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https://api.github.com/repos/langchain-ai/langchain/issues/16179 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16179/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16179/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16179/events | https://github.com/langchain-ai/langchain/issues/16179 | 2,087,527,966 | I_kwDOIPDwls58bSYe | 16,179 | How can I embed chat logs into the specified collection 'QDRANT_COLLECTION_NAME' in Qrdant, and what approach should I use to write the chain for short-term memory? | {
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] | open | false | null | [] | null | 1 | 2024-01-18T05:22:40 | 2024-01-18T05:32:44 | null | NONE | null | ### Issue with current documentation:
import os
import qdrant_client
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings.huggingface import HuggingFaceInstructEmbeddings
from langchain.llms import OpenAI
from langchain.vectorstores import Qdrant
os.environ['OPENAI_API_KEY'] = "mykey"
# memory=
def get_vector_store():
client = qdrant_client.QdrantClient(
os.getenv('QDRANT_HOST'),
)
embeddings = HuggingFaceInstructEmbeddings(model_name="all-MiniLM-L6-v2")
vector_store = Qdrant(
client=client,
collection_name=os.getenv('QDRANT_COLLECTION_NAME'),
embeddings=embeddings,
)
return vector_store
def main():
load_dotenv()
vectorstore = get_vector_store()
qa = RetrievalQA.from_chain_type(
llm=OpenAI(
temperature=0.7,
max_tokens=100,
),
chain_type="stuff",
retriever=vectorstore.as_retriever(search_type="similarity_score_threshold",
search_kwargs={"score_threshold": 0.1})
)
while True:
user_question = input("ask:")
if user_question == "bye":
break
answer = qa.invoke(user_question)
print(f"Ai: {answer}")
if __name__ == "__main__":
main()
### Idea or request for content:
How can I embed chat logs into the specified collection 'QDRANT_COLLECTION_NAME' in Qrdant, and what approach should I use to write the chain for short-term memory? | {
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] | open | false | null | [] | null | 2 | 2024-01-18T05:01:53 | 2024-01-18T05:08:49 | null | CONTRIBUTOR | null | - **Description:** add milvus multitenancy doc, it is an example for this [pr](https://github.com/langchain-ai/langchain/pull/15740) .
- **Issue:** No,
- **Dependencies:** No,
- **Twitter handle:** No
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Decreased dependency on `runnables` | {
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] | open | false | null | [] | null | 1 | 2024-01-18T03:34:34 | 2024-01-18T03:41:01 | null | NONE | null | Hi, I'm from the LanceDB team.
Improves LanceDB integration by making it easier to use - now you aren't required to create tables manually and pass them in the constructor, although that is still backward compatible.
Bug fix - pandas was being used even though it's not a dependency for LanceDB or langchain
PS - this issue was raised a few months ago but lost traction. It is a feature improvement for our users kindly review this , Thanks ! | {
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] | open | false | null | [] | null | 1 | 2024-01-18T02:54:59 | 2024-01-18T03:57:12 | null | COLLABORATOR | null | This PR adds `astream_events` method to Runnables to make it easier to stream data from arbitrary chains.
* Streaming only works properly in async right now
* One should use `astream()` with if mixing in imperative code as might be done with tool implementations
* Astream_log has been modified with minimal additive changes, so no breaking changes are expected
* Underlying callback code / tracing code should be refactored at some point to handle things more consistently (OK for now)
- [ ] check if there are handled callbacks by other LangChain runnables (e.g., embedders)
- [ ] verify event for on_retry
- [ ] Any rrenaming? Should we rename "event" to "hook"?
- [ ] Any other feedback from community?
## Example
See this [Example Notebook](https://github.com/langchain-ai/langchain/blob/dbbc7fa0d66bcdde420760baa0ddb9918c12c349/docs/docs/modules/agents/how_to/streaming_events.ipynb) for an example with streaming in the context of an Agent
## Event Hooks Reference
Here is a reference table that shows some events that might be emitted by the various Runnable objects.
Definitions for some of the Runnable are included after the table.
| event | name | chunk | input | output |
|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | |
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | |
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | {"generations": [...], "llm_output": None, ...} |
| on_llm_start | [model name] | | {'input': 'hello'} | |
| on_llm_stream | [model name] | 'Hello' | | |
| on_llm_end | [model name] | | 'Hello human!' |
| on_chain_start | format_docs | | | |
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
| on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" |
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | | |
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
| on_retriever_chunk | [retriever name] | {documents: [...]} | | |
| on_retriever_end | [retriever name] | | {"query": "hello"} | {documents: [...]} |
| on_prompt_start | [template_name] | | {"question": "hello"} | |
| on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
Here are declarations associated with the events shown above:
`format_docs`:
```python
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
```
`some_tool`:
```python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
```
`prompt`:
```python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
```
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] | open | false | null | [] | null | 1 | 2024-01-18T02:20:45 | 2024-01-18T02:27:02 | null | NONE | null | **Description** : New documents loader for visio files (with extension .vsdx)
A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with extension .vsdx) is associated with Microsoft Visio, a diagram creation software. It stores information about the structure, layout, and graphical elements of a diagram. This format facilitates the creation and sharing of visualizations in areas such as business, engineering, and computer science.
A Visio file can contain multiple pages. Some of them may serve as the background for others, and this can occur across multiple layers. This loader extracts the textual content from each page and its associated pages, enabling the extraction of all visible text from each page, similar to what an OCR algorithm would do.
