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* Added documentation to the existing Pydantic Parser notebook, with an example using LCEL and `with_retry()` on `OutputParserException`.
* Added an additional output example to the prompt
* More lenient parser in terms of LLM output format
* Amended unit test
FYI @hwchase17 | {
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] | closed | false | null | [] | null | 7 | 2023-12-13T19:02:58 | 2023-12-20T11:24:15 | 2023-12-13T22:53:53 | NONE | null | ### Feature request
With Gemini Pro going GA today (Dec. 13th).
When can users of LangChain expect an update to use the new LLM?
### Motivation
This will allow users of LangChain to use the latest LLM that Google is providing along with their safety settings.
### Your contribution
I can try and help. Happy to contribute where needed | {
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- **Description:** added new models on VertexAI
- **Twitter handle:** @lkuligin | {
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] | null | 4 | 2023-12-13T18:09:03 | 2024-01-02T18:33:46 | 2024-01-02T00:33:47 | CONTRIBUTOR | null | - **Description:** Update to tools.py to allow aliased tool inputs to be included in the _parse_input function output
- **Issue:** This fixes [Issue #14663](https://github.com/langchain-ai/langchain/issues/14663)
- **Twitter handle:** [@consultchrisq ](https://twitter.com/consultchrisq?lang=en)
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https://api.github.com/repos/langchain-ai/langchain/issues/14666 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14666/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14666/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14666/events | https://github.com/langchain-ai/langchain/pull/14666 | 2,040,209,084 | PR_kwDOIPDwls5h7Hf5 | 14,666 | Include scores in MongoDB Atlas QA chain results | {
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] | open | false | null | [] | null | 1 | 2023-12-13T17:54:29 | 2024-01-17T22:54:44 | null | CONTRIBUTOR | null | Adds the ability to return similarity scores when using `RetrievalQA.from_chain_type` with `MongoDBAtlasVectorSearch`. Requires that `return_source_documents=True` is set.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vector_search.as_retriever(search_kwargs={"additional": ["similarity_score"]}),
return_source_documents=True
)
...
docs = qa({"query": "..."})
docs["source_documents"][0].metadata["score"] # score will be here
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a vector search index. | {
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] | open | false | null | [] | null | 1 | 2023-12-13T16:51:16 | 2023-12-13T16:57:36 | null | CONTRIBUTOR | null | ### System Info
Langchain: 0.0.349
Langchain-community: v0.0.1
Langchain-core: 0.0.13
Python: 3.12
Platform: Mac OS
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have the following BaseModel and BaseTool classes created.
```python
class TaskPost(BaseModel):
"""
TaskPost
""" # noqa: E501
due_date: Optional[datetime] = Field(default=None, description="ISO 8601 Due date on the task. REQUIRED for scheduled tasks", alias="dueDate")
duration: Optional[TaskDuration] = None
status: Optional[StrictStr] = Field(default=None, description="Defaults to workspace default status.")
auto_scheduled: Optional[AutoScheduledInfo] = Field(default=None, alias="autoScheduled")
name: Annotated[str, Field(min_length=1, strict=True)] = Field(description="Name / title of the task")
project_id: Optional[StrictStr] = Field(default=None, alias="projectId")
workspace_id: StrictStr = Field(alias="workspaceId")
description: Optional[StrictStr] = Field(default=None, description="Input as GitHub Flavored Markdown")
priority: Optional[StrictStr] = 'MEDIUM'
labels: Optional[List[StrictStr]] = None
assignee_id: Optional[StrictStr] = Field(default=None, description="The user id the task should be assigned to", alias="assigneeId")
__properties: ClassVar[List[str]] = ["dueDate", "duration", "status", "autoScheduled", "name", "projectId", "workspaceId", "description", "priority", "labels", "assigneeId"]
model_config = {
"populate_by_name": True,
"validate_assignment": True
}
class CreateTaskTool(BaseTool):
name = "create_task"
description = (
"""Use this to create a new task from all available args that you have. Always make sure date and time inputs are in ISO format""")
args_schema: Type[BaseModel] = openapi_client.TaskPost
verbose = True
```
The agent will use the alias names instead of the field name. i.e (workspaceId, dueDate) instead of (workspace_id, due_date)
```linux
[tool/start] [1:chain:AgentExecutor > 7:tool:create_task] Entering Tool run with input:
"{'name': 'Update code', 'workspaceId': 'xxxxxyyyyyzzzzz', 'dueDate': '2023-12-14T00:00:00'}"
```
When the agent calls `_parse_input` function from [langchian_core/tools.py and reaches line 247](https://github.com/langchain-ai/langchain/blob/14bfc5f9f477fcffff3f9aa564a864c5d5cd5300/libs/core/langchain_core/tools.py#L247) the results are filtered out because the results have the field names and the tool_input has the alias names which do not match.
```
CreateTaskTool -> _parse_input -> parse_obj -> result: due_date=datetime.datetime(2023, 12, 14, 0, 0) duration=None status=None auto_scheduled=None name='Update code' project_id=None workspace_id='xxxxxyyyyyzzzzz' description=None priority='MEDIUM' labels=None assignee_id=None
CreateTaskTool -> _parse_input -> parse_obj -> result keys: dict_keys(['due_date', 'duration', 'status', 'auto_scheduled', 'name', 'project_id', 'workspace_id', 'description', 'priority', 'labels', 'assignee_id'])
CreateTaskTool -> _parse_input -> parse_obj -> tool_input keys: dict_keys(['name', 'workspaceId', 'dueDate'])
CreateTaskTool -> _parse_input -> parse_obj -> finalResults: {'name': 'Update code'}
```
### Expected behavior
```
CreateTaskTool -> _parse_input -> parse_obj -> result keys: dict_keys(['due_date', 'duration', 'status', 'auto_scheduled', 'name', 'project_id', 'workspace_id', 'description', 'priority', 'labels', 'assignee_id'])
CreateTaskTool -> _parse_input -> parse_obj -> tool_input keys: dict_keys(['name', 'workspaceId', 'dueDate'])
CreateTaskTool -> _parse_input -> finalResults: {'name': 'Update code','workspaceId': 'xxxxxyyyyyzzzzz', 'dueDate': '2023-12-14T00:00:00'}
or
CreateTaskTool -> _parse_input -> finalResults: {'name': 'Update code','workspace_Id': 'xxxxxyyyyyzzzzz', 'due_date': '2023-12-14T00:00:00'}
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14662 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14662/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14662/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14662/events | https://github.com/langchain-ai/langchain/pull/14662 | 2,040,101,932 | PR_kwDOIPDwls5h6wJh | 14,662 | Fixing the Issue with DashScopeEmbeddings Handling More than 25 Rows of Data | {
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] | closed | false | null | [] | null | 2 | 2023-12-13T16:44:38 | 2024-01-02T19:39:47 | 2024-01-02T00:50:13 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
This change addresses the issue where DashScopeEmbeddingAPI limits requests to 25 lines of data, and DashScopeEmbeddings did not handle cases with more than 25 lines, leading to errors. I have implemented a fix to manage data exceeding this limit efficiently.
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https://api.github.com/repos/langchain-ai/langchain/issues/14661 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14661/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14661/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14661/events | https://github.com/langchain-ai/langchain/pull/14661 | 2,040,094,732 | PR_kwDOIPDwls5h6ukc | 14,661 | Add Gemini Notebook | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14660 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14660/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14660/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14660/events | https://github.com/langchain-ai/langchain/issues/14660 | 2,040,091,551 | I_kwDOIPDwls55mVOf | 14,660 | SQLDatabaseChain with LlamaCpp Llama2 "Chain Run Errored With Error: ArgumentError: <class 'TypeError'>: wrong type" | {
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] | open | false | null | [] | null | 2 | 2023-12-13T16:38:34 | 2023-12-13T17:06:04 | null | NONE | null | ### System Info
LangChain version 0.0.348
Python version 3.10
Operating System MacOS Monterey version 12.6
SQLAlchemy version 2.0.23
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```# Connect to db
# Create an SQLAlchemy engine
engine = create_engine("mysql+mysqlconnector://user:pass@host/database")
# Test the database connection
try:
# Connect and execute a simple query
with engine.connect() as connection:
query = text("SELECT 1")
result = connection.execute(query)
for row in result:
print("Connection successful, got row:", row)
except Exception as e:
print("Error connecting to database:", e)
# Create an instance of SQLDatabase
db = SQLDatabase(engine)
# using Llama2
llm = LlamaCpp(
model_path="/path_to/llama-2-7b.Q4_K_M.gguf",
verbose=True,
n_ctx=2048)
# using Default and Suffix prompt template
PROMPT = PromptTemplate(
input_variables=["input", "table_info", "dialect", "top_k"],
template=_DEFAULT_TEMPLATE + PROMPT_SUFFIX,
)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, return_sql=True,
prompt=PROMPT)
langchain.debug = True
response = db_chain.run(formatted_prompt)
print(response
### Expected behavior
Expected Behavior: Expected to generate SQL queries without errors.
