecutor chain...
Could not parse LLM output: That's a broad topic, Sagar. Could you tell me what you already understand about farming? This will help me provide you with the most relevant information.
Observation: Could not parse LLM output: That's a broad topic,
Thought:Could not parse LLM output: I'm sorry for the confusion, Sagar. You mentioned that you want to understand more about farming. Could you please specify what exactly you know about farming? This will help me to provide you with the most relevant information.
Observation: Could not parse LLM output: I'm sorry for the conf
Thought:Could not parse LLM output: I'm sorry for the confusion, Sagar. You mentioned that you want to understand more about farming. Could you please specify what exactly you know about farming? This will help me to provide you with the most relevant information.
Observation: Could not parse LLM output: I'm sorry for the conf
Thought:
> Finished chain.
LLM : Agent stopped due to iteration limit or time limit.
===============================
``` ```
It was supposed to output the final answer after the first answer (right after the user said "farming"). But it is running in loop and sometime it also consider that the user have already answered.
Can I make some corrections in the code where I can agent produce final answer right where it needs too.
Thanks,
Sagar
### Who can help?
@hwchase17 @agola11
### 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
Provided in the question itself
### Expected behavior
provided in the explaination
|
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| 1,957,115,754 |
I_kwDOIPDwls50pzdq
| 12,156 |
Langchain crashes when retrieving results from vertexai codey models
|
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[] | null | 1 | 2023-10-23T13:02:18 | 2023-10-23T13:04:34 | null |
CONTRIBUTOR
| null |
### System Info
Hey folks, I think i stumbled on a bug (or i'm using langchain wrong)
Langchain version : 0.0.320
Platform Ubuntu 23.04
Python: 3.11.4
### Who can help?
@hwchase17 , @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the following:
`
llm = llms.VertexAI(model_name="code-bison@001", max_output_tokens=1000, temperature=0.0)
prediction = llm.predict(""" write a fibonacci sequence in python""")
from pprint import pprint
pprint(prediction)`
### Expected behavior
We get a prediction
(adding more info since the form has ran out of fields)
I think the bug is in [llms/vertexai.py:301](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/vertexai.py#L301C23-L301C23) . Variable res is a TextGenerationResponse as opposed to MultiCandidateTextGenerationResponse
Hence there are no "candidates" as you would expect from a chat model.
This happens because: Google's sdk (vertexai/language_models_language_models.py)
method:
>_ChatSessionBase. _parse_chat_prediction_response
returns a `MultiCandidateTextGenerationResponse`
but both `CodeChatSession` and `CodeGenerationModel` return a `TextGenerationResponse`
I think the fix might be replacing
`generations.append([_response_to_generation(r) for r in res.candidates])`
with
```
if self.is_codey_model:
generations.append([_response_to_generation(res)])
else:
generations.append([_response_to_generation(r) for r in res.candidates])
```
happy to send a pr if I helps
|
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| 1,957,048,261 |
I_kwDOIPDwls50pi_F
| 12,154 |
MatchingEngine.add_texts() doesn't work
|
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[] | null | 1 | 2023-10-23T12:28:00 | 2023-11-19T18:10:42 | 2023-11-19T18:10:42 |
NONE
| null |
### System Info
`langchain==0.0.320`
### 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
```py
from langchain.vectorstores import MatchingEngine
texts = [
"The cat sat on",
"the mat.",
"I like to",
"eat pizza for",
"dinner.",
"The sun sets",
"in the west.",
]
vector_store = MatchingEngine.from_components(
texts=texts,
project_id="<my_project_id>",
region="<my_region>",
gcs_bucket_uri="<my_gcs_bucket>",
index_id="<my_matching_engine_index_id>",
endpoint_id="<my_matching_engine_endpoint_id>",
)
vector_store.add_texts(texts=texts)
```
### Expected behavior
I think line 136 in matching_engine.py should be
```py
jsons.append(json_)
```
|
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| 1,956,875,719 |
PR_kwDOIPDwls5dg9bR
| 12,153 |
fix: doc string of default params in chat_models, llm qianfan
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[] | null | 2 | 2023-10-23T10:44:33 | 2023-10-23T18:03:19 | 2023-10-23T18:03:18 |
CONTRIBUTOR
| null |
- **Description:** a fix of the doc string in Qianfan
- **Issue:** no
- **Dependencies:** no
- **Tag maintainer:** @baskaryan
- **Twitter handle:** no
|
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PR_kwDOIPDwls5dg6J_
| 12,152 |
Dev qf
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[] | null | 1 | 2023-10-23T10:35:51 | 2023-10-23T10:36:36 | 2023-10-23T10:36:36 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50obxQ
| 12,151 |
CharacterTextSplitter return incorrect chunks
|
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[] | null | 1 | 2023-10-23T09:37:40 | 2023-10-23T09:44:36 | null |
NONE
| null |
### System Info
`langchain==0.0.320`
Example file: https://drive.google.com/file/d/1zDj3VXohUO7x4udu9RY9KxWbzBUIrsvb/view?usp=sharing
```
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
# Specify the PDF file to be processed
pdf = "Deep Learning.pdf"
# Initialize a PdfReader object to read the PDF file
pdf_reader = PdfReader(pdf)
# Initialize an empty string to store the extracted text from the PDF
text = ""
for i, page in enumerate(pdf_reader.pages):
text += f" ### Page {i}:\n\n" + page.extract_text()
assert text.count(" ### ") == 100
text_splitter = CharacterTextSplitter(
separator=" ### ",
)
chunks = text_splitter.split_text(text)
len(chunks) ## = 76
```
Length chunks = 76 while there are 100 counts of the separator ` ### `. All the splits by ` ### ` look decent, so not sure why they are being merged strangely. Please help advice! Thanks!
