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https://api.github.com/repos/langchain-ai/langchain/issues/13240
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PR_kwDOIPDwls5fNHow
| 13,240 |
Add dockerfile template
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[] | null | 2 | 2023-11-11T07:26:50 | 2023-11-13T15:33:02 | 2023-11-13T15:33:01 |
CONTRIBUTOR
| null |
Description: This pr adds a dockerfile to an app template for easy containerization of your production application.
Dependencies: None
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PR_kwDOIPDwls5fNGRf
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Fix typo in timescalevector.ipynb
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[] | null | 1 | 2023-11-11T07:17:03 | 2023-11-13T17:03:08 | 2023-11-13T17:03:08 |
CONTRIBUTOR
| null |
enviornment -> environment
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52iX9F
| 13,238 |
Trouble Accessing Document ID in WeaviateHybridSearchRetriever Results
|
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[] | null | 4 | 2023-11-11T04:39:30 | 2023-11-11T08:51:07 | null |
NONE
| null |
### System Info
langchain: 0.0.334
python: 3.11.6
weaviate-client: 3.25.3
### Who can help?
_No response_
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to implement the WeaviateHybridSearchRetriever to retrieve documents from Weaviate. My schema indicates the document ID is stored in the _id field based on the shardingConfig.
When setting up the retriever, I included _id in the attributes list:
````
hybrid_retriever = WeaviateHybridSearchRetriever(
attributes=["_id", "aliases", "categoryid", "name", "page_content", "ticker"]
)
````
However, when I try to access _id on the returned Document objects, I get an error that _id is not found.
For example:
````
results = hybrid_retriever.get_relevant_documents(query="some query")
print(results[0]._id) # Error!_id not found
````
I have tried variations like id, document_id instead of _id but still cannot seem to access the document ID field.
Any suggestions on what I am missing or doing wrong when trying to retrieve the document ID from the Weaviate results using the _id field specified in the schema?
Let me know if any other details would be helpful in troubleshooting this issue!
**Schema Details**
````
{
"classes":[
{
"class":"Category_taxonomy",
"invertedIndexConfig":{
"bm25":{
"b":0.75,
"k1":1.2
},
"cleanupIntervalSeconds":60,
"stopwords":{
"additions":"None",
"preset":"en",
"removals":"None"
}
},
"moduleConfig":{
"text2vec-openai":{
"baseURL":"https://api.openai.com",
"model":"ada",
"modelVersion":"002",
"type":"text",
"vectorizeClassName":true
}
},
"multiTenancyConfig":{
"enabled":false
},
"properties":[
{
"dataType":[
"text"
],
"description":"Content of the page",
"indexFilterable":true,
"indexSearchable":true,
"moduleConfig":{
"text2vec-openai":{
"skip":false,
"vectorizePropertyName":false
}
},
"name":"page_content",
"tokenization":"word"
},
{
"dataType":[
"number"
],
"description":"Identifier for the category",
"indexFilterable":true,
"indexSearchable":false,
"moduleConfig":{
"text2vec-openai":{
"skip":false,
"vectorizePropertyName":false
}
},
"name":"categoryid"
},
{
"dataType":[
"text"
],
"description":"Ticker symbol",
"indexFilterable":true,
"indexSearchable":true,
"moduleConfig":{
"text2vec-openai":{
"skip":false,
"vectorizePropertyName":false
}
},
"name":"ticker",
"tokenization":"word"
},
{
"dataType":[
"text"
],
"description":"Name of the entity",
"indexFilterable":true,
"indexSearchable":true,
"moduleConfig":{
"text2vec-openai":{
"skip":false,
"vectorizePropertyName":false
}
},
"name":"name",
"tokenization":"word"
},
{
"dataType":[
"text"
],
"description":"Aliases for the entity",
"indexFilterable":true,
"indexSearchable":true,
"moduleConfig":{
"text2vec-openai":{
"skip":false,
"vectorizePropertyName":false
}
},
"name":"aliases",
"tokenization":"word"
}
],
"replicationConfig":{
"factor":1
},
"shardingConfig":{
"virtualPerPhysical":128,
"desiredCount":1,
"actualCount":1,
"desiredVirtualCount":128,
"actualVirtualCount":128,
"key":"_id",
"strategy":"hash",
"function":"murmur3"
},
"vectorIndexConfig":{
"skip":false,
"cleanupIntervalSeconds":300,
"maxConnections":64,
"efConstruction":128,
"ef":-1,
"dynamicEfMin":100,
"dynamicEfMax":500,
"dynamicEfFactor":8,
"vectorCacheMaxObjects":1000000000000,
"flatSearchCutoff":40000,
"distance":"cosine",
"pq":{
"enabled":false,
"bitCompression":false,
"segments":0,
"centroids":256,
"trainingLimit":100000,
"encoder":{
"type":"kmeans",
"distribution":"log-normal"
}
}
},
"vectorIndexType":"hnsw",
"vectorizer":"text2vec-openai"
}
]
}
````
**Example Document**
````
{
"class": "Category_taxonomy",
"creationTimeUnix": 1699553747601,
"id": "ad092eb1-e4a6-4d93-a7d2-c507c33c3837",
"lastUpdateTimeUnix": 1699553747601,
"properties": {
"aliases": "Binance Coin, Binance Smart Chain",
"categoryid": 569,
"name": "BNB",
"page_content": "ticker: bnb\nname: BNB\naliases: Binance Coin, Binance Smart Chain",
"ticker": "bnb"
},
"vectorWeights": null
}
````
Example Search Result
````
{
"status":"success",
"results":[
{
"page_content":"ticker: bnb\nname: BNB\naliases: Binance Coin, Binance Smart Chain",
"metadata":{
"_additional":{
"explainScore":"(vector) [-0.0067740963 -0.03091735 0.00511335 0.0016186031 -0.016120477 0.017543973 -0.0072548385 -0.023063144 0.015246399 -0.0020884196]... \n(hybrid) Document ad092eb1-e4a6-4d93-a7d2-c507c33c3837 contributed 0.00819672131147541 to the score",
"score":"0.008196721"
},
"aliases":"Binance Coin, Binance Smart Chain",
"categoryid":569,
"name":"BNB",
"ticker":"bnb"
},
"type":"Document"
}
]
}
````
App Code
````
# Prepare global variables
WEAVIATE_URL = os.getenv('WEAVIATE_URL')
WEAVIATE_API_KEY = os.getenv('WEAVIATE_API_KEY')
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
INDEX_NAME = "Category_taxonomy"
TEXT_KEY = "page_content"
# Dependency provider function for Weaviate client
def get_weaviate_vectorstore():
# Initialize the Weaviate client with API key authentication
client = weaviate.Client(
url=WEAVIATE_URL,
auth_client_secret=weaviate.AuthApiKey(WEAVIATE_API_KEY),
additional_headers={
"X-Openai-Api-Key": OPENAI_API_KEY,
}
)
# Initialize embeddings with a specified model
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY, model='text-embedding-ada-002')
# Initialize vector store with attributes and schema
vectorstore = Weaviate(
client=client,
index_name=INDEX_NAME,
text_key=TEXT_KEY,
embedding=embeddings,
attributes=["aliases", "categoryid", "name", "page_content", "ticker"],
by_text=False
)
return client, vectorstore
def get_weaviate_hybrid_retriever(k: int = 5):
# Directly call the function to get the client and vectorstore
client, vectorstore = get_weaviate_vectorstore()
# Instantiate the retriever with the settings from the vectorstore
hybrid_retriever = WeaviateHybridSearchRetriever(
client=client,
index_name=INDEX_NAME,
text_key=TEXT_KEY,
attributes=["aliases", "categoryid", "name", "page_content", "ticker"],
k=k,
create_schema_if_missing=True
)
return hybrid_retriever
async def parse_query_params(request: Request) -> Dict[str, List[Any]]:
parsed_values = defaultdict(list)
for key, value in request.query_params.multi_items():
# Append the value for any key directly
parsed_values[key].append(value)
return parsed_values
@router.get("/hybrid_search_category_taxonomy/")
async def hybrid_search_category_taxonomy(parsed_values: Dict[str, List[Any]] = Depends(parse_query_params), query: Optional[str] = None, k: int = 5):
categoryids = parsed_values.get('categoryid', [])
tickers = parsed_values.get('ticker', [])
names = parsed_values.get('name', [])
aliasess = parsed_values.get('aliases', [])
# Use a partial function to pass 'k' to 'get_weaviate_hybrid_retriever'
retriever = get_weaviate_hybrid_retriever(k=k)
# Initialize the where_filter with an 'And' operator if there are any filters provided
logging.info(
f"query: {query}, "
f"categoryID: {categoryids}, "
f"ticker: {tickers}, "
f"name: {names}, "
f"aliases: {aliasess}, "
f"k: {k}"
)
# Adjustments to reference parameters from 'parse_query_params'
where_filter = {"operator": "And", "operands": []} if any([categoryids, tickers, names, aliasess]) else None
# Add filters for categoryid and ticker with the 'Equal' operator
if categoryids:
category_operands = [{"path": ["categoryid"], "operator": "Equal", "valueNumber": cid} for cid in categoryids]
if category_operands:
where_filter["operands"].append({"operator": "Or", "operands": category_operands})
if tickers:
ticker_operands = [{"path": ["ticker"], "operator": "Equal", "valueText": ticker} for ticker in tickers]
if ticker_operands:
where_filter["operands"].append({"operator": "Or", "operands": ticker_operands})
if names:
name_operands = [{"path": ["name"], "operator": "Equal", "valueText": name} for name in names]
if name_operands:
where_filter["operands"].append({"operator": "Or", "operands": name_operands})
if aliasess:
aliases_operands = [{"path": ["aliases"], "operator": "Equal", "valueText": aliases} for aliases in aliasess]
if aliases_operands:
where_filter["operands"].append({"operator": "Or", "operands": aliases_operands})
try:
# Format the results for the response
effective_query = " " if not query or not query.strip() else query
# Log the where_filter before fetching documents
logging.info(f"where_filter being used: {where_filter}")
# Fetch the relevant documents using the hybrid retriever instance
results = retriever.get_relevant_documents(effective_query, where_filter=where_filter, score=True)
# Format the results for the response
response_data = [vars(doc) for doc in results]
return {"status": "success", "results": response_data}
except Exception as e:
logger.error(f"Error while processing request: {str(e)}", exc_info=True)
raise HTTPException(detail=str(e), status_code=500)
````
### Expected behavior
**Expected Behavior**
When using the WeaviateHybridSearchRetriever for document retrieval, I expect that including the _id attribute in the attributes list will allow me to access the document ID of each retrieved document without any issues. Specifically, after setting up the WeaviateHybridSearchRetriever like so:
````
hybrid_retriever = WeaviateHybridSearchRetriever(
attributes=["_id", "aliases", "categoryid", "name", "page_content", "ticker"]
)
````
I anticipate that executing a query and attempting to print the _id of the first result should successfully return the unique identifier of the document, as per the below code snippet:
````
results = hybrid_retriever.get_relevant_documents(query="some query")
print(results[0]._id) # Expecting this to print the _id of the first result
````
In this scenario, my expectation is that the _id field, being specified in the attributes parameter, should be readily accessible in each Document object returned by the get_relevant_documents method. This behavior is crucial for my application as it relies on the unique document IDs for further processing and analysis of the retrieved data.
|
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PR_kwDOIPDwls5fMzyo
| 13,237 |
Add OpenAI API v1 support for ChatAnyscale and fixed a bug with openai_api_key
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[] | null | 2 | 2023-11-11T04:33:50 | 2023-11-13T17:02:02 | 2023-11-13T17:01:54 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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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.
