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https://api.github.com/repos/langchain-ai/langchain/issues/13682
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| 2,005,164,418 |
I_kwDOIPDwls53hGGC
| 13,682 |
How to add prompt template to create_csv_agent?
|
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[] | null | 4 | 2023-11-21T21:26:28 | 2023-12-08T18:08:18 | null |
NONE
| null |
### Issue with current documentation:
Hey guys! Below is the code which i'm working on
```
import pandas as pd
from IPython.display import Markdown, display
from langchain.agents import create_csv_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
import os
import pandas as pd
os.environ["OPENAI_API_KEY"] = ""
df = pd.read_csv('https://gist.githubusercontent.com/armgilles/194bcff35001e7eb53a2a8b441e8b2c6/raw/92200bc0a673d5ce2110aaad4544ed6c4010f687/pokemon.csv')
df.to_csv('pokemon.csv', index=False)
# Load the agent
agent = create_csv_agent(OpenAI(temperature=0), "pokemon.csv", verbose=True)
gpt4_agent = create_csv_agent(
ChatOpenAI(temperature=0, model_name="gpt-4"), "pokemon.csv", verbose=True
)
agent.run("Which pokemon has the highest attack and which has lowest defense?")
```
In the above code, you can see the query has the normal question. How do i add the below prompt template as the question?
```
# Now you can use the json_data in your prompt
prompt_template = f'''If the dataset has the following columns: {json_data}
Understand user questions with different column names and convert them to a JSON format. Here's an example:
Example 1:
User Question: Top 2 zones by Housing Loan in the West and South in the year 2019 excluding small loans and with Discount > 5 and Term Deposit between 10k and 15k
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Month",
"agg_columns": ["Housing Loan"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone"],
"filters": {{"Zone": ["West", "South"]}},
"not_in": {{"Loan Type": ["Small"]}},
"num_filter": {{
"gt": [
["Discount", 5],
["Term Deposit", 10000]
],
"lt": [
["Term Deposit", 15000]
]
}},
"percent": false,
"top": "2",
"bottom": null
}}
Example 2:
User Question: What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020
{{
"start_date": "01-01-2020",
"end_date": "31-01-2020",
"time_stamp_col": "Due Date",
"agg_columns": ["Loan Amount", "Loan Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": [],
"filters": {{"RM Name": ["James"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 3:
User Question: Which RM Name with respect to Region has the Highest Interest Outstanding and Principal Outstanding in the year 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Interest Outstanding", "Principal Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["RM Name", "Region"],
"filters": {{}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 4:
User Question: Which Branch in North Carolina with respect to Cibil Score Bucket has the Highest Cibil Score in 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Cibil Score", "DPD Bucket"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
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"filters": {{"Region": ["North Carolina"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 5:
User Question: With respect to Zone, Region, Branch, RM Name what is the Highest Loan Amount, Loan Tenure, Loan Outstanding, EMI Pending, Principal Outstanding
{{
"start_date": null,
"end_date": null,
"time_stamp_col": null,
"agg_columns": ["Loan Amount", "Loan Tenure", "Loan Outstanding", "EMI Pending", "Principal Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone", "Region", "Branch", "RM Name"],
"filters": {{}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 6:
User Question: Top 2 zones by Housing Loan in the year 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Housing Loan"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone"],
"filters": {{"Product": ["Home Loan"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "2",
"bottom": null
}}
Our test dataset has the following columns: {json_data}
User Question (to be converted): {{user_question}}'''
user_question = "Which pokemon has the highest attack and which has lowest defense?"
```
Can anyone assist with this?
### Idea or request for content:
_No response_
|
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| 2,005,150,371 |
I_kwDOIPDwls53hCqj
| 13,681 |
Returning "ValidationError: 1 validation error for LLMChain prompt value is not a valid dict (type=type_error.dict)" while trying to run the LLMChain
|
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[] | null | 1 | 2023-11-21T21:15:55 | 2023-11-21T21:20:40 | null |
NONE
| null |
### Issue with current documentation:
I have run the below code which will try to go through the csv file and return the answer
```
import pandas as pd
import json
from langchain.llms import OpenAI
from langchain.chains import LLMChain
openai_api_key = ''
# Read CSV file into a DataFrame
df = pd.read_csv('https://gist.githubusercontent.com/armgilles/194bcff35001e7eb53a2a8b441e8b2c6/raw/92200bc0a673d5ce2110aaad4544ed6c4010f687/pokemon.csv')
# del df['Due Date']
# del df['Closing Date']
# Create a dictionary of unique values per column
unique_values_per_column = {}
for column in df.select_dtypes(include=['object']).columns:
# Convert ndarray to list
unique_values_per_column[column] = df[column].unique().tolist()
# Convert the dictionary to JSON
json_data = json.dumps(unique_values_per_column, indent=4)
# Now you can use the json_data in your prompt
prompt_template = f'''If the dataset has the following columns: {json_data}
Understand user questions with different column names and convert them to a JSON format. Here's an example:
Example 1:
User Question: Top 2 zones by Housing Loan in the West and South in the year 2019 excluding small loans and with Discount > 5 and Term Deposit between 10k and 15k
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Month",
"agg_columns": ["Housing Loan"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone"],
"filters": {{"Zone": ["West", "South"]}},
"not_in": {{"Loan Type": ["Small"]}},
"num_filter": {{
"gt": [
["Discount", 5],
["Term Deposit", 10000]
],
"lt": [
["Term Deposit", 15000]
]
}},
"percent": false,
"top": "2",
"bottom": null
}}
Example 2:
User Question: What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020
{{
"start_date": "01-01-2020",
"end_date": "31-01-2020",
"time_stamp_col": "Due Date",
"agg_columns": ["Loan Amount", "Loan Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": [],
"filters": {{"RM Name": ["James"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 3:
User Question: Which RM Name with respect to Region has the Highest Interest Outstanding and Principal Outstanding in the year 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Interest Outstanding", "Principal Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["RM Name", "Region"],
"filters": {{}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 4:
User Question: Which Branch in North Carolina with respect to Cibil Score Bucket has the Highest Cibil Score in 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Cibil Score", "DPD Bucket"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Branch"],
"filters": {{"Region": ["North Carolina"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 5:
User Question: With respect to Zone, Region, Branch, RM Name what is the Highest Loan Amount, Loan Tenure, Loan Outstanding, EMI Pending, Principal Outstanding
{{
"start_date": null,
"end_date": null,
"time_stamp_col": null,
"agg_columns": ["Loan Amount", "Loan Tenure", "Loan Outstanding", "EMI Pending", "Principal Outstanding"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone", "Region", "Branch", "RM Name"],
"filters": {{}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "1",
"bottom": null
}}
Example 6:
User Question: Top 2 zones by Housing Loan in the year 2019
{{
"start_date": "01-01-2019",
"end_date": "31-12-2019",
"time_stamp_col": "Due Date",
"agg_columns": ["Housing Loan"],
"trend": null,
"to_start_date": null,
"to_end_date": null,
"growth": null,
"variables_grpby": ["Zone"],
"filters": {{"Product": ["Home Loan"]}},
"not_in": {{}},
"num_filter": {{}},
"percent": false,
"top": "2",
"bottom": null
}}
Our test dataset has the following columns: {json_data}
User Question (to be converted): {{user_question}}'''
# Use langchain to process the prompt
llm = OpenAI(api_key=openai_api_key, temperature=0.9)
chain = LLMChain(llm=llm, prompt=prompt_template)
user_question = "What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020"
response = chain.run(user_question)
print(response)
```
Below is the error it's returning
```
WARNING! api_key is not default parameter.
api_key was transferred to model_kwargs.
Please confirm that api_key is what you intended.
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_22224\1265770286.py in <cell line: 169>()
167 # Use langchain to process the prompt
168 llm = OpenAI(api_key=openai_api_key, temperature=0.9)
--> 169 chain = LLMChain(llm=llm, prompt=prompt_template)
170
171
~\anaconda3\lib\site-packages\langchain\load\serializable.py in __init__(self, **kwargs)
72
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
76
~\anaconda3\lib\site-packages\pydantic\main.cp310-win_amd64.pyd in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LLMChain
prompt
value is not a valid dict (type=type_error.dict)
```
Can anyone assist what exactly is the error?
### Idea or request for content:
_No response_
|
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| 2,005,148,049 |
PR_kwDOIPDwls5gEU07
| 13,680 |
fixed import error for BashOutputParser
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[] | null | 1 | 2023-11-21T21:14:05 | 2023-11-22T01:23:33 | 2023-11-22T00:33:41 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gET6P
| 13,679 |
Embaas - added backoff retries for network requests
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[] | null | 1 | 2023-11-21T21:12:21 | 2023-12-05T00:21:35 | 2023-12-05T00:21:35 |
CONTRIBUTOR
| null |
Running a large number of requests to Embaas' servers (or any server) can result in intermittent network failures (both from local and external network/service issues). This PR implements exponential backoff retries to help mitigate this issue.
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PR_kwDOIPDwls5gEJRB
| 13,678 |
[OBSOLETE] added CrossEncoderReranker with HuggingFaceCrossEncoder and SagemakerEndpointCrossEncoder
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[] | null | 1 | 2023-11-21T20:43:32 | 2023-12-01T17:24:31 | 2023-11-21T23:07:21 |
CONTRIBUTOR
| null |
- **Description:** Support reranking based on cross encoder models available from HuggingFace.
- Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and `SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it. Please let me know if anything else needs to be done to make it visible on the table-of-contents navigation bar on the left, or on the card list on [retrievers documentation page](https://python.langchain.com/docs/integrations/retrievers).
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
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PR_kwDOIPDwls5gEGH_
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CLI 0.0.19
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[] | null | 1 | 2023-11-21T20:33:40 | 2023-11-21T20:34:40 | 2023-11-21T20:34:39 |
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PR_kwDOIPDwls5gEF9q
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Update name
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I_kwDOIPDwls53gvyy
| 13,675 |
Issue: Queries on conversational retreival chain prompt
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[] | null | 3 | 2023-11-21T20:28:07 | 2023-11-21T20:48:23 | null |
NONE
| null |
### Issue you'd like to raise.
I am using conversational retreival chain .from_llm(). If i try to print the final prompt that is been sent to LLM there i see LLM calls total 2 times. I will paste both the prompt in both calls.
Below is the code i am using
`qa_chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type=chain_type,
retriever=vector_database.as_retriever(),
return_source_documents=True)
qa_chain({'question': 'summarize this document', "chat_history": [('what is this', 'this is something'),('who you are', 'i am nothing')]})`
Below i can see the prompt sending to LLM 2 times
**1st call to LLM:**
Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
human: what is this
assistant: this is something
human: who you are
assistant: i am nothing
Follow Up Input: summarize this document
Standalone question:"""
**2nd call to LLM:**
Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
Title: Details
Title:Details
Tile:Details
Question: what is the summary of the document?
Helpful Answers:
In the above mentioned 2 calls i can see clearly whatever i have passed in **chat_history** as a question and answer tuple pair that is used to frame my question in the **1st call prompt**. and i can see clearly my original question **summarize this document** is been modified to **what is the summary of the document?**
My question or i am trying to understand is, In place of context i see 3 times Title:Details. what is this?? Can you please explain? . is this the output of vectordb.asretreiver? if yes why i am seeing Title:Details 3 times?. please help me understand what is **Title:Details.**? in the **2nd prompt call**
Let me know if u need additional context to understand my questions.
### Suggestion:
_No response_
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PR_kwDOIPDwls5gDtqp
| 13,673 |
update langserve to v0.0.30
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[] | null | 1 | 2023-11-21T19:12:54 | 2023-11-21T19:17:48 | 2023-11-21T19:17:47 |
COLLABORATOR
| null |
Upgrade langserve template version to 0.0.30 to include new improvements
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| 2,004,956,076 |
PR_kwDOIPDwls5gDqyZ
| 13,672 |
fix templates dockerfile
|
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[] | null | 1 | 2023-11-21T19:02:56 | 2023-11-21T19:09:57 | 2023-11-21T19:09:56 |
COLLABORATOR
| null |
- **Description:** We need to update the Dockerfile for templates to also copy your README.md. This is because poetry requires that a readme exists if it is specified in the pyproject.toml
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PR_kwDOIPDwls5gDbJz
| 13,671 |
Updated integration with Clarifai python SDK functions
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[] | null | 8 | 2023-11-21T18:18:26 | 2023-12-06T04:08:00 | 2023-12-06T04:08:00 |
CONTRIBUTOR
| null |
Description :
Updated the functions with new Clarifai python SDK.
