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CONTRIBUTOR
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Meroka Managed Security: Enjoy all the benefits that AI and LLM based platforms have to offer, while maintaining a high standard for your privacy and compliance needs
Meroka provides software vendors that use LangChain, the ability for their users to have fine grain control over how their sensitive data (PII/PHI/etc) are being handled by the vendor. They will also gain visibility and insight, around how to improve their data hygiene
Twitter: @RayzerCA
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<!-- Thank you for contributing to LangChain!
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/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.
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<!-- Thank you for contributing to LangChain!
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PR_kwDOIPDwls5aYbXw
| 10,598 |
fix replicate output type
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[] | null | 1 | 2023-09-14T20:59:37 | 2023-09-14T21:02:03 | 2023-09-14T21:02:02 |
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PR_kwDOIPDwls5aYU2d
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Refactor Fireworks and add ChatFireworks (#3)
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[] | null | 1 | 2023-09-14T20:34:08 | 2023-09-27T03:11:55 | 2023-09-27T03:11:55 |
CONTRIBUTOR
| null |
Description
* Refactor Fireworks within Langchain LLMs.
* Remove FireworksChat within Langchain LLMs.
* Add ChatFireworks (which uses chat completion api) to Langchain chat models.
* Users have to install `fireworks-ai` and register an api key to use the api.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin @baskaryan
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PR_kwDOIPDwls5aYJYh
| 10,596 |
vertex default model_name changed to the stable version
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[] | null | 2 | 2023-09-14T19:57:27 | 2023-11-20T15:32:47 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** the default model_name for VertexAI llm is "text-bison", this model points to text-bison@latest, which is NOT the stable version of text-bison.,
- **Issue:** llm results when no model_name are not stable, as it is pointing to the latest/experimental model and not to the stable version ,
- **Dependencies:** no,
- **Tag maintainer:**
- **Twitter handle:**
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/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5aYErW
| 10,595 |
Fixed some grammatical and spelling errors
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[] | null | 2 | 2023-09-14T19:40:56 | 2023-09-15T00:43:37 | 2023-09-15T00:43:37 |
CONTRIBUTOR
| null |
Fixed some grammatical and spelling errors
@baskaryan, @eyurtsev.
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PR_kwDOIPDwls5aYBug
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RunnableBranch
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[] | null | 2 | 2023-09-14T19:30:19 | 2023-09-18T15:31:08 | 2023-09-18T15:31:07 |
COLLABORATOR
| null |
Runnable Branch implementation, no streaming logic yet
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PR_kwDOIPDwls5aX8C2
| 10,593 |
Fix intermediate steps example in docs : replaced json.dumps with Langchain's dumps()
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[] | null | 1 | 2023-09-14T19:10:29 | 2023-09-27T18:00:30 | 2023-09-27T18:00:30 |
CONTRIBUTOR
| null |
The intermediate steps example in docs has an example on how to retrieve and display the intermediate steps.
But the intermediate steps object is of type AgentAction which cannot be passed to json.dumps (it raises an error).
I replaced it with Langchain's dumps function (from langchain.load.dump import dumps) which is the preferred way to do so.
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[] | null | 2 | 2023-09-14T18:57:33 | 2023-09-17T17:31:20 | 2023-09-17T17:31:20 |
CONTRIBUTOR
| null |
This PR enhances the utility of the `ArcGISLoader` by introducing a document transformer and chains for converting query results into natural language summaries. The following features have been added:
* `langchain.chains.arcgis.row.base.ArcGISRowSummaryChain`
* `langchain.document_transformers.ArcGISRowSummaryTransformer`
* `langchain.chains.arcgis.layer.base.ArcGISLayerSummaryInnerChain`
* `langchain.chains.arcgis.layer.base.ArcGISLayerStuffSummaryChain`
* Notebooks demonstrating the functionalities of these new classes
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PR_kwDOIPDwls5aXc7J
| 10,591 |
Added template_format to from_template_file
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[] | null | 6 | 2023-09-14T17:24:09 | 2023-12-07T22:30:01 | 2023-12-07T22:30:01 |
CONTRIBUTOR
| null |
Issue: #10519
There is no clear reason for `PromptTemplate.from_template` and `PromptTemplate.from_file` to be implemented so differently. I added the ability to `PromptTemplate.from_file` to infer `input_variables` from the template when they are not given by using the new `template_format` parameter. Also made sure to maintain backward compatibility. Added a few tests for the new functionality.
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I_kwDOIPDwls5xEeh5
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Llama - n_ctx defaults to 512 even if overide passed during invocation
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[] | null | 2 | 2023-09-14T17:17:33 | 2023-12-21T16:05:59 | 2023-12-21T16:05:58 |
NONE
| null |
### System Info
Langain 0.288
Windows 11
Python 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
called using the below code, where model_n_ctx is set to 1024
llm = LlamaCpp(model_path=model_path, max_tokens=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=model_verbose, echo=True)
when executing inference getting error inputs token exceed 512
### Expected behavior
called using the below code, where model_n_ctx is set to 1024
llm = LlamaCpp(model_path=model_path, max_tokens=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=model_verbose, echo=True)
when executing inference getting error inputs token exceed 512
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I_kwDOIPDwls5xEXt1
| 10,589 |
Add Google Cloud Document AI integration
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[] | null | 2 | 2023-09-14T16:57:14 | 2023-10-09T15:05:54 | 2023-10-09T15:05:54 |
CONTRIBUTOR
| null |
### Feature request
Add integration for [Document AI](https://cloud.google.com/document-ai/docs/overview) from Google Cloud for intelligent document processing.
### Motivation
This product offers Optical Character Recognition, specialized processors from specific document types, and built in Generative AI processing for Document Summarization and entity extraction.
### Your contribution
I can implement this myself, I mostly want to understand where and how this could fit into the library.
Should it be a document transformer? An LLM? An output parser? A Retriever? Document AI does all of these in some capacity.
Document AI is designed as a platform that non-ML engineers can use to extract information from documents, and I could see several features being useful to Langchain (Like Document OCR to extract text and fields before sending it to an LLM) or using the Document AI Processors with Generative AI directly for the summarization/q&a output.
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| 1,896,963,859 |
PR_kwDOIPDwls5aXTlq
| 10,588 |
Refactor: Generated speech in-memory
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[] | null | 2 | 2023-09-14T16:52:30 | 2023-11-30T01:10:52 | 2023-11-30T01:10:51 |
CONTRIBUTOR
| null |
### Description:
Keeps speech in memory instead of saving it into the temporary file.
- Temporary files will probably stay there forever, whereas there is no evident reason to keep them.
- Demonstration notebooks have been updated according to the new implementation.
### Twitter handle:
@deepsense_ai, @matt_wosinski
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PR_kwDOIPDwls5aW9zp
| 10,587 |
add routing notebook
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[] | null | 1 | 2023-09-14T15:52:10 | 2023-09-15T20:48:37 | 2023-09-15T20:48:36 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5aW7BJ
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bump 289
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[] | null | 1 | 2023-09-14T15:45:15 | 2023-09-14T15:53:52 | 2023-09-14T15:53:50 |
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/hwchase17/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_kwDOIPDwls5aWb3F
| 10,585 |
Fix typo in URL document loader example
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[] | null | 2 | 2023-09-14T14:33:29 | 2023-09-21T18:35:34 | 2023-09-21T18:35:27 |
CONTRIBUTOR
| null |
- **Description:** Fix typo in URL document loader example
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** not urgent
|
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PR_kwDOIPDwls5aWYFc
| 10,584 |
Refactor/clickup
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[] | null | 1 | 2023-09-14T14:24:19 | 2023-09-14T14:24:47 | 2023-09-14T14:24:47 |
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I_kwDOIPDwls5xDSXo
| 10,583 |
PythonAstREPLTool won't execute code with functions/lambdas
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[] | null | 3 | 2023-09-14T14:22:45 | 2023-12-25T16:08:00 | 2023-12-25T16:07:59 |
NONE
| null |
### System Info
langchain==0.0.288
python==3.10
This bug is reproducible on older langchain version (0.0.240) and different os (Windows, Debian).
### 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
```python
from langchain.tools.python.tool import PythonAstREPLTool
query = """
import pandas as pd
import random
import string
def generate_random_text():
return ''.join(random.choices(string.ascii_letters + string.digits, k=128))
df = pd.DataFrame({
'Column1': [generate_random_text() for _ in range(1000)],
'Column2': [generate_random_text() for _ in range(1000)],
'Column3': [generate_random_text() for _ in range(1000)]
})
df
"""
ast_repl = PythonAstREPLTool()
ast_repl(query)
>>> "NameError: name 'generate_random_text' is not defined"
```
### Expected behavior
I expect it to return a df.
|
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| 1,896,670,788 |
I_kwDOIPDwls5xDOZE
| 10,582 |
Issue: an agent that can get the current time
|
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[] | null | 5 | 2023-09-14T14:14:19 | 2023-09-27T17:30:33 | 2023-09-27T17:30:33 |
NONE
| null |
### Issue you'd like to raise.
I am building an agent and need a tool that can get give the agent access to the current datetime.
### Suggestion:
_No response_
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| 1,896,670,748 |
I_kwDOIPDwls5xDOYc
| 10,581 |
Is score return from similarity_search_with_score in Weaviate is really cosine distance?
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[] | null | 4 | 2023-09-14T14:14:17 | 2023-12-25T16:08:05 | 2023-12-25T16:08:04 |
NONE
| null |
### System Info
langchain = 0.0.288
python = 3.8.0
### Who can help?
@hwchase17
@eyurtsev
### 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
I'm sorry, I'm not very familiar with this field,
but I don't quite understand how the description of this function differs from its actual operation.
```
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
"""
if self._embedding is None:
raise ValueError(
"_embedding cannot be None for similarity_search_with_score"
)
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
embedded_query = self._embedding.embed_query(query)
if not self._by_text:
vector = {"vector": embedded_query}
result = (
query_obj.with_near_vector(vector)
.with_limit(k)
.with_additional("vector")
.do()
)
else:
result = (
query_obj.with_near_text(content)
.with_limit(k)
.with_additional("vector")
.do()
)
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs_and_scores = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
score = np.dot(res["_additional"]["vector"], embedded_query)
docs_and_scores.append((Document(page_content=text, metadata=res), score))
return docs_and_scores
```
### Expected behavior
`score = np.dot(res["_additional"]["vector"], embedded_query)`
As you can see, the description mentions that the `score` corresponds to `cosine distance`, but the `code` seems to only calculate the `dot product`. Am I missing something?