-->
1. Add OpenAI API v1 support
2. Fixed a bug to call `get_secret_value` on a str value (values["openai_api_key"])
|
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| 1,988,719,895 |
PR_kwDOIPDwls5fMzoO
| 13,236 |
Move OAI assistants to langchain and add callbacks
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[] | null | 2 | 2023-11-11T04:31:37 | 2023-11-24T08:49:43 | 2023-11-14T01:42:07 |
COLLABORATOR
| null | null |
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| 1,988,660,318 |
I_kwDOIPDwls52iIxe
| 13,234 |
If i assign two SemanticSimilarityExampleSelector with different data in different variable but it combines
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[] | null | 4 | 2023-11-11T02:23:06 | 2023-11-11T05:35:01 | 2023-11-11T05:35:00 |
NONE
| null |
### System Info
I'm using colab
### Who can help?
_No response_
### 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
from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector
key_selector = SemanticSimilarityExampleSelector(vectorstore=few_shots,k=2)
few_shots_selector = SemanticSimilarityExampleSelector(vectorstore=few_shots,k=2)
key_selector.select_examples({"All": "who is in Bengaluru?"})
# It's return from few_shots_selector
### Expected behavior
key_selector.select_examples({"All": "who is in Bengaluru?"})
it's need to return from key_selector
|
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I_kwDOIPDwls52iG4k
| 13,233 |
Issue: <ConversationalRetrievalChain.from_llm and partition_pdf >
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[] | null | 2 | 2023-11-11T02:10:50 | 2023-11-11T02:19:25 | null |
NONE
| null |
### Issue you'd like to raise.
Why did I follow the tutorial below to generate vector library data, but I wanted to use ConversationalRetrievalChain.from_llm to answer my question, but couldn't answer the question? Or can I only answer with chain?
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb?ref=blog.langchain.dev
code show as below:
def read_item(query=Body(..., embed=True)):
question=query
print(question)
embeddings = OpenAIEmbeddings()
vector_store_path = r"/mnt/PD/VS"
docsearch = Chroma(persist_directory=vector_store_path, embedding_function=embeddings)
# Build prompt
template = """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. \
Use three sentences maximum. Keep the answer as concise as possible. Always say "thanks for asking!" \
at the end of the answer.
{context}
{chat_history}
Question: {question}
Helpful Answer:"""
# Build prompt
#QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
prompt = PromptTemplate(
input_variables=["chat_history", "context", "question"],
template=template,
)
store = InMemoryStore()
id_key = "doc_id"
# The retriever (empty to start)
retriever = MultiVectorRetriever(
vectorstore=docsearch,
docstore=store,
id_key=id_key,
)
llm = OpenAI(
temperature=0,max_tokens=1024,
model_name="gpt-4-1106-preview"
)
memory = ConversationKGMemory(llm=llm,memory_key='chat_history',return_messages=True,output_key='answer')
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
chain_type="stuff",
retriever=retriever,
memory=memory,
return_source_documents=True,
return_generated_question=True,
combine_docs_chain_kwargs={'prompt': prompt}
)
# 进行问答
result = qa({"question": question})
#print(qa.combine_documents_chain.memory)
return result
### Suggestion:
_No response_
|
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| 1,988,624,195 |
PR_kwDOIPDwls5fMfD1
| 13,232 |
PGVector needs to close its connection if it is garbage collected
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CONTRIBUTOR
| null |
Description: Without this change, PGVector was using lots of connections and would eventually be unable to keep making connections.
Issue: Resolves #9696
Dependencies: None
Twitter: @sumukhsridhara
More context:
The `__del__` method gets called when the object is about to be garbage collected. Without this, users (like us) have run into errors like the below
```
psql: FATAL: remaining connection slots are reserved for non-replication superuser connections
```
<!-- Thank you for contributing to LangChain!
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,988,591,377 |
PR_kwDOIPDwls5fMX6K
| 13,231 |
feat(llms): support Openai API v1 for Azure OpenAI completions
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[] | null | 2 | 2023-11-11T00:13:58 | 2023-11-14T20:10:19 | 2023-11-14T20:10:18 |
CONTRIBUTOR
| null |
Hi,
this PR adds support for OpenAI API v1 for Azure OpenAI completion API.
@baskaryan @hwchase17
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I_kwDOIPDwls52hrN2
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load_summarize_chain with map_reduce error : InvalidRequestError: This model's maximum context length is 8192 tokens. However, your messages resulted in 13516 tokens. Please reduce the length of the messages.
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[] | null | 2 | 2023-11-10T23:05:46 | 2023-11-10T23:20:05 | null |
NONE
| null |
### System Info
from langchain.text_splitter import CharacterTextSplitter
from langchain.docstore.document import Document
from langchain.chains.summarize import load_summarize_chain
from fastapi.encoders import jsonable_encoder
from langchain.chains.mapreduce import MapReduceChain
from time import monotonic
gpt_4_8k_max_tokens = 8000 #https://platform.openai.com/docs/models/gpt-4
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(model_name=model_name, chunk_size=gpt_4_8k_max_tokens, chunk_overlap=0)
verbose = False
# Initialize output dataframe with all the columns in the patient history class
column_names = list(PatientHistory.model_fields.keys())
df_AOAI_extracted_text = pd.DataFrame(columns=column_names)
# Create documents from the input text
texts = text_splitter.split_text(test_text)
docs = [Document(page_content=t) for t in texts]
print(f"Number of Documents {len(docs)}")
# Count the number of tokens in the document
num_tokens = num_tokens_from_string(test_text, model_name)
print(f"Number of Tokens {num_tokens}")
# call langchain summarizer to get the output for the given prompt
summaries = []
if num_tokens < gpt_4_8k_max_tokens:
#Stuffing is the simplest method, whereby you simply stuff all the related data into the prompt as context to pass to the language model. This is implemented in LangChain as the StuffDocumentsChain.
#This method is sutable for smaller piece of data.
chain = load_summarize_chain(llm, chain_type="stuff", prompt=TABLE_PROMPT, verbose=verbose)
else:
#MapReduceDocumentsChain is an advanced document processing technique that extends the capabilities of the conventional MapReduce framework.
#It goes beyond the typical MapReduce approach by executing a distinct prompt to consolidate the initial outputs.
#This method is designed to generate a thorough and cohesive summary or response that encompasses the entire document.
print('mapreduce')
chain = load_summarize_chain(llm, chain_type="map_reduce", map_prompt=TABLE_PROMPT, combine_prompt=TABLE_PROMPT, verbose=verbose, return_intermediate_steps=False)
start_time = monotonic()
summary = chain.run(docs)
print(summary)
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.text_splitter import CharacterTextSplitter
from langchain.docstore.document import Document
from langchain.chains.summarize import load_summarize_chain
from fastapi.encoders import jsonable_encoder
from langchain.chains.mapreduce import MapReduceChain
from time import monotonic
gpt_4_8k_max_tokens = 8000 #https://platform.openai.com/docs/models/gpt-4
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(model_name=model_name, chunk_size=gpt_4_8k_max_tokens, chunk_overlap=0)
verbose = False
# Initialize output dataframe with all the columns in the patient history class
column_names = list(PatientHistory.model_fields.keys())
df_AOAI_extracted_text = pd.DataFrame(columns=column_names)
# Create documents from the input text
texts = text_splitter.split_text(test_text)
docs = [Document(page_content=t) for t in texts]
print(f"Number of Documents {len(docs)}")
# Count the number of tokens in the document
num_tokens = num_tokens_from_string(test_text, model_name)
print(f"Number of Tokens {num_tokens}")
# call langchain summarizer to get the output for the given prompt
summaries = []
if num_tokens < gpt_4_8k_max_tokens:
#Stuffing is the simplest method, whereby you simply stuff all the related data into the prompt as context to pass to the language model. This is implemented in LangChain as the StuffDocumentsChain.
#This method is sutable for smaller piece of data.
chain = load_summarize_chain(llm, chain_type="stuff", prompt=TABLE_PROMPT, verbose=verbose)
else:
#MapReduceDocumentsChain is an advanced document processing technique that extends the capabilities of the conventional MapReduce framework.
#It goes beyond the typical MapReduce approach by executing a distinct prompt to consolidate the initial outputs.
#This method is designed to generate a thorough and cohesive summary or response that encompasses the entire document.
print('mapreduce')
chain = load_summarize_chain(llm, chain_type="map_reduce", map_prompt=TABLE_PROMPT, combine_prompt=TABLE_PROMPT, verbose=verbose, return_intermediate_steps=False)
start_time = monotonic()
summary = chain.run(docs)
print(summary)
### Expected behavior
Should go through all docs and provide the summary
|
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PR_kwDOIPDwls5fMHuE
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Fix "additional_kwargs["arguments"] already exists in this message, but with a different type." for Azure openai API.
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[] | null | 8 | 2023-11-10T22:46:51 | 2023-12-05T03:58:49 | 2023-12-05T03:58:48 |
NONE
| null |
- **Description:** This PR addresses an issue with the OpenAI API streaming response, where initially the key (arguments) is provided but the value is None. Subsequently, it updates with {"arguments": "{\n"}, leading to a type inconsistency that causes an exception. The specific error encountered is ValueError: additional_kwargs["arguments"] already exists in this message, but with a different type. This change aims to resolve this inconsistency and ensure smooth API interactions.
- **Issue:** None.
- **Dependencies:** None.
- **Tag maintainer:** @eyurtsev,
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Fix Document Loader Unit Test - Docusaurus
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[] | null | 1 | 2023-11-10T22:46:35 | 2023-11-10T22:52:02 | 2023-11-10T22:52:01 |
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Image template
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COLLABORATOR
| null |
example usage
```python
ffrom langchain.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([
("system", "foo bar"),
("human", [
"{question}",
{"text": "{second_question}"},
# image with var name 'first_image' and no preset values
{"image_url": "{first_image}"},
# image with var name 'second_image' and preset "detail" level
{"image_url": {"variable_name": "second_image", "detail": "low"}},
# constant image, not a var
{"type": "image_url", "image_url": {"url": "https://python.langchain.com/assets/images/langchain_stack-f195a2c417953cb5828f76e5f9039c9f.png"}}]),
])
prompt.format_messages(
question="what's the difference between these images",
second_question="what's upppppp",
# specify an image path and other params
first_image={
"path": "/Users/bagatur/Downloads/LangChain Stack - V8.png",
"detail":"auto"
},
# specify just a url. preset value "detail": "low" will be attached
second_image="data:blah"
)
```
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PR_kwDOIPDwls5fL-21
| 13,226 |
Supply format to Ollama
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I_kwDOIPDwls52gyC1
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Warning: model not found. Using cl100k_base encoding. with Azure Openai and load_summarize_chain when I am trying to implement map_reduce
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[] | null | 4 | 2023-11-10T20:00:32 | 2023-11-10T20:31:32 | null |
NONE
| null |
### System Info
Hi: I have tried several strategies to implement map reduce summarization using Azure OpenAi and Langchain . My model is "gpt-35-turbo-16k".