Enabled initialisation of Clarifai class with model URL.
Updated docs with new functions examples.
|
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PR_kwDOIPDwls5gDWjf
| 13,670 |
Bugfix duckduckgo_search news search
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[] | null | 2 | 2023-11-21T18:08:19 | 2023-12-05T00:48:20 | 2023-12-05T00:48:20 |
CONTRIBUTOR
| null |
- **Description:**
Bugfix duckduckgo_search news search
- **Issue:**
https://github.com/langchain-ai/langchain/issues/13648
- **Dependencies:**
None
- **Tag maintainer:**
@baskaryan
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PR_kwDOIPDwls5gDSUO
| 13,669 |
Fix SearchApi integration
|
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[] | null | 2 | 2023-11-21T17:57:54 | 2023-12-05T00:24:03 | 2023-12-05T00:24:03 |
CONTRIBUTOR
| null |
When integrating into the [Langflow](https://github.com/logspace-ai/langflow) repo, we noticed some issues with the integration, mainly with imports.
This PR fixes the imports and updates the documentation.
@baskaryan since you reviewed our last PR - https://github.com/langchain-ai/langchain/pull/11023.
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I_kwDOIPDwls53fyDq
| 13,667 |
Can not load chain with ChatPromptTemplate
|
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[] | null | 1 | 2023-11-21T17:40:19 | 2023-11-27T16:39:51 | 2023-11-27T16:39:51 |
CONTRIBUTOR
| null |
### System Info
LangChain version: `0.0.339`
Python version: `3.10`
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When serializing the `ChatPromptTemplate`, it saves it in a JSON/YAML format like this:
```
{'input_variables': ['question', 'context'],
'output_parser': None,
'partial_variables': {},
'messages': [{'prompt': {'input_variables': ['context', 'question'],
'output_parser': None,
'partial_variables': {},
'template': "...",
'template_format': 'f-string',
'validate_template': True,
'_type': 'prompt'},
'additional_kwargs': {}}],
'_type': 'chat'}
```
Note that the `_type` is "chat".
However, LangChain's `load_prompt_from_config` [does not recognize "chat" as the supported prompt type](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/prompts/loading.py#L19).
Here is a minimal example to reproduce the issue:
```python
from langchain.prompts import ChatPromptTemplate
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains.loading import load_chain
TEMPLATE = """Answer the question based on the context:
{context}
Question: {question}
Answer:
"""
chat_prompt = ChatPromptTemplate.from_template(TEMPLATE)
llm = OpenAI()
def get_retriever(persist_dir = None):
vectorstore = FAISS.from_texts(
["harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
return vectorstore.as_retriever()
chain_with_chat_prompt = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=get_retriever(),
chain_type_kwargs={"prompt": chat_prompt},
)
chain_with_prompt_saved_path = "./chain_with_prompt.yaml"
chain_with_prompt.save(chain_with_prompt_saved_path)
loaded_chain = load_chain(chain_with_prompt_saved_path, retriever=get_retriever())
```
The above script failed with the error:
`ValueError: Loading chat prompt not supported`
### Expected behavior
Load a chain that contains `ChatPromptTemplate` should work.
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PR_kwDOIPDwls5gDJxC
| 13,666 |
Updated Clarifai-gRPC with Clarifai python SDK functions
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[] | null | 1 | 2023-11-21T17:30:56 | 2023-11-21T17:58:30 | 2023-11-21T17:58:30 |
CONTRIBUTOR
| null |
Description :
Updated the functions with new Clarifai python SDK.
Enabled initialisation of Clarifai class with model URL.
Updated docs with new functions examples.
|
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PR_kwDOIPDwls5gC9Uu
| 13,665 |
more core fixes
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[] | null | 1 | 2023-11-21T16:55:15 | 2023-11-21T23:15:50 | 2023-11-21T23:15:49 |
COLLABORATOR
| null |
Fix some circular deps:
- move PromptValue into top level module bc both PromptTemplates and OutputParsers import
- move tracer context vars to `tracers.context` and import them in functions in `callbacks.manager`
- add core import tests
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PR_kwDOIPDwls5gC4OH
| 13,664 |
bump 0.0.339rc0
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[] | null | 1 | 2023-11-21T16:41:49 | 2023-11-21T16:42:00 | 2023-11-21T16:41:59 |
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I_kwDOIPDwls53fTLc
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Issue: How to validate Tool input arguments without raising ValidationError
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[] | null | 15 | 2023-11-21T16:23:53 | 2023-12-12T03:13:52 | null |
NONE
| null |
### Issue you'd like to raise.
I am creating a tool that takes a multiple input arguments (say input1, input2). I would like to validate their types and also make sure that the tool only receives input1 and input2. How do I validate this without breaking the llm chain. I would instead like to return a warning to the llm agent. something like, "The input passed were incorrect, please try again"
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gCrfT
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Add CometTracer
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[] | null | 2 | 2023-11-21T16:14:07 | 2023-12-05T00:46:48 | 2023-12-05T00:46:48 |
CONTRIBUTOR
| null |
Hi! I'm Alex, Python SDK Team Lead from [Comet](https://www.comet.com/site/).
This PR contains our new integration between langchain and Comet - `CometTracer` class which uses new `comet_llm` python package for submitting data to Comet.
No additional dependencies for the langchain package are required directly, but if the user wants to use `CometTracer`, `comet-llm>=2.0.0` should be installed. Otherwise an exception will be raised from `CometTracer.__init__`.
A test for the feature is included.
There is also an already existing callback (and .ipynb file with example) which ideally should be deprecated in favor of a new tracer. I wasn't sure how exactly you'd prefer to do it. For example we could open a separate PR for that.
I'm open to your ideas :)
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PR_kwDOIPDwls5gCnfl
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Fix Sagemaker Endpoint documentation
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[] | null | 1 | 2023-11-21T16:07:42 | 2023-12-05T00:28:30 | 2023-12-05T00:28:29 |
CONTRIBUTOR
| null |
- **Description:** fixed the transform_input method in the example.,
- **Issue:** example didn't work,
- **Dependencies:** None,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** @Ravidhu87
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PR_kwDOIPDwls5gCG1U
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adapt Jina Embeddings to new Jina AI Embedding API
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[] | null | 3 | 2023-11-21T15:13:37 | 2023-12-05T04:40:34 | 2023-12-05T04:40:33 |
CONTRIBUTOR
| null |
- **Description:** Adapt JinaEmbeddings to run with the new Jina AI Embedding platform
- **Twitter handle:** https://twitter.com/JinaAI_
|
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PR_kwDOIPDwls5gCGO8
| 13,657 |
Switched VertexAI models from preview
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[] | null | 3 | 2023-11-21T15:12:14 | 2023-11-29T01:38:04 | 2023-11-29T01:38:04 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** VertexAI models are now GA, moved away from using preview ones from the SDK
- **Issue:** #13606
|
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I_kwDOIPDwls53eZPH
| 13,656 |
Error in executing SPARQL query with GraphSparqlQAChain and custom LLM (llamacpp)
|
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[] | null | 5 | 2023-11-21T14:51:15 | 2023-11-27T13:40:43 | null |
NONE
| null |
### System Info
Langchain Version: 0.0.339
Python Version: 3.10.12
### Who can help?
@hwchase17
When trying to execute the example from the GraphSparqlQAChain docs a ValueError arises, in my understanding because it cannot parse the query correctly and execute it against the graph.
```
> Entering new GraphSparqlQAChain chain...
Llama.generate: prefix-match hit
The URI of Tim Berners-Lee's work homepage is <https://www.w3.org/People/Berners-Lee/>.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-13-432f483fbf1b>](https://localhost:8080/#) in <cell line: 2>()
1 llm_chain = GraphSparqlQAChain.from_llm(llm=llm, graph=graph, verbose=True)
----> 2 llm_chain.run("What is Tim Berners-Lee's work homepage?")
3 frames
[/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py](https://localhost:8080/#) in run(self, callbacks, tags, metadata, *args, **kwargs)
503 if len(args) != 1:
504 raise ValueError("`run` supports only one positional argument.")
--> 505 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
506 _output_key
507 ]
[/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py](https://localhost:8080/#) in __call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
[/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py](https://localhost:8080/#) in __call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
[/usr/local/lib/python3.10/dist-packages/langchain/chains/graph_qa/sparql.py](https://localhost:8080/#) in _call(self, inputs, run_manager)
101 intent = "UPDATE"
102 else:
--> 103 raise ValueError(
104 "I am sorry, but this prompt seems to fit none of the currently "
105 "supported SPARQL query types, i.e., SELECT and UPDATE."
ValueError: I am sorry, but this prompt seems to fit none of the currently supported SPARQL query types, i.e., SELECT and UPDATE.
```
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behaviour:
1. Follow the example as per the documentation: https://python.langchain.com/docs/use_cases/graph/graph_sparql_qa
2. Instead of the OpenAPI use a custom llamacpp deploy i.e.
```
llm = LlamaCpp(
model_path=model_path,
n_gpu_layers=32,
n_batch=512,
callback_manager=callback_manager,
n_ctx=2048,
verbose=True, # Verbose is required to pass to the callback manager
)
```
3. Run the chain:
```
llm_chain = GraphSparqlQAChain.from_llm(llm=llm, graph=graph, verbose=True)
llm_chain.run("What is Tim Berners-Lee's work homepage?")
```
### Expected behavior
Apart from giving the right result (which it does) the SPARQL query should be shown and run against the graph, as per documentation this should be the reply:
```
> Entering new GraphSparqlQAChain chain...
Identified intent:
SELECT
Generated SPARQL:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?homepage
WHERE {
?person foaf:name "Tim Berners-Lee" .
?person foaf:workplaceHomepage ?homepage .
}
Full Context:
[]
> Finished chain.
"Tim Berners-Lee's work homepage is http://www.w3.org/People/Berners-Lee/."
```
|
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| 2,004,439,132 |
I_kwDOIPDwls53eVBc
| 13,655 |
LlamaCppEmbeddings - fails if all available GPU layers are used.
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[] | null | 1 | 2023-11-21T14:42:36 | 2023-11-21T14:49:47 | null |
NONE
| null |
### System Info
Langchain 0.0.326
Windows
Python 3.11.5
Nvidia P6-16Q GPU
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Loading up all available GPU layers appears to cause LlamaCppEmbeddings to fail?
- Edit: this was my original submission because I was using -1 for the n_gpu_layers but I did further testing and noted that it was only if you used 34 or 35 - for 33 it works, but not 34 or 35.
For example this fails:
`llama = LlamaCppEmbeddings(model_path=r"mistral-7b-instruct-v0.1.Q5_K_M.gguf", n_ctx=512, n_gpu_layers=34)`
```
llm_load_tensors: ggml ctx size = 0.09 MB
llm_load_tensors: using CUDA for GPU acceleration
llm_load_tensors: mem required = 86.05 MB (+ 128.00 MB per state)
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloading v cache to GPU
llm_load_tensors: offloaded 34/35 layers to GPU
llm_load_tensors: VRAM used: 4840 MB
```
With this error message:
```
Traceback (most recent call last):
File "c:\Users\x\Desktop\Embedding\EmbeddingScript.py", line 11, in <module>
query_result = llama.embed_query("Hello World")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\embeddings\llamacpp.py", line 125, in embed_query
embedding = self.client.embed(text)
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_cpp\llama.py", line 860, in embed
return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_cpp\llama.py", line 824, in create_embedding
self.eval(tokens)
File "C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_cpp\llama.py", line 491, in eval
return_code = llama_cpp.llama_eval(
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_cpp\llama_cpp.py", line 808, in llama_eval
return _lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
OSError: exception: access violation writing 0x0000000000000000
```
However, this does not fail:
`llama = LlamaCppEmbeddings(model_path=r"mistral-7b-instruct-v0.1.Q5_K_M.gguf", n_ctx=512, n_gpu_layers=33)`
```
lm_load_tensors: ggml ctx size = 0.09 MB
llm_load_tensors: using CUDA for GPU acceleration
llm_load_tensors: mem required = 86.05 MB (+ 128.00 MB per state)
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/35 layers to GPU
llm_load_tensors: VRAM used: 4808 MB
```
It may not be a langchain problem, I am not knowledgeable enough to know - but not sure why you can't load up all the available layers of the GPU?