Actual Behavior: Received TypeError: wrong type and context window exceedance errors.
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/llama_cpp/llama.py", line 1325, in _create_completion
f"Requested tokens ({len(prompt_tokens)}) exceed context window of {llama_cpp.llama_n_ctx(self._ctx)}"
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/llama_cpp/llama_cpp.py", line 612, in llama_n_ctx
return _lib.llama_n_ctx(ctx)
ctypes.ArgumentError: argument 1: <class 'TypeError'>: wrong type``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14659 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14659/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14659/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14659/events | https://github.com/langchain-ai/langchain/issues/14659 | 2,040,074,533 | I_kwDOIPDwls55mREl | 14,659 | Getting "object has no field "documents" error with extended BaseRetriever classs | {
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] | open | false | null | [] | null | 3 | 2023-12-13T16:29:16 | 2023-12-13T17:38:18 | null | NONE | null | ### Issue you'd like to raise.
I am attempting to call an instance of ConversationalRetrieverChain with a list of dictionary objects that I've pre-processed with a similarity search and cohere reranker. I've created an extension of BaseRetriever in order to pass my list of dictionary objects to the "retriever=" parameter. However when my extended class instantiates, I get an error saying "seai_retriever object has no field "documents". My code is below. What am I doing wrong?
```
from langchain.schema.retriever import BaseRetriever
from langchain.schema.document import Document
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from typing import List
class seai_retriever(BaseRetriever):
def __init__(self, documents):
self.documents = documents
def retrieve(self, query, top_n=10):
retrieved_docs = [doc for doc in self.documents if query.lower() in doc['content'].lower()]
retrieved_docs = sorted(retrieved_docs, key=lambda x: x['content'].find(query), reverse=True)[:top_n]
return retrieved_docs
def _get_relevant_documents(self, query: str, *, run_manager: CallbackManagerForRetrieverRun) -> List[Document]:
retrieved_docs = [doc for doc in self.documents if query.lower() in doc['content'].lower()]
retrieved_docs = sorted(retrieved_docs, key=lambda x: x['content'].find(query), reverse=True)[:top_n]
return retrieved_docs
```
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14658 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14658/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14658/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14658/events | https://github.com/langchain-ai/langchain/issues/14658 | 2,040,021,283 | I_kwDOIPDwls55mEEj | 14,658 | UnstructuredXMLLoader import error (circular import) | {
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] | open | false | null | [] | null | 1 | 2023-12-13T16:02:17 | 2023-12-13T16:09:05 | null | NONE | null | ### System Info
Mac Studio
M1 Max 32GB
macOS 14.1.2
Using rye
Python 3.11.6
langchain==0.0.350
langchain-community==0.0.2
langchain-core==0.1.0
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Error**
```
File "/venv/lib/python3.11/site-packages/langchain_community/document_loaders/xml.py", line 41, in _get_elements
from unstructured.partition.xml import partition_xml
ImportError: cannot import name 'partition_xml' from partially initialized module 'unstructured.partition.xml' (most likely due to a circular import) (/venv/lib/python3.11/site-packages/unstructured/partition/xml.py)
```
I tried to load XML document like this link(from langchain document)
https://python.langchain.com/docs/integrations/document_loaders/xml
```
from langchain.document_loaders import UnstructuredXMLLoader
loader = UnstructuredXMLLoader(
"aaa.xml",
)
docs = loader.load()
docs[0]
```
### Expected behavior
no circular import | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14657 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14657/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14657/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14657/events | https://github.com/langchain-ai/langchain/pull/14657 | 2,039,993,587 | PR_kwDOIPDwls5h6Y1c | 14,657 | Update GuardrailsOutputParser for Guardrails 0.3.0, add docs | {
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- **Issue:** none
- **Dependencies:** none
- **Tag maintainer:** @efriis
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Hi, I'm trying to use StreamingStdOutCallbackHandler for a conversation chain. But it prints out the memory after the response. Is there a way to not printing memory without using an agent?
### Suggestion:
_No response_ | {
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- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @bfortunato
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- **Issue:**
- **Dependencies:** the Kinetica Python API `pip install gpudb==7.2.0.0b`,
- **Tag maintainer:** @baskaryan,
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` {'action_name': 'search_items', 'action': {'term': 'pizza'}}`
sometimes, specially with gpt3.5 it comes like:
`{'action_name': 'search_items', 'term': 'pizza'}`
and it fails.
This PR is a way to make it work in both scenarios.
issues releated: #6624
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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https://api.github.com/repos/langchain-ai/langchain/issues/14651 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14651/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14651/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14651/events | https://github.com/langchain-ai/langchain/issues/14651 | 2,039,714,207 | I_kwDOIPDwls55k5Gf | 14,651 | Issue: I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain. | {
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] | open | false | null | [] | null | 3 | 2023-12-13T13:27:30 | 2023-12-20T12:41:57 | null | NONE | null | ### Issue you'd like to raise.
I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain.
How can we achieve this, below is my code
` loader = UnstructuredURLLoader(urls=urls)
urlDocument = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
texts = text_splitter.split_documents(documents=urlDocument)`
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14650 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14650/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14650/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14650/events | https://github.com/langchain-ai/langchain/pull/14650 | 2,039,528,489 | PR_kwDOIPDwls5h4yUx | 14,650 | Add CometLLM integration notebook example | {
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] | closed | false | null | [] | null | 3 | 2023-12-13T11:38:05 | 2024-01-09T18:28:38 | 2024-01-09T18:28:37 | CONTRIBUTOR | null | I also added LANGCHAIN_COMET_TRACING to enable the CometLLM tracing integration similar to other tracing integrations. This is easier for end-users to enable it rather than importing the callback and pass it manually.
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https://api.github.com/repos/langchain-ai/langchain/issues/14649 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14649/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14649/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14649/events | https://github.com/langchain-ai/langchain/issues/14649 | 2,039,411,662 | I_kwDOIPDwls55jvPO | 14,649 | Bug in Text splitting while using langchain.text_splitter.split_text_on_tokens¶ | {
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] | open | false | null | [] | null | 3 | 2023-12-13T10:31:27 | 2023-12-18T14:40:32 | null | NONE | null | ### System Info
Langchain Version = 0.0.311
Python Version = 3.9
Tried it on my local system as well on Company's hosted Jupyter Hub as well
### Who can help?
@eyurtsev @agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.text_splitter import TokenTextSplitter
token_splitter_model_name = "gpt-3.5-turbo"
SPLIT_CHUNK_SIZE = 1024
CHUNK_OVERLAP = 256
text_splitter = TokenTextSplitter.from_tiktoken_encoder(model_name=token_splitter_model_name, chunk_size=SPLIT_CHUNK_SIZE , chunk_overlap = CHUNK_OVERLAP)
blog_content= ' your text here'
blog_splits=text_splitter.split_text(blog_content)
```
### Expected behavior
The ways this token text splitter works , isn't how it is intended to work .
For Example :-
When specified chunk_size = 1024 and overlap = 256 with input text tokens = 991 , it made two chunks ;
first = token[0 : 991]
second = token[768 : 991]
but logically it should work this ways .
If input text token >1024 , there should be two chunks .
first = token[0 : 1024]
second = token[768 : ]
and so on ..............
If input text token <=1024 , there should be one chunk only .
first = token[ 0 : ]
More details on this issue raised previously :- https://github.com/langchain-ai/langchain/issues/5897 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14647 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14647/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14647/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14647/events | https://github.com/langchain-ai/langchain/pull/14647 | 2,039,236,707 | PR_kwDOIPDwls5h3yVQ | 14,647 | fix: correct spelling mistakes of "seperate, intialise, pre-defined" | {
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] | closed | false | null | [] | null | 1 | 2023-12-13T08:51:12 | 2023-12-22T19:49:36 | 2023-12-22T19:49:36 | CONTRIBUTOR | null | fix spellings
**seperate -> separate**: found more occurrences, see https://github.com/langchain-ai/langchain/pull/14602
**initialise -> intialize**: the latter is more common in the repo
**pre-defined > predefined**: adding a comma after a prefix is a delicate matter, but this is a generally accepted word
also, another word that appears in the repo is "fs" (stands for filesystem), e.g., in `libs/core/langchain_core/prompts/loading.py`
` """Unified method for loading a prompt from LangChainHub or local fs."""`
Isn't "filesystem" better?