And Here is some definition from Weaviate:
[https://weaviate.io/blog/distance-metrics-in-vector-search#cosine-similarity](url)
Thanks for your kind help!
|
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GoogleCloudEnterpriseSearchRetriever fails to where location is "us"
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[] | null | 3 | 2023-09-14T13:11:37 | 2023-11-01T05:59:16 | 2023-11-01T05:59:16 |
NONE
| null |
### System Info
'OS_NAME': 'DEBIAN_10'
Langchain version : '0.0.288'
python : 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This issue only occurs when region != "global". The retriever works well when region is set to "global"
Steps to reproduce :
1. Create a Enterprise search App with region = 'us'
3. Import langchain version 0.0.288
```
import langchain
from langchain.retrievers import GoogleCloudEnterpriseSearchRetriever
PROJECT_ID = "<PROJECT_ID>" # Set to your Project ID
SEARCH_ENGINE_ID = "<SEARCH_ENGINE_ID>"#"# Set to your data store ID
retriever = GoogleCloudEnterpriseSearchRetriever(
project_id=PROJECT_ID,
search_engine_id=SEARCH_ENGINE_ID,
max_documents=3 ,
location_id = "us"
)
retriever.get_relevant_documents("What is capital of India?")
```
4. Code errors out with below
```
---------------------------------------------------------------------------
_InactiveRpcError Traceback (most recent call last)
File /opt/conda/envs/python310/lib/python3.10/site-packages/google/api_core/grpc_helpers.py:72, in _wrap_unary_errors.<locals>.error_remapped_callable(*args, **kwargs)
71 try:
---> 72 return callable_(*args, **kwargs)
73 except grpc.RpcError as exc:
File /opt/conda/envs/python310/lib/python3.10/site-packages/grpc/_channel.py:1161, in _UnaryUnaryMultiCallable.__call__(self, request, timeout, metadata, credentials, wait_for_ready, compression)
1155 (
1156 state,
1157 call,
1158 ) = self._blocking(
1159 request, timeout, metadata, credentials, wait_for_ready, compression
1160 )
-> 1161 return _end_unary_response_blocking(state, call, False, None)
File /opt/conda/envs/python310/lib/python3.10/site-packages/grpc/_channel.py:1004, in _end_unary_response_blocking(state, call, with_call, deadline)
1003 else:
-> 1004 raise _InactiveRpcError(state)
_InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.NOT_FOUND
details = "DataStore projects/PROJECT_ID/locations/us/collections/default_collection/dataStores/SEARCH_ENGINE_ID not found."
debug_error_string = "UNKNOWN:Error received from peer ipv4:172.253.120.95:443 {created_time:"2023-09-14T12:55:00.327037809+00:00", grpc_status:5, grpc_message:"DataStore projects/PROJECT_ID/locations/us/collections/default_collection/dataStores/SEARCH_ENGINE_ID not found."}"
>
The above exception was the direct cause of the following exception:
NotFound Traceback (most recent call last)
Cell In[365], line 1
----> 1 retriever.get_relevant_documents("What is capital of India?")
File ~/.local/lib/python3.10/site-packages/langchain/schema/retriever.py:208, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, **kwargs)
206 except Exception as e:
207 run_manager.on_retriever_error(e)
--> 208 raise e
209 else:
210 run_manager.on_retriever_end(
211 result,
212 **kwargs,
213 )
File ~/.local/lib/python3.10/site-packages/langchain/schema/retriever.py:201, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, **kwargs)
199 _kwargs = kwargs if self._expects_other_args else {}
200 if self._new_arg_supported:
--> 201 result = self._get_relevant_documents(
202 query, run_manager=run_manager, **_kwargs
203 )
204 else:
205 result = self._get_relevant_documents(query, **_kwargs)
File ~/.local/lib/python3.10/site-packages/langchain/retrievers/google_cloud_enterprise_search.py:254, in GoogleCloudEnterpriseSearchRetriever._get_relevant_documents(self, query, run_manager)
251 search_request = self._create_search_request(query)
253 try:
--> 254 response = self._client.search(search_request)
255 except InvalidArgument as e:
256 raise type(e)(
257 e.message + " This might be due to engine_data_type not set correctly."
258 )
File /opt/conda/envs/python310/lib/python3.10/site-packages/google/cloud/discoveryengine_v1beta/services/search_service/client.py:577, in SearchServiceClient.search(self, request, retry, timeout, metadata)
570 metadata = tuple(metadata) + (
571 gapic_v1.routing_header.to_grpc_metadata(
572 (("serving_config", request.serving_config),)
573 ),
574 )
576 # Send the request.
--> 577 response = rpc(
578 request,
579 retry=retry,
580 timeout=timeout,
581 metadata=metadata,
582 )
584 # This method is paged; wrap the response in a pager, which provides
585 # an `__iter__` convenience method.
586 response = pagers.SearchPager(
587 method=rpc,
588 request=request,
589 response=response,
590 metadata=metadata,
591 )
File /opt/conda/envs/python310/lib/python3.10/site-packages/google/api_core/gapic_v1/method.py:113, in _GapicCallable.__call__(self, timeout, retry, *args, **kwargs)
110 metadata.extend(self._metadata)
111 kwargs["metadata"] = metadata
--> 113 return wrapped_func(*args, **kwargs)
File /opt/conda/envs/python310/lib/python3.10/site-packages/google/api_core/grpc_helpers.py:74, in _wrap_unary_errors.<locals>.error_remapped_callable(*args, **kwargs)
72 return callable_(*args, **kwargs)
73 except grpc.RpcError as exc:
---> 74 raise exceptions.from_grpc_error(exc) from exc
NotFound: 404 DataStore projects/PROJECT_ID/locations/us/collections/default_collection/dataStores/SEARCH_ENGINE_ID not found.
```
### Expected behavior
Code should return three relevant documents from Enterprise Search
|
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I_kwDOIPDwls5xCtgb
| 10,579 |
Document' object has no attribute '_lc_kwargs
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[] | null | 5 | 2023-09-14T13:04:51 | 2023-12-26T16:02:15 | null |
NONE
| null |
### System Info
langchain version 0.0.285
langsmith version 0.0.28
Python version 3.11.2
### Who can help?
@hwchase17
### Information
- [X ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I followed tutorial in RAG cookbook with "With Memory and returning source documents"
Steps for changing behaviour
1. use FAISS.from documents and save to files
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(documents, embeddings)
vectorstore.save_local("data/faiss_index")
2. load from file
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local("data/faiss_index", embeddings)
retriever = vectorstore.as_retriever()
3. error raised "Document' object has no attribute '_lc_kwargs" in step 5
final_inputs = {
"context": lambda x: _combine_documents(x["docs"]),
"question": itemgetter("question")
}
here is the screen shot when using langsmith
<img width="543" alt="RAG" src="https://github.com/langchain-ai/langchain/assets/105797032/038b1b98-4dde-47a1-a79b-4061014c05a2">
### Expected behavior
Expected behaviour is LLM give result without error
|
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PR_kwDOIPDwls5aVu5a
| 10,578 |
Fix typing of RecursiveUrlLoader's exclude_dirs
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[] | null | 2 | 2023-09-14T12:44:17 | 2023-09-27T18:16:53 | 2023-09-27T18:16:53 |
NONE
| null |
- **Description:** This fixes an issue in `RecursiveUrlLoader` where a typing appears to be incorrect. The `exclude_dirs` parameter is iterated over, yet is defined as `Optional[str]` - this PR changes that to `Optional[Iterable[str]]`.
- **Issue:** None - this is a find-and-fix.
- **Dependencies:** None
- **Tag maintainer:** Maybe @rlancemartin?
- **Twitter handle:** @benc_uk
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Filtering graph schema for Cypher generation
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[] | null | 8 | 2023-09-14T11:11:38 | 2023-09-27T07:28:59 | 2023-09-25T21:14:15 |
CONTRIBUTOR
| null |
Sometimes you don't want the LLM to be aware of the whole graph schema, and want it to ignore parts of the graph when it is constructing Cypher statements.
@katarinasupe @brettdbrewer : Can you check if the MemGraph integration works?
@jexp
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I_kwDOIPDwls5xBdUG
| 10,575 |
The RetrievalQAWithSourcesChain doesn't return SOURCES.
|
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[] | null | 4 | 2023-09-14T10:01:45 | 2023-11-11T16:58:58 | null |
NONE
| null |
### System Info
I have a question&answer over docs chatbot application, that uses the RetrievalQAWithSourcesChain and ChatPromptTemplate. In langchain version 0.0.238 it used to return sources but this seems to be broken in the releases since then.
Python version: Python 3.11.4
LangChain version: 0.0.287
Example response with missing sources:
> Entering new RetrievalQAWithSourcesChain chain...
> Finished chain.
{'question': 'what is sql injection', 'answer': 'SQL injection is a web security vulnerability that allows an attacker to interfere with the queries that an application makes to its database. By manipulating the input data, an attacker can execute their own malicious SQL queries, which can lead to unauthorized access, data theft, or modification of the database. This vulnerability can be exploited to view sensitive data, modify or delete data, or even take control of the database server. SQL injection is a serious issue that can result in high-profile data breaches and compromises of user accounts. It is important for developers to implement proper input validation and parameterized queries to prevent SQL injection attacks.\n\n', 'sources': ''}
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import pickle
import gradio as gr
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import PromptLayerChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
pkl_file_path = "faiss_store.pkl"
event = {"question": "what is sql injection"}
system_template = """
Use the provided articles delimited by triple quotes to answer questions. If the answer cannot be found in the articles, write "I could not find an answer."
If you don't know the answer, just say "Hmm..., I'm not sure.", don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.
Example of your response should be:
The answer is foo
SOURCES:
1. abc
2. xyz
Begin!
----------------
{summaries}
"""
def get_chain(store: FAISS, prompt_template: ChatPromptTemplate):
return RetrievalQAWithSourcesChain.from_chain_type(
PromptLayerChatOpenAI(
pl_tags=["burpbot"],
temperature=0,
),
chain_type="stuff",
retriever=store.as_retriever(),
chain_type_kwargs={"prompt": prompt_template},
reduce_k_below_max_tokens=True,
verbose=True,
)
def create_prompt_template() -> ChatPromptTemplate:
return ChatPromptTemplate.from_messages(
[
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
def load_remote_faiss_store() -> FAISS:
with open(pkl_file_path, "rb") as f:
return pickle.load(f)
def main() -> dict:
prompt_template = create_prompt_template()
store: FAISS = load_remote_faiss_store()
chain = get_chain(store, prompt_template)
result = chain(event)
print(result)
```
### Expected behavior
expected output:
>{'question': 'what is sql injection', 'answer': 'SQL injection is a web security vulnerability that allows an attacker to interfere with the queries that an application makes to its database. By manipulating the input data, an attacker can execute their own malicious SQL queries, which can lead to unauthorized access, data theft, or modification of the database. This vulnerability can be exploited to view sensitive data, modify or delete data, or even take control of the database server. SQL injection is a serious issue that can result in high-profile data breaches and compromises of user accounts. It is important for developers to implement proper input validation and parameterized queries to prevent SQL injection attacks.\n\n', 'sources': 'https://example.net/web-security/sql-injection'}
|
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| 1,896,046,235 |
PR_kwDOIPDwls5aULIH
| 10,573 |
fix 'Thought:' prefix for structured chat agent
|
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| false | null |
[] | null | 1 | 2023-09-14T08:38:39 | 2023-11-12T07:11:19 | 2023-11-12T07:00:01 |
CONTRIBUTOR
| null |
The current implementation of the `StructuredChatAgent` includes a system prompt that is less than ideal for the following reasons:
- The first "Thought:" prefix is added to the system prompt before the human message.