### Expected behavior
I would have thought that using all available layers of the GPU would not cause it to fail.
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| 2,004,415,809 |
PR_kwDOIPDwls5gBy-k
| 13,654 |
Tests: Fix regression in `ConsistentFakeEmbeddings.embed_query`
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[] | null | 3 | 2023-11-21T14:31:09 | 2023-12-05T00:46:30 | 2023-12-05T00:46:29 |
NONE
| null |
**Description**: Fixes a regression in `ConsistentFakeEmbeddings.embed_query`.
**Issue**: The function did not return the correct values.
**Dependencies**: -,
**Tag maintainer**: @baskaryan
----
Hi there,
first things first: Thanks a stack for conceiving and maintaining LangChain.
At https://github.com/crate-workbench/langchain/pull/1, we are working on a database adapter for [CrateDB](https://github.com/crate/crate) [^1], where we may have discovered a minor flaw recently introduced to the test utility `ConsistentFakeEmbeddings`.
The function `ConsistentFakeEmbeddings.embed_query` was changed to only return the embedding of the query expression, without considering the known texts, as advertised. It was the recent commit a28eea57674a9 which may have changed it inadvertently this way.
The fix works well on behalf of the test cases we are running (`pytest -vvv tests/ -k cratedb`) [^2], but we didn't run it on any other vector store adapters. It may well have some impact, depending on where `ConsistentFakeEmbeddings` has been used, and how its outcomes are being evaluated on behalf of other test cases.
With kind regards,
Andreas.
[^1]: After sorting out some remaining issues, we hope to be able to contribute this adapter soon, and hope it will be well received.
[^2]: Actually, it was needed by a test case we added with https://github.com/crate-workbench/langchain/pull/15, where it also has been discovered.
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I_kwDOIPDwls53eBxc
| 13,653 |
Issue: Unable to Serialize AgentExecutor Object for Centralized Storage
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[] | null | 2 | 2023-11-21T14:03:40 | 2023-11-21T14:15:11 | null |
NONE
| null |
### Issue you'd like to raise.
To develop a scalable solution, I hoped to store the AgentExecutor object in centralized memory, such as Redis. However, due to its non-serializable nature, this has not been possible. Is there a workaround to achieve this?
### Suggestion:
_No response_
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PR_kwDOIPDwls5gBZ3i
| 13,652 |
fix the problem that redis vector store will change metadata with val…
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[] | null | 3 | 2023-11-21T13:33:13 | 2023-11-22T04:16:35 | 2023-11-22T04:16:24 |
CONTRIBUTOR
| null |
- **Description:** This commit fixed the problem that Redis vector store will change the value of a metadata from 0 to empty when saving the document, which should be an un-intended behavior.
- **Issue:** N/A
- **Dependencies:** N/A
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I_kwDOIPDwls53dvhB
| 13,651 |
Issue: getting error in deletion of embeddings for a file
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[] | null | 1 | 2023-11-21T13:25:26 | 2023-11-21T13:33:34 | null |
NONE
| null |
### Issue you'd like to raise.
def delete_embeddings(file_path, persist_directory):
chroma_db = chromadb.PersistentClient(path=persist_directory)
collection = chroma_db.get_or_create_collection(name="langchain")
ids = collection.get(where={"source": file_path})['ids']
collection.delete(where={"source": file_path},ids=ids)
print("delete successfully")
below is the error i am getting
File "/home/aaditya/DBChat/CustomBot/user_projects/views.py", line 1028, in project_pages
delete_embeddings(names, persist_directory)
File "/home/aaditya/DBChat/CustomBot/accounts/common_langcain_qa.py", line 134, in delete_embeddings
chroma_db = chromadb.PersistentClient(path=persist_directory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/aaditya/DBChat/chat/lib/python3.11/site-packages/chromadb/__init__.py", line 106, in PersistentClient
return Client(settings)
^^^^^^^^^^^^^^^^
File "/home/aaditya/DBChat/chat/lib/python3.11/site-packages/chromadb/__init__.py", line 143, in Client
api = system.instance(API)
^^^^^^^^^^^^^^^^^^^^
File "/home/aaditya/DBChat/chat/lib/python3.11/site-packages/chromadb/config.py", line 248, in instance
impl = type(self)
^^^^^^^^^^
File "/home/aaditya/DBChat/chat/lib/python3.11/site-packages/chromadb/api/segment.py", line 81, in __init__
self._sysdb = self.require(SysDB)
^^^^^^^^^^^^^^^^^^^
File "/home/aaditya/DBChat/chat/lib/python3.11/site-packages/chromadb/config.py", line 189, in require
inst = self._system.instance(type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/aaditya/DBChat/chat/lib/python3.11/site-packages/chromadb/config.py", line 248, in instance
impl = type(self)
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gBSyC
| 13,650 |
Enable support for async streaming on VertexAI
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NONE
| null |
- **Description:** Enable support for async streaming on VertexAI based on release [1.36.0](https://github.com/googleapis/python-aiplatform/releases/tag/v1.36.0) from vertexai SDK
- **Issue:** N/A
- **Dependencies:** Users will need google-cloud-aiplatform>=1.36.0
- **Tag maintainer:** @baskaryan @leo-gan
PS:
I couldn't find unit test for vertex ai that's why I did not add those.
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PR_kwDOIPDwls5gBBPD
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issue langchain-ai#12165 mask API key and IAM token in YandexGPT
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NONE
| null |
- **Description:** The PR masks API key and IAM token secrets for the YandexGPT model.
- **Issue:** #12165
- **Dependencies:** yandexcloud,
- **Tag maintainer:** @eyurtsev ,
- **Twitter handle:** cheparukhin_s
|
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| 2,004,139,873 |
I_kwDOIPDwls53dL9h
| 13,648 |
ddg error: backend "news" is obsolete so should raise an error and example should be updated
|
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[] | null | 5 | 2023-11-21T12:10:21 | 2023-11-21T16:02:01 | null |
CONTRIBUTOR
| null |
### System Info
Ubuntu 22
python 3.10.12
Langchain==0.0.339
duckduckgo-search==3.9.6
### Who can help?
@timonpalm who created [the PR](https://github.com/langchain-ai/langchain/pull/8292)
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps are straight from [this help page](https://python.langchain.com/docs/integrations/tools/ddg) which is [this notebook](https://github.com/langchain-ai/langchain/blob/611e1e0ca45343b86debc0d24db45703ee63643b/docs/docs/integrations/tools/ddg.ipynb#L69)
```
from langchain.tools import DuckDuckGoSearchResults
search = DuckDuckGoSearchResults(backend="news")
search.run("Obama")
```
This results in error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/USER/.local/lib/python3.10/site-packages/langchain/tools/base.py", line 365, in run
raise e
File "/home/USER/.local/lib/python3.10/site-packages/langchain/tools/base.py", line 337, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "/home/USER/.local/lib/python3.10/site-packages/langchain/tools/ddg_search/tool.py", line 61, in _run
res = self.api_wrapper.results(query, self.num_results, backend=self.backend)
File "/home/USER/.local/lib/python3.10/site-packages/langchain/utilities/duckduckgo_search.py", line 108, in results
for i, res in enumerate(results, 1):
File "/home/USER/.local/lib/python3.10/site-packages/duckduckgo_search/duckduckgo_search.py", line 96, in text
for i, result in enumerate(results, start=1):
UnboundLocalError: local variable 'results' referenced before assignment
```
Looking at the source code of duckduckgo-search package I see this function:
```
def text(
self,
keywords: str,
region: str = "wt-wt",
safesearch: str = "moderate",
timelimit: Optional[str] = None,
backend: str = "api",
max_results: Optional[int] = None,
) -> Iterator[Dict[str, Optional[str]]]:
"""DuckDuckGo text search generator. Query params: https://duckduckgo.com/params
Args:
keywords: keywords for query.
region: wt-wt, us-en, uk-en, ru-ru, etc. Defaults to "wt-wt".
safesearch: on, moderate, off. Defaults to "moderate".
timelimit: d, w, m, y. Defaults to None.
backend: api, html, lite. Defaults to api.
api - collect data from https://duckduckgo.com,
html - collect data from https://html.duckduckgo.com,
lite - collect data from https://lite.duckduckgo.com.
max_results: max number of results. If None, returns results only from the first response. Defaults to None.
Yields:
dict with search results.
"""
if backend == "api":
results = self._text_api(keywords, region, safesearch, timelimit, max_results)
elif backend == "html":
results = self._text_html(keywords, region, safesearch, timelimit, max_results)
elif backend == "lite":
results = self._text_lite(keywords, region, timelimit, max_results)
for i, result in enumerate(results, start=1):
yield result
if max_results and i >= max_results:
break
```
My assessment is that langchain should raise an exception if backend is not part of ["api", "html", "lite"] and the notebook should not mention this "news" feature anymore.
### Expected behavior
Crashing when creating instance of ` DuckDuckGoSearchResults` with invalid `backend` argument and updating the documentation.
|
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| 2,004,123,075 |
I_kwDOIPDwls53dH3D
| 13,647 |
SQL Agent not working well with Claude v2 model
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| false | null |
[] | null | 2 | 2023-11-21T12:00:56 | 2023-11-22T09:22:29 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Im trying to develop an sql agent chatbot that will generate and exceute SQL queries based on the user free-text questions.
so far I have developed a generic and basic agent with ChatGPT-4 and it worked pretty well (generated complex SQL and returned an answer).
When I tried to switch the LLM with Claude v2 (hosted on AWS Bedrock) things started to break.
sometimes the agent get stuck in a loop of the same input (for example):
```
> Entering new AgentExecutor chain...
Here is my thought process and actions to answer the question "how many alerts?":
Thought: I need to first see what tables are available in the database.
Action: sql_db_list_tables
Action Input:
Observation: alerts, detectors
Thought: Here is my response to the question "how many alerts?":
Thought: I need to first see what tables are available in the database.
Action: sql_db_list_tables
Action Input:
Observation: alerts, detectors
Thought: Here is my response to the question "how many alerts?":
Thought: I need to first see what tables are available in the database.
Action: sql_db_list_tables
Action Input:
Observation: alerts, detectors
Thought: Here is my response to the question "how many alerts?":
Thought: I need to first see what tables are available in the database.
Action: sql_db_list_tables
Action Input:
Observation: alerts, detectors
Thought: Here is my response to the question "how many alerts?":
```
sometimes it gets to the point that it actually query the db and get the result but don't stop the run and return the answer.
Here is how I configured the LLM and agent:
```
def aws_bedrock():
config = AwsBedrockConfig()
client = boto3.client(
'bedrock-runtime',
region_name=config.region_name,
aws_access_key_id=config.aws_access_key_id,
aws_secret_access_key=config.aws_secret_access_key,
)
model_kwargs = {
"prompt": "\n\nHuman: Hello world\n\nAssistant:",
"max_tokens_to_sample": 100000,
"temperature": 0,
"top_k": 10,
"top_p": 1,
"stop_sequences": [
"\n\nHuman:"
],
"anthropic_version": "bedrock-2023-05-31"
}
return Bedrock(
client=client,
model_id=config.model_id,
model_kwargs=model_kwargs
)
```
(also tried to change the model arguments with no success)
```
memory = ConversationBufferMemory(memory_key="history", chat_memory=history)
toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
self.agent = create_sql_agent(
llm=self.llm,
toolkit=toolkit,
extra_tools=[TableCommentsTool(db=db)],
prefix=self.format_sql_prefix(filters),
suffix=HISTORY_SQL_SUFFIX,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
agent_executor_kwargs={"memory": memory},
verbose=True,
input_variables=["input", "history", "agent_scratchpad"]
)
```
### Suggestion:
_No response_
|
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| 2,004,082,712 |
PR_kwDOIPDwls5gAp1L
| 13,646 |
Update bedrock.py to fix bug
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[] | null | 2 | 2023-11-21T11:37:41 | 2023-11-22T01:28:38 | 2023-11-22T01:28:38 |
CONTRIBUTOR
| null |
fix bug
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gAjym
| 13,645 |
Added partial_variables to BaseStringMessagePromptTemplate.from_template(...)