I have tried several experiments but always get to the same warning:
from langchain.schema.document import Document
from langchain.chains.mapreduce import MapReduceChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader
llm_summary = AzureChatOpenAI(
openai_api_base= azure_api_base,
openai_api_version=azure_openai_api_version,
deployment_name=azure_deployment_name,
openai_api_key=azure_openai_api_key,
openai_api_type= azure_api_type,
model_name=azure_model_name,
temperature=azure_model_temperature
)
text="""The ReduceDocumentsChain handles taking the document mapping results and reducing them into a single output.\
It wraps a generic CombineDocumentsChain (like StuffDocumentsChain) but adds the ability to collapse documents before passing it
to the CombineDocumentsChain if their cumulative size exceeds token_max. In this example, we can actually re-use our chain for combining
our docs to also collapse our docs."""
text1=""" You can continue with your English studies and never use Inversion in sentences. That’s perfectly okay. However, if you are preparing for a Cambridge or IELTS exam or other exams or situations where you need to demonstrate an extensive use of English, you will be expected to know about Inversion.
Let’s start with why and when. After all, if you don’t know why we use Inversion, you won’t know when to use it.
WHY & WHEN do we use INVERSION?
Inversion is mainly used for EMPHASIS. The expressions used (never, rarely, no sooner, only then, etc.) have much more impact when used at the beginning of a sentence than the more common pronoun subject, especially as most of them are negative.
Negatives are more dramatic. Consider negative contractions: don’t, won’t, can’t, haven’t, etc. They usually have strong stress in English whilst positive contractions: I’m, he’ll, she’s, we’ve, I’d, etc. usually have weak stress.
"""
doc= [Document(page_content=text1)]
chain = load_summarize_chain(llm_summary, chain_type="map_reduce") #chain_type="map_reduce"
chain.run(doc)`
and Strategy 2 with text_splitter:
`from langchain import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=50)
chunks = text_splitter.create_documents([text1])
chain = load_summarize_chain(
llm_summary,
chain_type='map_reduce',
verbose=False
)
summary = chain.run(chunks)
summary
I always get the same output:
<img width="1473" alt="image" src="https://github.com/langchain-ai/langchain/assets/7675634/fc871a72-4f5f-43ff-9725-52a718ebaeac">
I have some questions:
1) How to fix this warning?
2) Can I trust in the output when the model is not found?
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run this chunks of code in any notebook
### Expected behavior
I want to fix this warning helping to langchain to find the model.
|
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PR_kwDOIPDwls5fLYAD
| 13,223 |
Comprehend moderation: update to default threshold and callback property for prompt safety
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[] | null | 2 | 2023-11-10T19:57:51 | 2023-11-13T19:06:38 | null |
CONTRIBUTOR
| null |
This PR has following minor updates:
1. Updated the callback block for `prompt safety`. The callback property name was incorrect.
2. Updated the default threshold for prompt safety from 0.5 to 0.8.
|
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| 1,988,290,829 |
PR_kwDOIPDwls5fLVaN
| 13,222 |
Add mongo parent template to index
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PR_kwDOIPDwls5fLU6k
| 13,221 |
fleet_context docs update
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[] | null | 1 | 2023-11-10T19:50:43 | 2023-11-10T22:53:57 | 2023-11-10T22:53:57 |
CONTRIBUTOR
| null |
- **Description:** Changed the fleet_context documentation to use `context.download_embeddings()` from the latest release from our package. More details here: https://github.com/fleet-ai/context/tree/main#api
- **Issue:** n/a
- **Dependencies:** n/a
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @andrewthezhou
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PR_kwDOIPDwls5fLPu6
| 13,219 |
Upgrade cohere embedding model to v3
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[] | null | 1 | 2023-11-10T19:36:09 | 2023-11-11T00:25:59 | 2023-11-11T00:25:58 |
COLLABORATOR
| null |
Just updates API docs, doesn't change default param from 2.0 (could be breaking change)
|
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I_kwDOIPDwls52gc35
| 13,218 |
Vector Search on GCP Private Network gives AttributeError: 'MatchingEngineIndexEndpoint' object has no attribute '_public_match_client'
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[] | null | 2 | 2023-11-10T19:10:23 | 2023-11-10T19:17:11 | null |
NONE
| null |
### System Info
langchain 0.0.332
Python 3.10.12
Platform: GCP
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code to reproduce the problem:
```
from google.cloud import aiplatform
from langchain.embeddings import VertexAIEmbeddings
from langchain.vectorstores.matching_engine import MatchingEngine
embeddings = VertexAIEmbeddings()
embeddings
vector_store = MatchingEngine.from_components(
index_id=INDEX,
region=REGION,
embedding=embeddings,
project_id=PROJECT_ID,
endpoint_id=ENDPOINT,
gcs_bucket_name=DOCS_EMBEDDING)
vector_store.similarity_search('hello world', k=8)
```
Traceback:
```Traceback (most recent call last):
File "/opt/conda/envs/classifier/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3526, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-13-c6115207e7f5>", line 1, in <module>
relevant_documentation = vector_store.similarity_search('hello world', k=8)
File "/opt/conda/envs/classifier/lib/python3.10/site-packages/langchain/vectorstores/matching_engine.py", line 291, in similarity_search
docs_and_scores = self.similarity_search_with_score(
File "/opt/conda/envs/classifier/lib/python3.10/site-packages/langchain/vectorstores/matching_engine.py", line 202, in similarity_search_with_score
return self.similarity_search_by_vector_with_score(
File "/opt/conda/envs/classifier/lib/python3.10/site-packages/langchain/vectorstores/matching_engine.py", line 234, in similarity_search_by_vector_with_score
if self.endpoint._public_match_client:
AttributeError: 'MatchingEngineIndexEndpoint' object has no attribute '_public_match_client'
```
**Expected Behavior**:
No Error
**Analysis**:
The newest changes in https://github.com/langchain-ai/langchain/pull/10056 added the following to matching engine. [Source](https://github.com/langchain-ai/langchain/blob/869df62736f9084864ab907e7ec5736dd19f05d4/libs/langchain/langchain/vectorstores/matching_engine.py#L234)
`if self.endpoint._public_match_client:`
However, in GCP's MatchingEngineIndexEndpoint Class, object _public_match_client does not get instantiated until the following check passes. [Source](https://github.com/googleapis/python-aiplatform/blob/fcf05cb6da15c83e91e6ce5f20ab3e6649983685/google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py#L132-L133)
```
if self.public_endpoint_domain_name:
self._public_match_client = self._instantiate_public_match_client()
```
Therefore I think the if self.endpoint._public_match_client check may be preventing all Private Network users from using vector search with matching Engine.
|
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PR_kwDOIPDwls5fLA2h
| 13,217 |
Bagatur/langserve blurb
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[] | null | 1 | 2023-11-10T18:56:44 | 2023-11-10T19:05:45 | 2023-11-10T19:05:44 |
COLLABORATOR
| null | null |
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| 1,988,191,412 |
PR_kwDOIPDwls5fK_CJ
| 13,216 |
Adding support for amazon personalize Integration
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[] | null | 7 | 2023-11-10T18:51:54 | 2023-12-05T18:48:10 | null |
NONE
| null |
## Amazon Personalize support on Langchain
This PR introduces an integration with [Amazon Personalize](https://aws.amazon.com/personalize/) to help you to retrieve recommendations and use them in your natural language applications. This integration provides two new components:
1. An `AmazonPersonalize` client, that provides a wrapper around the Amazon Personalize API.
2. An `AmazonPersonalizeChain`, that provides a chain to pull in recommendations using the client, and then generating the response in natural language.
Here is some sample code to explain the usage.
```python
from langchain.utilities import AmazonPersonalize
from langchain.chains import AmazonPersonalizeChain
from langchain.llms.bedrock import Bedrock
recommender_arn = "<insert_arn>"
client=AmazonPersonalize(
credentials_profile_name="default",
region_name="us-west-2",
recommender_arn=recommender_arn
)
bedrock_llm = Bedrock(
model_id="anthropic.claude-v2",
region_name="us-west-2"
)
chain = AmazonPersonalizeChain.from_llm(
llm=bedrock_llm,
client=client
)
response = chain({'user_id': '1'})
```
Reviewer: @3coins
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vectara rag mq
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[] | null | 1 | 2023-11-10T18:04:10 | 2023-11-10T18:08:46 | 2023-11-10T18:08:45 |
COLLABORATOR
| null |
Description: another Vectara template for MultiQuery RAG flow
Twitter handle: @ofermend
Fixes to #13106
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| 13,213 |
update stack diagram
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| 13,212 |
Add token usage tracker module
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[] | null | 2 | 2023-11-10T17:43:13 | 2023-11-20T10:33:17 | null |
CONTRIBUTOR
| null |
- **Description:** A new, flexible, extensible token usage tracker module
- **Dependencies:** only optional dependencies base on the LLM used,
- **Tag maintainer:** @agola11 @baskaryan @hwchase17
- **Twitter handle:** jtolgyesi
The highly customizable `token_usage` module offers to track the number of consumed tokens, measure the latency of the LLM responses, and even estimate the usage cost of your application. You can analyze the token usage of your application directly from the code, or send the usage data to a metrics repository such as Amazon CloudWatch.
The `token_usage` module splits the task of collecting the usage metrics from the LLMs via standard Callbacks in the `handlers` submodule, and the processing of the collected metrics in the `reporters` submodule.
Some LLM APIs (for example, OpenAI) sends you back the number of consumed tokens after each call. Other APIs do not provide such usage information. In this case you can configure a `LocalTokenUsageCallbackHandler` instance to track the token usage directly on client side (your application's code).
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PR_kwDOIPDwls5fKYr1
| 13,211 |
bump 334
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COLLABORATOR
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PR_kwDOIPDwls5fKYlP
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fix: Reduce post-processing time for `DocAIParser`
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[] | null | 2 | 2023-11-10T17:11:15 | 2023-11-27T18:47:23 | 2023-11-20T17:41:44 |
CONTRIBUTOR
| null |
- Remove `WrappedDocument` introduced in https://github.com/langchain-ai/langchain/pull/11413
- https://github.com/googleapis/python-documentai-toolbox/issues/198 in Document AI Toolbox to improve initialization time for `WrappedDocument` object.
@lkuligin
@baskaryan
@hwchase17
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PR_kwDOIPDwls5fKRIC
| 13,209 |
Delegate to from_template
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[] | null | 1 | 2023-11-10T16:49:46 | 2023-12-05T04:05:40 | 2023-12-05T04:05:40 |
CONTRIBUTOR
| null |
-**Description:** Delegate from_file to from_template() so that they behave identical. Keep a deprecation in place for `input_variables`.
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PR_kwDOIPDwls5fKOnM
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Update
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PR_kwDOIPDwls5fKNif
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Mask API key for SerpApi.com tools
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[] | null | 1 | 2023-11-10T16:38:23 | 2023-11-10T16:51:05 | null |
CONTRIBUTOR
| null |
* **Description:** Mask API key for SerpApi.com tools.
* Mask SerpApi API key in `SerpAPIWrapper` and `GoogleScholarAPIWrapper`
* Use [`SerpApiClient#pagination`](https://github.com/serpapi/google-search-results-python#pagination-using-iterator) in the Google Scholar Search Tool (related to https://github.com/langchain-ai/langchain/issues/11513)
* **Issue:** https://github.com/langchain-ai/langchain/issues/12165
* **Tag maintainer:** @eyurtsev
@keenborder786, please review the changes in `GoogleScholarAPIWrapper`.
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PR_kwDOIPDwls5fKEjE
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Update README.md - Added notebook for extraction_openai_tools
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[] | null | 1 | 2023-11-10T16:14:19 | 2023-11-13T08:12:47 | 2023-11-13T08:12:47 |
CONTRIBUTOR
| null |
added Parallel Function Calling for Structured Data Extraction notebook
<!-- 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_kwDOIPDwls52ez-p
| 13,204 |
NameError: name '_httpx_client' is not defined
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[] | null | 3 | 2023-11-10T14:58:08 | 2023-11-10T16:53:18 | null |
NONE
| null |
### System Info
Hello All,
I have just installed the helm chart with some small additions to the basic values.yaml and the "langchain-langsmith-backend" contain keeps breaking with the following error, Has anyone had this before?