- Subsequent "Thought:" prefixes are added everytime the `agent_scratchpad` is created.
As a result, the input format presented to the LLMs in the first round appears as follows:
```
You are an agent ...
...
Begin!
Thought:
Human: {input}
```
In the subsequent rounds, the input format is as follows:
```
You are an agent ...
...
Begin!
Thought:
Human: {input}
This was your previous work (but I haven\'t seen any of it! I only see what you return as final answer):
Action: ...
Observation: ...
Thought:
```
The issue here is that the first "Thought:" prefix precedes the human input, which can be misleading to the LLM, causing it to sometimes skip generating any thoughts in the first round.
This misplaced "Thought:" also disrupts the intended format instruction, which is designed to guide the LLM to follow the routing sequence of Question -> Thought -> Action -> Observation -> ... -> Final Answer.
This PR fixes this issue by removing the "Thought:" prefix in the system prompt, and add it back in the `agent_scratchpad`
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| 1,895,728,355 |
I_kwDOIPDwls5w_oTj
| 10,572 |
agent got "No I need to use a tool? No" response from llmm,which CANNOT be parsed
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[] | null | 2 | 2023-09-14T05:33:02 | 2023-12-15T05:47:20 | 2023-12-15T05:47:12 |
NONE
| null |
### System Info
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
if f"{self.ai_prefix}:" in text:
return AgentFinish(
{"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text
)
regex = r"Action: (.*?)[\n]*Action Input: (.*)"
match = re.search(regex, text)
if not match:
raise OutputParserException(f"Could not parse LLM output: `{text}`")
action = match.group(1)
action_input = match.group(2)
return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. init an agent
2. ask the agent a simple question which it can solve without using any tools
### Expected behavior
DONT RAISE ERROR
|
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PR_kwDOIPDwls5aSvjB
| 10,571 |
Feature/csharp text splitter doc
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[] | null | 2 | 2023-09-14T04:27:24 | 2023-10-05T19:22:55 | 2023-10-05T19:22:55 |
CONTRIBUTOR
| null |
- **Description:** Just docs related to csharp code splitter
- **Issue:** It's related to a request made by @baskaryan in a comment on my previous PR #10350
- **Dependencies:** None
- **Twitter handle:** @ather19
|
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| 1,895,610,544 |
PR_kwDOIPDwls5aSqh7
| 10,570 |
Removed duplicate heading
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[] | null | 3 | 2023-09-14T03:55:36 | 2023-09-14T19:36:53 | 2023-09-14T19:35:37 |
CONTRIBUTOR
| null |
**I recently reviewed the content and identified that there heading appeared twice on the docs.**
@baskaryan , @hwchase17
|
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I_kwDOIPDwls5w_JIh
| 10,569 |
Link in the Readme is invalid.
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[] | null | 3 | 2023-09-14T03:40:51 | 2023-12-27T16:05:23 | 2023-12-27T16:05:23 |
NONE
| null |
### Issue with current documentation:
https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain
<img width="399" alt="WX20230914-113935@2x" src="https://github.com/langchain-ai/langchain/assets/34183928/1d61724a-152f-4ad2-8197-0dfc0fd44f98">
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5w-8a4
| 10,567 |
Can not use QianfanLLMEndpoint
|
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[] | null | 6 | 2023-09-14T02:32:37 | 2023-12-26T16:05:47 | 2023-12-26T16:05:46 |
NONE
| null |
### System Info
Unresolved reference 'QianfanLLMEndpoint'
Name: langchain
Version: 0.0.288
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
### Expected behavior
I hope to use Qianfan model but I can't import it, even though i have update my langchain, it can't work.
|
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I_kwDOIPDwls5w-6Y9
| 10,566 |
Issue: Agent keeps using the wrong type
|
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[] | null | 10 | 2023-09-14T02:22:18 | 2023-09-27T20:09:53 | 2023-09-27T20:09:52 |
NONE
| null |
### Issue you'd like to raise.
Attempting to make a google calendar agent, however it keeps making a field that should be a datetime object a string object.
the prompt:
```prefix = """Date format: datetime(2023, 5, 2, 10, 0, 0)
Based on this event description: "Joey birthday tomorrow at 7 pm",
output a json of the following parameters:
Today's datetime on UTC time datetime(2023, 5, 2, 10, 0, 0), it's Tuesday and timezone
of the user is -5, take into account the timezone of the user and today's date.
1. summary
2. start
3. end
4. location
5. description
6. user_timezone
event_summary:
{{
"summary": "Joey birthday",
"start": "datetime(2023, 5, 3, 19, 0, 0)",
"end": "datetime(2023, 5, 3, 20, 0, 0)",
"location": "",
"description": "",
"user_timezone": "America/New_York"
}}
Date format: datetime(YYYY, MM, DD, hh, mm, ss)
Based on this event description: "Create a meeting for 5 pm on Saturday with Joey",
output a json of the following parameters:
Today's datetime on UTC time datetime(2023, 5, 4, 10, 0, 0), it's Thursday and timezone
of the user is -5, take into account the timezone of the user and today's date.
1. summary
2. start
3. end
4. location
5. description
6. user_timezone
event_summary:
{{
"summary": "Meeting with Joey",
"start": "datetime(2023, 5, 6, 17, 0, 0)",
"end": "datetime(2023, 5, 6, 18, 0, 0)",
"location": "",
"description": "",
"user_timezone": "America/New_York"
}}
"""```
the tool
```
class CalnederEventTool(BaseTool):
"""A tool used to create events on google calendar."""
name = "custom_search"
description = "a tool used to create events on google calendar"
def _run(
self,
summary: str,
start: datetime,
end: Union[datetime, None],
recurrence: Optional[str] = None, # Changed from Optional[Recurrence] to Optional[str]
run_manager: Optional['CallbackManagerForToolRun'] = None,
) -> str:
GOOGLE_EMAIL = environ.get('GOOGLE_EMAIL')
CREDENTIALS_PATH = environ.get('CREDENTIALS_PATH')
calendar = GoogleCalendar(
GOOGLE_EMAIL,
credentials_path=CREDENTIALS_PATH
)
event = Event(summary=summary, start=start, end=end)
calendar.add_event(event)
async def _arun(
self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
```
```
> Entering new AgentExecutor chain...
Action:
```
{
"action": "custom_search",
"action_input": {
"summary": "Going to the bar",
"start": "2020-09-01T17:00:00",
"end": "2020-09-01T18:00:00",
"recurrence": ""
}
}
```
### Suggestion:
_No response_
|
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| 1,895,511,997 |
PR_kwDOIPDwls5aSWq0
| 10,565 |
implement get_num_tokens to use google's count_tokens function
|
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[] | null | 13 | 2023-09-14T01:53:22 | 2023-10-30T22:10:07 | 2023-10-30T22:10:06 |
CONTRIBUTOR
| null |
can get the correct token count instead of using gpt-2 model
**Description:**
Implement get_num_tokens within VertexLLM to use google's count_tokens function. (https://cloud.google.com/vertex-ai/docs/generative-ai/get-token-count). So we don't need to download gpt-2 model from huggingface, also when we do the mapreduce chain we can get correct token count.
**Tag maintainer:**
@lkuligin
**Twitter handle:**
My twitter: @abehsu1992626
|
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| 1,895,419,748 |
PR_kwDOIPDwls5aSGIe
| 10,564 |
Update LangSmith Walkthrough
|
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[] | null | 1 | 2023-09-14T00:05:26 | 2023-09-14T00:13:19 | 2023-09-14T00:13:18 |
COLLABORATOR
| null | null |
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| 1,895,410,296 |
PR_kwDOIPDwls5aSEI6
| 10,563 |
CassandraCache and CassandraSemanticCache can handle any "Generation"
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[] | null | 6 | 2023-09-13T23:52:46 | 2023-09-18T16:16:05 | 2023-09-14T15:33:06 |
CONTRIBUTOR
| null |
Hello,
this PR improves coverage for caching by the two Cassandra-related caches (i.e. exact-match and semantic alike) by switching to the more general `dumps`/`loads` serdes utilities.
This enables cache usage within e.g. `ChatOpenAI` contexts (which need to store lists of `ChatGeneration` instead of `Generation`s), which was not possible as long as the cache classes were relying on the legacy `_dump_generations_to_json` and `_load_generations_from_json`).
Additionally, a slightly different init signature is introduced for the cache objects:
- named parameters required for init, to pave the way for easier changes in the future connect-to-db flow (and tests adjusted accordingly)
- added a `skip_provisioning` optional passthrough parameter for use cases where the user knows the underlying DB table, etc already exist.