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[] | null | 2 | 2023-11-21T11:21:33 | 2023-11-22T09:21:06 | 2023-11-22T01:48:38 |
CONTRIBUTOR
| null |
**Description:** BaseStringMessagePromptTemplate.from_template was passing the value of partial_variables into cls(...) via **kwargs, rather than passing it to PromptTemplate.from_template. Which resulted in those *partial_variables being* lost and becoming required *input_variables*.
**twitter handle**: @josep_pon
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| 2,004,052,880 |
I_kwDOIPDwls53c2uQ
| 13,644 |
Support for streaming in the langchain chains (eg., load_summarize_chain)
|
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[] | null | 3 | 2023-11-21T11:20:41 | 2023-11-24T16:19:26 | null |
NONE
| null |
### Feature request
Hello,
I'm not sure if this is already supported or not, I couldn't find anything in the documentation.
Is there a way to make chains support streaming ? It would be nice if we can get it working with something like the `load_summarize_chain`.
Or something like this:
```
doc_prompt = PromptTemplate.from_template("{page_content}")
chain = (
{
"content": lambda docs: "\n\n".join(
format_document(doc, doc_prompt) for doc in docs
)
}
| PromptTemplate.from_template("Summarize the following content:\n\n{content}")
| OpenAI(
temperature=1,
model_name=llm_model,
stream=True,
)
| StrOutputParser()
)
docs = [
Document(
page_content=split,
metadata={"source": "https://en.wikipedia.org/wiki/Nuclear_power_in_space"},
)
for split in text.split()
]
for partial_result in chain.invoke(docs):
print(partial_result)
```
### Motivation
I have long documents to summarize, so I would like to show the partial results in streaming mode and not make the user wait so long to get the final result.
### Your contribution
No. If it's not possible, I'm willing to implement the summarization chain from scratch and use the OpenAI lib.
|
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| 13,643 |
Use pytest asyncio auto mode
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[] | null | 1 | 2023-11-21T10:17:31 | 2023-11-21T15:00:15 | 2023-11-21T15:00:13 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5gAD5A
| 13,642 |
Adjust Cobol Splitter. Take into consideration all divisions
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[] | null | 2 | 2023-11-21T10:07:12 | 2023-12-05T00:35:55 | null |
NONE
| null |
- **Description:** Enhancements to the CobolSegmenter
1- Improved COBOL code validation by checking for the presence of essential COBOL division keywords (IDENTIFICATION, DATA, PROCEDURE, ENVIRONMENT) using a regular expression.
2- Refinements made to the _process_lines method, which iterates through all lines in the COBOL code. It now effectively identifies the start index when inside a relevant section.
3- The regular expression for identifying paragraphs has been updated to exclude certain keywords (e.g., "END_PERFORM," "END-IF," etc.), preventing them from being mistakenly categorized as paragraphs and split.
4- With the comprehensive division splitting and consideration of all code segments, the need for "simplify_code" is eliminated.
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| 2,003,863,458 |
PR_kwDOIPDwls5f_5um
| 13,641 |
Add dstack services integration
|
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[] | null | 4 | 2023-11-21T09:43:42 | 2023-12-01T14:00:44 | 2023-12-01T14:00:44 |
NONE
| null |
- **Description:** This PR adds `Dstack` LLM and `ChatDstack` chat model. It allows easily call LLMs deployed as a dstack service.
- **Twitter handle:** @dstackai
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I_kwDOIPDwls53cGFj
| 13,640 |
DOC: Template "rag-gpt-crawler" doc is incorrect
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[] | null | 2 | 2023-11-21T09:39:28 | 2023-11-22T22:06:11 | 2023-11-22T22:06:11 |
NONE
| null |
### Issue with current documentation:
Regarding:
https://github.com/langchain-ai/langchain/blob/master/templates/rag-gpt-crawler/README.md
I found 2 issues:
1. no such file as "server.py" ( there is "rag_gpt_crawler.ipynb" instead )
2. in docs linked line
`from rag_chroma import chain as rag_gpt_crawler`
is not the same as shown in terminal
`from rag_gpt_crawler import chain as rag_gpt_crawler_chain`
I think the first is incorrect.
### Idea or request for content:
_No response_
|
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DOC: Text metadata
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[] | null | 2 | 2023-11-21T09:39:12 | 2023-11-24T10:28:28 | 2023-11-24T10:28:28 |
NONE
| null |
### Issue with current documentation:
The documentation on [creating documents](https://js.langchain.com/docs/modules/data_connection/document_loaders/how_to/creating_documents) covers optional document metadata but doesn't mention that it's possible to create text metadata in `page_content`. For example if only a filename is given to `CSVLoader` it will assume the header is metadata and [delimit each key-value pair with a newline](https://github.com/aws-samples/multi-tenant-chatbot-using-rag-with-amazon-bedrock/pull/14).
Chat LangChain will talk about this source of metadata but I can't find any additional information in the provided references.
> In the context of the RAG model, if the CSV data is used as a source for retrieval or generation, the fieldnames can be utilized in several ways:
>
> • Retrieval: The fieldnames can be used as query terms to retrieve relevant documents from the CSV dataset. The RAG model can leverage the fieldnames to understand the user's query and retrieve documents that match the specified criteria.
>
> • Generation: The fieldnames can provide context and constraints for generating responses. The RAG model can use the fieldnames as prompts or conditioning information to generate responses that are specific to the content of the corresponding columns in the CSV file.
>
> By incorporating the CSV fieldnames into the retrieval and generation processes, the RAG model can produce more accurate and contextually relevant results based on the specific attributes and structure of the CSV dataset.
### Idea or request for content:
A description of the consequences of including text metadata for different scenarios, models, data stores.
Strategies for including text metadata.
|
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| 2,003,810,406 |
I_kwDOIPDwls53b7hm
| 13,637 |
Add progress bar to GooglePalmEmbeddings
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[] | null | 2 | 2023-11-21T09:16:16 | 2023-11-21T09:18:40 | null |
CONTRIBUTOR
| null |
### Feature request
Add a progress bar to `GooglePalmEmbeddings.embed_documents()` function. [tqdm](https://github.com/tqdm/tqdm) would work just fine.
In my opinion, all embedders should have a progress bar.
### Motivation
When processing embeddings the user should have an idea of how much time is going to take to embed the data. While using GooglePalmEmbeddings, which are not the fastest, I couldn't see a progress bar and that was frustrating me because I had no idea if it was even accessing GooglePalm correctly or how much time it was going to take.
### Your contribution
```python
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from tqdm import tqdm
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.schema.embeddings import Embeddings
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
...
class GooglePalmEmbeddings(BaseModel, Embeddings):
"""Google's PaLM Embeddings APIs."""
client: Any
google_api_key: Optional[str]
model_name: str = "models/embedding-gecko-001"
"""Model name to use."""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate api key, python package exists."""
google_api_key = get_from_dict_or_env(
values, "google_api_key", "GOOGLE_API_KEY"
)
try:
import google.generativeai as genai
genai.configure(api_key=google_api_key)
except ImportError:
raise ImportError("Could not import google.generativeai python package.")
values["client"] = genai
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return [self.embed_query(text) for text in tqdm(texts)]
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
embedding = embed_with_retry(self, self.model_name, text)
return embedding["embedding"]
```
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I_kwDOIPDwls53bw-R
| 13,636 |
TypeError in Jira Wrapper When Parsing Empty Ticket Information
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[] | null | 2 | 2023-11-21T08:52:06 | 2023-11-21T08:54:27 | null |
NONE
| null |
### System Info
When using the Jira wrapper in LangChain to parse data from Jira tickets, the application encounters a TypeError if the ticket information is empty. This issue occurs specifically when the priority field of a ticket is not set, leading to a 'NoneType' object is not subscriptable error.
### Environment Details
LangChain version: [specify version]
Jira Wrapper version: [specify version]
Python version: 3.10
Operating System: [specify OS]
### Error Logs/Stack Traces
```
Traceback (most recent call last):
...
File "/path/to/langchain/tools/jira/tool.py", line 53, in _run
return self.api_wrapper.run(self.mode, instructions)
...
File "/path/to/langchain/utilities/jira.py", line 72, in parse_issues
priority = issue["fields"]["priority"]["name"]
TypeError: 'NoneType' object is not subscriptable
```
### Proposed Solution
I propose adding a check before parsing the ticket information. If the information is empty, return an empty string instead of 'None'. This modification successfully prevented the application from breaking in my tests.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to Reproduce
1- Execute a JQL query to fetch issues from a Jira project (e.g., project = SN and sprint = 'SN Sprint 8 + 9' and labels = 'fe').
2- Ensure that one of the fetched issues has an empty priority field.
3- Observe the application breaking with a TypeError.
### Expected behavior
The Jira wrapper should handle cases where ticket information, such as the priority field, is empty, without causing the application to break.
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I_kwDOIPDwls53bo6r
| 13,635 |
Chain Type Refine Error: 1 validation error for RefineDocumentsChain prompt extra fields not permitted
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[] | null | 2 | 2023-11-21T08:36:32 | 2023-11-21T09:33:37 | null |
NONE
| null |
### System Info
langchain 0.0.326
### Who can help?
_No response_
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
# This text splitter is used to create the parent documents - The big chunks
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=400)
# This text splitter is used to create the child documents - The small chunks
# It should create documents smaller than the parent
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
from chromadb.errors import InvalidDimensionException
try:
vectorstore = Chroma(collection_name="split_parents", embedding_function=bge_embeddings, persist_directory="chroma_db")
except InvalidDimensionException:
Chroma().delete_collection()
vectorstore = Chroma(collection_name="split_parents", embedding_function=bge_embeddings, persist_directory="chroma_db")
#vectorstore = Chroma(collection_name="split_parents", embedding_function=bge_embeddings)
# The storage layer for the parent documents
store = InMemoryStore()
big_chunks_retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
)
big_chunks_retriever.add_documents(documents)
qa_template = """
Nutze die folgenden Informationen aus dem Kontext (getrennt mit <ctx></ctx>), um die Frage zu beantworten.
Antworte nur auf Deutsch, weil der Nutzer kein Englisch versteht! \
Falls du die Antwort nicht weißt, antworte mit "Leider fehlen mir dazu die Informationen." \
Wenn du nicht genügend Informationen unten findest, antworte ebenfalls mit "Leider fehlen mir dazu die Informationen." \
------
<ctx>
{context}
</ctx>
------
{query}
Answer:
"""
prompt = PromptTemplate(template=qa_template,
input_variables=['context','history', 'question'])
chain_type_kwargs={
"verbose": True,
"prompt": prompt,
"memory": ConversationSummaryMemory(
llm=build_llm(),
memory_key="history",
input_key="question",
return_messages=True)}
refine = RetrievalQA.from_chain_type(llm=build_llm(),
chain_type="refine",
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs,
retriever=big_chunks_retriever,
verbose=True)
query = "Hi, I am Max, can you help me??"
refine(query)
```
### Expected behavior
Hi,
in the code you see how I built my RAG model with the ParentDocumentRetriever from Langchain and with Memory. At the moment I am using the RetrievalQA-Chain with the default `chain_type="stuff"`. However I want to try different chain types like "map_reduce" or "refine". But when replacing `chain_type="refine"` and creating the Retrieval QA chain, I get the following Error:
```
ValidationError: 1 validation error for RefineDocumentsChain
prompt
extra fields not permitted (type=value_error.extra)
```
How can I solve this?
|
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PR_kwDOIPDwls5f-Tf-
| 13,634 |
iMessage loader: implement message content extraction from attributed…
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[] | null | 1 | 2023-11-21T03:34:19 | 2023-11-28T20:45:44 | 2023-11-28T20:45:43 |
CONTRIBUTOR
| null |
- **Description:** We are adding functionality to extract message content from the `attributedBody` field of the database, in case the content is not in the `text` field.