INFO: Started server process [1]
--
Fri, Nov 10 2023 2:41:24 pm | INFO: Waiting for application startup.
Fri, Nov 10 2023 2:41:24 pm | INFO: Application startup complete.
Fri, Nov 10 2023 2:41:24 pm | INFO: Uvicorn running on http://0.0.0.0:1984 (Press CTRL+C to quit)
Fri, Nov 10 2023 2:42:18 pm | INFO: Shutting down
Fri, Nov 10 2023 2:42:18 pm | INFO: Waiting for application shutdown.
Fri, Nov 10 2023 2:42:19 pm | ERROR:root:Error closing httpx client for clickhouse name '_httpx_client' is not defined
Fri, Nov 10 2023 2:42:19 pm | Traceback (most recent call last):
Fri, Nov 10 2023 2:42:19 pm | File "/code/smith-backend/app/main.py", line 140, in shutdown_event
Fri, Nov 10 2023 2:42:19 pm | File "/code/lc_database/lc_database/clickhouse.py", line 36, in close_clickhouse_client
Fri, Nov 10 2023 2:42:19 pm | await _httpx_client.aclose()
Fri, Nov 10 2023 2:42:19 pm | ^^^^^^^^^^^^^
Fri, Nov 10 2023 2:42:19 pm | NameError: name '_httpx_client' is not defined
Fri, Nov 10 2023 2:42:19 pm | INFO: Application shutdown complete.
Fri, Nov 10 2023 2:42:19 pm | INFO: Finished server process [1]
### 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
helm repo add langchain https://langchain-ai.github.io/helm/
helm install langchain/langsmith . --values values.yaml --namespace it-dev-langchain
Running this on an on prem Rancher cluster.
### Expected behavior
The contain runs normally.
|
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PR_kwDOIPDwls5fJlIg
| 13,203 |
Fix #11737 issue (extra_tools option of create_pandas_dataframe_agent is not working)
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[] | null | 1 | 2023-11-10T14:56:24 | 2023-12-05T04:54:08 | 2023-12-05T04:54:08 |
CONTRIBUTOR
| null |
- **Description:** Fix #11737 issue (extra_tools option of create_pandas_dataframe_agent is not working),
- **Issue:** #11737 ,
- **Dependencies:** no,
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17 I needed this method at work, so I modified it myself and used it. There is a similar issue(#11737) and PR(#13018) of @PyroGenesis, so I combined my code at the original PR.
You may be busy, but it would be great help for me if you checked. Thank you.
- **Twitter handle:** @lunara_x
If you need an .ipynb example about this, please tag me.
I will share what I am working on after removing any work-related content.
|
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| 1,987,762,764 |
I_kwDOIPDwls52etpM
| 13,202 |
Inconsistent output format of ChatVertexAI compared to ChatOpenAI
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[] | null | 1 | 2023-11-10T14:42:40 | 2023-11-10T14:45:38 | null |
NONE
| null |
### System Info
I tried to use `ChatVertexAI` as a replacement for `ChatOpenAI` as the latter is quite slow these days.
I have this code for Chat OpenAI
```
template_string = """ # some explanation
give me suggestions in JSON format where the suggestions are a list of dictionaries with the following keys:
- before:
- after:
- reason:
"""
chat = ChatOpenAI(
temperature=0.0,
openai_api_key=self.session_info.openai_api_key,
model=llm_model,
)
prompt_template = ChatPromptTemplate.from_template(template_string)
service_messages = prompt_template.format_messages(
text=self.text
)
response = chat(service_messages)
info = json.loads(response.content)
```
Then I print `response.content`, here is the format as expected:
```
[{"before": ..., "after": ...}, ...]
```
Then I use `ChatVertexAI` with the same `template_string`
```
chat = ChatVertexAI(
temperature=0.0,
google_api_key=google_api_key,
model="codechat-bison",
max_output_tokens=2048,
)
prompt_template = ChatPromptTemplate.from_template(template_string)
service_messages = prompt_template.format_messages(
text=self.text
)
response = chat(service_messages)
```
I print the `response.content` coming from Vertex AI and the output format is encapsulated by
```(3 backticks)JSON [{...}] (3 backticks)```. To make `response.content` the same, I used the following code
```
json_match = re.search(r'```JSON(.*?)\n```', response.content, re.DOTALL)
if json_match:
json_data = json_match.group(1).strip()
info = json.loads(json_data)
else:
info = {}
print("No JSON data found in the input string.")
```
I am not sure of the potential failures of this solution, but unless this is intentional, it might be better to make it consistent.
I will try to use the other LLMs and see how others work.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I shared my codes above, but the steps are:
1. setting up openai_api_key
2. setting up gcp environment
3. running ChatOpenAI
4. running ChatVertexAI
5. comparing the responses (i.e. `response.content`)
### Expected behavior
My expectation is to obtain the outputs of different models the same way. i.e., `json.loads` should output a list of dictionary in each case. Users should not have to deal with `regex` to obtain the data in the expected format.
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PR_kwDOIPDwls5fJdj-
| 13,201 |
dalle add model parameter
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CONTRIBUTOR
| null |
- **Description:** dalle_image_generator adding a new model parameter,
- **Issue:** N/A,
- **Dependencies:**
- **Tag maintainer: @hwchase17
- **Twitter handle:**
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| 1,987,751,284 |
PR_kwDOIPDwls5fJdhA
| 13,200 |
Adds an in-memory implementation of RecordManager
|
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[] | null | 3 | 2023-11-10T14:36:01 | 2023-12-15T12:59:06 | null |
CONTRIBUTOR
| null |
**Description:**
langchain offers three technologies to save data:
- [vectorstore](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [docstore](https://js.langchain.com/docs/api/schema/classes/Docstore)
- [record manager](https://python.langchain.com/docs/modules/data_connection/indexing)
If you want to combine these technologies in a sample persistence stategy you need a common implementation for each. `DocStore` propose `InMemoryDocstore`.
We propose the class `MemoryRecordManager` to complete the system.
This is the prelude to another full-request, which needs a consistent combination of persistence components.
**Tag maintainer:**
@baskaryan
**Twitter handle:**
@pprados
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| 1,987,735,492 |
I_kwDOIPDwls52em_E
| 13,199 |
More than 800 errors detected with the latest version of mypy
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[] | null | 3 | 2023-11-10T14:26:34 | 2023-11-11T08:43:03 | null |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.333
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
The current mypy crash with `make lint` and propose to install the last master version.
So, after a `git clone` and `poetry install --with dev,test,lint`, run
```bash
pip install git+https://github.com/python/mypy.git
make lint
```
**Mypy found 834 errors in 238 files**
### Expected behavior
0 errors
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| 1,987,672,041 |
I_kwDOIPDwls52eXfp
| 13,197 |
MlflowAIGatewayEmbeddings : Default Batch size incompatible with Azure OpenAI text-embedding-ada-002
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[] | null | 3 | 2023-11-10T13:52:34 | 2023-11-10T14:40:38 | null |
NONE
| null |
### System Info
The text-embedding-ada-002 OpenAI embedding model on Azure OpenAI has a maximum batch size of 16. MlflowAIGatewayEmbeddings has a hard-coded batch size of 20 which results in it being unusable with Azure OpenAI's text-embedding-ada-002.
The best fix would be to allow a configurable batchsize as an argument to MlflowAIGatewayEmbeddings,
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a gateway route to the text-embedding-ada-002 on azure openai
```
ROUTE_NAME = "azure-ada-002"
# Try to delete the route if it already exists
try:
delete_route(ROUTE_NAME)
print("Route deleted")
except:
print("Route does not exist, creating..")
create_route(
name=ROUTE_NAME,
route_type="llm/v1/embeddings",
model = {
"name" : "text-embedding-ada-002",
"provider" : "openai",
"openai_config" : {
"openai_api_type" : azure_openai_type,
"openai_api_key": azure_openai_key,
"openai_deployment_name": "ada-embed-v1",
"openai_api_base": azure_openai_base,
"openai_api_version": "2023-05-15"
}
}
)
```
2. Initialize the `MlflowAIGatewayEmbeddings` and try to embed more than 16 documents
```
from langchain.embeddings import MlflowAIGatewayEmbeddings
import pandas as pd
azure_ada = MlflowAIGatewayEmbeddings(route="azure-ada-002")
test_strings = [
"aaaa" for i in range(20)
]
azure_ada.embed_documents(test_strings)
```
3. Observe the error
```
HTTPError: 400 Client Error: Bad Request for url: https://ohio.cloud.databricks.com/gateway/azure-ada-002/invocations. Response text: {
"error": {
"message": "Too many inputs. The max number of inputs is 16. We hope to increase the number of inputs per request soon. Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions.",
"type": "invalid_request_error",
"param": null,
"code": null
}
}
```
### Expected behavior
I should be able to use the `MlflowAIGatewayEmbeddings` class with Azure OpenAI.
|
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| 1,987,564,233 |
I_kwDOIPDwls52d9LJ
| 13,196 |
langchain Retrieval
|
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[] | null | 3 | 2023-11-10T12:47:23 | 2023-11-16T05:15:18 | null |
NONE
| null |
### Issue you'd like to raise.
i have the following code:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from silly import no_ssl_verification
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with no_ssl_verification():
# load the document and split it into chunks
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# hfemb = HuggingFaceEmbeddings()
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
i must use retrieval mechanism. and i am using Turkish language data. please change my code
### Suggestion:
_No response_
|
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| 1,987,527,709 |
I_kwDOIPDwls52d0Qd
| 13,195 |
Example for chat chain with documents retrieval and history capability
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[] | null | 2 | 2023-11-10T12:23:44 | 2023-11-10T12:34:43 | null |
NONE
| null |
### System Info
Latest langchain, Mac
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi Community,
I'm trying to build a chain with Chroma database as context, AzureOpenAI embeddings and AzureOpenAI GPT model.
The following libs work fine for me and doing their work
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import AzureChatOpenAI
But I can't figure out how to build the chain itself and what kind of chain I should use.
Could you kindly provide any suggestions?
Thanks, Artem.
### Expected behavior
Desired behaviour:
- user asks question
- code searches it in Chroma using AzureOpenAI embeddings to transform text
- In prompt for AzureOpenAI GPT there is context with info retrieved from Chroma and current chat history with Human/AI questions and answers
- Also there should be a custom prompt template used
- Also I want to wrap it in Flask server, so users chat history could be stored in session with username as key until it's forcibly cleared
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| 1,987,527,490 |
I_kwDOIPDwls52d0NC
| 13,194 |
The Agent is not using Custom Tools.
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[] | null | 10 | 2023-11-10T12:23:33 | 2023-11-17T14:19:54 | null |
NONE
| null |
### System Info
I just updated langchain to newest version and my Agent is not working anymore.
Tool structure :
```
class Data_Retriever(BaseModel):
db : Any
class Config:
extra = Extra.forbid
def run(self,request) -> str:
data = self.db.get(request)
return data
```
When Agent uses the above tool, it will always stop at "Action Input" step :
```
> Entering new AgentExecutor chain...
AI: Thought: Do I need to use a tool? Yes
Action: Data_Retriever
Action Input: DATA
> Finished chain.