Thank you for a review!
|
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PR_kwDOIPDwls5aRyW2
| 10,562 |
Francisco/sql agent improvements
|
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[] | null | 2 | 2023-09-13T22:02:43 | 2023-11-20T19:51:50 | null |
COLLABORATOR
| null |
A few changes to the sql agent in the spirit of making it much more efficient without compromising accuracy:
- removed the instruction to use the query checker before running a query
- table names are not a tool, but are appended automatically to the prompt
- dynamic retrievers (including few shots) can be used automatically by the agent instead of being selected as a chosen tool
|
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| 1,895,297,545 |
I_kwDOIPDwls5w9_IJ
| 10,561 |
Issue: Dynamically select from multiple retrievers
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[] | null | 1 | 2023-09-13T21:34:13 | 2023-09-13T22:29:56 | 2023-09-13T22:29:56 |
NONE
| null |
### Issue you'd like to raise.
chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)
I'm receiving this error when I try to call the above:(I'm following this doc https://python.langchain.com/docs/use_cases/question_answering/how_to/multi_retrieval_qa_router)
```
ValidationError Traceback (most recent call last)
Cell In[7], line 1
----> 1 chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos)
File ~/anaconda3/lib/python3.11/site-packages/langchain/chains/router/multi_retrieval_qa.py:66, in MultiRetrievalQAChain.from_retrievers(cls, llm, retriever_infos, default_retriever, default_prompt, default_chain, **kwargs)
64 prompt = r_info.get("prompt")
65 retriever = r_info["retriever"]
---> 66 chain = RetrievalQA.from_llm(llm, prompt=prompt, retriever=retriever)
67 name = r_info["name"]
68 destination_chains[name] = chain
File ~/anaconda3/lib/python3.11/site-packages/langchain/chains/retrieval_qa/base.py:84, in BaseRetrievalQA.from_llm(cls, llm, prompt, callbacks, **kwargs)
74 document_prompt = PromptTemplate(
75 input_variables=["page_content"], template="Context:\n{page_content}"
76 )
77 combine_documents_chain = StuffDocumentsChain(
78 llm_chain=llm_chain,
79 document_variable_name="context",
80 document_prompt=document_prompt,
81 callbacks=callbacks,
82 )
---> 84 return cls(
85 combine_documents_chain=combine_documents_chain,
86 callbacks=callbacks,
87 **kwargs,
88 )
File ~/anaconda3/lib/python3.11/site-packages/langchain/load/serializable.py:75, in Serializable.__init__(self, **kwargs)
74 def __init__(self, **kwargs: Any) -> None:
---> 75 super().__init__(**kwargs)
76 self._lc_kwargs = kwargs
File ~/anaconda3/lib/python3.11/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for RetrievalQA
retriever
Can't instantiate abstract class BaseRetriever with abstract method _get_relevant_documents (type=type_error)
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5aRiHW
| 10,560 |
explicitly check openllm return type
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[] | null | 1 | 2023-09-13T20:54:26 | 2023-09-13T21:13:17 | 2023-09-13T21:13:15 |
COLLABORATOR
| null |
cc @aarnphm
|
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PR_kwDOIPDwls5aRZFW
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ArcGISLoader: add keyword arguments, error handling, and better tests
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[] | null | 2 | 2023-09-13T20:23:01 | 2023-09-13T21:41:40 | 2023-09-13T21:12:42 |
CONTRIBUTOR
| null |
* More clarity around how geometry is handled. Not returned by default; when returned, stored in metadata. This is because it's usually a waste of tokens, but it should be accessible if needed.
* User can supply layer description to avoid errors when layer properties are inaccessible due to passthrough access.
* Enhanced testing
* Updated notebook
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I_kwDOIPDwls5w9K8M
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RetrievalQA custom prompt to accept prompts other than context and question e.g. language for use in Sequential Chain
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[] | null | 2 | 2023-09-13T18:59:17 | 2023-10-19T21:42:54 | null |
NONE
| null |
### Feature request
Update RetrievalQA chain so that custom prompts can accept parameters other than input_documents and question.
Current functionality is limited by call to StuffDocumentsChain:
answer = self.combine_documents_chain.run(
input_documents=docs, question=question, callbacks=_run_manager.get_child()
)
Any additional parameters required aren't passed, including chat history.
2 line code update required:
inputs['input_documents'] = docs
answer = self.combine_documents_chain.run(
inputs, callbacks=_run_manager.get_child()
)
### Motivation
Improve flexibility of the RetrievalQA chain enabling system message to be customised, chat history to be passed so GPT can reference back to previous answers. Customise language in answer in QA chain etc.
### Your contribution
Will submit PR with above change in-line with contributing guidelines.
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PR_kwDOIPDwls5aQz1x
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add mmr support to redis retriever
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[] | null | 1 | 2023-09-13T18:28:25 | 2023-09-14T15:55:16 | 2023-09-14T15:43:50 |
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/hwchase17/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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| 1,894,977,351 |
I_kwDOIPDwls5w8w9H
| 10,555 |
LangChain's Chroma similarity_search return results from other db
|
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open
| false | null |
[] | null | 6 | 2023-09-13T17:42:19 | 2023-12-06T13:20:08 | null |
NONE
| null |
### System Info
LangChain 0.0.281
Platform: Centos
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi,
I have two vector stores:
splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=50)
splits_1 = splitter.split_documents(docs_1)
splits_2 = splitter.split_documents(docs_2)
store1 = Chroma.from_documents(documents=splits_1, embedding=HuggingFaceEmbeddings())
store2 = Chroma.from_documents(documents=splits_2, embedding=HuggingFaceEmbeddings())
Then I use store2 to do similarity search, it returns results from splits_1, that's very wired. Can someone please help?
Thanks
Tom
### Expected behavior
Different vector store should use its own pool to do the similarity search
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| 1,894,953,189 |
I_kwDOIPDwls5w8rDl
| 10,554 |
Issue: Parsing issue
|
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[] | null | 2 | 2023-09-13T17:23:30 | 2023-12-20T16:05:06 | 2023-12-20T16:05:05 |
NONE
| null |
### Issue you'd like to raise.
import pandas as pd
import pandas_gpt
df = pd.read_csv('aisc-shapes-database-v16.0.csv', index_col=0, header=0, usecols = ["A:F"], names = [
"Type", "EDI_Std_Nomenclature", "AISC_Manual_Label", "T_F", "W", "Area"])
df.ask('what is the area of W12X12?')
Need help getting this file to read with pandas.read_csv
[aisc-shapes-database-v16.0.csv](https://github.com/langchain-ai/langchain/files/12600284/aisc-shapes-database-v16.0.csv)
### Suggestion:
This is my error message
File "c:\Users\camer\import pandas as pd.py", line 4, in <module>
df = pd.read_csv('aisc-shapes-database-v16.0.csv', index_col=0, header=0, usecols = ["A:F"], names = [
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\camer\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\parsers\readers.py", line 948, in read_csv
return _read(filepath_or_buffer, kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\camer\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\parsers\readers.py", line 611, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\camer\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\parsers\readers.py", line 1448, in __init__
self._engine = self._make_engine(f, self.engine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\camer\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\parsers\readers.py", line 1723, in _make_engine
return mapping[engine](f, **self.options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\camer\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\parsers\c_parser_wrapper.py", line 93, in __init__
self._reader = parsers.TextReader(src, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "parsers.pyx", line 579, in pandas._libs.parsers.TextReader.__cinit__
File "parsers.pyx", line 668, in pandas._libs.parsers.TextReader._get_header
File "parsers.pyx", line 879, in pandas._libs.parsers.TextReader._tokenize_rows
File "parsers.pyx", line 890, in pandas._libs.parsers.TextReader._check_tokenize_status
File "parsers.pyx", line 2050, in pandas._libs.parsers.raise_parser_error
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x96 in position 703: invalid start byte
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| 1,894,880,317 |
I_kwDOIPDwls5w8ZQ9
| 10,553 |
Issue: Can I load THUDM/chatglm-6b?
|
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[] | null | 4 | 2023-09-13T16:32:12 | 2023-11-11T02:27:34 | null |
NONE
| null |
I've been searching for a large-context LLM with a relatively low parameter count suitable for local execution on multiple T4 GPUs or a single A100. My primary goal is to summarize extensive financial reports. While I came across FinGPT v1, it seems it isn't hosted on HuggingFace.
However, I did find chatglm-6b, which serves as the foundation for FinGPT v1. This model is accessible on HuggingFace, but I'm facing issues loading it.
Here's a snippet that successfully loads and uses the model outside Langchain:
```
from transformers import AutoModel, AutoTokenizer
model_name = "THUDM/chatglm2-6b"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
# 按需修改，目前只支持 4/8 bit 量化
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()
import torch
has_cuda = torch.cuda.is_available()
# has_cuda = False # force cpu
if has_cuda:
#model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4",trust_remote_code=True).half().cuda() # 3.92
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()
else:
model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4",trust_remote_code=True).half() # float()
response, history = model.chat(tokenizer, f"Summarize this in a few words: {a}", history=[])
```
But, when I try the following, to use in Langchain:
```
from langchain.llms import HuggingFacePipeline
llm = HuggingFacePipeline.from_model_id(
model_id="THUDM/chatglm-6b",
task="text-generation",
model_kwargs={"temperature": 0, "max_length": 64},
)
```
I encounter this error:
```
ValueError: Tokenizer class ChatGLMTokenizer does not exist or is not currently imported.
```
From what I gather, the ChatGLM model cannot be passed directly to HuggingFace's pipeline. While the Langchain documentation does mention using ChatGLM as a local model, it seems to primarily focus on using it via an API endpoint:
```
endpoint_url = "http://127.0.0.1:8000"
# direct access endpoint in a proxied environment
# os.environ['NO_PROXY'] = '127.0.0.1'
llm = ChatGLM(
endpoint_url=endpoint_url,
max_token=80000,
history=[["我将从美国到中国来旅游，出行前希望了解中国的城市", "欢迎问我任何问题。"]],
top_p=0.9,
model_kwargs={"sample_model_args": False},
)
```
Would anyone have insights on how to correctly load ChatGLM for tasks within Langchain?
### Suggestion:
_No response_
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| 1,894,837,821 |
I_kwDOIPDwls5w8O49
| 10,552 |
DOC: inexistent documentation for index
|
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[] | null | 2 | 2023-09-13T16:04:16 | 2023-12-20T16:05:11 | 2023-12-20T16:05:10 |
NONE
| null |
### Issue with current documentation:
The `index` [API Reference document](https://api.python.langchain.com/en/latest/indexes/langchain.indexes._api.index.html) that is linked in the [Indexing documentation](https://python.langchain.com/docs/modules/data_connection/indexing#quickstart) returns a 404 error.
### Idea or request for content:
Please include detailed documentation for `index` to use correctly `SQLRecordManager`.
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I_kwDOIPDwls5w8Mv-
| 10,551 |
SQLRecordManager index adds documents outside existing Redis vectorstore index
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[] | null | 3 | 2023-09-13T15:58:42 | 2023-12-25T16:01:50 | null |
NONE
| null |
### System Info
langchain_version: "0.0.287"
library: "langchain"
library_version: "0.0.287"
platform: "Linux-6.1.0-12-amd64-x86_64-with-glibc2.36"
py_implementation: "CPython"
runtime: "python"
runtime_version: "3.11.2"
### 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
After following the [indexing instructions](https://python.langchain.com/docs/modules/data_connection/indexing), `index` stores the documents in a Redis vectorstore, but it does so outside the vectorstore's index.