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https://api.github.com/repos/langchain-ai/langchain/issues/14646 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14646/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14646/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14646/events | https://github.com/langchain-ai/langchain/pull/14646 | 2,039,163,008 | PR_kwDOIPDwls5h3iTa | 14,646 | Fix RRF and lucene escape characters for neo4j vector store | {
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] | closed | false | null | [] | null | 1 | 2023-12-13T08:01:47 | 2023-12-13T17:09:51 | 2023-12-13T17:09:50 | CONTRIBUTOR | null | * Remove Lucene special characters (fixes https://github.com/langchain-ai/langchain/issues/14232)
* Fixes RRF normalization for hybrid search | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14645 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14645/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14645/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14645/events | https://github.com/langchain-ai/langchain/issues/14645 | 2,039,150,807 | I_kwDOIPDwls55ivjX | 14,645 | TypeError: object Document can't be used in 'await' expression | {
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] | open | false | null | [] | null | 1 | 2023-12-13T07:52:41 | 2023-12-13T07:58:57 | null | NONE | null | ### System Info
from langchain.document_transformers import DoctranTextTranslator
from langchain.schema import Document
documents = [Document(page_content=sample_text)]
qa_translator = DoctranTextTranslator(language="spanish")
translated_document = await qa_translator.atransform_documents(documents)
TypeError Traceback (most recent call last)
[<ipython-input-18-c526f9c55393>](https://localhost:8080/#) in <cell line: 8>()
6 openai_api_model="gpt-3.5-turbo", language="chinese")
7
----> 8 translated_document = await qa_translator.atransform_documents(documents)
9
10
[/usr/local/lib/python3.10/dist-packages/langchain_community/document_transformers/doctran_text_translate.py](https://localhost:8080/#) in atransform_documents(self, documents, **kwargs)
61 ]
62 for i, doc in enumerate(doctran_docs):
---> 63 doctran_docs[i] = await doc.translate(language=self.language).execute()
64 return [
65 Document(page_content=doc.transformed_content, metadata=doc.metadata)
TypeError: object Document can't be used in 'await' expression
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
test
### Expected behavior
test | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14644 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14644/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14644/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14644/events | https://github.com/langchain-ai/langchain/pull/14644 | 2,039,145,722 | PR_kwDOIPDwls5h3eh7 | 14,644 | add api_base to _client_params (community version of #14393) | {
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] | closed | false | null | [] | null | 1 | 2023-12-13T07:48:43 | 2024-01-02T19:37:34 | 2024-01-02T00:53:16 | CONTRIBUTOR | null | - **Description:** This PR adds `api_base` to `_client_params` in the `chat_model` of LiteLLM to ensure it's included in API calls. Previously, `api_base` was set on the client but was not included in the parameters passed to the completion function. This change ensures that `api_base` is correctly passed to all API calls.
- **Issue:** #14338
- **Tag maintainer:** @hwchase17 @agola11
- **Twitter handle:** @LMS_David_RS | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14643 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14643/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14643/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14643/events | https://github.com/langchain-ai/langchain/issues/14643 | 2,039,122,156 | I_kwDOIPDwls55iojs | 14,643 | ParentDocumentRetriever.get_relevant_documents() got empty result | {
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] | closed | false | null | [] | null | 5 | 2023-12-13T07:31:59 | 2024-01-11T20:34:16 | 2023-12-19T23:58:20 | NONE | null | I have two code.
The 1st code is to load PDF documents and use ParentDocumentRetriever to add_documents() and save vectorstore to local disk.
The 2nd code is to load vectorstore and call ParentDocumentRetriever.get_relevant_documents
The problem is, the ParentDocumentRetriever.get_relevant_documents() got empty result.
Any idea??
What is the inMemoryStore for??
Here are the two codes:
### the 1st code: LoadDocumentsAndSaveVectorstore.py
```
loader = DirectoryLoader('data/',
glob='**/*.pdf',
loader_cls=PyPDFLoader)
docs = loader.load()
store = InMemoryStore()
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=128, chunk_overlap=64)
texts = [""]
vectorstore = FAISS.from_texts(texts, embedding_function)
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
)
retriever.add_documents(docs, ids=None)
vectorstore.save_local("ppstore")
print("save data to ppstore")
```
The 1st code works fine.
The 2nd file is to load the vectorstore and query.
The strange thing is, the **retriever1.get_relevant_documents(query)** got empty result.
### the 2nd code: load vectorstore and use retriever to get relevant_documents
```
print("load ppstore")
store = InMemoryStore()
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=128, chunk_overlap=64)
db = FAISS.load_local("ppstore", embedding_function)
retriever1 = ParentDocumentRetriever(
vectorstore=db,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
)
query = "Please describe Sardina dataset"
print("query:", query)
sub_docs = db.similarity_search(query)
print("=== check small chunk ===")
print(sub_docs[0].page_content)
print(len(sub_docs[0].page_content)) ## this respond OK, the len is a little bit smaller than 128
retrieved_docs = retriever1.get_relevant_documents(query) ## I got empty result
print("=== check larger chunk ===")
print(retrieved_docs[0].page_content)
print(len(retrieved_docs[0].page_content))
```
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https://api.github.com/repos/langchain-ai/langchain/issues/14642 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14642/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14642/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14642/events | https://github.com/langchain-ai/langchain/pull/14642 | 2,039,121,880 | PR_kwDOIPDwls5h3ZRh | 14,642 | pipeline template fixes; overlapping variables and non-pipelined variables | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14640 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14640/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14640/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14640/events | https://github.com/langchain-ai/langchain/issues/14640 | 2,039,051,024 | I_kwDOIPDwls55iXMQ | 14,640 | (RedisSemanticCache + ChatOpenAI + OpenAIEmbeddings) - Not working as expected - Wanted to understand, if I am doing something wrong here. | {
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# Dependency Versions
langchain==0.0.349
langchain-community==0.0.1
langchain-core==0.0.13
openai==1.3.8
# Python Version
Python 3.11.4
# Redis Stack Version
redis-stack-server 6.2.0
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.globals import set_llm_cache
from langchain.cache import RedisSemanticCache
from langchain.embeddings import OpenAIEmbeddings
from langchain_community.chat_models import ChatOpenAI
from redis import Redis
import time
llm = ChatOpenAI(openai_api_key='<OPENAI_API_KEY>', model_name='gpt-3.5-turbo')
cache = RedisSemanticCache(redis_url="redis://localhost:6379",
embedding=OpenAIEmbeddings(openai_api_key='<OPENAI_API_KEY>'),
score_threshold=0.95)
set_llm_cache(cache)
start = time.time()
response = llm.predict("""Tell me about USA in only two sentences""")
print(time.time()-start)
print(response)
start = time.time()
response = llm.predict("""Tell me about INDIA in only two sentences""")
print(time.time()-start)
print(response)
start = time.time()
response = llm.predict("""What is LLMs in the context of GEN AI ?""")
print(time.time()-start)
print(response)
### Expected behavior
As the score_threshold is set to 0.98, I expect all the three prompts to give three different responses. But we are getting one response for all the three prompts.
Output from running the script :
4.252941131591797
The United States of America is a federal republic consisting of 50 states, a federal district, five major self-governing territories, and various possessions. It is a diverse and influential country known for its cultural and economic power on the global stage.
0.3903520107269287
The United States of America is a federal republic consisting of 50 states, a federal district, five major self-governing territories, and various possessions. It is a diverse and influential country known for its cultural and economic power on the global stage.
0.6625611782073975
The United States of America is a federal republic consisting of 50 states, a federal district, five major self-governing territories, and various possessions. It is a diverse and influential country known for its cultural and economic power on the global stage.
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https://api.github.com/repos/langchain-ai/langchain/issues/14639 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14639/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14639/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14639/events | https://github.com/langchain-ai/langchain/issues/14639 | 2,039,002,274 | I_kwDOIPDwls55iLSi | 14,639 | Issue: bedrock is throwing an error for the langchain stuff method using the anthropic model for the summarization. | {
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] | closed | false | null | [] | null | 1 | 2023-12-13T05:48:17 | 2023-12-15T07:47:12 | 2023-12-15T07:47:11 | NONE | null | ### Issue you'd like to raise.