- **Issue:** Closes #13326 and #10680
- **Dependencies:** None.
- **Tag maintainer:** @eyurtsev, @hwchase17
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PR_kwDOIPDwls5f-Fs4
| 13,633 |
remove openai api key from cookbook
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[] | null | 3 | 2023-11-21T02:21:06 | 2023-11-22T02:08:05 | 2023-11-22T01:25:06 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 2,003,278,993 |
PR_kwDOIPDwls5f970u
| 13,632 |
Lint for imports
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[] | null | 1 | 2023-11-21T01:22:35 | 2023-11-24T08:44:24 | 2023-11-22T01:42:56 |
COLLABORATOR
| null |
- Adds pydantic/import linting to core
- Adds a check for `langchain_experimental` imports to langchain
|
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I_kwDOIPDwls53ZoNA
| 13,631 |
Langchain.schema.runnable now missing from docs?
|
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[] | null | 4 | 2023-11-20T23:53:15 | 2023-11-24T14:21:53 | null |
NONE
| null |
### System Info
Lanchain V: 0.339
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Following any example that uses a `langchain.schema.runnable` object. For example the "[Adding memory](https://python.langchain.com/docs/expression_language/cookbook/memory)" tutorial uses `RunnableLambda` and `RunnablePassthrough`.
- I no longer see `langchain.schema` in the API docs (see image below).
- Searching in the API docs also doesn't return any results when searching for `RunnablePassthrough`
<img width="363" alt="image" src="https://github.com/langchain-ai/langchain/assets/94480542/1b3ded17-c669-406d-8309-4f953f42c1f6">
### Expected behavior
- I don't see anything in the release notes about the `langchain.schema.runnables` being removed or relocated.
- I would have expected to see them in the API docs, or at least for them to still be returned when searching for them.
- Not sure if this is a documentation build issue and the modules are still importable as I have not updated my Langchain version yet. I was just using the docs as a reference and then starting getting 404 errors upon page refresh (e.g. [this](https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.AIMessageChunk.html#langchain.schema.messages.AIMessageChunk) page for `AIMessageChunk` also no longer exists)
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| 2,003,181,541 |
PR_kwDOIPDwls5f9m55
| 13,629 |
BUG fix anthropic models on bedrock
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[] | null | 1 | 2023-11-20T23:26:43 | 2023-11-22T01:40:30 | 2023-11-22T01:40:29 |
COLLABORATOR
| null |
Introduced in #13403
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| 2,003,171,128 |
I_kwDOIPDwls53Zfc4
| 13,628 |
Potential Bug in ConversationalRetrievalChain - TypeError: can only concatenate str (not "CTransformers") to str | TypeError: can only concatenate str (not "AzureChatOpenAI") to str
|
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[] | null | 3 | 2023-11-20T23:14:44 | 2023-11-30T06:34:23 | null |
NONE
| null |
### System Info
LangChain Version: 0.0.339
Python version: 3.10.8
Windows 10 Enterprise 21H2
When creating a ConversationalRetrievalChain as follows:
CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER = ConversationalRetrievalChain(
combine_docs_chain=combine_docs_chain,
memory=summary_memory,
retriever=rag_retriever,
question_generator=question_generator_chain
)
With
LLM = AzureChatOpenAI(...)
The following error occurs:
"
Traceback (most recent call last):
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\threading.py", line 1016, in _bootstrap_inner
self.run()
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\om403f\Documents\Applied_Research\Deep_Learning\web_app\app.py", line 1456, in llm_task
history_rag_buffer_result = CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER.invoke({'question':user_query, 'chat_history':summary_memory})
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py", line 87, in invoke
return self(
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py", line 310, in __call__
raise e
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 135, in _call
chat_history_str = get_chat_history(inputs["chat_history"])
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 44, in _get_chat_history
ai = "Assistant: " + dialogue_turn[1]
TypeError: can only concatenate str (not "AzureChatOpenAI") to str
"
Alternately, when using CTransformers as follows:
LLM = CTransformers(model=llm_model, model_type="llama", config=config, streaming=True, callbacks=[StreamingStdOutCallbackHandler()])
The following error occurs:
"
Traceback (most recent call last):
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\threading.py", line 1016, in _bootstrap_inner
self.run()
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\om403f\Documents\Applied_Research\Deep_Learning\web_app\app.py", line 1456, in llm_task
history_rag_buffer_result = CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER.invoke({'question':user_query, 'chat_history':summary_memory})
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py", line 87, in invoke
return self(
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py", line 310, in __call__
raise e
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 135, in _call
chat_history_str = get_chat_history(inputs["chat_history"])
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 44, in _get_chat_history
ai = "Assistant: " + dialogue_turn[1]
TypeError: can only concatenate str (not "CTransformers") to str
"
Hope this is an accurate bug report and it helps! Apologies if this is in fact a dumb report and actually an error at my end.
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema.vectorstore import VectorStoreRetriever
from langchain.memory import ConversationSummaryBufferMemory
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.vectorstores import Chroma
from langchain.chains import LLMChainn
from langchain.chains import StuffDocumentsChain
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import CTransformers
VECTOR_STORE = Chroma(persist_directory=VECTORDB_SBERT_FOLDER, embedding_function=HuggingFaceEmbeddings())
LLM = CTransformers(model=llm_model, model_type="llama", config=config, streaming=True, callbacks=[StreamingStdOutCallbackHandler()])
document_prompt = PromptTemplate(
input_variables=["page_content"],
template="{page_content}"
)
document_variable_name = "context"
temp_StuffDocumentsChain_prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
llm_chain_for_StuffDocumentsChain = LLMChain(llm=LLM, prompt=temp_StuffDocumentsChain_prompt)
combine_docs_chain = StuffDocumentsChain(
llm_chain=llm_chain_for_StuffDocumentsChain,
document_prompt=document_prompt,
document_variable_name=document_variable_name
)
summary_memory = ConversationSummaryBufferMemory(llm=LLM, max_token_limit=100)
retriever=VECTOR_STORE.as_retriever()
rag_retriever = VectorStoreRetriever(vectorstore=VECTOR_STORE)
temp_template = (
"""
Combine the chat history and qustion into a standalone question:
Chat history: {chat_history}
question: {user_query}
"""
)
temp_prompt = PromptTemplate.from_template(temp_template)
question_generator_chain = LLMChain(llm=LLM, prompt=temp_prompt)
CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER = ConversationalRetrievalChain(
combine_docs_chain=combine_docs_chain,
memory=summary_memory,
retriever=rag_retriever,
question_generator=question_generator_chain
)
CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER.invoke({'question':user_query, 'chat_history':summary_memory})
### Expected behavior
Should work according to code example and API specs as described in the official LangChain API docs:
https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html#langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain
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| 2,003,135,534 |
PR_kwDOIPDwls5f9cz7
| 13,627 |
Core refac
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[] | null | 1 | 2023-11-20T22:41:30 | 2023-11-21T16:35:31 | 2023-11-21T16:35:30 |
COLLABORATOR
| null |
proposed changes:
- remove langchain_core/schema since no clear distinction b/n schema and non-schema modules
- make every module that doesn't end in -y plural
- where easy have 1-2 classes per file
- no more than one level of nesting
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PR_kwDOIPDwls5f9b98
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chore: add serializer for tool message
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[] | null | 1 | 2023-11-20T22:38:27 | 2023-11-24T08:37:02 | 2023-11-22T02:20:58 |
CONTRIBUTOR
| null |
**Description:** Currently, if we pass in a ToolMessage back to the chain, it crashes with error
`Got unsupported message type: `
This fixes it.
Tested locally
|
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PR_kwDOIPDwls5f9XHa
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Add template for gpt-crawler
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[] | null | 1 | 2023-11-20T22:17:29 | 2023-11-21T05:32:58 | 2023-11-21T05:32:57 |
COLLABORATOR
| null |
Template for RAG using [gpt-crawler](https://github.com/BuilderIO/gpt-crawler).
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Potential Bug in Retriever.py: Can't instantiate abstract class BaseRetriever with abstract method _get_relevant_documents
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[] | null | 4 | 2023-11-20T21:27:06 | 2024-01-10T14:30:12 | null |
NONE
| null |
### System Info
LangChain Version: 0.0.339
Python version: 3.10.8
Windows 10 Enterprise 21H2
When creating a ConversationalRetrievalChain as follows:
CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER = ConversationalRetrievalChain(
combine_docs_chain=combine_docs_chain,
memory=summary_memory,
retriever=rag_retriever,
question_generator=question_generator_chain
)
With rag_retriever = VectorStoreRetrieverMemory(retriever=VECTOR_STORE.as_retriever())
The following error occurs:
"
Traceback (most recent call last):
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\threading.py", line 1016, in _bootstrap_inner
self.run()
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\om403f\Documents\Applied_Research\Deep_Learning\web_app\app.py", line 1438, in llm_task
CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER = ConversationalRetrievalChain(
File "C:\Users\om403f\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\load\serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ConversationalRetrievalChain
retriever
Can't instantiate abstract class BaseRetriever with abstract method _get_relevant_documents (type=type_error)
"
Name mangling may be occurring as described here: https://stackoverflow.com/questions/31457855/cant-instantiate-abstract-class-with-abstract-methods
retriever.py implements the abstract method _get_relevant_documents: https://github.com/langchain-ai/langchain/blob/4eec47b19128fa168e58b9a218a9da049275f6ce/libs/langchain/langchain/schema/retriever.py#L136
Hope this is an accurate bug report and it helps! Apologies if this is in fact a dumb report and actually an error at my end.
### 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.memory import ConversationSummaryBufferMemory
from langchain.memory import VectorStoreRetrieverMemory
from langchain.prompts import PromptTemplate
from langchain.vectorstores import Chroma
from langchain.chains import LLMChain
from langchain.chains import RetrievalQA
from langchain.chains import ConversationChain
from langchain.chains import StuffDocumentsChain
from langchain.chains import ConversationalRetrievalChain
VECTOR_STORE = Chroma(persist_directory=VECTORDB_SBERT_FOLDER, embedding_function=HuggingFaceEmbeddings())
LLM = AzureChatOpenAI()
document_prompt = PromptTemplate(
input_variables=["page_content"],
template="{page_content}"
)
document_variable_name = "context"
temp_StuffDocumentsChain_prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
llm_chain_for_StuffDocumentsChain = LLMChain(llm=LLM, prompt=temp_StuffDocumentsChain_prompt)
combine_docs_chain = StuffDocumentsChain(
llm_chain=llm_chain_for_StuffDocumentsChain,
document_prompt=document_prompt,
document_variable_name=document_variable_name
)
summary_memory = ConversationSummaryBufferMemory(llm=LLM, max_token_limit=100)
retriever=VECTOR_STORE.as_retriever()
rag_retriever = VectorStoreRetrieverMemory(retriever=retriever)
temp_template = (
"""
Combine the chat history and qustion into a standalone question:
Chat history: {chat_history}
question: {user_query}
"""
)
temp_prompt = PromptTemplate.from_template(temp_template)
question_generator_chain = LLMChain(llm=LLM, prompt=temp_prompt)
CONVERSATION_RAG_CHAIN_WITH_SUMMARY_BUFFER = ConversationalRetrievalChain(
combine_docs_chain=combine_docs_chain,
memory=summary_memory,
retriever=rag_retriever,
question_generator=question_generator_chain
)
### Expected behavior
Example code here works: https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html#langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain
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PR_kwDOIPDwls5f9EM0
| 13,623 |
Add core as dep
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[] | null | 1 | 2023-11-20T21:19:49 | 2023-11-20T22:38:11 | 2023-11-20T22:38:10 |
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| 2,003,012,832 |
I_kwDOIPDwls53Y4zg
| 13,622 |
Configurable timeout for Ollama
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[] | null | 3 | 2023-11-20T21:10:54 | 2023-11-20T21:36:40 | 2023-11-20T21:36:40 |
NONE
| null |
### Feature request
I have been using Ollama with Langchain for various tasks, but sometimes Ollama takes too long to respond depending on my local hardware. Is it possible to add a configurable timeout to the Ollama base class so that I can adjust this setting to avoid timeouts when using agents. Currently, I am getting a `httpx` timeout error when using Ollama.