```
import os, time, json, openai
from langchain.vectorstores.redis import Redis
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.indexes import SQLRecordManager, index
from langchain.schema import Document
from datetime import datetime
from pathlib import Path
openai.api_key = os.environ['OPENAI_API_KEY']
VECTORS_INDEX_NAME = 'Vectors'
COLLECTION_NAME = 'DocsDB'
NAMESPACE = f"Redis/{COLLECTION_NAME}"
REDIS_URL = "redis://10.0.1.21:6379"
embeddings = OpenAIEmbeddings()
record_manager = SQLRecordManager(NAMESPACE, db_url="sqlite:///cache_Redis.sql")
record_manager.create_schema()
rds_vectorstore = Redis.from_existing_index(
embeddings,
index_name=VECTORS_INDEX_NAME,
redis_url=REDIS_URL,
schema='Redis_schema.yaml'
)
index(
document,
record_manager,
rds_vectorstore,
cleanup = "full", # None: for first document load; "incremental": for following documents
source_id_key = "title",
)
```
When exploring the Redis vectorstore, all `documents` loaded outside the specified `VECTORS_INDEX_NAME`.
When `documents` are loaded to the vectorstore without `RecordManager` `index`, they are created inside the specified `VECTORS_INDEX_NAME` when using the following code:
```
rds = Redis.from_documents(
document,
embeddings,
index_name=VECTORS_INDEX_NAME,
redis_url=REDIS_URL,
index_schema='Redis_schema.yaml'
)
```
### Expected behavior
`Documents` loaded into a Redis vectorstore using `index` `RecordManager` should be created inside the vectorstore's index.
|
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| 1,894,829,007 |
I_kwDOIPDwls5w8MvP
| 10,550 |
MMR search_type not implemented for Google Vertex AI Matching Engine Vector Store (new name of Matching Engine- Vector Search)
|
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[] | null | 1 | 2023-09-13T15:58:41 | 2023-12-01T14:01:06 | null |
NONE
| null |
### Feature request
MMR search_type is not implemented for Google Vertex AI Matching Engine Vector Store (new name of Matching Engine- Vector Search).
I am getting the error `NotImplementedError`
Below is the code that I had used
`retriever = me.as_retriever(
search_type="mmr",
search_kwargs={
"k": 10,
"search_distance": 0.6,
"fetch_k": 15,
"lambda_mult": 0.7 }}`
Please implement it at the earliest, request the team if they can provide ETA too
### Motivation
I am working for a client where they are using only Google Vertex AI components for creating LLM chatbot agents using various unstructured document types. We are not getting optimal results with the default `search_type="similarity"` , we understand that results can improve a lot with MMR search. Hence kindly requesting the team to add the `search_type="mmr"` feature
### Your contribution
Can provide feedback on the new feature performance
|
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| 1,894,815,399 |
I_kwDOIPDwls5w8Jan
| 10,549 |
Issue: Not sure whether my transformation using the model I created was correct, as I am getting an error.
|
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[] | null | 5 | 2023-09-13T15:50:31 | 2023-12-20T16:05:16 | 2023-12-20T16:05:15 |
NONE
| null |
### Issue you'd like to raise.
# The code for my model for sentiment analysis (this works, the problem is in the next part of my code)
from datasets import load_dataset,Dataset
from sentence_transformers.losses import CosineSimilarityLoss
from setfit import SetFitModel, SetFitTrainer, sample_dataset
from transformers import pipeline
import pandas as pd
import langchain
# df = pd.read_csv("C:/Users/sanja/OneDrive/Desktop/Trillo InternShip/train.csv",encoding='ISO-8859-1')
df = pd.read_csv("C:/Users/sanja/OneDrive/Desktop/Trillo InternShip/train-utf-8.csv")
# Create a mapping from string labels to integer labels
label_mapping = {"negative": 0, "neutral": 1, "positive": 2} # Customize this mapping as needed
# Apply the mapping to the "sentiment" column
df['label'] = df['label'].map(label_mapping)
# Specify the columns for text (input) and label (output)
text_column = "selected_text"
label_column = "label"
# Assuming you have already preprocessed and tokenized your text data
dataset = Dataset.from_pandas(df)
num_samples_per_class = 8
# Simulate the few-shot regime by sampling 8 examples per class
train_dataset = sample_dataset(dataset, label_column=label_column, num_samples=num_samples_per_class)
eval_dataset = dataset # Assuming you want to evaluate on the same DataFrame
# Load a SetFit model from Hub
model = SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2")
# Create trainer
trainer1 = SetFitTrainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
loss_class=CosineSimilarityLoss,
metric="accuracy",
batch_size=16,
num_iterations=20, # The number of text pairs to generate for contrastive learning
num_epochs=1, # The number of epochs to use for contrastive learning
column_mapping={text_column: "text", label_column: "label"} # Map dataset columns to text/label expected by trainer
)
# Train and evaluate
trainer1.train()
metrics = trainer1.evaluate()
# Pushing model to hub
trainer1.push_to_hub("Sanjay1234/Trillo-Project")
# But here I get a problem when I do transformation,
from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain
from sentence_transformers.losses import CosineSimilarityLoss
from setfit import SetFitModel, SetFitTrainer, sample_dataset
from transformers import pipeline
def transform_func(text):
shortened_text = "\n\n".join(text.split("\n\n")[:3])
return shortened_text
transform_chain = TransformChain(
input_variables=["text"], output_variables=["output_text"], transform=transform_func
)
# I get a problem here
llm_chain = LLMChain(
llm={"llm": "Sanjay1234/Trillo-Project"}, # Provide the llm parameter as a dictionary
prompt={"prompt": "Summarize this text:"}
)
sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])
text = "This is a long text. I want to transform it to only the first 3 paragraphs."
transformed_text = sequential_chain.run(text)
print(transformed_text)
I get the following error.-
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[26], line 1
----> 1 llm_chain = LLMChain(
2 llm={"llm": "Sanjay1234/Trillo-Project"}, # Provide the llm parameter as a dictionary
3 prompt={"prompt": "Summarize this text:"}
4 )
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\langchain\load\serializable.py:74, in Serializable.__init__(self, **kwargs)
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\pydantic\main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 2 validation errors for LLMChain
prompt
Can't instantiate abstract class BasePromptTemplate with abstract methods format, format_prompt (type=type_error)
llm
Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt, apredict, apredict_messages, generate_prompt, invoke, predict, predict_messages (type=type_error)
### Suggestion:
_No response_
|
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| 1,894,798,632 |
PR_kwDOIPDwls5aP-3e
| 10,548 |
bump 288
|
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[] | null | 1 | 2023-09-13T15:41:22 | 2023-09-13T15:57:44 | 2023-09-13T15:57:43 |
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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| 1,894,742,556 |
PR_kwDOIPDwls5aPyqy
| 10,546 |
Fix `GoogleEnterpriseSearchRetriever`
|
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[] | null | 2 | 2023-09-13T15:08:51 | 2023-09-13T18:45:24 | 2023-09-13T18:45:08 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- Description: fixed Google Enterprise Search Retriever where it was consistently returning empty results,
- Issue: related to [issue 8219](https://github.com/langchain-ai/langchain/issues/8219),
- Dependencies: no dependencies,
- Tag maintainer: @hwchase17 ,
- Twitter handle: [Tomas Piaggio](https://twitter.com/TomasPiaggio)!
|
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| 1,894,630,727 |
I_kwDOIPDwls5w7cVH
| 10,544 |
Issue: Want to get this to run, im suspecting that the csv. file is causing the problem
|
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[] | null | 2 | 2023-09-13T14:15:08 | 2023-12-20T16:05:20 | 2023-12-20T16:05:20 |
NONE
| null |
### Issue you'd like to raise.
imports
import langchain
import os
from apikey import apikey
import openai
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA
import streamlit as st
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
import nltk
nltk.download("punkt")
#loading file
loader = UnstructuredFileLoader("aisc-shapes-database-v16.0.csv","aisc-shapes-database-v16.0_a1085.pdf")
documents = loader.load()
len(documents)
text_splitter = CharacterTextSplitter(chunk_size =1000000000, chunk_overlap = 0)
text = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()#(openai_api_key = os.environ['OPENAI_API_KEY'])
doc_search = Chroma.from_documents(text, embeddings)
chain = RetrievalQA.from_chain_type(llm =OpenAI(), chain_type="stuff", retriever=doc_search.as_retriever(search_kwargs={"k":1}))
query = "What is the area of wide flange W44X408"
result = chain.run(query)
print(result)
model.save('CIVEN-GPT')
### Suggestion:
It runs without the csv. file so im assuming it is that however I would like to be able to include the data in the file in the training.
|
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PR_kwDOIPDwls5aPNgn
| 10,543 |
openai_functions_multi_agent: solved the case when the "arguments" is valid JSON but it does not contain `actions` key
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[] | null | 2 | 2023-09-13T13:45:41 | 2023-10-05T18:14:14 | 2023-10-05T18:08:09 |
CONTRIBUTOR
| null |
Description: There are cases when the output from the LLM comes fine (i.e. function_call["arguments"] is a valid JSON object), but it does not contain the key "actions". So I split the validation in 2 steps: loading arguments as JSON and then checking for "actions" in it.
|
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I_kwDOIPDwls5w7MvQ
| 10,542 |
Get input prompt in a callback handler
|
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[] | null | 0 | 2023-09-13T13:42:49 | 2023-10-31T18:15:10 | null |
NONE
| null |
### Feature request
I want to intercept the input prompt and the output of a chain, so I added a custom callback to the chain (derived from _BaseCallbackHandler_), but the input prompt seems quite tricky to retrieve.
The _on_chain_start_ method has the information hidden in the "serialized" variable, but accessing it is quite cumbersome. I let you judge by yourself:
```
def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> Any:
"""Run when chain starts running."""
print(serialized["kwargs"]["prompt"]["kwargs"]["messages"][0]["kwargs"]["prompt"]["kwargs"]["template"])
```
Note that the format of _serialized_ changes from time to time for a reason I ignore, and it doesn't seem to be documented. This makes it unusable. Moreover, the "template" value is not the final prompt passed to the LLM after replacement of variables.
As for the _on_text_ method, it contains a formatted and colored text:
> Prompt after formatting:
> Human: prompt in green
Are there simpler ways to retrieve the input prompt from a callback handler?
### Motivation
Showing both input and output could help debugging and it may be desirable to customize the outputs given by the _verbose_ mode.
### Your contribution
Maybe simply add the input message in the parameters of the _on_chain_start_ method, regardless of the way it has been generated.
|
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| 1,894,532,433 |
I_kwDOIPDwls5w7EVR
| 10,541 |
Issue: Agents using GPTQ models from huggingface is really slow.
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[] | null | 2 | 2023-09-13T13:26:28 | 2023-12-20T16:05:26 | 2023-12-20T16:05:25 |
NONE
| null |
### Issue you'd like to raise.
While trying to load a GPTQ model through a HuggingFace Pipeline and then run an agent on it, the inference time is really slow.