Issue 1:
I am working on the summarization using the stuff and map-reduce of the Langchain. I have integrated it with the AWS Bedrock's anthropic llm which has a token limit of 100000. It is working fine but when the pdf with 40000 tokens is passed, the bedrock is throwing an error:
i) with VPN connected:
An error occurred: Error raised by bedrock service: Could not connect to the endpoint URL: "https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/invoke".
ii) without VPN connected:
An error occurred: Error raised by bedrock service: An error occurred (ExpiredTokenException) when calling the InvokeModel operation: The security token included in the request is expired
Any reason why this is happening? thanks in advance!
Issue 2:
2) map-reduce takes a lot of time to produce the summary for the 40000+ tokens documents when the anthropic threshold is reduced to 40000. Sometimes it is taking a lot of time but erroring out. Any help is appreciated. Thanks in advance!
### Suggestion:
_No response_ | {
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] | closed | false | null | [] | null | 1 | 2023-12-13T03:25:03 | 2024-01-02T19:40:35 | 2024-01-02T00:55:52 | CONTRIBUTOR | null | The fix #14221 has broken default gitlab url which is forcing the users to specify GITLAB_URL for default one. With this fix if GITLAB_URL is not set, the default gitlab url will be taken.
- **Description:** Add the GITHUB URL instead of None
- **Issue:** the issue #14221 has broken the default github URL
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** manjunath_shiva
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- added missed tools
- added links and descriptions | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14636 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14636/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14636/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14636/events | https://github.com/langchain-ai/langchain/pull/14636 | 2,038,834,641 | PR_kwDOIPDwls5h2bMz | 14,636 | Milvus allows to store metadata as json field | {
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] | closed | false | null | [] | null | 3 | 2023-12-13T02:19:16 | 2024-01-03T00:50:42 | 2024-01-03T00:12:00 | CONTRIBUTOR | null | Because Milvus doesn't support nullable fields, but document metadata is very rich, so it makes more sense to store it as json.
https://github.com/milvus-io/pymilvus/issues/1705#issuecomment-1731112372
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Hello, after forking and cloning the repo on my machine, I tried to open it using docker and specifically in VS Code with the option to "Reopen in Container". While building, the final command of [dev.Dockerfile](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/dev.Dockerfile#L44) resulted in the following error:
```logs
#0 1.241 Directory ../core does not exist
```
After investigating, I found out that the issue lies in [pyproject.toml](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/pyproject.toml) which is using relative paths like `../core` and `../community` in some occasions. Additionally, even after replacing `../` with `libs/` (which I am not sure if it breaks something else), the actual `core` and `community` directories are never copied over in [dev.Dockerfile](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/dev.Dockerfile). These should also be copied in the created docker container, similarly to [line 41](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/dev.Dockerfile#L41).
After making these two changes, the container was successfully built. I'll check out whether the change of paths in pyproject.toml is affecting any other files, and if not I will create a PR for this.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Fork and clone the repo on your machine
2. Open it with VS Code (with Dev Containers extension installed)
3. Run the VS Code command: "Dev Containers: Rebuild Container"
### Expected behavior
Build the development docker container without errors | {
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] | closed | false | null | [] | null | 1 | 2023-12-12T23:06:17 | 2023-12-13T22:25:36 | 2023-12-13T22:25:35 | COLLABORATOR | null | Fix bug shown in #14458. Namely, that saving inputs to history fails when the input to base runnable is a list of messages | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14628 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14628/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14628/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14628/events | https://github.com/langchain-ai/langchain/pull/14628 | 2,038,668,203 | PR_kwDOIPDwls5h14MK | 14,628 | add support for weaviate vectorstore using weaviate's v4 beta client | {
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] | open | false | null | [] | null | 2 | 2023-12-12T22:56:11 | 2024-01-15T10:40:31 | null | NONE | null | Hi there, a few days ago I created #14531 and asked to add support for Weaviate's new v4 python client which is currently in beta. As I mentioned in that issue, I started working on it myself and I believe I have created a working weaviate vectorstore using their new client.
## Changelog
* Uses the new weaviate-client **v4** with updated syntax and functions. The new client is faster and safer to use because of Weaviate's new [gRPC API](https://weaviate.io/developers/weaviate/api/grpc).
* Added a new function to support [BM25 keyword search](https://weaviate.io/developers/weaviate/search/bm25).
* Changed the delete function to use batch for faster execution (there was a [TODO](https://github.com/langchain-ai/langchain/blob/b9ef92f2f4bb1e4322dff040c4474c0922fbf546/libs/community/langchain_community/vectorstores/weaviate.py#L526) for it)
* The function names and attributes have remained the same from the original weaviate vectorstore.
As this version of weaviate's client is still in beta, I think it is wise to add this as a new vectorstore, without replacing the existing one. For this reason, I did not change the original weaviate.py vectorstore file, but I added a new one called weaviate_v4.py. This way users using the original weaviate vectorstore will not be affected, but they'll be able to try the new vectorstore if they wish.
The are no dependencies other than having installed weaviate-client pre-release >= v4.
I also added a file in docs/docs, which is the same as the original one for weaviate but with the new client's structure.
I ran `make format`, `make lint` and `make test` before committing. :)
I did not create a unit test file because I saw there are almost none for other vectorstores. Let me know if it's essential and I may try to create one.
My Twitter (or X) handle is @mantzouranidis. Thanks for your work, I hope my contribution helps. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14627 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14627/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14627/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14627/events | https://github.com/langchain-ai/langchain/pull/14627 | 2,038,652,867 | PR_kwDOIPDwls5h101t | 14,627 | add anthropic package | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14626 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14626/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14626/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14626/events | https://github.com/langchain-ai/langchain/pull/14626 | 2,038,648,668 | PR_kwDOIPDwls5h1z7R | 14,626 | Update Docugami Cookbook | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14625 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14625/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14625/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14625/events | https://github.com/langchain-ai/langchain/issues/14625 | 2,038,636,476 | I_kwDOIPDwls55gx-8 | 14,625 | DALLEAPIWrapper to support Azure OpenAI | {
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] | open | false | null | [] | null | 2 | 2023-12-12T22:22:43 | 2023-12-13T01:03:43 | null | NONE | null | ### Feature request
Azure OpenAI now previews the DALLE-3 model. Today, DALLEAPIWrapper only supports the openai API.
### Motivation
My customers are using Azure OpenAI and would like to use DALL-E-3 in their solutions.
### Your contribution
PR may not be possible but I'd like to help anyway I can. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14624 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14624/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14624/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14624/events | https://github.com/langchain-ai/langchain/pull/14624 | 2,038,535,808 | PR_kwDOIPDwls5h1bUh | 14,624 | docs: fix links in readme | {
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Add first-class support for Vertex AI Endpoints in Langchain. This would involve providing a similar interface to the existing SageMakerEndpoint class, allowing users to easily connect to and interact with Vertex AI Endpoints.
### Motivation
Although VertexAIModelGarden already exist, there may be instances where users require custom models with unique input and output formats. To address this need, a more versatile class could be developed, upon which VertexAIModelGarden could be built. This would allow for seamless integration of custom models without compromising the functionality of the existing Model Garden class.
### Your contribution
The implementation taking inspiration from SageMakerEndpoint if pertinent. | {
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Still todo: add a deprecation warning in PALM | {
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Hi, it seems the only DocStore available are InMemory or Google. I'd like to submit a feature request for an S3DocStore.
### Motivation
Many people have raised issues related to limited DocStore options. | {
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The second issue is that the "API" menu item is actually not for the whole API docs, but only for the "langchain" package.
I've updated menu to: "Core/Langchain/Community/Experimental/Docs"
Now, "Core" is the top namespace, then "Langchain".
Notes:
- "Langchain", not "LangChain" (the capital "C" inside). Reason: namespaces are in the lower case. So, here the capitalization helps to visually separate one menu item from another. "LangChain" messes with this visual comprehension.
- The last item, "Python Docs" replaced with "Docs". Reason: A 2-word item makes an impression of two menu items. Now we have more menu items and all but this are 1-word items. This single 2-word item looks like two menu items. Since we don't have a link to the JS Docs, removing "Python" is OK.