### Motivation
This feature will help to leverage local LLMs on a variety of hardware and let's experiment and build with local LLMs before using any third party APIs.
### Your contribution
If this is something that would be considered as a feature I am happy to add in a PR for this feature.
|
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PR_kwDOIPDwls5f8_XI
| 13,621 |
Add Databricks Vector Search as a new vector store
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[] | null | 1 | 2023-11-20T21:02:33 | 2023-11-27T19:07:27 | 2023-11-27T19:07:26 |
CONTRIBUTOR
| null |
**Description:**
This PR adds Databricks Vector Search as a new vector store in LangChain.
- [x] Add `DatabricksVectorSearch` in `langchain/vectorstores/`
- [x] Unit tests
- [x] Add [`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/) as a new optional dependency
We ran the following checks:
- `make format` passed ✅
- `make lint` failed but the failures were caused by other files
+ Files touched by this PR passed the linter ✅
- `make test` passed ✅
- `make coverage` failed but the failures were caused by other files. Tests added by or related to this PR all passed
+ langchain/vectorstores/databricks_vector_search.py test coverage 94% ✅
- `make spell_check` passed ✅
The example notebook and updates to the [provider's documentation page](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/providers/databricks.md) will be added later in a separate PR.
**Dependencies:**
Optional dependency: [`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
<!-- Thank you for contributing to LangChain!
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PR_kwDOIPDwls5f89lI
| 13,620 |
VoyageEmbeddings embed_general_texts
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[] | null | 1 | 2023-11-20T20:57:10 | 2023-11-22T02:33:07 | 2023-11-22T02:33:07 |
CONTRIBUTOR
| null |
- **Description:** add method embed_general_texts in VoyageEmebddings to support input_type
- **Issue:**
- **Dependencies:**
- **Tag maintainer:**
- **Twitter handle:** @Voyage_AI_
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PR_kwDOIPDwls5f81Bz
| 13,619 |
feat: implement max marginal relevance for momento vector index
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[] | null | 5 | 2023-11-20T20:28:37 | 2023-12-05T00:50:23 | 2023-12-05T00:50:23 |
CONTRIBUTOR
| null |
**Description**
Implements `max_marginal_relevance_search` and `max_marginal_relevance_search_by_vector` for the Momento Vector Index vectorstore.
Additionally bumps the `momento` dependency in the lock file and adds logging to the implementation.
**Dependencies**
✅ updates `momento` dependency in lock file
**Tag maintainer**
@baskaryan
**Twitter handle**
Please tag @momentohq for Momento Vector Index and @mloml for the contribution 🙇
<!-- Thank you for contributing to LangChain!
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PR_kwDOIPDwls5f8nMy
| 13,618 |
office365 toolkit bug fixes
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[] | null | 1 | 2023-11-20T19:53:31 | 2023-11-28T17:10:06 | 2023-11-28T16:49:25 |
COLLABORATOR
| null |
Several bug fixes:
- emails: instead of `bcc` the `cc` is used.
- errors in the truncation descriptions
- no truncation of the `message_search`
Several updates:
- generalized UTC format
- truncation limit can be changed now in _call()
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I_kwDOIPDwls53YUl1
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Issue: `pgvector` versions in `poetry.lock` are really old
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[] | null | 3 | 2023-11-20T19:32:27 | 2023-12-09T13:20:44 | null |
NONE
| null |
### Issue you'd like to raise.
The compatible versions for library `pgvector` listed in `poetry.lock` are really old: `"pgvector (>=0.1.6,<0.2.0)"`.
Are we able to update them to more recent ones?
### Suggestion:
Update versions to recent ones.
|
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BookendAI Embeddings
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[] | null | 3 | 2023-11-20T17:57:18 | 2023-12-05T00:53:29 | 2023-12-05T00:53:29 |
CONTRIBUTOR
| null |
- **Description:** https://bookend.ai/ langchain embeddings integration
- **Dependencies:** n/a (just using `requests`)
- **Tag maintainer:** stvhu-bookend
- **Twitter handle:** https://twitter.com/bookendai
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PR_kwDOIPDwls5f8AI0
| 13,615 |
DOCS: update rag use case images
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PR_kwDOIPDwls5f7-mQ
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Rag redis template dependency update
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CONTRIBUTOR
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- **Description:** Update RAG Redis template readme and dependencies.
- **Tag maintainer:** @efriis @baskaryan
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bump 339
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[] | null | 1 | 2023-11-20T17:45:02 | 2023-11-20T17:56:44 | 2023-11-20T17:56:43 |
COLLABORATOR
| null | null |
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| 2,002,664,790 |
PR_kwDOIPDwls5f71dq
| 13,612 |
standardise metadata_field handling
|
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[] | null | 1 | 2023-11-20T17:18:34 | 2023-11-28T08:51:47 | null |
NONE
| null |
- **Description:** updated func max_marginal_relevance_search handling of metadata_field to be same as similarity_search_with_score. Allow flatten Opensearch mapping, where meta fields are not nested into any field.
- **Issue:** #10524
- **Maintainer:** @hwchase17, @baskaryan
|
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I_kwDOIPDwls53XF1R
| 13,611 |
ValueError: Redis failed to connect:
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[] | null | 2 | 2023-11-20T16:10:48 | 2023-11-20T16:17:33 | null |
NONE
| null |
### System Info
# Create and load Redis with documents
vectorstore = RedisVectorStore.from_texts(
texts=texts,
metadatas=metadatas,
embedding=embedding,
index_name=index_name,
redis_url=redis_url
)
The error i faced
Redis cannot be used as a vector database without RediSearch >=2.4Please head to https://redis.io/docs/stack/search/quick_start/to know more about installing the RediSearch module within Redis Stack.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
.
### Expected behavior
.
|
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I_kwDOIPDwls53Wwcw
| 13,610 |
Type Checking issue: CacheBackedEmbeddings.split_documents does not take a list of Documents
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| false | null |
[] | null | 2 | 2023-11-20T15:27:25 | 2023-11-20T15:34:24 | null |
NONE
| null |
### System Info
I encountered an exception and a type checking notice in PyCharm while working with the following code snippet:
```
split_documents = text_splitter.split_documents(raw_documents)
cached_embedder.embed_documents(**split_documents**)
```
The type checking notice indicates that there is a mismatch in the expected type for a Document. According to the type definition, a Document should have properties such as page_content, type, and metadata. However, the function embed_documents seems to be designed to handle a list of strings instead of documents.
To align with the expected type for a Document, it is suggested to consider renaming the function from embed_documents to something like embed_strings or embed_texts. This change would accurately reflect the input type expected by the function and help avoid type-related issues during development.
Thank you for your attention to this matter.
```
cached_embedder.embed_documents(split_documents)
...
File "venv/lib/python3.11/site-packages/langchain/embeddings/cache.py", line 26, in _hash_string_to_uuid
hash_value = hashlib.sha1(input_string.encode("utf-8")).hexdigest()
^^^^^^^^^^^^^^^^^^^
AttributeError: 'Document' object has no attribute 'encode'
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
loader = DirectoryLoader(
"./data",
glob="**/*.md",
show_progress=True,
use_multithreading=True,
loader_cls=UnstructuredMarkdownLoader,
loader_kwargs={"mode": "elements"},
)
raw_documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
split_documents = text_splitter.split_documents(raw_documents)
cached_embedder.embed_documents(split_documents)
### Expected behavior
method should either accept a list of Documents or be renamed to embed_strings
|
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| 2,002,224,474 |
PR_kwDOIPDwls5f6VLd
| 13,608 |
Added support for partial_variables to BaseStringMessagePromptTemplate
|
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[] | null | 1 | 2023-11-20T13:36:03 | 2023-11-21T11:05:58 | 2023-11-21T11:05:55 |
CONTRIBUTOR
| null |
**Description:** BaseStringMessagePromptTemplate.from_template was passingg the value of partial_variables into cls(...) via **kwargs, rather than passing it to PromptTemplate.from_template.
**Tag maintainer:** @eyurtsev
**Twitter handle:** @josep_pon
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I_kwDOIPDwls53V19y
| 13,607 |
Issue: Validation errors for ConversationalRetrievalChain (combine_docs_chain)
|
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[] | null | 2 | 2023-11-20T13:30:37 | 2023-11-20T13:40:30 | null |
NONE
| null |
### Issue you'd like to raise.
Hello, I am working on a conversational chatbot, here is a snippet of the code :
```
general_system_template = ""You a chatbot...
---
{summaries}"""
general_user_template = "Question: {question}"
messages = [
SystemMessagePromptTemplate.from_template(general_system_template),
HumanMessagePromptTemplate.from_template(general_user_template)
]
qa_prompt = ChatPromptTemplate(
messages=messages,
input_variables=['question', 'summaries']
)
q = Queue()
llm_chat = ChatVertexAI(
temperature=0,
model_name="chat-bison",
streaming=True,
callbacks=[QueueCallback(q)],
verbose=False
)
retriever = docsearch.as_retriever(
search_type="similarity",
search_kwargs={
'k': 2,
'filter': {'source': {'$in': sources}}
}
)
llm_text = VertexAI(
temperature=0,
model_name="text-bison"
)
combine_docs_chain = load_qa_with_sources_chain(
llm=llm_chat,
chain_type="stuff",
prompt=qa_prompt
)
condense_question_template = (
"""Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History: {chat_history}
Follow Up Input: {question}"""
)
condense_question_prompt = PromptTemplate.from_template(condense_question_template)
condense_chain = LLMChain(
llm=llm_text,
prompt=condense_question_prompt,
verbose=True
)
chain = ConversationalRetrievalChain(
combine_docs_chain_=combine_docs_chain,
retriever=retriever,
question_generator=condense_chain
)
```
When running the code I have the following error :
```
pydantic.error_wrappers.ValidationError: 2 validation errors for ConversationalRetrievalChain
combine_docs_chain
field required (type=value_error.missing)
combine_docs_chain_
extra fields not permitted (type=value_error.extra)
```
How could I solve this ? Is there any way to have a more detailed error ?
### Suggestion:
_No response_
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| 2,002,142,951 |
I_kwDOIPDwls53Vkbn
| 13,606 |
Changed import of VertexAI
|
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[] | null | 2 | 2023-11-20T12:55:25 | 2023-11-20T13:02:41 | null |
NONE
| null |
### System Info
python = "^3.8.10"
langchain = "^0.0.336"
google-cloud-aiplatform = "^1.36.3"
### Who can help?
@hwchase17 @agol
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.llms.vertexai import VertexAI
model = VertexAI(
model_name="text-bison@001",
temperature=0.2,
max_output_tokens=1024,
top_k=40,
top_p=0.8
)
model.client
# <vertexai.preview.language_models._PreviewTextGenerationModel at ...>
# it should be <vertexai.language_models.TextGenerationModel at ...>
```
### Expected behavior
Code reference: https://github.com/langchain-ai/langchain/blob/78a1f4b264fbdca263a4f8873b980eaadb8912a7/libs/langchain/langchain/llms/vertexai.py#L255C77-L255C77
The VertexAI API is now using vertexai.language_models.TextGenerationModel.
Instead, here we are still importing it from from vertexai.preview.language_models.