```
# Load configuration from the model to avoid warnings
generation_config = GenerationConfig.from_pretrained(model_name_or_path)
# Create a pipeline for text generation
pipe = pipeline(
task="text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=1024,
do_sample=True,
repetition_penalty=1.15,
generation_config=generation_config,
use_cache=False
)
local_llm = HuggingFacePipeline(pipeline=pipe)
logging.info("Local LLM Loaded")
```
The model is getting loaded on GPU
However the inference is really slow. I am waiting around 10 minutes for one iteration to complete.
`agent = create_csv_agent(
local_llm,
"titanic.csv",
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True
)`
`agent.run("What is the total number of rows in titanic.csv")
`
Also, I get an error message -` Observation: len() is not a valid tool, try one of [python_repl_ast].` How to enable all tools so that the agent can use them?
### Suggestion:
No suggestion, require help.
|
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| 1,894,516,796 |
PR_kwDOIPDwls5aPBJR
| 10,540 |
Add language detection example
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[] | null | 2 | 2023-09-13T13:18:02 | 2023-10-05T17:39:09 | 2023-10-05T17:39:09 |
CONTRIBUTOR
| null |
### Description
Adds language detection examples based on [langdetect](https://github.com/Mimino666/langdetect/tree/master/langdetect) and [fasttext](https://github.com/facebookresearch/fastText/) libraries. These frameworks can be especially useful together with components that require selection of the language (e.g. data-anonymizer)
### Twitter handle
@deepsense_ai, @matt_wosinski
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| 1,894,471,329 |
I_kwDOIPDwls5w61ah
| 10,539 |
Will langchain be able to support Baidu Qianfan in the future?
|
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[] | null | 2 | 2023-09-13T12:55:51 | 2023-09-28T01:19:12 | 2023-09-28T01:19:12 |
NONE
| null |
### Feature request
New features to support Baudu's Qianfan
### Motivation
I believe that Baidu's recently launched LLM platform, Qianfan, which offers a range of APIs, will soon become widely adopted. It would be beneficial to consider incorporating features that facilitate seamless integration between Langchain and Qianfan, making it easier for developers to build applications.
### Your contribution
https://github.com/langchain-ai/langchain/pull/10496
|
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| 1,894,462,413 |
I_kwDOIPDwls5w6zPN
| 10,538 |
SimpleJsonOutputParser not working
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[] | null | 2 | 2023-09-13T12:50:53 | 2023-12-20T16:05:31 | 2023-12-20T16:05:30 |
CONTRIBUTOR
| null |
### System Info
Langchain 0.0.287
In output_parsers there is a `SimpleJsonOutputParser` defined (json.py). This looks very reasonable for easily getting answers back in structured a format. However, the class does not work as it does not specify the method `get_format_instructions`and thus calling the parse method raises a `NotImplementedError`. In addition, there is no documentation and the class is not imported into the `__init__.py` of the directory.
Is this intended behavior ? I am ok to submit a small patch - for my case the class comes very handy and has less complexity than the approach via Pydantic.
### 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
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce
1. from langchain.output_parsers.json import SimpleJsonOutputParser
2. output_parser = SimpleJsonOutputParser()
3. format_instructions = output_parser.get_format_instructions()
### Expected behavior
SimpleJsonOutputParser works like any other output parser.
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I_kwDOIPDwls5w6Y0D
| 10,536 |
Issue: google calendar agent
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[] | null | 1 | 2023-09-13T11:46:22 | 2023-09-14T02:37:27 | 2023-09-14T02:37:27 |
NONE
| null |
### Issue you'd like to raise.
Is there an agent toolkit for google calendar?
### Suggestion:
_No response_
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PR_kwDOIPDwls5aOdPl
| 10,535 |
Added Streaming Capability to SageMaker LLMs
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[] | null | 10 | 2023-09-13T11:45:36 | 2023-10-25T16:08:00 | 2023-10-05T18:08:44 |
CONTRIBUTOR
| null |
This PR adds the ability to declare a Streaming response in the SageMaker LLM by leveraging the `invoke_endpoint_with_response_stream` capability in `boto3`. It is heavily based on the AWS Blog Post announcement linked [here](https://aws.amazon.com/blogs/machine-learning/elevating-the-generative-ai-experience-introducing-streaming-support-in-amazon-sagemaker-hosting/).
It does not add any additional dependencies since it uses the existing `boto3` version.
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| 1,894,334,794 |
PR_kwDOIPDwls5aOZOT
| 10,534 |
Fix SingleStoreDB
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[] | null | 2 | 2023-09-13T11:33:27 | 2023-09-18T07:17:22 | 2023-09-13T19:09:46 |
CONTRIBUTOR
| null |
After the refactoring #6570, the DistanceStrategy class was moved to another module and this introduced a bug into the SingleStoreDB vector store, as the `DistanceStrategy.EUCLEDIAN_DISTANCE` started to convert into the 'DistanceStrategy.EUCLEDIAN_DISTANCE' string, instead of just 'EUCLEDIAN_DISTANCE' (same for 'DOT_PRODUCT').
In this change, I check the type of the parameter and use `.name` attribute to get the correct object's name.
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I_kwDOIPDwls5w6DAs
| 10,533 |
Issue: Is LangSmith playground compatible with Azure OpenAI?
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[] | null | 11 | 2023-09-13T10:48:45 | 2023-10-11T10:43:39 | null |
NONE
| null |
### Issue you'd like to raise.
1. I have downloaded original LangSmith walkthrough notebook and modified it to run AzureOpenAI llm instead of OpenAI
2. After successful run of the first example I went to Langsmith, selected first LLM call and opened it in the Playground.
3. I have filled up OpenAI key and hit 'Start'
Here is the error I get:
Error: Invalid namespace: $ -> {"id":["langchain","chat_models","azure_openai","AzureChatOpenAI"],"lc":1,"type":"constructor","kwargs":{"temperature":0,"openai_api_key":{"id":["xxx"],"lc":1,"type":"secret"},"deployment_name":"chat-gpt","openai_api_base":"yyy","openai_api_type":"azure","openai_api_version":"2023-03-15-preview"}}
I have played with different ways of setting OPEN_API_KEY but none of them works, the same error is consistently displayed.
So it is a bug or Azure Open AI does not work by design in the Playground?
### Suggestion:
_No response_
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PR_kwDOIPDwls5aNzDk
| 10,532 |
Add support for Exllama
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NONE
| null |
- Description: Integrate exllama as an LLM,
- Issue: #8385 ,
- Dependencies: https://github.com/jllllll/exllama
Referenced https://github.com/CoffeeVampir3/exllama-langchain-example/tree/master
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I_kwDOIPDwls5w5j__
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ValueError: Missing some input keys: {'input'}
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[] | null | 7 | 2023-09-13T09:36:13 | 2024-01-05T17:21:22 | null |
NONE
| null |
### System Info
langchain deplopment on sagemaker
### Who can help?
_No response_
### 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
- [x] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from typing import Dict
import json
class HFContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_dict = {
"input": {
"question": prompt,
"context": model_kwargs
}
}
input_str = json.dumps(input_dict)
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = output.read().decode('utf-8')
res = json.loads(response_json)
# Stripping away the input prompt from the returned response
ans = res[0]['generated_text'][self.len_prompt:]
ans = ans[:ans.rfind("Human")].strip()
return ans
# Example parameters
parameters = {
'do_sample': True,
'top_p': 0.3,
'max_new_tokens': 1024,
'temperature': 0.6,
'watermark': True
}
llm = SagemakerEndpoint(
endpoint_name="huggingface-pytorch-inference-**********",
region_name="us-east-1",
model_kwargs=parameters,
content_handler=HFContentHandler(),
)
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
memory = ConversationBufferMemory()
# Creating a chain with buffer memory to keep track of conversations
chain = ConversationChain(llm=llm, memory=memory)
chain.predict({"input": {"question": "this is test", "context": "this is answer"}})
### Expected behavior
there is error in content handler please help to correct it.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[87], line 8
5 # Creating a chain with buffer memory to keep track of conversations
6 chain = ConversationChain(llm=llm, memory=memory)
----> 8 chain.predict({"input": {"question": "this is test", "context": "this is answer"}})
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/chains/llm.py:255, in LLMChain.predict(self, callbacks, **kwargs)
240 def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
241 """Format prompt with kwargs and pass to LLM.
242
243 Args:
(...)
253 completion = llm.predict(adjective="funny")
254 """
--> 255 return self(kwargs, callbacks=callbacks)[self.output_key]
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/chains/base.py:268, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
232 def __call__(
233 self,
234 inputs: Union[Dict[str, Any], Any],
(...)
241 include_run_info: bool = False,
242 ) -> Dict[str, Any]:
243 """Execute the chain.
244
245 Args:
(...)
266 `Chain.output_keys`.
267 """
--> 268 inputs = self.prep_inputs(inputs)
269 callback_manager = CallbackManager.configure(
270 callbacks,
271 self.callbacks,
(...)
276 self.metadata,
277 )
278 new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/chains/base.py:425, in Chain.prep_inputs(self, inputs)
423 external_context = self.memory.load_memory_variables(inputs)
424 inputs = dict(inputs, **external_context)
--> 425 self._validate_inputs(inputs)
426 return inputs
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/chains/base.py:179, in Chain._validate_inputs(self, inputs)
177 missing_keys = set(self.input_keys).difference(inputs)
178 if missing_keys:
--> 179 raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {'input'}
|
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| 1,894,127,953 |
I_kwDOIPDwls5w5hlR
| 10,530 |
Can not access to url: https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers
|
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[] | null | 3 | 2023-09-13T09:30:31 | 2023-12-25T16:08:20 | 2023-12-25T16:08:19 |
NONE
| null |
### Issue you'd like to raise.
https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers is unreachable.
### Suggestion:
_No response_
|
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| 1,894,107,226 |
I_kwDOIPDwls5w5cha
| 10,529 |
compress content when using gpt-4
|
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[] | null | 2 | 2023-09-13T09:20:29 | 2023-12-20T16:05:41 | 2023-12-20T16:05:40 |
NONE
| null |
hi team,
In langchain agent, any recommendations to compress the content? Hoping to reduce the token usage.
Setting max token was not working to reduce the token usage.
|
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| 1,894,078,446 |
I_kwDOIPDwls5w5Vfu
| 10,528 |
Handle by default `ToolException`
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[] | null | 1 | 2023-09-13T09:05:35 | 2023-10-25T17:07:51 | null |
NONE
| null |
### Feature request
As of today, if a tool crashes, the whole agent or chain crashes. From a user point-of-view, it is understandable that a specific tool is not available.
### Motivation
The user experience should be maintained if a dependency is broken. Plus, catching by default tool error can enhance the software reliability.