- "Docs" menu item is bold. It visually separates Docs from other menu items. Other menu items are linked to API Refernce, but Docs is linked to Documentation. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14614 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14614/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14614/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14614/events | https://github.com/langchain-ai/langchain/pull/14614 | 2,038,352,269 | PR_kwDOIPDwls5h0zqL | 14,614 | changed default for VertexAIEmbeddings | {
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Replace this entire comment with:
- **Description:** @kurtisvg has raised a point that it's a good idea to have a fixed version for embeddings (since otherwise a user might run a query with one version vs a vectorstore where another version was used).
In order to avoid breaking changes, I'd suggest to give users a warning, and make a `model_name` a required argument in 1.5 months. | {
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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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,
2. an example notebook showing its use. It lives in `docs/extras` directory.
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https://api.github.com/repos/langchain-ai/langchain/issues/14609 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14609/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14609/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14609/events | https://github.com/langchain-ai/langchain/pull/14609 | 2,038,258,200 | PR_kwDOIPDwls5h0fM8 | 14,609 | added example and updated docs | {
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- **Description:** adjust docs and added an example
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https://api.github.com/repos/langchain-ai/langchain/issues/14608 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14608/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14608/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14608/events | https://github.com/langchain-ai/langchain/issues/14608 | 2,038,224,081 | I_kwDOIPDwls55fNTR | 14,608 | SlackToolkit() does not support proxy configuration | {
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langchain Version: 0.0.348
Python: 3.9.16
Docs suggests to use proxy entry as follows but it does not work:
from slacktoolkit import SlackToolkit
# Proxy settings
proxies = {
'http': 'http://proxy.example.com:8080',
'https': 'https://proxy.example.com:8080'
}
# Initialize SlackToolkit with proxy
slack_toolkit = SlackToolkit(proxies=proxies)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [x] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Proxy settings
proxies = {
'http': 'your_proxy',
'https': 'your_proxy'
}
# Initialize SlackToolkit with proxy
toolkit = SlackToolkit(proxies=proxies)
tools = toolkit.get_tools()
llm = OpenAI(temperature=0)
agent = initialize_agent(
tools=toolkit.get_tools(),
llm=llm,
verbose=True,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
)
agent.run("Send a greeting to my coworkers in the #slack1 channel. Your name is chatbot. Set the sender name as chatbot.")
### Expected behavior
slack message sent in #slack1 channel | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14606 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14606/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14606/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14606/events | https://github.com/langchain-ai/langchain/pull/14606 | 2,038,169,402 | PR_kwDOIPDwls5h0L3z | 14,606 | allow other namespaces | {
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- **Issue:** #14583
- **Dependencies:** no dependensies
- **Tag maintainer:**
- **Twitter handle:** @AmirMalekiz | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14601 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14601/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14601/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14601/events | https://github.com/langchain-ai/langchain/pull/14601 | 2,037,930,539 | PR_kwDOIPDwls5hzW8z | 14,601 | Add cohere librarian template | {
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It can:
use a vector database to reccommend books
<img width="840" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/96543a18-217b-4445-ab4b-950c7cced915">
Use a prompt template to provide information about the library
<img width="834" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/996c8e0f-cab0-4213-bcc9-9baf84f1494b">
Use Cohere RAG to provide grounded results
<img width="822" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/7bb4a883-5316-41a9-9d2e-19fd49a43dcb">
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https://api.github.com/repos/langchain-ai/langchain/issues/14598 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14598/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14598/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14598/events | https://github.com/langchain-ai/langchain/pull/14598 | 2,037,839,839 | PR_kwDOIPDwls5hzDB0 | 14,598 | WatsonxLLM updates/enhancements | {
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- **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan
- **Twitter handle:** details in comment below.
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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https://api.github.com/repos/langchain-ai/langchain/issues/14596 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14596/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14596/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14596/events | https://github.com/langchain-ai/langchain/issues/14596 | 2,037,780,695 | I_kwDOIPDwls55dhDX | 14,596 | QaWithSources default prompt is massive | {
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] | open | false | null | [] | null | 4 | 2023-12-12T13:40:37 | 2023-12-13T08:28:38 | null | CONTRIBUTOR | null | ### System Info
Not relevant
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use the QA With Sources chain with the default prompt. If the chain type is `stuff` or `map_reduce` the default prompts used are [this](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/qa_with_sources/stuff_prompt.py) and [this](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/qa_with_sources/map_reduce_prompt.py) respectively. These files are massive, and easily adds a 1000+ tokens to every request. In models like PALM2, there's barely any tokens left for other questions.
### Expected behavior
These prompts should be much more short, even if as a sample input. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14595 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14595/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14595/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14595/events | https://github.com/langchain-ai/langchain/issues/14595 | 2,037,760,278 | I_kwDOIPDwls55dcEW | 14,595 | Sources returned in QaWithSources can be optimised | {
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As of the current implementation of the QA with sources chain, if `return_source_documents` is set to `True`, [all the sources](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/langchain/langchain/chains/qa_with_sources/base.py#L165) that are retrieved from the vector DB are returned. The `sources` field returns a list of file names that were used by the LLM. I propose we could do something like this
1. Assign each `Document` a unique UUID as the `source` before passing it to the LLM.
2. Once the LLM returns the relevant sources, we can backmap this to the actual `Document`s that were used by the LLM as opposed to getting just the filename.
### Motivation
This information seems vastly more useful than the entire response from the vector DB. For our current use cases, we've ended up overriding these functions to add this functionality.
### Your contribution
I can raise a PR adding this if this is something that you'd find useful as well.
An additional issue to report, the `_split_sources` [function](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/langchain/langchain/chains/qa_with_sources/base.py#L124) splits at the first instance of `SOURCE` which seems to be a bit problematic. I can fix this to split at the last occurence. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14594 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14594/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14594/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14594/events | https://github.com/langchain-ai/langchain/issues/14594 | 2,037,660,794 | I_kwDOIPDwls55dDx6 | 14,594 | How to use custom tracing tool like opentelemetry or tempo | {
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] | open | false | null | [] | null | 1 | 2023-12-12T12:34:07 | 2023-12-12T12:41:42 | null | NONE | null | ### Feature request
How to use custom tracing tool like opentelemetry or tempo
### Motivation
If I don't want to use LangSmith
### Your contribution
N/A | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14593 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14593/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14593/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14593/events | https://github.com/langchain-ai/langchain/issues/14593 | 2,037,539,816 | I_kwDOIPDwls55cmPo | 14,593 | Not able to inference deepseek-coder-6.7b-instruct.Q5_K_M.gguf | {
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] | open | false | null | [] | null | 3 | 2023-12-12T11:20:20 | 2023-12-15T13:29:13 | null | NONE | null | ### System Info
OS: Apple M1 Max
______________________
Name: langchain
Version: 0.0.349
Summary: Building applications with LLMs through composability
Home-page: https://github.com/langchain-ai/langchain
Author:
Author-email:
License: MIT
Requires: aiohttp, async-timeout, dataclasses-json, jsonpatch, langchain-community, langchain-core, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity
Required-by:
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
I have followed the instructions provided here : https://python.langchain.com/docs/integrations/llms/llamacpp.
Though not able inference it correctly.
Model path : https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF
```
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain, QAGenerationChain
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
template = """Question: {question}
Answer: Let's work this out in a step by step way to be sure we have the right answer."""
prompt = PromptTemplate(template=template, input_variables=["question"])
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
n_gpu_layers = 1 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
llm = LlamaCpp(
model_path="../models/deepcoder-gguf/deepseek-coder-6.7b-instruct.Q2_K.gguf",
n_gpu_layers=n_gpu_layers,
max_tokens=2000,
top_p=1,
n_batch=n_batch,
callback_manager=callback_manager,
f16_kv=True,
verbose=True, # Verbose is required to pass to the callback manager
)
llm(
"Question: Write python program to add two numbers ? Answer:"
)
```
Result: ` < """"""""""""""""""""""/"`
Requesting you to look into it.
Please let me know in case you need more information.
Thank you.
I have tried the same model file with **[llama-cpp-python](https://github.com/abetlen/llama-cpp-python)** package and it works as expected.
Please find below the code that I have tried:
```
import json
import time
from llama_cpp import Llama
n_gpu_layers = 1 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512
llm = Llama(model_path="../models/deepcoder-gguf/deepseek-coder-6.7b-instruct.Q5_K_M.gguf" , chat_format="llama-2", n_gpu_layers=n_gpu_layers,n_batch=n_batch)
start_time = time.time()
pp = llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are an python language assistant."},
{
"role": "user",
"content": "Write quick sort ."