Thank you so much!
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
# Specify the PDF file to be processed
pdf = "Deep Learning.pdf"
# Initialize a PdfReader object to read the PDF file
pdf_reader = PdfReader(pdf)
# Initialize an empty string to store the extracted text from the PDF
text = ""
for i, page in enumerate(pdf_reader.pages):
text += f" ### Page {i}:\n\n" + page.extract_text()
assert text.count(" ### ") == 100
text_splitter = CharacterTextSplitter(
separator=" ### ",
)
chunks = text_splitter.split_text(text)
len(chunks) ## = 76
```
### Expected behavior
`len(chunks) == 100`
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I_kwDOIPDwls50oZmM
| 12,150 |
LlamaCpp truncates the response from LLM, and CTransformers gives a token overrun error
|
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[] | null | 1 | 2023-10-23T09:32:40 | 2023-11-07T10:43:57 | null |
NONE
| null |
### System Info
Ubuntu: 22.04.3 LTS
python: 3.10
pip: 22.0.2
langchain: 0.0.319
vector stores: faiss
llm model: llama-2-13b-chat.Q4_K_M.gguf and mistral-7b-openorca.Q4_K_M.gguf
embeddings model: thenlper/gte-large
### Who can help?
anyone
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Prompt is created via PromptTemplate.
In Prompt:
```
You are a financial consultant. Try to answer the question based on the information below.
If you cannot answer the question based on the information, say that you cannot find the answer or are unable to find the answer.
Therefore, try to understand the context of the question and answer only on the basis of the information given. Do not write answers that are irrelevant.
If you do not have enough information to end a sentence with a period, then do not write that sentence.
Context: {context}
Question: {question}
Helpful answer:
```
2. Load vectoron storage using HuggingFaceEmbeddings and FAISS:
```
embeddings = HuggingFaceEmbeddings(model_name="thenlper/gte-large", model_kwargs={'device': 'cpu'})
db = FAISS.load_local(persist_directory, embeddings)
retriever = db.as_retriever(search_kwargs={"k": 2})
```
3. Load the LLM via LlamaCpp or CTransformers
LlamaCpp code:
```
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(
model_path=model_path,
n_gpu_layers=-1,
use_mlock=True,
n_batch=512,
n_ctx=2048,
temperature=0.8,
f16_kv=True,
callback_manager=callback_manager,
verbose=True,
top_k=1
)
```
CTransformers Code:
```
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = CTransformers(
model=model_path,
model_type="llama",
config={'max_new_tokens': 2048, 'temperature': 0.8},
callbacks=callback_manager
)
```
4. Create Retriever using RetrievalQA:
```
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=return_source_documents,
chain_type_kwargs={"prompt": custom_prompt}
)
```
5. Query the created retriever: qa("your question")
### Expected behavior
I look forward to receiving a completed response from LLM.
However, when using LlamaCpp, the response reaches about 1000 characters and ends. The last sentence from LLM is abruptly cut off.
When CTransformers are used, the response can reach different lengths, after which an error occurs: "Number of tokens (513) exceeded maximum context length (512)" and there are many of these errors. After that I get a response where the beginning is correct and then the same words are repeated. The repetition of words starts from the moment the errors about the number of tokens begin.