### Relates
- https://github.com/langchain-ai/langchain/issues/8348
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| 1,893,881,183 |
I_kwDOIPDwls5w4lVf
| 10,526 |
Issue: How to retrieve and search from multiple collections or directories?
|
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[] | null | 2 | 2023-09-13T07:03:24 | 2023-12-20T16:05:46 | 2023-12-20T16:05:45 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Is there any way in langchain to fetch documents from multiple vectorstores, and then combine them to ask the question.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5aMlOR
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Bagatur/elevenlabs
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[] | null | 1 | 2023-09-13T06:01:10 | 2023-09-13T06:11:05 | 2023-09-13T06:11:04 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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| 1,893,792,079 |
I_kwDOIPDwls5w4PlP
| 10,524 |
Opensearch metadata_field and metadata_filter not working
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[] | null | 5 | 2023-09-13T05:51:57 | 2023-10-17T11:22:52 | null |
NONE
| null |
### System Info
langchain version = 0.0.281
python = 3.11
opensearch-py = 2.3.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to do metadata based filtering alongside the query execution using `OpensearchVectorSearch.similarity_search()`. But when I use `metadata_field` and `metadata_filter`, the search doesn't seems to take that into account and still returns results outside of those filters.
Here is myr code:
`response = es.similarity_search( query = "<sample query text>", K =4, metadata_field = "title", metadata_filter = {"match":{"title": "<sample doc title>}}, )`
Here `es` is the `OpenSearchVectorSearch` object for `index1`
The output structure is like this:
`[Document(page_content = ' ', metadata={'vector_field' : [], 'text' : ' ', 'metadata' : {'source' : ' ', 'title' : ' ' }})]`
Here the title I see is not the title I specified in my query.
Steps to reproduce:
1. Create an Opensearch index with multiple documents.
2. Run similarity_search() query with a metadata_field and/or metadata_filter
### Expected behavior
The query should be run against the specified `metadata_field` and `metadata_filter` and in output, I should only see the correct document name I specified in `metadata_field` and `metadata_filter`
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| 1,893,725,262 |
PR_kwDOIPDwls5aMUx8
| 10,523 |
fix: unify generation outputs on newer openllm release
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[] | null | 2 | 2023-09-13T04:37:36 | 2023-09-13T20:49:17 | 2023-09-13T20:49:17 |
CONTRIBUTOR
| null |
update newer generation format from OpenLLm where it returns a dictionary for one shot generation
cc @baskaryan
Signed-off-by: Aaron <[email protected]>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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| 1,893,647,164 |
PR_kwDOIPDwls5aMDZV
| 10,522 |
Additional Weaviate Filter Comparators
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[] | null | 4 | 2023-09-13T03:17:45 | 2023-10-05T17:40:05 | 2023-10-05T17:40:05 |
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,
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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,
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
### Description
When using Weaviate Self-Retrievers, certain common filter comparators generated by user queries were unimplemented, resulting in errors. This PR implements some of them. All linting and format commands have been run and tests passed.
### Issue
#10474
### Dependencies
timestamp module
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| 1,893,622,488 |
I_kwDOIPDwls5w3mLY
| 10,521 |
only use past x messages
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[] | null | 2 | 2023-09-13T02:42:51 | 2023-12-20T16:05:51 | 2023-12-20T16:05:50 |
NONE
| null |
### Feature request
An input for conversational chains to be able to limit their context to a set number of chats
### Motivation
I am in the process of building a document analysis tool using langchain but when the chat chain becomes too long, I just get an error stating that the limit for the no of openai tokens has been reached because the context keeps becoming longer and longer. is there some way i could limit the context to only a certain no of messages and not take all of them in.
### Your contribution
No I am very new to using langchain and having a hard time understanding the codebase. so i am afraid their is nothing i could do to help.
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I_kwDOIPDwls5w3YdL
| 10,520 |
Reduce azure openai token usage
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[] | null | 2 | 2023-09-13T01:23:29 | 2023-12-20T16:05:56 | 2023-12-20T16:05:55 |
NONE
| null |
hi team,
I am using the Azure openai gpt4-32k as llm in langchain. I implemented openai plugin by agent, but the cost is increasing at an incredible rate. I think the agent would ask gpt4 modal to understand the plugin openapi json that make the token usage increasing. any recommendations to reduce the token usage in agent?
thanks
|
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I_kwDOIPDwls5w3Vyc
| 10,519 |
Support jinja2 template format when using ChatPromptTemplate.from_template_file
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[] | null | 6 | 2023-09-13T01:10:23 | 2023-10-07T10:26:42 | null |
NONE
| null |
### Feature request
BaseStringMessagePromptTemplate.from_template supports the template_format variable, while BaseStringMessagePromptTemplate.from_template_file does not.
### Motivation
All supported template formats (including Jinja2) should be supported by all template loaders equally.
### Your contribution
I'm not experienced enough with the Langchain codebase to submit PRs at this time.
|
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PR_kwDOIPDwls5aLs0c
| 10,518 |
add replicate stream
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[] | null | 1 | 2023-09-13T00:49:58 | 2023-09-14T15:49:45 | 2023-09-14T15:44:06 |
COLLABORATOR
| null |
support direct replicate streaming. cc @cbh123 @tjaffri
|
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PR_kwDOIPDwls5aLaJv
| 10,517 |
cache replicate version
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[] | null | 2 | 2023-09-12T23:12:18 | 2023-09-14T15:34:06 | 2023-09-14T15:34:05 |
COLLABORATOR
| null |
In subsequent pr will update _call to use replicate.run directly when not streaming, so version object isn't needed at all
cc @cbh123 @tjaffri
|
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| 1,893,413,058 |
PR_kwDOIPDwls5aLUsm
| 10,516 |
Bagatur/replicate inputs
|
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[] | null | 1 | 2023-09-12T22:46:51 | 2023-09-12T22:52:14 | 2023-09-12T22:52:13 |
COLLABORATOR
| null | null |
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| 1,893,387,846 |
PR_kwDOIPDwls5aLPHK
| 10,515 |
fixed PR template
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[] | null | 1 | 2023-09-12T22:22:37 | 2023-09-13T16:36:20 | 2023-09-13T16:35:48 |
COLLABORATOR
| null |
@hwchase17
|
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| 1,893,322,151 |
PR_kwDOIPDwls5aLEo1
| 10,514 |
Moved from InferenceApi to InferenceClient to avoid deprecation warning for huggingface_hub
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[] | null | 4 | 2023-09-12T21:43:33 | 2023-12-16T20:28:56 | 2023-12-16T20:28:55 |
CONTRIBUTOR
| null |
Updated huggingface_hub llm to use the new InferenceClient to avoid deprecation warning.
Resolves the issue: #10483
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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PR_kwDOIPDwls5aK_7P
| 10,513 |
fix: Update Google Cloud Enterprise Search to Vertex AI Search
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[] | null | 26 | 2023-09-12T21:25:48 | 2023-10-06T15:11:45 | 2023-10-05T17:47:47 |
CONTRIBUTOR
| null |
- Description: Google Cloud Enterprise Search was renamed to Vertex AI Search
- https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-search-and-conversation-is-now-generally-available
- This PR updates the documentation and Retriever class to use the new terminology.
- Changed retriever class from `GoogleCloudEnterpriseSearchRetriever` to `GoogleVertexAISearchRetriever`
- Updated documentation to specify that `extractive_segments` requires the new [Enterprise edition](https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#enterprise-features) to be enabled.
- Fixed spelling errors in documentation.
- Change parameter for Retriever from `search_engine_id` to `data_store_id`
- When this retriever was originally implemented, there was no distinction between a data store and search engine, but now these have been split.
- Fixed an issue blocking some users where the api_endpoint can't be set
|
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PR_kwDOIPDwls5aK7c_
| 10,512 |
Fix fine-tuned replicate models with faster cold boot
|
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[] | null | 1 | 2023-09-12T21:11:54 | 2023-09-17T18:54:22 | 2023-09-12T22:40:55 |
CONTRIBUTOR
| null |
With the latest support for faster cold boot in replicate https://replicate.com/blog/fine-tune-cold-boots it looks like the replicate LLM support in langchain is broken since some internal replicate inputs are being returned.
Screenshot below illustrates the problem:
<img width="1917" alt="image" src="https://github.com/langchain-ai/langchain/assets/749277/d28c27cc-40fb-4258-8710-844c00d3c2b0">
As you can see, the new replicate_weights param is being sent down with x-order = 0 (which is causing langchain to use that param instead of prompt which is x-order = 1)
FYI @baskaryan this requires a fix otherwise replicate is broken for these models. I have pinged replicate whether they want to fix it on their end by changing the x-order returned by them.
Update: per suggestion I updated the PR to just allow manually setting the prompt_key which can be set to "prompt" in this case by callers... I think this is going to be faster anyway than trying to dynamically query the model every time if you know the prompt key for your model.
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I_kwDOIPDwls5w2D1b
| 10,511 |
APIError: HTTP code 203 from API when running from a Click CLI app on a local Windows terminal
|
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[] | null | 2 | 2023-09-12T20:33:16 | 2023-12-19T00:47:23 | 2023-12-19T00:47:22 |
NONE
| null |
### System Info
Device name LAPTOP-3BD5HR1V
Processor AMD Ryzen 5 3500U with Radeon Vega Mobile Gfx 2.10 GHz
Installed RAM 20.0 GB (17.9 GB usable)
Device ID F8ACB5C8-80FB-46C6-AE6D-33AD019A5728
Product ID 00325-82110-59554-AAOEM
System type 64-bit operating system, x64-based processor
Pen and touch No pen or touch input is available for this display
Edition Windows 11 Home
Version 22H2
Installed on 10/5/2022
OS build 22621.2134
Serial number PF2WCKPH
Experience Windows Feature Experience Pack 1000.22659.1000.0
Python 3.11.2
langchain 0.0.272
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have created a Python CLI tool called 'dir-diary' that uses Chain.run to make API calls. The tool is built on `click`. When I run the tool from a terminal window with a Python virtual environment activated, the tool works okay. It also appears to work okay from both Linux-based and Windows-based Github Actions runners. But when I run it from a vanilla Windows terminal on my own machine, langchain fails to authenticate with Azure DevOps after several retries.
There's a whole lot of text returned with the error. The most helpful bits are:
.APIError: HTTP code 203 from API.