}
])
end_time = time.time()
print("execution time:", {end_time - start_time})
print(pp["choices"][0]["message"]["content"])
```
Output :
```
## Quick Sort Algorithm in Python
Here is a simple implementation of the quicksort algorithm in Python:
```python
def partition(arr, low, high):
i = (low-1) # index of smaller element
pivot = arr[high] # pivot
for j in range(low , high):
if arr[j] <= pivot:
i += 1
arr[i],arr[j] = arr[j],arr[i]
arr[i+1],arr[high] = arr[high],arr[i+1]
return (i+1)
def quickSort(arr, low, high):
if low < high:
pi = partition(arr,low,high)
quickSort(arr, low, pi-1)
quickSort(arr, pi+1, high)
# Test the code
n = int(input("Enter number of elements in array: "))
print("Enter elements: ")
arr = [int(input()) for _ in range(n)]
quickSort(arr,0,n-1)
print ("Sorted array is:")
for i in range(n):
print("%d" %arr[i]),
This code first defines a helper function `partition()` that takes an array and two indices. It then rearranges the elements of the array so that all numbers less than or equal to the pivot are on its left, while all numbers greater than the pivot are on its right. The `quickSort()` function is then defined which recursively applies this partitioning process until the entire array is sorted.
The user can input their own list of integers and the program will output a sorted version of that list.
[/code]
Conclusion
In conclusion, Python provides several built-in functions for sorting lists such as `sort()` or `sorted()` but it's also possible to implement quick sort algorithm from scratch using custom function. This can be useful in situations where you need more control over the sorting process or when dealing with complex data structures.
```
### Expected behavior
It should inference the model just like the native [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) package. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14592 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14592/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14592/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14592/events | https://github.com/langchain-ai/langchain/issues/14592 | 2,037,519,560 | I_kwDOIPDwls55chTI | 14,592 | Issue: <How to store/export the output of a chatbot to excel ' prefix> | {
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i have created a chatbot to chat with the sql database using openai and langchain, but how to store or output data into excel using langchain.
I got some idea from chatgpt which i have integrated with my code, but there is an error while importing the modules
below is my code
import pandas as pd
import sqlalchemy as sal
import os, sys, openai
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
# import ChatOpenAI
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
from langchain import PromptTemplate
from langchain.models import ChatGPTClient
from langchain.utils import save_conversation
os.environ['OPENAI_API_KEY'] = openapi_key
def chat(question):
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
# custom_suffix = """""
# If the SQLResult is empty, the Answer should be "No results found". DO NOT hallucinate an answer if there is no result."""
engine = create_engine(connection_uri)
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
# db = SQLDatabase(engine, view_support=True, include_tables=['egv_emp_acadamics_ChatGPT'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
from langchain.prompts import PromptTemplate
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the answer.
The question: {db_chain.run}
"""
prompt_template = """" Use the following pieces of context to answer the question at the end.
If you don't know the answer, please think rationally and answer from your own knowledge base.
Don't consider the table which are not mentioned, if no result is matching with the keyword Please return the answer as invalid question
{context}
Question: {questions}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "questions"]
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
return db_chain.run(question)
# answer=chat("give the names of employees who have completed PG")
answer= chat("give the list of employees joined in january and february of 2023 with Employee ID, Name, Department,Date of join")
print(answer)
conversation_data= chatgpt.chat(prompt="convert into .csv and .xlsx only if the multiple values are asked in the question, if one a single thing is asked, just give the answer in chatbot no need to save the answer")
# conversation_data = chat("convert into .csv and .xlsx only if the multiple values are asked in the question, if one a single thing is asked, just give the answer in chatbot no need to save the answer")
save_conversation(conversation_data, "chat_data.csv")
df = pd.read_csv("chat_data.csv")
path = r"C:\Users\rndbcpsoft\OneDrive\Desktop\test\chat_data.xlsx"
df.to_excel(path, index=False)
print(f"Conversation data has been saved to '{path}' in Excel format.")
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14591 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14591/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14591/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14591/events | https://github.com/langchain-ai/langchain/pull/14591 | 2,037,447,520 | PR_kwDOIPDwls5hxtB_ | 14,591 | Fix detection of Amazon OpenSearch Serverless | {
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] | closed | false | null | [] | null | 3 | 2023-12-12T10:27:50 | 2024-01-09T10:14:07 | 2024-01-09T10:14:06 | CONTRIBUTOR | null | - **Description:** Fixes detection Amazon OpenSearch Serverless (AOSS) when using the `RequestsAWSV4SignerAuth` utility from [opensearch-py][1] for authentication. Keeps compatibility with using [requests-aws4auth][2].
- **Issue:** #14129
- **Dependencies:** [opensearch-py][3], [requests-aws4auth][2] (for testing purposes only)
- **Tag maintainer:** @naveentatikonda @eyurtsev
[1]: https://github.com/opensearch-project/opensearch-py/blob/c4e53d444aef698e08801bfccd55a2478721a97c/opensearchpy/helpers/signer.py#L73C21-L73C21
[2]: https://pypi.org/project/requests-aws4auth/
[3]: https://pypi.org/project/opensearch-py/ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14590 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14590/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14590/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14590/events | https://github.com/langchain-ai/langchain/issues/14590 | 2,037,430,016 | I_kwDOIPDwls55cLcA | 14,590 | Caching with Bedrock does not distinguish models or params | {
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] | open | false | null | [] | null | 1 | 2023-12-12T10:17:59 | 2023-12-12T10:20:16 | null | NONE | null | ### System Info
Python 3.9.12, LangChain 0.0.346
### Who can help?
@agola11 @3coins
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When you use caching with LangChain, it does not distinguish different LLM models. For example, the response for LLama2 was used for a prompt for Claude 2.
```
def ask_name(model_id):
question = 'what is your name?'
bedrock = Bedrock(model_id=model_id, model_kwargs={'temperature': 0.1})
print('me: ' + question)
t0 = datetime.datetime.now()
print(f'{bedrock.model_id}: ' + bedrock(question).strip())
print('({:.2f} sec)'.format((datetime.datetime.now() - t0).total_seconds()))
print()
model_ids = ['meta.llama2-70b-chat-v1','anthropic.claude-v2',]
for model_id in model_ids:
ask_name(model_id)
ask_name(model_id)
```
==>
```
me: what is your name?
meta.llama2-70b-chat-v1: Answer: My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI.
(2.24 sec)
me: what is your name?
meta.llama2-70b-chat-v1: Answer: My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI.
(0.00 sec)
me: what is your name?
anthropic.claude-v2: Answer: My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI.
(0.00 sec)
```
This is because of https://github.com/langchain-ai/langchain/blob/db6bf8b022c17353b46f97ab3b9f44ff9e88a488/libs/langchain/langchain/llms/bedrock.py#L235
```
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"model_kwargs": _model_kwargs},
}
```
My current workaround is subclassing the `Bedrock` class:
```
class MyBedrock(Bedrock):
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
'model_id': self.model_id,
**BedrockBase._identifying_params.__get__(self)
}
```
This seems working.
### Expected behavior
LLama 2 should replay with "My name is LLaMa..." while Claude 2 should reply with "My name is Claude." | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14589 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14589/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14589/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14589/events | https://github.com/langchain-ai/langchain/issues/14589 | 2,037,428,125 | I_kwDOIPDwls55cK-d | 14,589 | Unable to dump/load custom classes since langchain_core 0.0.13 | {
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] | closed | false | null | [] | null | 3 | 2023-12-12T10:16:56 | 2023-12-13T10:44:16 | 2023-12-12T17:10:00 | NONE | null | ### System Info
python: 3.11
langchain: 0.0.347-0.0.349
langchain_core: 0.0.12, 0.0.13
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import langchain_core.load
class Custom(langchain_core.load.Serializable):
@classmethod
def is_lc_serializable(cls) -> bool:
return True
out = langchain_core.load.dumps(Custom())
langchain_core.load.loads(
out,
valid_namespaces=["langchain", "__main__"],
)
```
### Expected behavior
I'm expecting it's possible to make a custom class serializable, but since langchain_core 0.0.13 the `valid_namespaces` is effectively ignored as it relies on a whitelist of what can be serialized `SERIALIZABLE_MAPPING`.