```
Does anyone know how to fix this ? I'm using langchain==0.0.332
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.agents import AgentType, initialize_agent,load_tools
tools = [
Tool(
name="Music Search",
func=lambda x: "'All I Want For Christmas Is You' by Mariah Carey.", # Mock Function
description="A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'",
),
]
agent = initialize_agent(
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
tools=tools,
llm=llm,
verbose=True)
agent.run(input = "Random song")
```
### Expected behavior
The output should be All I Want For Christmas Is You' by Mariah Carey.
My agent stopped once it hit the action input step.
> Entering new AgentExecutor chain...
AI: Thought: Do I need to use a tool? Yes
Action: Music Search
Action Input: "Random song"
> Finished chain.
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Templates/rag lancedb
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added template code for lancedb
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Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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### Issue you'd like to raise.
_No response_
### Suggestion:
my code:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from silly import no_ssl_verification
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with no_ssl_verification():
# load the document and split it into chunks
loader = TextLoader("nazim.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# hfemb = HuggingFaceEmbeddings()
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "When was Nâzım Hikmet entered the Naval School?"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
my nazim.txt:
```
Nâzım Hikmet was born on 21 November 1901 in Salonica, but his birth was certified as 15 January 1902 in order to prevent his age appearing older by a year older on account of 40 days. He died on 3 June 1963 in Moscow.
His paternal grandfather Nâzım Pasha the governor was a liberal and a poet. He belonged to Mevlevi Mysticism. He was a close friend to Mithat Pasha. His father, Hikmet was graduated from Mekteb-i Sultani (later the Galatasaray Lycée). He firstly dealt with trade but when he had been unsuccessful in that area, he became a civil servant at the Ministry of Foreign Affairs (Kalem-i Ecnebiye).
His mother was the daughter of Enver Pasha who was a linguist and an educator. Celile Hanım spoke French, played the piano and painted pictures as well as an artist.
His family environment, with its progressive thoughts, had tremendous effect on the education of Nâzım Hikmet. He was first trained at a school where the language of instruction was French and later attended the Numune Mektep (Taş Mektep) in Göztepe in Istanbul. After graduating primary school, he attended the prep class of the Mekteb-i Sultani with his friend, Vâlâ Nurettin. The year after, because of the financial strait in which his family found themselves, he changed his school to the Nişantaşı Junior High School.
In this period, with the influence of his grandfather, he started to write poetry. During a family meeting, Cemal Pasha, the Minister of the Navy, evinced to very much moved when Nâzım Hikmet read a heroic poem he had written about sailors. Cemal Pasha offered to send him to the Heybeliada Naval School and after the acceptance of the offer by the family, he helped Nâzım enter this school.
Nâzım Hikmet entered the Naval School in 1917 and graduated thence in 1919 and started to work as intern deck officer on the Hamidiye Cruiser. But in winter of the same year his pleurisy illness repeated. After a medical treatment period of nearly two months, during which he was under control of the head doctor of the Navy Hospital, Hakkı Şinasi Pasha, he was given permission to go home for two months. But he did not recover enough to return to work as a navy officer. By a Health Council Report he was discharged as unfit for duty in May 1920.
At this time, he was going to be known among Syllabist Poets as a young voice. Nâzım Hikmet admired Yahya Kemal who was his history and literature teacher and also his family friend, and showed him his poems for his critique. In 1920, the Alemdar Newspaper organised a contest where the jury consisted of famous poets. Nâzım was elected recipient of the award. Young masters, such as Faruk Nafiz, Yusuf Ziya, Orhan Seyfi talked about him with great admiration.
Istanbul had been under occupation and Nâzım Hikmet was writing resistance poems reflecting the ebullient patriotism. In the last days of 1920 the poem "Gençlik" (Youth) called the young generation to fight for the liberation of the country.
On 1 January 1921, with the help of illegal organisation which provided weapons to Mustafa Kemal, four poets (Faruk Nafiz,Yusuf Ziya, Nâzım Hikmet and Vâlâ Nurettin) secretly got on the Yeni Dünya (New World) Ship in Sirkeci. In order to gain passage to Ankara, one had to wait nearly 5 or 6 days at Inebolu. The passage was granted only to Nâzım Hikmet and Vâlâ Nurettin by Ankara.
During the days in Inebolu, they met young students coming from Germany who were waiting for permission to go to Ankara, like them. Among them there were Sadık Ahi ( later Mehmet Eti- Parliamentary of CHP),Vehbi(Prof. Vehbi Sarıdal) Nafi Atuf(Kansu-General Secretary of CHP). These were called as Spartans and they defended socialism and praised SSCB that was the first country which accepted Turkey's National Pact (Misak-ı Milli) Borders. These ideas were new for Nâzım and his friend Vâlâ Nureddin.
When they reached Ankara the first duty given to them was to write a poem to summon the youth of Istanbul to the national Struggle. They finished this three-page poem within three days. It was published by the Matbuat Müdürlüğü (Directory of Press) in four pages in 11.5 x 18 cm. format and served out in ten-thousand copies in March 1921. The impact of the poem was so great that the members of the National Assembly started to argue how to resolve the enthusiastic upheaval which was caused by the poem. Muhittin Birgen, the Director of Press, was criticised in the negative because of publishing and serving out of the poem.
It would have been a great problem to find jobs if the youth of Istanbul had come to Ankara. Discomforted by having been compelled to report to the National Assembly on the matter, Muhittin Birgen decided to transfer Nâzım Hikmet and Vâlâ Nureddin to the auspices of the Ministry of Education.
At this time, İsmail Fazıl Pasha, one of the relatives of Celile Hanım, summoned to the Assembly these two talented poets whose poem had caused such a stir, and introduced them to Mustafa Kemal Pasha.
```
QUESTİON:
My output should be one line and correct answer according to question "Nâzım Hikmet entered the Naval School in 1917". What should i change in my code?
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I_kwDOIPDwls52dQPe
| 13,191 |
langchain & chroma - Basic Example
|
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[] | null | 2 | 2023-11-10T10:46:38 | 2023-11-10T10:53:55 | null |
NONE
| null |
### Issue you'd like to raise.
_No response_
### Suggestion:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from silly import no_ssl_verification
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with no_ssl_verification():
# load the document and split it into chunks
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# hfemb = HuggingFaceEmbeddings()
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
i use this code but i get the following error:
ValueError: Expected EmbeddingFunction.__call__ to have the following signature: odict_keys(['self', 'input']), got odict_keys(['self', 'args', 'kwargs'])
Please see https://docs.trychroma.com/embeddings for details of the EmbeddingFunction interface.
Please note the recent change to the EmbeddingFunction interface: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023
can you give me fixed code and explain?
|
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| 1,987,369,601 |
PR_kwDOIPDwls5fIJ1M
| 13,190 |
Add a Wrapper vectorstore, compatible with SelfQueryRetriever
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[] | null | 2 | 2023-11-10T10:39:45 | 2023-12-01T13:45:35 | 2023-11-27T09:44:58 |
CONTRIBUTOR
| null |
**Description:**
The [`SelfQueryRetriever`](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/) component is able to convert filter clauses for different vectorstore implementations.
If we wish to offer advanced use of vectorstore, by modifying for example certain methods, via a wrapper type approach, we lose the ability to use `SelfQueryRetriever`.
We propose to standardize vectorstore wrapper exploitation, to allow flexible integration with `SelfQueryRetriever`.
The idea is to add to `_get_builtin_translator()`, taking into account the wrapper.
Thus, every vectorstore wrapper automatically benefits from the SelfQueryRetriever.
This is the prelude to another full-request, which needs a vectorstore wrapper, while being compatible
with `SelfQueryRetriever`.
**Tag maintainer:**
@baskaryan
**Twitter handle:**
@pprados
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,987,366,533 |
PR_kwDOIPDwls5fIJK6
| 13,189 |
Make it easier to subclass runnable binding with custom init args
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[] | null | 1 | 2023-11-10T10:37:45 | 2023-11-11T09:01:18 | 2023-11-11T09:01:17 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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I_kwDOIPDwls52dKVf
| 13,188 |
Binding stop to a local llm does not work?
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[] | null | 3 | 2023-11-10T10:31:00 | 2023-11-10T10:59:39 | null |
NONE
| null |
### System Info
newest version
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When loading a local model it keeps hallucinating a conversation, how to make it stop at "Human:" ? In the create_llm function you see two ways I tried, giving kwars and binding, but both did not work. Is the bind specifically made for LCEL?
Is there some other way to make the stopping work?
```
from langchain.llms import CTransformers
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
def create_llm(model_path='./models/mistral-7b-instruct-v0.1.Q4_K_M.gguf', model_type="mistral"):
llm = CTransformers(model=model_path, model_type=model_type,kwargs={'stop': ['Human:']})
llm.bind(stop=["Human:"])
return llm
def create_memory():
memory = ConversationBufferMemory(memory_key="history")
return memory
def create_memory_prompt():
template = """You are an AI chatbot having a conversation with a human. Answer his questions.
Previous conversation: {history}
Human: {human_input}
AI: """
prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
return prompt
def create_normal_chain(llm, prompt, memory):
llm_chain = LLMChain(llm=llm, prompt=prompt, memory=memory)
return llm_chain
chain = create_normal_chain(create_llm(), create_memory_prompt(), create_memory())
out = chain.invoke({"human_input" : "Hello"})
print(out)
```
### Expected behavior
Generation stops at "Human:".
|
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I_kwDOIPDwls52c9S0
| 13,187 |
cookbook/Semi_Structured_RAG.ipynb ERROR：IndexError: list index out of range
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[] | null | 3 | 2023-11-10T09:59:01 | 2023-11-15T07:58:08 | null |
NONE
| null |
### System Info
cenos7
python3.9
langchai 0.0.333
Reading error PDF file here：
[llm.pdf](https://github.com/langchain-ai/langchain/files/13318006/llm.pdf)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from lxml import html
from pydantic import BaseModel
from typing import Any, Optional
from unstructured.partition.pdf import partition_pdf
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
import uuid,os
from langchain.vectorstores import Chroma
from langchain.storage import InMemoryStore
from langchain.schema.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers.multi_vector import MultiVectorRetriever
os.environ["OPENAI_API_KEY"] = ''
path=rf"/mnt/PD/PDF/"
# Get elements
raw_pdf_elements = partition_pdf(
filename=path + "llm.pdf",
# Unstructured first finds embedded image blocks
extract_images_in_pdf=False,
# Use layout model (YOLOX) to get bounding boxes (for tables) and find titles
# Titles are any sub-section of the document
infer_table_structure=True,
# Post processing to aggregate text once we have the title
chunking_strategy="by_title",
# Chunking params to aggregate text blocks
# Attempt to create a new chunk 3800 chars
# Attempt to keep chunks > 2000 chars
max_characters=4000,
new_after_n_chars=3800,
combine_text_under_n_chars=2000,
image_output_dir_path=path,
)
### Expected behavior
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb?ref=blog.langchain.dev
When testing langchain's Semi_Structured_RAG.ipynb, I tested the PDF files used. Some PDFs were read normally, but some PDFs reported errors. The error log is as follows:
File "/mnt/PD/test.py", line 20, in <module>
raw_pdf_elements = partition_pdf(
File "/usr/local/lib/python3.9/site-packages/unstructured/documents/elements.py", line 372, in wrapper
elements = func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/unstructured/file_utils/filetype.py", line 591, in wrapper
elements = func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/unstructured/file_utils/filetype.py", line 546, in wrapper
elements = func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/unstructured/chunking/title.py", line 297, in wrapper
elements = func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/pdf.py", line 182, in partition_pdf
return partition_pdf_or_image(
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/pdf.py", line 312, in partition_pdf_or_image
_layout_elements = _partition_pdf_or_image_local(
File "/usr/local/lib/python3.9/site-packages/unstructured/utils.py", line 179, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/pdf.py", line 413, in _partition_pdf_or_image_local
final_layout = process_file_with_ocr(
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/ocr.py", line 170, in process_file_with_ocr
raise e
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/ocr.py", line 159, in process_file_with_ocr
merged_page_layout = supplement_page_layout_with_ocr(
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/ocr.py", line 238, in supplement_page_layout_with_ocr
page_layout.elements[:] = supplement_element_with_table_extraction(
File "/usr/local/lib/python3.9/site-packages/unstructured/partition/ocr.py", line 274, in supplement_element_with_table_extraction
element.text_as_html = table_agent.predict(cropped_image, ocr_tokens=table_tokens)
File "/usr/local/lib/python3.9/site-packages/unstructured_inference/models/tables.py", line 54, in predict
return self.run_prediction(x, ocr_tokens=ocr_tokens)
File "/usr/local/lib/python3.9/site-packages/unstructured_inference/models/tables.py", line 191, in run_prediction
prediction = recognize(outputs_structure, x, tokens=ocr_tokens)[0]
IndexError: list index out of range
|
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| 1,987,293,567 |
I_kwDOIPDwls52c7F_
| 13,186 |
QAGenerationChain output in different languages
|
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[] | null | 2 | 2023-11-10T09:53:25 | 2023-11-10T10:00:53 | null |