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I_kwDOIPDwls53VhLr
| 13,605 |
Issue: retrieve multi index from vector store using Faiss in Langchain
|
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[] | null | 2 | 2023-11-20T12:47:43 | 2023-11-21T14:58:27 | 2023-11-21T14:58:27 |
NONE
| null |
### Issue you'd like to raise.
I've created a multi-level directory vector store using Faiss. How can I retrieve all indices within one or multiple subdirectories?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5f5pAz
| 13,604 |
Fix broken link on Meilisearch vector-store documentation
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[] | null | 1 | 2023-11-20T11:49:12 | 2023-11-29T09:56:37 | 2023-11-28T20:49:33 |
CONTRIBUTOR
| null |
- **Description:** dead link replacement
- **Issue:** no open issue
**Note:**
Hi langchain team,
Sorry to open a PR for this concern but we realized that one of the links present in the documentation booklet was broken 😄
|
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PR_kwDOIPDwls5f5S2A
| 13,603 |
Exclude Bedrock client and credentials_profile_name fields from serialisation
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[] | null | 2 | 2023-11-20T10:58:18 | 2023-11-28T21:34:47 | 2023-11-28T21:34:46 |
CONTRIBUTOR
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| 2,001,851,127 |
I_kwDOIPDwls53UdL3
| 13,602 |
Structured tools not able to pass structured data to each other
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[] | null | 11 | 2023-11-20T10:21:21 | 2023-11-20T14:06:32 | null |
NONE
| null |
### System Info
LangChain 0.0.338
Python 3.11.5
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I was trying to combine multiple structured `Tool`s, one that produces a `List` of values and another that consumes it, but couldn't get it to work. I asked the LangChain support bot whether it was possible and it said yes and produced the following example. But it does not work :)
```python
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import BaseTool
from typing import List
# Define the first structured tool that returns a list of strings
class ListTool(BaseTool):
name = "List Tool"
description = "Generates a list of strings."
def _run(self) -> List[str]:
"""Return a list of strings."""
return ["apple", "banana", "cherry"]
tool1 = ListTool()
# Define the second structured tool that accepts a list of strings
class ProcessListTool(BaseTool):
name = "Process List Tool"
description = "Processes a list of strings."
def _run(self, input_list: List[str]) -> str:
"""Process the list of strings."""
# Perform the processing logic here
processed_list = [item.upper() for item in input_list]
return f"Processed list: {', '.join(processed_list)}"
tool2 = ProcessListTool()
llm = OpenAI(temperature=0)
agent_executor = initialize_agent(
[tool1, tool2],
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
output = agent_executor.run("Process the list")
print(output) # Output: 'Processed list: APPLE, BANANA, CHERRY'
```
Full output:
```
> Entering new AgentExecutor chain...
Action:
{
"action": "Process List Tool",
"action_input": {
"input_list": {
"title": "Input List",
"type": "array",
"items": {
"type": "string"
}
}
}
}
Observation: Processed list: TITLE, TYPE, ITEMS
Thought: I have the processed list
Action:
{
"action": "Final Answer",
"action_input": "I have processed the list and it contains the following: TITLE, TYPE, ITEMS"
}
> Finished chain.
```
### Expected behavior
Expected output:
```
Processed list: APPLE, BANANA, CHERRY'
```
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I_kwDOIPDwls53UdDD
| 13,601 |
Neo4j - ConfigurationError: username not supported in the URI
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[] | null | 4 | 2023-11-20T10:21:02 | 2023-11-20T10:50:00 | null |
NONE
| null |
### System Info
Langchain==0.0.338
python==3.8.1
neo4j latest
this is the error:
---------------------------------------------------------------------------
ConfigurationError Traceback (most recent call last)
[/Users/m1/Desktop/LangChain/Untitled.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/Untitled.ipynb) Cell 1 line 5
[2](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=1) import os
[4](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=3) uri, user, password = os.getenv("NEO4J_URI"), os.getenv("NEO4J_USERNAME"), os.getenv("NEO4J_PASSWORD")
----> [5](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=4) graph = Neo4jGraph(
[6](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=5) url=uri,
[7](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=6) username=user,
[8](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=7) password=password,
[9](vscode-notebook-cell:/Users/m1/Desktop/LangChain/Untitled.ipynb#W0sZmlsZQ%3D%3D?line=8) )
File [~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:69](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:69), in Neo4jGraph.__init__(self, url, username, password, database)
[66](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:66) password = get_from_env("password", "NEO4J_PASSWORD", password)
[67](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:67) database = get_from_env("database", "NEO4J_DATABASE", database)
---> [69](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:69) self._driver = neo4j.GraphDatabase.driver(url, auth=(username, password))
[70](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:70) self._database = database
[71](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/langchain/graphs/neo4j_graph.py:71) self.schema: str = ""
File [~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:190](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:190), in GraphDatabase.driver(cls, uri, auth, **config)
[170](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:170) @classmethod
[171](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:171) def driver(
[172](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:172) cls, uri: str, *,
ref='~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:0'>0</a>;32m (...)
[177](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:177) **config
[178](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/_sync/driver.py:178) ) -> Driver:
...
--> [486](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/api.py:486) raise ConfigurationError("Username is not supported in the URI")
[488](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/api.py:488) if parsed.password:
[489](https://file+.vscode-resource.vscode-cdn.net/Users/m1/Desktop/LangChain/~/Desktop/LangChain/KG_openai/lib/python3.8/site-packages/neo4j/api.py:489) raise ConfigurationError("Password is not supported in the URI")
ConfigurationError: Username is not supported in the URI
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?bbc3ed55-b69e-4557-b7e7-e9913806eb86) or open in a [text editor](command:workbench.action.openLargeOutput?bbc3ed55-b69e-4557-b7e7-e9913806eb86). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.graphs import Neo4jGraph
import os
uri, user, password = os.getenv("NEO4J_URI"), os.getenv("NEO4J_USERNAME"), os.getenv("NEO4J_PASSWORD")
graph = Neo4jGraph(
url= uri,
username=user,
password=password,
)
### Expected behavior
This driver formation is running fine in v264. however its giving me error in v338 version. at last in the driver stub, its parsing the url and then the username from the parsed url is being checked. If its present then its raising this above config error.
|
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| 2,001,666,284 |
I_kwDOIPDwls53TwDs
| 13,599 |
Issue: Filling `ConversationSummaryMemory` with existing conversation from an SQLite database
|
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[] | null | 17 | 2023-11-20T08:52:20 | 2023-11-30T03:27:24 | 2023-11-26T16:03:52 |
NONE
| null |
### Issue you'd like to raise.
I'd like to make `ConversationSummaryMemory` is filled with the previous questions and answers for a specific conversation from an SQLite database so I can have my agent already aware of previous conversation with the user.
Here's my current code:
```py
import os
import sys
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.indexes import VectorstoreIndexCreator
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
from langchain.vectorstores.chroma import Chroma
from langchain.memory import ConversationSummaryMemory
from langchain.tools import Tool
from langchain.agents.types import AgentType
from langchain.agents import initialize_agent
from dotenv import load_dotenv
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
query = " ".join(sys.argv[1:]) if len(sys.argv) > 1 else None
retriever = # retriever stuff here for the `local-docs` tool
llm = ChatOpenAI(temperature=0.7, model="gpt-3.5-turbo-1106")
memory = ConversationSummaryMemory(
llm=llm,
memory_key="chat_history",
return_messages=True,
)
chain = ConversationalRetrievalChain.from_llm(
llm=llm,
memory=memory,
chain_type="stuff",
retriever=index.vectorstore.as_retriever(search_kwargs={"k": 4}),
get_chat_history=lambda h: h,
verbose=False,
)
system_message = (
"Be helpful to your users".
)
tools = [
Tool(
name="local-docs",
func=chain,
description="Useful when you need to answer docs-related questions",
)
]
def ask(input: str) -> str:
result = ""
try:
result = executor({"input": input})
except Exception as e:
response = str(e)
if response.startswith("Could not parse LLM output: `"):
response = response.removeprefix(
"Could not parse LLM output: `"
).removesuffix("`")
return response
else:
raise Exception(str(e))
return result
chat_history = []
executor = initialize_agent(
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
tools=tools,
llm=llm,
memory=memory,
agent_kwargs={"system_message": system_message},
verbose=True,
max_execution_time=30,
max_iterations=6,
handle_parsing_errors=True,
early_stopping_method="generate",
stop=["\nObservation:"],
)
result = ask(query)
print(result["output"])
```
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| 2,001,501,651 |
I_kwDOIPDwls53TH3T
| 13,597 |
add multimodal support
|
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[] | null | 2 | 2023-11-20T07:00:43 | 2023-11-20T07:03:04 | null |
NONE
| null |
### Feature request
add support for other multimodal models like Llava, Fuyu, Bakllava... This would help with RAG, where documents have non text data.
### Motivation
I have a lot of tables and images to proccess in PDFs when doing RAG, and right now this is not ideal.
### Your contribution
no time :(
|
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| 2,001,474,706 |
I_kwDOIPDwls53TBSS
| 13,596 |
Issue: How to add randomness when using max_marginal_relevance_search with Qdrant
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[] | null | 2 | 2023-11-20T06:38:24 | 2023-11-20T06:44:07 | null |
NONE
| null |
### Issue you'd like to raise.
I am using Qdrant as my vector store, and now, every time I use 'max_marginal_relevance_search' with a fix k parameter. It will always return the same documents. How to add some randomness? So it will return something different(Still within the score_threshold) each time. Here is my sample code of using 'max_marginal_relevance_search':
related_docs = vectorstore.max_marginal_relevance_search(target_place, k=fetch_amount, score_threshold=0.5, filter=rest.Filter(must=[rest.FieldCondition(
key='metadata.category',
match=rest.MatchValue(value=category),
),rest.FieldCondition(
key='metadata.related_words',
match=rest.MatchAny(any=related_words),
)]))
### Suggestion:
_No response_
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PR_kwDOIPDwls5f3gZi
| 13,595 |
Bagatur/cogniswitch
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[] | null | 2 | 2023-11-20T05:19:00 | 2023-12-14T02:53:28 | 2023-12-14T02:53:28 |
COLLABORATOR
| null | null |
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| 2,001,365,132 |
PR_kwDOIPDwls5f3bVE
| 13,594 |
Update mongodb_atlas.py to fix ImportError: cannot import name 'DriverInfo' from 'pymongo'
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[] | null | 2 | 2023-11-20T04:54:52 | 2023-12-05T03:26:00 | 2023-12-05T03:26:00 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** fix ImportError: cannot import name 'DriverInfo' from 'pymongo'
- **Issue:** As above
- **Dependencies:** NA
- **Tag maintainer:** @efriis @jarib
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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| 2,001,330,836 |
I_kwDOIPDwls53SeKU
| 13,593 |
[SelfQueryRetriever] Generated Query Mismatched Timestamp Type
|
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[] | null | 2 | 2023-11-20T04:16:00 | 2023-11-20T04:23:23 | null |
NONE
| null |
### System Info
langchain==0.0.266
### Who can help?
@eyurtsev @hwchase17
### 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
```python
import datetime
import chainlit
from dotenv import load_dotenv
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.chat_models import ChatOpenAI
from langchain.docstore.document import Document # noqa
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import SelfQueryRetriever
from langchain.vectorstores import Chroma
chainlit.debug = True
load_dotenv()
llm = ChatOpenAI()
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
metadata={"released_at": 1700190868, "rating": 7.7, "genre": "science fiction"},
),
Document(
page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
metadata={"released_at": 1700190868, "director": "Christopher Nolan", "rating": 8.2},
),
Document(
page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",
metadata={"released_at": 1700190868, "director": "Satoshi Kon", "rating": 8.6},
),
Document(
page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
metadata={"released_at": 1700190868, "director": "Greta Gerwig", "rating": 8.3},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"released_at": 1700190868, "genre": "animated"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={"released_at": 1700190868, "director": "Andrei Tarkovsky", "genre": "thriller", "rating": 9.9},
),
]
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
metadata_field_info = [
AttributeInfo(
name="released_at",
description="Time the movie was released. It's second timestamp.",
type="integer",
),
]
document_content_description = "Brief summary of a movie"
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
)
result = retriever.invoke(
f"What's a movie in this month that's all about toys, and preferably is animated. Current time is: {datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}.",
)
print(result)
```
### Expected behavior
I declared the `metadata_field_info` which includes the `released_at` field with the data type `integer`.
## I expected the following:
When my query involves the time/timerange of the release time, the query should compare using `integer` instead of `date time`.