So I get the error:
> ValueError: Trying to deserialize something that cannot be deserialized in current version of langchain-core: ('__main__', 'Custom')
Triggered in [load.py#L68](https://github.com/langchain-ai/langchain/blob/v0.0.349/libs/core/langchain_core/load/load.py#L68)
---
I'm not sure if serialization was ever intended to be part of the public API, but I've found it convenient to be able to make my custom parts of chains also abide by the serialization protocol and still be able to dump/load my chains | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14588 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14588/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14588/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14588/events | https://github.com/langchain-ai/langchain/issues/14588 | 2,037,349,229 | I_kwDOIPDwls55b3tt | 14,588 | OutputFixingParser should use callbacks | {
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] | open | false | null | [] | null | 1 | 2023-12-12T09:35:53 | 2023-12-12T09:38:12 | null | NONE | null | ### System Info
Python 3.9.13, LangChain 0.0.347, Windows 10
### Who can help?
@hwchase17 @agola
### Information
- [ ] The official example notebooks/scripts
- [X] 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Running this snippet:
```python
answers = (answer for answer in ["answer1", "answer2"])
class CustomLLM(LLM):
def _llm_type(self) -> str:
return "custom"
def _call(self, prompt: str, **_) -> str:
return next(answers)
class CustomOutputParser(BaseOutputParser[str]):
reject_output: bool = True
def parse(self, text: str) -> str:
if self.reject_output:
self.reject_output = False
raise OutputParserException(f"Parsing failed")
return text
def get_format_instructions(self) -> str:
return "format instructions"
class CustomCallbackHandler(BaseCallbackHandler):
def on_llm_end(self, response: LLMResult, **_):
print(f"received LLM response: {response}")
llm = CustomLLM()
chain = LLMChain(
llm=llm,
prompt=PromptTemplate.from_template("Testing prompt"),
output_key="chain_output",
verbose=True,
output_parser=OutputFixingParser.from_llm(llm, CustomOutputParser()),
)
result = chain({}, callbacks=[CustomCallbackHandler()])
print(f"Chain result is {result}")
```
produces the following output:
```
> Entering new LLMChain chain...
Prompt after formatting:
Testing prompt
received LLM response: generations=[[Generation(text='answer1')]] llm_output=None run=None
> Finished chain.
Chain result is {'chain_output': 'answer2'}
```
### Expected behavior
The output line `received LLM response: generations=[[Generation(text='answer1')]] llm_output=None run=None` should also appear with `answer2`, because that one is also generated while running the chain that was configured to use the `CustomCallbackHandler`.
The callbacks should also be used in the chain that is created inside the OutputFixingParser. In my opinion, the chain doing the retry, should have the overall chain as a parent in the callback methods (`on_chain_start()` an so on). | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14587 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14587/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14587/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14587/events | https://github.com/langchain-ai/langchain/pull/14587 | 2,037,340,664 | PR_kwDOIPDwls5hxVzc | 14,587 | fix a bug in RedisNum filter againt value 0 | {
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- **Description:** There is a bug in RedisNum filter that filter towards value 0 will be parsed as "*". This is a fix to it.
- **Issue:** NA
- **Dependencies:** NA
- **Tag maintainer:** NA
- **Twitter handle:** NA
| {
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https://api.github.com/repos/langchain-ai/langchain/issues/14586 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14586/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14586/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14586/events | https://github.com/langchain-ai/langchain/issues/14586 | 2,037,287,446 | I_kwDOIPDwls55booW | 14,586 | Issue: Getting error while integrating Confluence Spaces including attachments | {
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"name": "area: doc loader",
"color": "D4C5F9",
"default": false,
"description": "Related to document loader module (not documentation)"
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] | closed | false | null | [] | null | 2 | 2023-12-12T09:00:13 | 2023-12-12T11:25:18 | 2023-12-12T11:24:03 | NONE | null | ### Issue you'd like to raise.
below is my code
` confluence_url = config.get("confluence_url", None)
username = config.get("username", None)
api_key = config.get("api_key", None)
space_key = config.get("space_key", None)
documents = []
embedding = OpenAIEmbeddings()
loader = ConfluenceLoader(
url=confluence_url,
username=username,
api_key=api_key
)
for space_key in space_key:
documents.extend(loader.load(space_key=space_key,include_attachments=True,limit=100))`
The error I am getting:
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://api.media.atlassian.com/file/UNKNOWN_MEDIA_ID/binary?token=sometoken&name=Invalid%20file%20id%20-%20UNKNOWN_MEDIA_ID&max-age=2940
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14585 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14585/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14585/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14585/events | https://github.com/langchain-ai/langchain/issues/14585 | 2,037,282,618 | I_kwDOIPDwls55bnc6 | 14,585 | ValidationError: 2 validation errors for DocArrayDoc returned when try to execute the RAG Search Example | {
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] | open | false | null | [] | null | 15 | 2023-12-12T08:57:21 | 2024-01-04T06:45:44 | null | NONE | null | ### System Info
Langchain Version: 0.0.335
Platform: Win11
Python Version: 3.11.5
Hi experts,
I'm trying to execute the RAG Search Example on the Langchain Doc: https://python.langchain.com/docs/expression_language/get_started
**Here is the code:**
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
vectorstore = DocArrayInMemorySearch.from_texts(
["harrison worked at kensho", "bears like to eat honey"],
embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
output_parser = StrOutputParser()
setup_and_retrieval = RunnableParallel(
{"context": retriever, "question": RunnablePassthrough()}
)
chain = setup_and_retrieval | prompt | model | output_parser
chain.invoke("where did harrison work?")
**but the example fails with the ValidationError: 2 validation errors for DocArrayDoc.**
**Here is the error details:**
C:\Project\pythonProjectAI\.venv\Lib\site-packages\pydantic\_migration.py:283: UserWarning: `pydantic.error_wrappers:ValidationError` has been moved to `pydantic:ValidationError`.
warnings.warn(f'`{import_path}` has been moved to `{new_location}`.')
Traceback (most recent call last):
File "C:\Project\pythonProjectAI\AI.py", line 28, in <module>
chain.invoke("where did harrison work?")
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\runnable\base.py", line 1427, in invoke
input = step.invoke(
^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\runnable\base.py", line 1938, in invoke
output = {key: future.result() for key, future in zip(steps, futures)}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\runnable\base.py", line 1938, in <dictcomp>
output = {key: future.result() for key, future in zip(steps, futures)}
^^^^^^^^^^^^^^^
File "C:\Program Files\Python\Lib\concurrent\futures\_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python\Lib\concurrent\futures\_base.py", line 401, in __get_result
raise self._exception
File "C:\Program Files\Python\Lib\concurrent\futures\thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\retriever.py", line 112, in invoke
return self.get_relevant_documents(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\retriever.py", line 211, in get_relevant_documents
raise e
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\retriever.py", line 204, in get_relevant_documents
result = self._get_relevant_documents(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\vectorstore.py", line 657, in _get_relevant_documents
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\vectorstores\docarray\base.py", line 127, in similarity_search
results = self.similarity_search_with_score(query, k=k, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\vectorstores\docarray\base.py", line 106, in similarity_search_with_score
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\pydantic\main.py", line 164, in __init__
__pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
pydantic_core._pydantic_core.ValidationError: 2 validation errors for DocArrayDoc
text
Field required [type=missing, input_value={'embedding': [-0.0192381..., 0.010137099064823456]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing
metadata
Field required [type=missing, input_value={'embedding': [-0.0192381..., 0.010137099064823456]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing
### 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
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
vectorstore = DocArrayInMemorySearch.from_texts(
["harrison worked at kensho", "bears like to eat honey"],
embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
output_parser = StrOutputParser()
setup_and_retrieval = RunnableParallel(
{"context": retriever, "question": RunnablePassthrough()}
)
chain = setup_and_retrieval | prompt | model | output_parser
chain.invoke("where did harrison work?")