NONE
| null |
### Issue you'd like to raise.
**My code:**
loader_pdf = PyMuPDFLoader("/Users/python/test_pdf.pdf")
doc_pdf = loader_pdf.load()
llm = ChatOpenAI(temperature=0)
chain = QAGenerationChain.from_llm(llm=llm)
print("pdf:\n",doc_pdf[3].page_content)
qa_pdf = chain.run(doc_pdf[3].page_content)
The PDF I am using is a Chinese data file，but the output is in English.
There are no relevant records in the document. I would like to know how to solve this problem or can I set a Prompt to solve it?
### Suggestion:
_No response_
|
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| 1,987,257,994 |
I_kwDOIPDwls52cyaK
| 13,185 |
Setting a custom http_client fails with pydantic ConfigError
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[] | null | 5 | 2023-11-10T09:36:13 | 2023-11-27T18:50:16 | 2023-11-27T18:50:15 |
NONE
| null |
### System Info
<pre>
platform = macOS-10.16-x86_64-i386-64bit
version = 3.10.13 (main, Sep 11 2023, 08:39:02) [Clang 14.0.6 ]
langchain.version = 0.0.332
pydantic.version = 1.10.13
openai.version = 1.2.0
</pre>
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm trying to initialize an instance of AzureChatOpenAI with a custom http_client. Following is the code I'm using -
<pre>
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
import httpx
messages = [
SystemMessage(content="You are a helpful assistant that translates English to Spanish"),
HumanMessage(content="Good morning Vietnam!")
]
client = httpx.Client()
chat = AzureChatOpenAI(
openai_api_key="my-secret-key",
openai_api_version="2023-07-01-preview",
model="gpt-4",
deployment_name="my-deployment-name",
azure_endpoint="https://my-instance.openai.azure.com",
http_client=client
)
# AzureChatOpenAI.update_forward_refs()
chat(messages)
</pre>
This fails with the following pydantic config error:
<pre>
Traceback (most recent call last):
File "azure-custom.py", line 24, in <module>
chat = AzureChatOpenAI(
File "langchain-env/lib/python3.10/site-packages/langchain/load/serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1076, in pydantic.main.validate_model
File "pydantic/fields.py", line 860, in pydantic.fields.ModelField.validate
pydantic.errors.ConfigError: field "http_client" not yet prepared so type is still a ForwardRef, you might need to call AzureChatOpenAI.update_forward_refs().
</pre>
### Expected behavior
As per the [ChatOpenAI API Doc](https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html), `http_client` seems to be a valid parameter to set in order to customize the httpx client in the background. But I'm unable to do that customization.
Adding `AzureChatOpenAI.update_forward_refs()` also does not solve this issue - the code raises the error even before hitting that line.
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| 1,987,247,637 |
PR_kwDOIPDwls5fHvC5
| 13,184 |
Remove `_get_kwarg_value` function
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[] | null | 1 | 2023-11-10T09:29:31 | 2023-11-13T08:21:49 | 2023-11-13T08:09:54 |
CONTRIBUTOR
| null |
`_get_kwarg_value` function is useless, one can rely on python builtin functionalities to do the exact same thing.
- **Description:** Removed `_get_kwarg_value`. Helps with code readability.
- **Issue:** the issue # it fixes (if applicable),
- **Twitter handle:** @Guillem_96
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| 1,987,237,194 |
PR_kwDOIPDwls5fHsyC
| 13,183 |
docugami cookbook
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[] | null | 1 | 2023-11-10T09:22:54 | 2023-11-21T20:02:21 | 2023-11-21T20:02:21 |
CONTRIBUTOR
| null |
Adds a cookbook for semi-structured RAG via Docugami. This follows the same outline as the semi-structured RAG with Unstructured cookbook: https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb
The main change is this cookbook uses Docugami instead of Unstructured to find text and tables, and shows how XML markup in the output helps with retrieval and generation.
We are \@docugami on twitter, I am \@tjaffri
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PR_kwDOIPDwls5fHqiW
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Llamacppserver client
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[] | null | 1 | 2023-11-10T09:16:38 | 2023-12-05T04:15:16 | null |
NONE
| null |
- **Description:** a client for the llama.cpp native server
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** emanuel248
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,987,207,110 |
PR_kwDOIPDwls5fHmQd
| 13,181 |
Add SQLDocStore
|
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[] | null | 1 | 2023-11-10T09:04:19 | 2023-11-27T09:26:14 | null |
CONTRIBUTOR
| null |
**Description:**
langchain offers three technologies to save data:
- [vectorstore](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [docstore](https://js.langchain.com/docs/api/schema/classes/Docstore)
- [record manager](https://python.langchain.com/docs/modules/data_connection/indexing)
If you want to combine these technologies in a single transaction, to guarantee the consistency of all backups,
you need a common implementation for each. This allows, for example, to combine the RecordManager with a vector
store using an SQL database.
But, there is no SQL docstore.
**Tag maintainer:**
@baskaryan
**Twitter handle:**
@pprados
|
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| 1,987,191,884 |
I_kwDOIPDwls52ciRM
| 13,180 |
DOC: Setting proxy for the whole langchain library, especially web_research_retriever
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[] | null | 3 | 2023-11-10T08:54:16 | 2023-11-10T10:37:03 | null |
NONE
| null |
### Issue with current documentation:
Hello guys,
I have to use a proxy to access Azure OpenAi because I'm using a VPN for my company.
However when I try to use the [](https://python.langchain.com/docs/modules/data_connection/retrievers/web_research) , I block at fetching the pages because the code inside the langchain library does not use the proxy.
So I managed to make it work for GoogleSearchAPIWrapper by setting it in the os.environ but it does not work for the inside request and html_load call in the langchain library.
### Idea or request for content:
So I would like to have a proxy parameter when I set up a web_research_retriever = WebResearchRetriever.from_llm(
vectorstore=vectorstore, llm=llm, search=search
)
Or any other tips that can help me to dodge this proxy issue.
Thanks in advance
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| 1,987,177,608 |
I_kwDOIPDwls52ceyI
| 13,179 |
Document retriever from Knowledge-graph based source
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[] | null | 1 | 2023-11-10T08:44:39 | 2023-11-13T09:08:19 | 2023-11-13T09:08:18 |
NONE
| null |
### Feature request
As the title suggests, there is currently no built-in method to retrieve chunks linked through edges in graph-based structures.
This is especially relevant in cases where documents are not self-contained and chunks reference other chunks, either within the same document or in other documents. These "children" chunks are often necessary to properly answer user queries.
Specifically, I would like to have the ability to perform the following within the same chain:
```
1. Get the user query
2. Embed it
3. Perform approximate nearest neighbor (ANN) lookup and retrieve the most relevant chunk
4. Given the ID of the chunk, expand the set of matches by navigating the graph
5. Retrieve all of these to provide to the Large Language Model (LLM)
6. Send everything to the LLM to answer the user query
```
To further contextualize my suggestion, I have been considering the following approach to begin with:
```python
import networkx as nx
GraphType = Union[nx.DiGraph, nx.Graph]
class NXInMemoryGraphRetriever(BaseRetriever):
def __init__(
self,
bootstrap_retriever: BaseRetriever,
graph: GraphType,
navigate_graph_function: Callable[[GraphType, str], List[Document]],
):
self._graph = graph
self._navigate_graph_fn = navigate_graph_function
...
def get_relevant_documents(
self,
query: str,
*,
callbacks: Optional[List[Callable]] = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
base_documents = self.bootstrap_retriever.get_relevant_documents(...)
expanded_set_of_docs = set(base_documents)
for document in base_documents:
expanded_set_of_docs.update(
self.navigate_graph_function(self.graph, document.metadata["id"])
)
...
return list(expanded_set_of_documents)
# Example of callable function to pass
def get_everything_one_hop_away(graph: GraphType, source_doc_id: str) -> List[Documents]:
related_docs = []
for source_doc, related_doc in nx.dfs_edges(
graph, source=source_doc_id, depth_limit=1
):
related_docs.add(Document(...)) # From the related docs id.
return related_docs
```
Additionally, expanding this to support Neo4J or similar should be rather straightforward. Don't pass the graph but the DB driver and the `navigate_graph_function` with a cypher query string/literal.
### Motivation
Use langchain to navigate graph related datasets easily.
### Your contribution
Happy to send a PR your way :)
|
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| 1,987,161,235 |
PR_kwDOIPDwls5fHcaS
| 13,178 |
Typo fix to quickstart.mdx
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[] | null | 1 | 2023-11-10T08:33:09 | 2023-11-13T08:02:18 | 2023-11-13T08:02:18 |
CONTRIBUTOR
| null |
- **Description:** I fixed a very small typo in the quickstart docs (BaeMessage -> BaseMessage)
|
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| 1,987,156,699 |
I_kwDOIPDwls52cZrb
| 13,177 |
Support OpenAI seed for deterministic outputs
|
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[] | null | 5 | 2023-11-10T08:29:51 | 2023-12-08T22:26:31 | null |
NONE
| null |
### Feature request
Support OpenAI seed and fingerprint parameters to get more consistent outputs for the same inputs and model version.
https://cookbook.openai.com/examples/deterministic_outputs_with_the_seed_parameter#seed
https://platform.openai.com/docs/api-reference/chat/create#chat-create-seed
### Motivation
Running unit tests that are dependent on LLM responses often leads to random flakiness.
### Your contribution
PR
|
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| 1,987,140,842 |
I_kwDOIPDwls52cVzq
| 13,176 |
Simple Retrieval QA Example
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[] | null | 4 | 2023-11-10T08:20:00 | 2023-11-10T10:45:56 | null |
NONE
| null |
### Issue you'd like to raise.
`
# Import necessary libraries
from llama_index import (
LangchainEmbedding,
)
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.vector_stores import ChromaVectorStore
from llama_index.storage.storage_context import StorageContext
import chromadb
# Create client and a new collection
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
hfemb = HuggingFaceEmbeddings()
embed_model = LangchainEmbedding(hfemb)
documents = SimpleDirectoryReader("./docs/examples/data/paul_graham").load_data()
# Set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
service_context = ServiceContext.from_defaults(llm=None, embed_model=embed_model)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, service_context=service_context, show_progress=True
)
# Query Data
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)
`
I used this code with llama-index retrieval mechanism for question&answer with documents. How can i write this code's LangChain form? I should'nt use model. I retrieve answer from documents. And i should use HuggingFaceEmbeddings for embedding.