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I_kwDOIPDwls50oPvi
| 12,149 |
LCEL not working with MultiPromptChain
|
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[] | null | 6 | 2023-10-23T09:10:57 | 2023-12-01T18:33:34 | null |
NONE
| null |
### System Info
python == 3.10.12
langchain == 0.0.312
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```open_llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY, model_name='gpt-3.5-turbo-16k', max_tokens=3000)
# test chain (LCEL)
human_message = HumanMessagePromptTemplate.from_template('test prompt: {input}')
system_message = SystemMessagePromptTemplate.from_template('system')
all_messages = ChatPromptTemplate.from_messages([system_message, human_message])
test_chain = all_messages | open_llm | StrOutputParser()
# LLMChain
class MyLLMChain(LLMChain):
output_key: str = "output" #: :meta private
prompt_infos = [
{
"name": "mastermind",
"description": "for general questions.",
"chain": MyLLMChain(llm=openai_chatbot, prompt=PromptTemplate(template='''{input}''',
input_variables=["input"]
))
},
{
"name": "test",
"description": "for testing",
"chain": test_chain
}]
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
# output_parser=OpenAIFunctionsAgentOutputParser()
# output_parser=RouterOutputParser(),
output_parser=OutputFixingParser.from_llm(parser=RouterOutputParser(), llm=openai_chatbot),
)
router_chain = LLMRouterChain.from_llm(openai_chatbot, router_prompt)
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
chain = p_info["chain"]
destination_chains[name] = chain
default_chain = ConversationChain(llm=openai_chatbot, output_key="output")
agent_yan_chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True
)
```
Error message:
```
ValidationError: 1 validation error for MultiPromptChain
destination_chains -> test
Can't instantiate abstract class Chain with abstract methods _call, input_keys, output_keys (type=type_error)
```
### Expected behavior
LCEL should work with MultiPromptChain
|
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| 1,956,539,711 |
I_kwDOIPDwls50nm0_
| 12,147 |
Langchain Document schema explodes with FastAPI
|
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[] | null | 2 | 2023-10-23T07:35:17 | 2023-10-23T13:17:32 | 2023-10-23T13:17:32 |
NONE
| null |
### System Info
Python 3.11, langchain 0.0.315 and mac
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a FastAPI endpoint/router
```python
from fastapi import APIRouter, Response
from pydantic import BaseModel
from typing import Optional
from langchain.schema import Document
router = APIRouter()
class QueryOut(BaseModel):
answer: str
sources: list[Document]
@router.post("/query")
async def get_answer():
sources = [Document(page_content="foo", metadata={"baa": "baaaa"})]
return QueryOut(answer="answer", sources=sources)
```
Then call it and boom
```bash
File "/Users/FRANCESCO.ZUPPICHINI/Documents/dbi_alphaai_knowledge_bot/.venv/lib/python3.11/site-packages/pydantic/main.py", line 164, in __init__
__pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
TypeError: BaseModel.validate() takes 2 positional arguments but 3 were given
```
Is this linked to the fact that langchain is still using pydantic v1? If so, I love you guys but put that 30M to good use 😄
### Expected behavior
Should not explode
|
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PR_kwDOIPDwls5dfhpr
| 12,146 |
10634: Added the capability to inject boto3 client in SagemakerEndpointEmbeddings
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[] | null | 2 | 2023-10-23T06:31:57 | 2023-10-23T19:08:26 | 2023-10-23T19:08:26 |
CONTRIBUTOR
| null |
**Description: Allow to inject boto3 client for Cross account access type of scenarios in using SagemakerEndpointEmbeddings and also updated the documentation for same in the sample notebook**
**Issue:SagemakerEndpointEmbeddings cross account capability #10634 #10184**
Dependencies: None
Tag maintainer:
Twitter handle:lethargicoder
|
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PR_kwDOIPDwls5dfU7Q
| 12,145 |
Fix Typo in CONTRIBUTING.md file
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[] | null | 1 | 2023-10-23T05:38:35 | 2023-10-23T18:53:04 | 2023-10-23T18:53:03 |
CONTRIBUTOR
| null |
Fix Type & add suitable pronoun in CONTRIBUTING.md file
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50mZP2
| 12,144 |
How to show images from PDFs, PPT, DOCs documents as part of answer?
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[] | null | 1 | 2023-10-23T02:16:49 | 2023-10-23T02:23:22 | null |
NONE
| null |
### Issue you'd like to raise.
Hello, I have this use-case where there are different types of documents. I can parse documents using document loaders using langchain. But, there are images also in these documents. I want to store them as metadata and if answer generated from a context chunk it show the image also. Please help.
### Suggestion:
_No response_
|
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