### Expected behavior
The example runs well. | {
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] | open | false | null | [] | null | 1 | 2023-12-12T08:18:57 | 2023-12-12T08:29:13 | null | NONE | null | https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/langchain/langchain/chains/graph_qa/cypher.py#L266C2-L269C25
Hi, I am heavily using GraphCypherQAChain and sometimes cypher_query_corrector returns generated cypher as valid but have some syntax problems or completely it is not a cypher query; and during the execution of the neo4j, it raises errors. Where do you think that these errors should be handled, how to continue the chain execution without any interruption and return a plausible response to the user? @tomasonjo
Example:
<img width="1104" alt="image" src="https://github.com/langchain-ai/langchain/assets/9192832/0cac12a9-0a4c-4446-af30-2f54ac3290c8">
Thanks! | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14583 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14583/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14583/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14583/events | https://github.com/langchain-ai/langchain/issues/14583 | 2,037,136,239 | I_kwDOIPDwls55bDtv | 14,583 | passing down vectorstore additional argument in SemanticSimilarityExampleSelector | {
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] | open | false | null | [] | null | 1 | 2023-12-12T07:18:55 | 2023-12-12T07:21:11 | null | CONTRIBUTOR | null | ### Feature request
Currently, SemanticSimilarityExampleSelector only passes `k` as a parameter to vectorstore [see here](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/core/langchain_core/example_selectors/semantic_similarity.py#L55C10-L55C10). vectorstore, depending on the implementation can take multiple other arguments. most notably, `filters` can be passed down [see here](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/community/langchain_community/vectorstores/faiss.py#L505C13-L507C38)
### Motivation
Having the ability to filter down examples (on top of similarity search) would be very helpful in controlling the examples that are added to the prompt. This feature provides significant more control over examples selection.
### Your contribution
It is very easy to update this. add a new attribute `vectorstore_kwargs` to the class
class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
"""Example selector that selects examples based on SemanticSimilarity."""
vectorstore: VectorStore
"""VectorStore than contains information about examples."""
k: int = 4
"""Number of examples to select."""
example_keys: Optional[List[str]] = None
"""Optional keys to filter examples to."""
input_keys: Optional[List[str]] = None
"""Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables."""
vectorstore_kwargs: Optional[Dict[str, Any]] = None
"""additional arguments passed to vectorstore for similarity search."""
and then update the `select_examples` function with
`example_docs = self.vectorstore.similarity_search(query, k=self.k, **self.vectorstore_kwargs)` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14582 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14582/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14582/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14582/events | https://github.com/langchain-ai/langchain/pull/14582 | 2,037,128,657 | PR_kwDOIPDwls5hwocQ | 14,582 | Fixes and Upgrades for the Tongyi LLM and ChatTongyi Model | {
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] | closed | false | null | [] | null | 3 | 2023-12-12T07:12:52 | 2023-12-17T18:33:28 | 2023-12-16T11:22:54 | CONTRIBUTOR | null | - **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should be properly handled when set by environment variables or initialization parameters.
- Changed the `dashscope` response to incremental output by setting the parameter `incremental_output`, which eliminates the need for the prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`, `_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @baskaryan
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and `Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
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] | open | false | null | [] | null | 4 | 2023-12-12T06:39:49 | 2024-01-16T07:23:00 | null | NONE | null | ### System Info
python = "^3.10"
openai = "^1.3.8"
langchain = "^0.0.349"
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
````
import chromadb
from langchain.embeddings import AzureOpenAIEmbeddings
from langchain.vectorstores.chroma import Chroma
client = chromadb.HttpClient(
host=CHROMA_SERVER_HOST,
port=CHROMA_SERVER_HTTP_PORT,
)
embeddings = AzureOpenAIEmbeddings(
openai_api_type=AZURE_OPENAI_API_TYPE,
azure_endpoint=AZURE_OPENAI_API_BASE,
api_key=AZURE_OPENAI_API_KEY,
openai_api_version=AZURE_OPENAI_API_VERSION,
azure_deployment=AZURE_EMBEDDING_DEPLOYMENT_NAME,
)
vectordb = Chroma(
client=client,
collection_name=CHROMA_COLLECTION_NAME_FBIG_1000,
embedding_function=embeddings,
)
````
### Expected behavior
TypeError: cannot pickle '_thread.RLock' object
When I use openai = "0.28.1" it doesn't have the above error | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14580 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14580/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14580/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14580/events | https://github.com/langchain-ai/langchain/issues/14580 | 2,037,040,612 | I_kwDOIPDwls55asXk | 14,580 | An output parsing error occurred. Could not parse LLM output create_csv_agent | {
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] | open | false | null | [] | null | 2 | 2023-12-12T05:58:28 | 2023-12-12T07:48:08 | null | NONE | null | ### System Info
Ubuntu 20.04
CUDA 12.1
NVIDIA RTX 4070
### Who can help?
@hwchase17
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
template = """Question: {question} Answer: Your Answer"""
prompt = PromptTemplate(template=template, input_variables=["question"])
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
n_gpu_layers = 80 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.llm = LlamaCpp(model_path="/home/rtx-4070/Downloads/openorca-platypus2-13b.Q4_K_M.gguf",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
callback_manager=callback_manager,
verbose=True,
n_ctx=2048
)
from langchain_experimental.agents.agent_toolkits import create_csv_agent
from langchain.agents.agent_types import AgentType
agent = create_csv_agent(
llm,
"/home/rtx-4070/git_brainyneurals/langchain_local/docs/SPY.csv",
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True
)
agent.run("How many rows are there?")
```
I am running opeorca model which I have downloaded from HuggingFace. But I am facing this erros. Could you please help me with related solutions? Or Suggest any other ways or models. Would be grateful.
Thanks in advance.
### Expected behavior
I want to make work simple csv agent that runs on my RTX 4070 Desktop GPU with any open source models. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14579 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14579/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14579/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14579/events | https://github.com/langchain-ai/langchain/pull/14579 | 2,036,900,399 | PR_kwDOIPDwls5hv3cp | 14,579 | create mypy cache dir if it doesn't exist | {
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] | closed | false | null | [] | null | 1 | 2023-12-12T03:23:30 | 2023-12-12T23:34:50 | 2023-12-12T23:34:50 | CONTRIBUTOR | null | ### Description
When running `make lint` multiple times, i can see the error `mkdir: .mypy_cache: File exists`. Use `mkdir -p` to solve this problem.
<img width="1512" alt="Screenshot 2023-12-12 at 11 22 01 AM" src="https://github.com/langchain-ai/langchain/assets/10000925/1429383d-3283-4e22-8882-5693bc50b502">
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https://api.github.com/repos/langchain-ai/langchain/issues/14577 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14577/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14577/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14577/events | https://github.com/langchain-ai/langchain/pull/14577 | 2,036,797,874 | PR_kwDOIPDwls5hvhy3 | 14,577 | docs[patch]: update installation with core and community | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14576 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14576/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14576/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14576/events | https://github.com/langchain-ai/langchain/pull/14576 | 2,036,763,032 | PR_kwDOIPDwls5hvaRg | 14,576 | Fix embeddings example for Databricks | {
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] | closed | false | null | [] | null | 1 | 2023-12-12T00:31:09 | 2023-12-12T03:24:22 | 2023-12-12T00:55:23 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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See contribution guidelines for more information on how to write/run tests, lint, etc:
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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,
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.
-->
Fix `from langchain.llms import DatabricksEmbeddings` to `from langchain.embeddings import DatabricksEmbeddings`.
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https://api.github.com/repos/langchain-ai/langchain/issues/14575 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14575/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14575/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14575/events | https://github.com/langchain-ai/langchain/pull/14575 | 2,036,754,143 | PR_kwDOIPDwls5hvYWR | 14,575 | docs[patch], templates[patch]: Import from core | {
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] | closed | false | null | [] | null | 5 | 2023-12-12T00:21:12 | 2023-12-12T00:49:12 | 2023-12-12T00:49:11 | COLLABORATOR | null | Update imports to use core for the low-hanging fruit changes. Ran following
```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14574 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14574/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14574/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14574/events | https://github.com/langchain-ai/langchain/pull/14574 | 2,036,753,400 | PR_kwDOIPDwls5hvYLx | 14,574 | Rename LLMonitor Callback Handler | {
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We are renaming our product from LLMonitor to Lunary.ai.
This PR changes all references to LLMonitor throughout the code to Lunary.
Thanks! | {
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Should be run from `libs/partners` | {
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Fixes to rag-semi-structured template.
- Added required libraries
- pdfminer was causing issues when installing with pip. pdfminer.six works best
- Changed the pdf name for demo from llama2 to llava
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Lanchain: 0.0.348
Python 3.12.0
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the error:
1. Run the [notebook](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/llms/predibase.ipynb) with the latest version of Langchain. The error occurs specficially in 13 `review = overall_chain.run("Tragedy at sunset on the beach")`
### Expected behavior
The expected behavior is simply to return the output from the LLM. | {
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