### Suggestion:
_No response_
|
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| 1,987,138,290 |
PR_kwDOIPDwls5fHXXr
| 13,175 |
Pprados/lite google drive
|
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[] | null | 1 | 2023-11-10T08:18:04 | 2023-12-05T04:31:22 | 2023-12-05T04:31:21 |
CONTRIBUTOR
| null |
- Fix bug in the document
- Add clarification on the use of langchain-google drive.
|
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PR_kwDOIPDwls5fG-gr
| 13,174 |
Add serve to quickstart
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PR_kwDOIPDwls5fG9yP
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OpenAI latest Update fixes
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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),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52b4f9
| 13,172 |
from langchain.indexes import VectorstoreIndexCreator report errors ""
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[] | null | 2 | 2023-11-10T06:47:43 | 2023-11-10T06:54:24 | null |
NONE
| null |
### System Info
python version: 3.11
I'm trying to run the sampe code "LangChain: Q&A over Documents", but when I run the below cell, it reports below error
```
pip install --upgrade langchain
from llm_commons.langchain.btp_llm import ChatBTPOpenAI
from llm_commons.langchain.btp_llm import BTPOpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.document_loaders import CSVLoader
from langchain.vectorstores import DocArrayInMemorySearch
from IPython.display import display, Markdown
file = 'OutdoorClothingCatalog_1000.csv'
loader = CSVLoader(file_path=file, encoding='utf-8')
from langchain.indexes import VectorstoreIndexCreator
```
ImportError Traceback (most recent call last)
Cell In[4], line 1
----> 1 from langchain.indexes import VectorstoreIndexCreator
File /opt/conda/lib/python3.11/site-packages/langchain/indexes/__init__.py:17
1 """Code to support various indexing workflows.
2
3 Provides code to:
(...)
14 documents that were derived from parent documents by chunking.)
15 """
16 from langchain.indexes._api import IndexingResult, aindex, index
---> 17 from langchain.indexes._sql_record_manager import SQLRecordManager
18 from langchain.indexes.graph import GraphIndexCreator
19 from langchain.indexes.vectorstore import VectorstoreIndexCreator
File /opt/conda/lib/python3.11/site-packages/langchain/indexes/_sql_record_manager.py:21
18 import uuid
19 from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence, Union
---> 21 from sqlalchemy import (
22 URL,
23 Column,
24 Engine,
25 Float,
26 Index,
27 String,
28 UniqueConstraint,
29 and_,
30 create_engine,
31 delete,
32 select,
33 text,
34 )
35 from sqlalchemy.ext.asyncio import (
36 AsyncEngine,
37 AsyncSession,
38 async_sessionmaker,
39 create_async_engine,
40 )
41 from sqlalchemy.ext.declarative import declarative_base
ImportError: cannot import name 'URL' from 'sqlalchemy' (/opt/conda/lib/python3.11/site-packages/sqlalchemy/__init__.py)
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
python version: 3.11
I'm trying to run the sampe code "LangChain: Q&A over Documents", but when I run the below cell, it reports below error
```
pip install --upgrade langchain
from llm_commons.langchain.btp_llm import ChatBTPOpenAI
from llm_commons.langchain.btp_llm import BTPOpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.document_loaders import CSVLoader
from langchain.vectorstores import DocArrayInMemorySearch
from IPython.display import display, Markdown
file = 'OutdoorClothingCatalog_1000.csv'
loader = CSVLoader(file_path=file, encoding='utf-8')
from langchain.indexes import VectorstoreIndexCreator
```
ImportError Traceback (most recent call last)
Cell In[4], line 1
----> 1 from langchain.indexes import VectorstoreIndexCreator
File /opt/conda/lib/python3.11/site-packages/langchain/indexes/__init__.py:17
1 """Code to support various indexing workflows.
2
3 Provides code to:
(...)
14 documents that were derived from parent documents by chunking.)
15 """
16 from langchain.indexes._api import IndexingResult, aindex, index
---> 17 from langchain.indexes._sql_record_manager import SQLRecordManager
18 from langchain.indexes.graph import GraphIndexCreator
19 from langchain.indexes.vectorstore import VectorstoreIndexCreator
File /opt/conda/lib/python3.11/site-packages/langchain/indexes/_sql_record_manager.py:21
18 import uuid
19 from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence, Union
---> 21 from sqlalchemy import (
22 URL,
23 Column,
24 Engine,
25 Float,
26 Index,
27 String,
28 UniqueConstraint,
29 and_,
30 create_engine,
31 delete,
32 select,
33 text,
34 )
35 from sqlalchemy.ext.asyncio import (
36 AsyncEngine,
37 AsyncSession,
38 async_sessionmaker,
39 create_async_engine,
40 )
41 from sqlalchemy.ext.declarative import declarative_base
ImportError: cannot import name 'URL' from 'sqlalchemy' (/opt/conda/lib/python3.11/site-packages/sqlalchemy/__init__.py)
### Expected behavior
the import should work as expected
|
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I_kwDOIPDwls52b11W
| 13,171 |
Problem With OpenAI Error update
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[] | null | 1 | 2023-11-10T06:37:20 | 2023-11-11T01:12:11 | null |
NONE
| null |
### System Info
langchain==0.0.333
openai == 1.2.2
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Make a simple OpenAI call with the latest OpenAI version
### Expected behavior
OpenAI in there recent changes have updated their error API so no longer it has to be referenced with OpenAI.error.
The error is in this file.
langchain/libs/langchain/langchain/chat_models/openai.py
So the error list that is
errors = [
openai.error.Timeout,
openai.error.APIError,
openai.error.APIConnectionError,
openai.error.RateLimitError,
openai.error.ServiceUnavailableError,
]
should be updated to
errors = [
openai.Timeout,
openai.APIError,
openai.APIConnectionError,
openai.RateLimitError,
openai.ServiceUnavailableError,
]
|
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| 1,986,917,411 |
PR_kwDOIPDwls5fGnSB
| 13,169 |
Improve youtube tool prompting and arg parsing
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[] | null | 1 | 2023-11-10T05:15:05 | 2023-12-05T04:33:55 | null |
CONTRIBUTOR
| null |
**Description:**
The youtube search tool does not work well with openai function calling. OpenAI infers that it should provide two arguments: `topic: str` and `num_results: int`, but the tool accepts a single argument `query: str` which it tries to parse into a topic and num_results.
This is a backwards compatible fix that adds an argument `num_results: Optional[int]` to the tool while maintaining the previous parsing logic if the argument is not provided.
This also improves the prompting for the tool. Previously the tool description mentioned only that you could search for a person on YouTube, when in fact the tool can be used to search for any topic.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
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I_kwDOIPDwls52bWKu
| 13,168 |
TypeError: multiple bases have instance layout conflict. DOC: <Quick start code dont work with version langchain ==0.333>
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[] | null | 3 | 2023-11-10T04:51:26 | 2023-11-11T07:50:51 | 2023-11-11T07:50:51 |
NONE
| null |
### Issue with current documentation:
in https://python.langchain.com/docs/get_started/quickstart
in the section LLM / Chat Model
with the code
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
# I recevce the error
File [~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:40](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/home/nghia_1660s/nghia_1660s_workspace/gates-llm-chatbots/learn_langchain/~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:40)
[29](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/home/nghia_1660s/nghia_1660s_workspace/gates-llm-chatbots/learn_langchain/~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:29) import yaml
[30](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/home/nghia_1660s/nghia_1660s_workspace/gates-llm-chatbots/learn_langchain/~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:30) from tenacity import (
[31](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/home/nghia_1660s/nghia_1660s_workspace/gates-llm-chatbots/learn_langchain/~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:31) RetryCallState,
[32](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/home/nghia_1660s/nghia_1660s_workspace/gates-llm-chatbots/learn_langchain/~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:32) before_sleep_log,
ref='~/nghia_1660s_workspace/gates-llm-chatbots/.venv/lib/python3.10/site-packages/langchain/llms/base.py:0'>0</a>;32m (...)
...
--> [106](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/usr/lib/python3.10/abc.py:106) cls = super().__new__(mcls, name, bases, namespace, **kwargs)
[107](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/usr/lib/python3.10/abc.py:107) _abc_init(cls)
[108](https://vscode-remote+ssh-002dremote-002bnghia-002d1660s.vscode-resource.vscode-cdn.net/usr/lib/python3.10/abc.py:108) return cls
**TypeError: multiple bases have instance lay-out conflict**
### Idea or request for content:
The TypeError: multiple bases have instance lay-out conflict is the error for
Here is an example illustrating the instance layout conflict in Python:
Let's say we have two extension types defined in C:
class Base1(object):
__slots__ = ()
_fields_ = ["field1"]
class Base2(object):
__slots__ = ()
_fields_ = ["field2"]
Now let's attempt multiple inheritance:
class Derived(Base1, Base2):
__slots__ = ()
Here you will get TypeError: multiple bases have instance layout conflict.
This error is somewhat specific to extension types and it's due to violating multiple inheritance compatibility in Python under the hood when working with extension types. In simpler terms, Python is complaining about a conflict that occurs due to Base1 and Base2 having a different memory layout. Python doesn't know how to resolve this ambiguity or conflict, so it throws a TypeError.
I use python the python I use is 3.10.12
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PR_kwDOIPDwls5fGei9
| 13,167 |
Bagatur/update agent docs
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improve duck duck go tool
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Add MyScaleWithoutJSON which allows user to wrap columns into Document's Metadata
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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| 1,986,745,002 |
I_kwDOIPDwls52a1Kq
| 13,162 |
getiing an error with openai v1
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[] | null | 6 | 2023-11-10T02:41:34 | 2023-11-24T02:40:25 | null |
CONTRIBUTOR
| null |
### System Info
Getting the below error when try to use load_qa_chain
openai.lib._old_api.APIRemovedInV1:
You tried to access openai.ChatCompletion, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API.
You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface.
Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`
A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run load_qa_chain
### Expected behavior
it should not error
|
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| 1,986,715,808 |
I_kwDOIPDwls52auCg
| 13,161 |
Issue: Can't get response from chain.predict()
|
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[] | null | 10 | 2023-11-10T02:09:17 | 2023-11-13T03:13:33 | 2023-11-13T03:05:42 |
NONE
| null |
### Issue you'd like to raise.
I'm using llama2 with langchain. To use gpu, I re-installed the llama-cpp-python with cublas options, but after that, I can not get response from langchain.LLMChain.predict() anymore.
The result is like:
```
User:hi
> Entering new LLMChain chain...
Prompt after formatting:
History:
Question: hi
Answer:
llama_print_timings: load time = 883.81 ms
llama_print_timings: sample time = 0.47 ms / 2 runs ( 0.24 ms per token, 4255.32 tokens per second)
llama_print_timings: prompt eval time = 883.77 ms / 14 tokens ( 63.13 ms per token, 15.84 tokens per second)
llama_print_timings: eval time = 228.71 ms / 1 runs ( 228.71 ms per token, 4.37 tokens per second)
llama_print_timings: total time = 1117.20 ms
> Finished chain.