'Microsoft Internet Explorer's Enhanced Security Configuration is currently enabled on your environment. This enhanced level of security prevents our web integration experiences from displaying or performing correctly. To continue with your operation please disable this configuration or contact your administrator'
'Unable to complete authentication for user due to looping logins'
'Traceback (most recent call last):
File "C:\Users\chris\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\api_requestor.py", line 755, in _interpret_response_line
data = json.loads(rbody)
^^^^^^^^^^^^^^^^^
File "C:\Users\chris\AppData\Local\Programs\Python\Python311\Lib\json\__init__.py", line 335, in loads
raise JSONDecodeError("Unexpected UTF-8 BOM (decode using utf-8-sig)",
json.decoder.JSONDecodeError: Unexpected UTF-8 BOM (decode using utf-8-sig): line 1 column 1 (char 0)'
Steps to reproduce:
I haven't fully figured out the secret to reproducing this yet. Obviously, if it works on a Windows runner, then it's not really a Windows problem. There must be something problematic about my local setup that I can't identify. FWIW, here are my steps:
1. run command `pip install -U dir-diary` from a Windows terminal
2. go to any code project folder
3. run command `summarize`
I have tried running as administrator and turning down the Internet security level through Internet Options in Control Panel, but neither of those solutions fixed the problem.
### Expected behavior
It's supposed to successfully query the API to summarize the project folder.
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PR_kwDOIPDwls5aKsVQ
| 10,510 |
Add support for showing IO to chain group
|
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[] | null | 1 | 2023-09-12T20:27:50 | 2023-09-17T07:47:53 | 2023-09-17T07:47:52 |
COLLABORATOR
| null |
As well as error propagation
|
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PR_kwDOIPDwls5aKsLT
| 10,509 |
add avg feedback
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[] | null | 1 | 2023-09-12T20:27:14 | 2023-09-12T21:05:19 | 2023-09-12T21:05:18 |
COLLABORATOR
| null |
in run_on_dataset agg feedback printout
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| 1,893,131,951 |
I_kwDOIPDwls5w1uav
| 10,506 |
Elasticsearch ELSER Timeout
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[] | null | 5 | 2023-09-12T19:32:37 | 2023-12-27T16:01:41 | null |
NONE
| null |
### System Info
Python 3.10.12
Google Colab
Elasticsearch Cloud 8.9.2
Langchain - latest
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps:
1. Load list of documents
2. Setup ElasticsearchStore of Langchain, with appropriate ES cloud credentials
3. Successfully create index with custom embedding model (HF embedding model, deployed on colab)
4. Deploy ELSER model and run it (with default model id).
5. Try creating index with SparseVectorRetrievalStrategy (ELSER) over the same list of documents.
6. Tried to change timeout, but didn't effect the outcome.
7. NOTE: It does start uploading docs and docs count is increasing, but it stops after about 10 sec. I tried to run the ELSER model on 3 nodes, but nothing changed.
### Expected behavior
WARNING:elastic_transport.node_pool:Node <Urllib3HttpNode([https://-------.us-central1.gcp.cloud.es.io:443](https:/------.us-central1.gcp.cloud.es.io/))> has failed for 1 times in a row, putting on 1 second timeout
---------------------------------------------------------------------------
ConnectionTimeout Traceback (most recent call last)
[<ipython-input-92----------cb>](https://-------colab.googleusercontent.com/outputframe.html?vrz=colab_20230911-060143_RC00_564310758#) in <cell line: 1>()
----> 1 elastic_elser_search = ElasticsearchStore.from_documents(
2 documents=split_texts,
3 es_cloud_id="cloudid",
4 index_name="search-tmd-elser",
5 es_user="elastic",
10 frames
[/usr/local/lib/python3.10/dist-packages/elastic_transport/_node/_http_urllib3.py](https://---XXXX-----0-colab.googleusercontent.com/outputframe.html?vrz=colab_---XXXX-----#) in perform_request(self, method, target, body, headers, request_timeout)
197 exception=err,
198 )
--> 199 raise err from None
200
201 meta = ApiResponseMeta(
ConnectionTimeout: Connection timed out
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PR_kwDOIPDwls5aKIwC
| 10,505 |
JSONLoader Documentation Fix
|
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[] | null | 3 | 2023-09-12T18:31:36 | 2023-09-21T18:37:41 | 2023-09-21T18:37:41 |
CONTRIBUTOR
| null |
- Description:
Updated JSONLoader usage documentation which was making it unusable
- Issue: JSONLoader if used with the documented arguments was failing on various JSON documents.
- Dependencies:
no dependencies
- Twitter handle: @TheSlnArchitect
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5aJ8iW
| 10,504 |
`fallback` definition
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[] | null | 4 | 2023-09-12T17:48:35 | 2023-10-03T19:51:06 | 2023-10-03T19:38:59 |
COLLABORATOR
| null |
I've added a definition to `fallback` and fixed couple misspells. It was not really clear what is the "fallback".
|
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PR_kwDOIPDwls5aJy2E
| 10,503 |
Update SQLDatabaseChain for intermediate SQLResults
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[] | null | 2 | 2023-09-12T17:24:40 | 2023-11-13T06:45:01 | 2023-11-13T06:45:01 |
NONE
| null |
Description:
In the call method within the SQLDatabaseChain class:
Amend the intermediate steps dictionary to include a new key, say sql_result, where the SQL results at different stages would be saved.
During the SQL execution step, save the SQL result into the sql_result key in the intermediate steps dictionary, similar to how sql_cmd is being saved currently.
Issue: #10500
Tag maintainer: zzfab
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PR_kwDOIPDwls5aJpfw
| 10,502 |
docs: `self-query` consistency
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[] | null | 2 | 2023-09-12T16:53:20 | 2023-09-13T22:02:32 | 2023-09-13T21:43:05 |
COLLABORATOR
| null |
The `self-que[ring` navbar](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/) has repeated `self-quering` repeated in each menu item. I've simplified it to be more readable
- removed `self-quering` from a title of each page;
- added description to the vector stores
- added description and link to the Integration Card (`integrations/providers`) of the vector stores when they are missed.
|
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PR_kwDOIPDwls5aJNtb
| 10,501 |
Instance anonymization
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[] | null | 3 | 2023-09-12T15:37:14 | 2023-10-05T18:23:03 | 2023-10-05T18:23:03 |
CONTRIBUTOR
| null |
### Description
Add instance anonymization - if `John Doe` will appear twice in the text, it will be treated as the same entity.
The difference between `PresidioAnonymizer` and `PresidioReversibleAnonymizer` is that only the second one has a built-in memory, so it will remember anonymization mapping for multiple texts:
```
>>> anonymizer = PresidioAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Brett Russell. Hi Brett Russell!'
```
```
>>> anonymizer = PresidioReversibleAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
```
### Twitter handle
@deepsense_ai / @MaksOpp
### Tag maintainer
@baskaryan @hwchase17 @hinthornw
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I_kwDOIPDwls5w0MLj
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Enhance SQLDatabaseChain with SQL Results in Intermediate Steps Dictionary
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[] | null | 2 | 2023-09-12T15:11:12 | 2023-12-19T00:47:27 | 2023-12-19T00:47:27 |
NONE
| null |
### Feature request
## Description
Currently, the SQLDatabaseChain class is designed to optionally return intermediate steps taken during the SQL command generation and execution. These intermediate steps are helpful in understanding the processing flow, especially during debugging or for logging purposes. However, these intermediate steps do not store the SQL results obtained at various steps, which could offer deeper insights and can aid in further optimizations or analyses.
This feature request proposes to enhance the SQLDatabaseChain class to save SQL results from intermediate steps into a dictionary, akin to how SQL commands are currently stored. This would not only facilitate a more comprehensive view of each step but also potentially help in identifying and fixing issues or optimizing the process further.
### Motivation
#### Insightful Debugging:
Storing SQL results in intermediate steps will facilitate deeper insights during debugging, helping to pinpoint the exact step where a potential issue might be occurring.
#### Enhanced Logging:
Logging the SQL results at each step can help in creating a more detailed log, which can be instrumental in analyzing the performance and identifying optimization opportunities.
Improved Analysis and Optimization: With the SQL results available at each step, it becomes feasible to analyze the results at different stages, which can be used to further optimize the SQL queries or the process flow.
### Your contribution
I propose to contribute to implementing this feature by:
#### Code Adaptation:
Modifying the _call_ method in the SQLDatabaseChain class to include SQL results in the intermediate steps dictionary, similar to how sql_cmd is currently being saved.
#### Testing:
Developing appropriate unit tests to ensure the correct functioning of the new feature, and that it does not break the existing functionality.
#### Documentation:
Updating the documentation to include details of the new feature, illustrating how to use it and how it can benefit the users.
#### Optimization:
Once implemented, analyzing the stored results to propose further optimizations or enhancements to the Langchain project.
## Proposed Changes
In the _call_ method within the SQLDatabaseChain class:
Amend the intermediate steps dictionary to include a new key, say sql_result, where the SQL results at different stages would be saved.
During the SQL execution step, save the SQL result into the sql_result key in the intermediate steps dictionary, similar to how sql_cmd is being saved currently.
```
if not self.use_query_checker:
...
intermediate_steps.append({"sql_cmd": sql_cmd, "sql_result": str(result)}) # Save sql result here
else:
...
intermediate_steps.append({"sql_cmd": checked_sql_command, "sql_result": str(result)}) # Save sql result here
```
I believe that this contribution would be a valuable addition to the Langchain project, and I am eager to collaborate with the team to make it a reality.
Looking forward to hearing your thoughts on this proposal.
|
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[] | null | 1 | 2023-09-12T14:52:51 | 2023-09-12T14:53:07 | null |
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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),
- 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.
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If you're adding a new integration, please include:
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2. an example notebook showing its use. These live is docs/extras directory.
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bump 287
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[] | null | 4 | 2023-09-12T14:15:52 | 2023-12-19T00:47:33 | 2023-12-19T00:47:32 |
NONE
| null |
### Issue you'd like to raise.
When I run the code I don't get any errors but I also don't get any output in the terminal or output area either? Can you help?
### Suggestion:
_No response_
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PR_kwDOIPDwls5aIp2w
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Add Baidu Qianfan endpoint for LLM
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[] | null | 3 | 2023-09-12T14:13:11 | 2023-09-13T23:33:17 | 2023-09-13T23:23:50 |
CONTRIBUTOR
| null |
- Description：
* Baidu AI Cloud's [Qianfan Platform](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) is an all-in-one platform for large model development and service deployment, catering to enterprise developers in China. Qianfan Platform offers a wide range of resources, including the Wenxin Yiyan model (ERNIE-Bot) and various third-party open-source models.
- Issue: none
- Dependencies:
* qianfan
- Tag maintainer: @baskaryan
- Twitter handle:
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PR_kwDOIPDwls5aInNx
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Update pdf.py comment for PyPDFLoader
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CONTRIBUTOR
| null |
PyPDF does not chunk at the character level to my understanding.
Description: PyPDF does not chunk at the character level, but instead breaks up content by page. Fixup comment
|
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