### Why I expected this:
- The data type declared in `metadata_field_info` should be utilized.
- In the implementations of `SelfQueryRetriever` (I tested `qdrant` and `chroma`), the accepted type in comparison operations (gte/lte) must be numeric, not a date.
### Identified Reason
I identified the problem due to the `"SCHEMA[s]"` in [langchain/chains/query_constructor/prompt.py](https://github.com/langchain-ai/langchain/blob/190952fe76d8f7bf1e661cbdaa2ba0a2dc0f5456/libs/langchain/langchain/chains/query_constructor/prompt.py#L117).
This line in prompt led the result:
```
Make sure that filters only use format `YYYY-MM-DD` when handling date data typed values
```
I guess that it works in some SQL queries such as `Postgresql`, which accepts 'YYY-MM-DD' as date query inputs.
However, we are working with metadata in vector records, which are structured like JSON objects with key-value pairs, it may not work.
### Proof of reason
I tryed modifing the PROMPTs by defining my own Classes and Functions such as `load_query_constructor_chain`, `_get_prompt`, `SelfQueryRetriever`.
After replacing the above line with the following, it worked as expected:
```
Make sure that filters only use timestamp in second (integer) when handling timestamp data typed values.
```
### Proposals
- Review the above problem. If metadata fields do not support querying with the date format 'YYYY-MM-DD' as specified in the prompt, please update it.
- If this prompt is specified for some use cases, please allow overriding the prompts.
|
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PR_kwDOIPDwls5f3QXy
| 13,591 |
Adrijanik patch 1
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[] | null | 1 | 2023-11-20T03:56:52 | 2023-12-05T03:26:52 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls53SZ0u
| 13,590 |
Support for Guidance
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[] | null | 2 | 2023-11-20T03:54:37 | 2023-11-20T03:56:53 | null |
NONE
| null |
### Feature request
Ability to use guidance.
https://github.com/guidance-ai/guidance
### Motivation
Not related to a problem.
### Your contribution
Not sure yet but I can look into it if it is something the community considers.
|
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I_kwDOIPDwls53SRSr
| 13,589 |
Issue: can i generate a nested dic output
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[] | null | 2 | 2023-11-20T03:10:22 | 2023-11-20T03:16:49 | null |
NONE
| null |
### Issue you'd like to raise.
I know I can generate a python dictionary output using StructuredOutputParser like { "a":1, "b":2, "c":3}. However, I would like to generate a nested dic like { "a":1, "b":2, "c":{"d":4, "e":5}}
How can I do it?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5f3DTe
| 13,588 |
Fix typo in MongoDB memory docs
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[] | null | 2 | 2023-11-20T02:42:11 | 2023-11-20T07:09:37 | 2023-11-20T03:13:36 |
CONTRIBUTOR
| null |
- **Description:** Fix typo in MongoDB memory docs
- **Tag maintainer:** @eyurtsev
<!-- Thank you for contributing to LangChain!
- **Description:** Fix typo in MongoDB memory docs
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** @baskaryan
- **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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PR_kwDOIPDwls5f2sH7
| 13,587 |
API Reference building script update
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[] | null | 3 | 2023-11-20T00:22:54 | 2023-12-07T19:48:39 | 2023-12-07T19:43:43 |
COLLABORATOR
| null |
The namespaces like `langchain.agents.format_scratchpad` clogging the API Reference sidebar.
This change removes those 3-level namespaces from sidebar (this issue was discussed with @efriis )
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PR_kwDOIPDwls5f2ceK
| 13,585 |
experimental[patch]: fixed namespace bug
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[] | null | 1 | 2023-11-19T22:49:11 | 2023-11-29T00:40:28 | 2023-11-29T00:40:28 |
COLLABORATOR
| null |
It was :
`from langchain.schema.prompts import BasePromptTemplate`
but because of the breaking change in the ns, it is now
`from langchain.schema.prompt_template import BasePromptTemplate`
This bug prevents building the API Reference for the langchain_experimental
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I_kwDOIPDwls53RYkD
| 13,584 |
Issue: can i access memory buffer through chain?
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[] | null | 4 | 2023-11-19T21:31:23 | 2023-11-19T22:11:42 | null |
NONE
| null |
### Issue you'd like to raise.
Hi, I'm using a conversationchain that contains memory. It is defined as:
llm = ChatOpenAI(temperature=0.0, model=llm_model)
memory = ConversationBufferMemory()
conversation = ConversationChain(
llm=llm,
memory = memory,
verbose=True
)
I know I can access the current memory by using "memory.buffer". However, I was wondering if there is a way to access memory only through ConversationChain instance "conversation"?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5f2Vkv
| 13,583 |
fix: Make YoutubeLoader support on demand language translation
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[] | null | 1 | 2023-11-19T21:22:14 | 2023-11-20T01:34:49 | 2023-11-20T01:34:48 |
CONTRIBUTOR
| null |
**Description:**
Enhance the functionality of YoutubeLoader to enable the translation of available transcripts by refining the existing logic.
**Issue:**
Encountering a problem with YoutubeLoader (#13523) where the translation feature is not functioning as expected.
Tag maintainers/contributors who might be interested:
@eyurtsev
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PR_kwDOIPDwls5f2Plx
| 13,581 |
fix: call _on_llm_error()
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CONTRIBUTOR
| null |
Description: There's a copy-paste typo where on_llm_error() calls _on_chain_error() instead of _on_llm_error().
Issue: #13580
Dependencies: None
Tag maintainer: @hwchase17
Twitter handle: @jwatte
"Run `make format`, `make lint` and `make test` to check this locally."
The test scripts don't work in a plain Ubuntu LTS 20.04 system.
It looks like the dev container pulling is stuck. Or maybe the internet is just ornery today.
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| 2,000,998,966 |
I_kwDOIPDwls53RNI2
| 13,580 |
The tracing on_llm_error() implementation calls _on_chain_error(), not _on_llm_error()
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[] | null | 3 | 2023-11-19T19:21:07 | 2023-11-22T00:14:24 | null |
CONTRIBUTOR
| null |
### System Info
Linux 20.04 LTS
Python 3.6
### Who can help?
@hwchase17 seems like this got introduced on 2023-11-16
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Attempt to use a tracer to trace an LLM error
2. Note that the tracer hook for _on_chain_error is called instead
### Expected behavior
_on_llm_error hook should be called.
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I_kwDOIPDwls53RK0Y
| 13,579 |
Confluence loader fails to retrieve specific pages when 'pages_ids' is given
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[] | null | 4 | 2023-11-19T18:54:14 | 2023-11-20T09:44:14 | null |
CONTRIBUTOR
| null |
### System Info
Mac M1
### Who can help?
@eyurtsev
Here:
https://github.com/langchain-ai/langchain/blob/78a1f4b264fbdca263a4f8873b980eaadb8912a7/libs/langchain/langchain/document_loaders/confluence.py#L284
We start adding the "max_pages" first pages to the "docs" list that will be the output of loader.load.
So we are sure that I cannot retrieve only one specific `page_id`.
`loader.load(..., page_ids=['1234'], max_pages=N)`
will output X pages where X in [min(N, # pages in my confluence), N + 1]
In other words, if I want only a specific page, I will always have at least 2 pages (in case max_pages = 1)
So page_ids does not work at all because space_key is mandatory.
adding ìf space_key and not page_ids` fix my problem but may lead to other problems (I did not check)
Dirty hack would be to collect the F last elements of the return list if pages where F is the number of found pages asked in page_ids
### 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
No time to do but easy when reading code
### Expected behavior
I can retrieve only the page_ids specified
|
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I_kwDOIPDwls53Q_Bb
| 13,578 |
ConversationChain failure after changing template text
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[] | null | 6 | 2023-11-19T16:56:45 | 2023-11-20T13:28:40 | 2023-11-20T13:25:40 |
NONE
| null |
I am having a wonderful time with my code, but after changing my template it now fails before I even get to give my input. Baffling!
all the required imports are not shown here nor is all the prompt text (containing no special characters)
template = '''Your task is to extract the relationships between terms in the input text,
Format your output as a json list. '''
prompt = ChatPromptTemplate.from_messages([
SystemMessagePromptTemplate.from_template(template),
HumanMessagePromptTemplate.from_template("{input}"),
MessagesPlaceholder(variable_name="history "),
])
llm = ChatOpenAI(temperature=0.8, model_name='gpt-4-1106-preview')
memory = ConversationBufferMemory(return_messages=True)
conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)](url)
Traceback .........
conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ConversationChain
__root__ Got unexpected prompt input variables. The prompt expects ['input', 'history '], but got ['history'] as inputs from memory, and input as the normal input key. (type=value_error)
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PR_kwDOIPDwls5f19P2
| 13,577 |
WIP: core
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[] | null | 1 | 2023-11-19T16:12:43 | 2023-11-20T21:09:31 | 2023-11-20T21:09:30 |
COLLABORATOR
| null | null |
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openapi/planner.py: Deal with json in markdown output cases
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[] | null | 3 | 2023-11-19T15:53:34 | 2023-12-05T04:27:23 | 2023-12-05T04:27:22 |
CONTRIBUTOR
| null |
- **Description:** In openapi/planner deal with json in markdown output cases
- **Issue:** In some cases LLMs could return json in markdown which can't be loaded.
- **Dependencies:**
- **Tag maintainer:** @eyurtsev
- **Twitter handle:**
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I_kwDOIPDwls53Qr53
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Implementing Hybrid Search (RRF) in LangChain Using OpenSearch Vector Store
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| false | null |
[] | null | 2 | 2023-11-19T13:59:02 | 2023-11-19T14:10:20 | null |
NONE
| null |
### Feature request
The feature request I am proposing involves the implementation of hybrid search, specifically using the Reciprocal Rank Fusion (RRF) method, in LangChain through the integration of OpenSearch's vector store.
This would enable the combination of keyword and similarity search. Currently, LangChain doesn't appear to support this functionality, even though OpenSearch has had this capability since its 2.10 release. The goal is to allow LangChain to call search pipelines using OpenSearch's vector implementation, enabling OpenSearch to handle the complexities of hybrid search.
**Relevant Links**:
https://opensearch.org/docs/latest/query-dsl/compound/hybrid
### Motivation
The motivation behind this request stems from the current limitation in LangChain regarding hybrid search capabilities. As someone working on a search project currently, I find it frustrating that despite OpenSearch supporting hybrid search since version 2.10, LangChain has not yet integrated this feature.
### Your contribution
I would gladly help as long as I get guidance..
|
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PR_kwDOIPDwls5f1Pzv
| 13,573 |
Update geodataframe.py
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| false | null |
[] | null | 3 | 2023-11-19T04:23:14 | 2023-12-05T03:44:31 | 2023-12-05T03:44:31 |
CONTRIBUTOR
| null |
here it is validating shapely.geometry.point.Point: if not isinstance(data_frame[page_content_column].iloc[0], gpd.GeoSeries): raise ValueError(
f"Expected data_frame[{page_content_column}] to be a GeoSeries" you need it to validate the geoSeries and not the shapely.geometry.point.Point
if not isinstance(data_frame[page_content_column], gpd.GeoSeries):
raise ValueError(
f"Expected data_frame[{page_content_column}] to be a GeoSeries"
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5f1MAj
| 13,572 |
Nasa api tool revision
|
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[] | null | 1 | 2023-11-19T02:42:48 | 2023-11-19T02:43:02 | 2023-11-19T02:43:02 |
CONTRIBUTOR
| null |
- compiled feature branch components into single working component
- trial ran integration tests
|
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PR_kwDOIPDwls5f1K2q
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MMR with Vectara
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[] | null | 2 | 2023-11-19T02:13:28 | 2023-12-14T21:12:36 | 2023-12-14T21:12:35 |
CONTRIBUTOR
| null |
- **Description:** Vectara implements MMR in the backend. This allows to invoke that MMR natively.
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ofermend
|
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PR_kwDOIPDwls5f1BcL
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experimental[patch]: release 0.0.43
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[] | null | 1 | 2023-11-18T23:22:14 | 2023-11-28T23:38:10 | 2023-11-28T23:38:09 |
COLLABORATOR
| null | null |
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