```
The following is how I define the llm and the chain:
```
llm = LlamaCpp(
model_path = model_path,
n_ctx=4096,
top_k=10,
top_p=0.9,
temperature=0.7, repeat_penalty=1.1,
verbose=True,
callback_manager=callback_manager,
n_gpu_layers=35,
n_batch=100,
stop=["Question:","Human:"]
)
memory = ConversationBufferMemory(memory_key='chat_history')
llm_chain = LLMChain(
prompt=prompt,
llm=llm,
memory=memory,
)
```
Actually when I used local llama.cpp and normally installed llama-cpp-python, everything went on well. The only change I made is the llama-cpp-python and all the code just remain the same. I wonder why will this happen.
### Suggestion:
_No response_
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| 1,986,656,960 |
PR_kwDOIPDwls5fFvtq
| 13,160 |
Update model i/o
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[] | null | 1 | 2023-11-10T01:05:34 | 2023-11-10T04:35:56 | 2023-11-10T04:35:56 |
COLLABORATOR
| null | null |
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| 1,986,646,789 |
I_kwDOIPDwls52adMF
| 13,159 |
Issue: Get cost estimates of GPT-4-Vision
|
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NONE
| null |
### Issue you'd like to raise.
Hi @baskaryan! Thanks for open-sourcing the [notebook](https://github.com/langchain-ai/langchain/blob/master/cookbook/openai_v1_cookbook.ipynb) related to GPT-4-Vision.
Is there a way, we can estimate the cost for the API calls similar to using `get_openai_callback`?
### Suggestion:
We could get the cost using callbacks as:
```
from langchain.callbacks import get_openai_callback
from langchain.prompts import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
human_message_prompt = HumanMessagePromptTemplate.from_template(prompt)
chat_prompt = ChatPromptTemplate.from_messages(
[system_message_prompt, human_message_prompt]
)
with get_openai_callback() as cb:
openai_chain = LLMChain(prompt=chat_prompt, llm=model)
response = openai_chain.run({})
n_tokens = cb.total_tokens
total_cost = cb.total_cost
```
How do I use `HumanMessage` with the callback?
|
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PR_kwDOIPDwls5fFmmy
| 13,158 |
fix module sidebar
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PR_kwDOIPDwls5fFk0I
| 13,157 |
Clean up Fireworks provider documentation
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CONTRIBUTOR
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moved `graph/` docs into `integrations/`
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COLLABORATOR
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`Graph` integrations docs mistakenly placed in the `use_cases/`.
- Moved `graph/` docs into the `integrations/`
- modified vercel.json to reroute old URLs
- fixed sidebar
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LCEL nits
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COLLABORATOR
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<!-- 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_kwDOIPDwls5fFM8j
| 13,154 |
OpenAI remove httpx typing
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[] | null | 1 | 2023-11-09T22:20:40 | 2023-11-09T22:32:10 | 2023-11-09T22:32:09 |
COLLABORATOR
| null |
Addresses #13124
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PR_kwDOIPDwls5fFIDO
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Pirate Speak Configurable Template
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[] | null | 1 | 2023-11-09T22:04:26 | 2023-11-09T22:13:47 | 2023-11-09T22:13:46 |
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PR_kwDOIPDwls5fFEGY
| 13,152 |
template readme's in docs
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PR_kwDOIPDwls5fE_Ed
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[LLMonitorCallbackHandler] Various improvements
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[] | null | 2 | 2023-11-09T21:30:27 | 2023-11-17T07:39:36 | 2023-11-17T07:39:36 |
CONTRIBUTOR
| null |
Small improvements for the llmonitor callback handler, like better support for non-openai models.
@baskaryan
|
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PR_kwDOIPDwls5fE9d7
| 13,150 |
Bagatur/docs serve context
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[] | null | 1 | 2023-11-09T21:24:45 | 2023-11-09T23:05:19 | 2023-11-09T23:05:18 |
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I_kwDOIPDwls52ZcEs
| 13,149 |
HTMLHeaderTextSplitter won't run (maxHead)
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[] | null | 2 | 2023-11-09T20:48:09 | 2023-11-09T20:54:35 | null |
NONE
| null |
### System Info
Notebook with latest langchain
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Trying in notebook the HTMLHeaderTextSplitter
```jupyter-notebook
from langchain.text_splitter import RecursiveCharacterTextSplitter
headers_to_split_on = [
("h1", "Header 1"),
("h2", "Header 2"),
("h3", "Header 3"),
("h4", "Header 4"),
]
html_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
html_header_splits = html_splitter.split_text_from_file("/content/X.html")
chunk_size = 500
chunk_overlap = 30
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
Split
splits = text_splitter.split_documents(html_header_splits)
splits[80:85]`
```
```jupyter-notebook
---------------------------------------------------------------------------
XSLTApplyError Traceback (most recent call last)
[<ipython-input-54-bd3edea942d1>](https://localhost:8080/#) in <cell line: 12>()
10 html_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
11
---> 12 html_header_splits = html_splitter.split_text_from_file("/content/X.html")
13
14 chunk_size = 500
[/usr/local/lib/python3.10/dist-packages/langchain/text_splitter.py](https://localhost:8080/#) in split_text_from_file(self, file)
586 xslt_tree = etree.parse(xslt_path)
587 transform = etree.XSLT(xslt_tree)
--> 588 result = transform(tree)
589 result_dom = etree.fromstring(str(result))
590
src/lxml/xslt.pxi in lxml.etree.XSLT.__call__()
XSLTApplyError: maxHead```
Is the HTML just to large to be handled by the textsplitter?
### Expected behavior
Load the html..
|
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PR_kwDOIPDwls5fEzNW
| 13,148 |
CLI interactivity
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[] | null | 1 | 2023-11-09T20:46:28 | 2023-11-14T17:53:30 | 2023-11-14T17:53:29 |
COLLABORATOR
| null |
Will implement more later
|
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PR_kwDOIPDwls5fEwFn
| 13,147 |
update oai tool agent doc
|
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[] | null | 1 | 2023-11-09T20:35:49 | 2023-11-09T20:57:01 | 2023-11-09T20:37:30 |
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Added ability to pass arguments to the Playwright browser
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[] | null | 1 | 2023-11-09T20:01:20 | 2023-12-05T04:48:10 | 2023-12-05T04:48:09 |
CONTRIBUTOR
| null |
- **Description:** Enhanced `create_sync_playwright_browser` and `create_async_playwright_browser` functions to accept a list of arguments. These arguments are now forwarded to `browser.chromium.launch()` for customizable browser instantiation.
- **Issue:** #13143
- **Dependencies:** None
- **Tag maintainer:** @eyurtsev,
- **Twitter handle:** Dr_Bearden
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I_kwDOIPDwls52ZKMY
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How to retrieve custom number of docs from parent retriver document , I have 100 docs and i don't want the reteiver to just retrieve only 4 docs
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[] | null | 7 | 2023-11-09T19:56:05 | 2023-11-13T11:08:08 | null |
NONE
| null |
### Issue you'd like to raise.
I have 100 docs , from which i am trying to retrieve top 10 docs which are most relevant to my query using parent document retriever. How can this be achieved.
### Suggestion:
_No response_
|
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| 1,986,245,356 |
PR_kwDOIPDwls5fEWNN
| 13,144 |
fix langsmith links
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I_kwDOIPDwls52Y6Qy
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Add ability to pass arguments when creating Playwright browser
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[] | null | 0 | 2023-11-09T19:09:40 | 2023-11-09T19:10:01 | null |
CONTRIBUTOR
| null |
### Feature request
Need the ability to pass arguments to `browser.chromium.launch()` in the `create_sync_playwright_browser` and `create_async_playwright_browser` functions located in [`langchain.tools.playwright.utils.py`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/tools/playwright/utils.py).
### Motivation
I created an agent that uses `PlayWrightBrowserToolkit`. All local testing worked, but I encounter errors when deploying to an AWS Lambda. I encountered two errors when testing within the AWS Lambda UI: `Target page, context or browser has been closed` and `Timeout 30000ms exceeded`. I did not experience these issues locally. I identified the issue occurred when the agent used the `navigate_browser` tool. I needed to launch the browser with `browser.chromium.launch(headless=True, args=["--disable-gpu", "--single-process"])`
### Your contribution
I have modified `utils.py` for Playwright and will submit a PR.
|
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PR_kwDOIPDwls5fES-3
| 13,142 |
Docs: combine LCEL index and why
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PR_kwDOIPDwls5fEPfJ
| 13,141 |
update modules sidebar
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PR_kwDOIPDwls5fD8_e
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Multimodal rag traces
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Bagatur/docs smith context
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PR_kwDOIPDwls5fD0JM
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OpenAIEmbeddings: retry_min_seconds/retry_max_seconds parameters
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[] | null | 6 | 2023-11-09T17:25:24 | 2023-12-12T16:49:08 | 2023-12-06T04:08:18 |
CONTRIBUTOR
| null |
- **Description:** new parameters in OpenAIEmbeddings() constructor (retry_min_seconds and retry_max_seconds) that allow parametrization by the user of the former min_seconds and max_seconds that were hidden in _create_retry_decorator() and _async_retry_decorator()
- **Issue:** #9298, #12986
- **Dependencies:** none
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @adumont
make format ✅
make lint ✅
make test ✅
|
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| 13,135 |
update oai cookbooks
|
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[] | null | 1 | 2023-11-09T16:02:17 | 2023-11-09T16:04:52 | 2023-11-09T16:04:51 |
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DOC: Install openai with langchain[openai]
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[] | null | 9 | 2023-11-09T15:42:53 | 2023-11-11T13:20:11 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
The documentation recommends to install openai with `pip install openai`.
If you do it currently (langchain 0.0.332) then an incompatible openai version is installed (1.1.2).
It's better to install it using `pip install langchain[openai]` as that will pick a version compatible with langchain.
So I propose to replace it in the docs.
WDYT ?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5fDHNM
| 13,133 |
Update huggingface_hub.py
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[] | null | 2 | 2023-11-09T15:34:33 | 2023-11-23T08:05:08 | 2023-11-23T08:05:08 |
NONE
| null |
also allows the e5 and gbe embedder families to be used
- **Description:** at the moment allowing only embedders from the sentence-transformer repo is too reductive, I have extended to two other repos with families of embeddings performing and integrated with sentence-transformer,
- **Issue:** #12069 ,
- **Tag maintainer:** @hwchase17,
|
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I_kwDOIPDwls52Xgz7
| 13,132 |
Import changed of Vertex chatModel
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[] | null | 1 | 2023-11-09T15:30:47 | 2023-11-09T15:31:17 | null |
NONE
| null |
https://github.com/langchain-ai/langchain/blob/c52725bdc5958d5295c2d563fa9b7fcb6ed09a3e/libs/langchain/langchain/chat_models/vertexai.py#L136C29-L136C29
seems there is an issue
from vertexai.preview.language_models import ChatModel
changed into
from vertexai.language_models import ChatModel
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PR_kwDOIPDwls5fDBfd
| 13,131 |
bump 333
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[] | null | 1 | 2023-11-09T15:24:40 | 2023-11-09T15:33:17 | 2023-11-09T15:33:16 |
COLLABORATOR
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I_kwDOIPDwls52W22i
| 13,129 |
Modify mysql query before execution
|
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[] | null | 13 | 2023-11-09T14:07:57 | 2023-11-09T17:22:12 | null |
NONE
| null |
### Feature request
We are using langchain create_sql_agent to build a chat engine with database.
generation and execution of query will be handled by create_sql_agent.
I want to modify the query before execution.
Please find below example:
original query : select * from transaction where type = IPC
modified query : select * from (select * from transaction where account_id = 123) where type = IPC
Can you please provide me way forward to achieve this ?
### Motivation
To protect some data from client, need this functionality
### Your contribution
I'm not familiar with the internal code, but I can help if guided.
|
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