**Dependencies** : xmltodict package | {
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] | open | false | null | [] | null | 1 | 2024-01-18T01:24:10 | 2024-01-18T01:30:41 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
none
### Description
In the HOME directory of the langchain source code, execute the following command:
poetry install --with lint,typing,test
However, the following errors are thrown, and I have a few questions to ask:
(1) Why do we need to perform a downgrade operation on modules like langchain-core, and how to avoid downgrading?
(2) Why can't the module langchain-monorepo be found?
(3) How to compile the source code of langchain in order to understand the principles of langchain and contribute code to langchain?
My development environment:
Python: Built on conda, Python version 3.8.18
Langchain: The master branch on GitHub.
Error message:
(langchain) [xiaobai@xiaobai langchain-master]$ poetry install --with lint,typing,test
Installing dependencies from lock file
Package operations: 0 installs, 4 updates, 0 removals
• Downgrading langchain-core (0.1.10 /home/xiaobai/data/pro/python/langchain-master/libs/core -> 0.1.3 /home/xiaobai/data/pro/python/langchain-master/libs/core)
• Downgrading langchain-community (0.0.12 /home/xiaobai/data/pro/python/langchain-master/libs/community -> 0.0.6 /home/xiaobai/data/pro/python/langchain-master/libs/community)
• Downgrading langchain (0.1.0 /home/xiaobai/data/pro/python/langchain-master/libs/langchain -> 0.0.352 /home/xiaobai/data/pro/python/langchain-master/libs/langchain)
• Downgrading langchain-experimental (0.0.49 /home/xiaobai/data/pro/python/langchain-master/libs/experimental -> 0.0.47 /home/xiaobai/data/pro/python/langchain-master/libs/experimental)
Installing the current project: langchain-monorepo (0.0.1)
The current project could not be installed: No file/folder found for package langchain-monorepo
If you do not want to install the current project use --no-root
### System Info
Python: Built on conda, Python version 3.8.18
Langchain: The master branch on GitHub.
### 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 | {
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} | [] | open | false | null | [] | null | 1 | 2024-01-18T00:43:01 | 2024-01-18T03:21:59 | null | COLLABORATOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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,
- **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` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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https://api.github.com/repos/langchain-ai/langchain/issues/16167 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16167/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16167/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16167/events | https://github.com/langchain-ai/langchain/pull/16167 | 2,087,245,482 | PR_kwDOIPDwls5kXo9o | 16,167 | Add revision identifier to run_on_dataset | {
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- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
def createAgent():
pinecone.init(environment="gcp-starter")
llm = ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview")
embeddings = OpenAIEmbeddings()
docsearch_ch = Chroma(persist_directory="../chroma_db", embedding_function=embeddings,
collection_name="company")
docsearch = Pinecone.from_existing_index(index_name="company", embedding=embeddings)
companyQa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch_ch.as_retriever(kwargs={"k": 15})
)
tools = [
Tool(
name="Company QA",
description="Useful to get informations about Company",
func=companyQa.run
)
]
return initialize_agent(
tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
```
### Description
I have Chroma index and Pinecone index created from exactly the same documents. It is a company website scraped through Apify. Below code is used to create both indices:
```
splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=50,
separators= ["\n", "", " ", "\n\n"])
docs = splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
Pinecone.from_documents(docs, embeddings, index_name="company")
Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db", collection_name="company")
```
The problem is that Chroma seems very limited in capabilities compared to Pinecone.I have provided the example agent code that I am using.
When we use the `docsearch_ch` and ask the following prompt: `What are some clients of the Company"?`. We are going to receive `To find out the clients of the Company, you would need to contact Company directly or visit their official website or check their press releases for announcements of new partnerships or clients. Company QA does not have access to this information.`
When we use the `docsearch` that is based on Pinecone. The same prompt returns `Some clients of the company include industry leaders such as Demandbase, New Relic, Nubank, Rapid7, and Skyscanner.`
If we try to do the similarity search manually using the below code:
```
print(docsearch_ch.similarity_search("List of the company clients.", k =10))
print(docsearch.similarity_search("List of the company clients.", k =10))
```
Both of the vector stores will return more or less correct results (altough seems that Pinecone results are better), but more importantly both of them will return the Document with following content:
```
page_content='by industry leaders worldwide, such as Demandbase, New Relic, Nubank, Rapid7, and Skyscanner.'
```
When I try to debug the code and setup the breakpoint in the [BaseCombineDocumentsChain](https://github.com/langchain-ai/langchain/blob/ca014d5b04b1d73fd8f0fe224def98a82600c991/libs/langchain/langchain/chains/combine_documents/base.py#L136) the inputs for Chroma based retriever are empty. Seems like this isn't the expected behaviour.
### System Info
langchain==0.0.342
langchain-community==0.0.13
langchain-core==0.0.7
langchain-experimental==0.0.49
Mac OS M1 Ventura 13.6
Python 3.9.6
### 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 | {
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] | open | false | null | [] | null | 3 | 2024-01-17T23:17:06 | 2024-01-18T05:37:29 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
I have noticed that the prompt produced by LangChain when using ```ChatOllama(model='mixtral')``` was not in the proper format.
This can be replicated using the following:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
##messages example
messages = [
{"role": "system", "content": "You are a chef"},
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
##converting messages example to LC format
messages_lc = [(m['role'],m['content']) for m in messages]
```
For the proper format:
```
tokenizer.apply_chat_template(messages,tokenize=False)
```
This would produce:
```
"<s>[INST] <<SYS>>\nYou are a chef\n<</SYS>>\n\nWhat is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen! </s><s>[INST] Do you have mayonnaise recipes? [/INST]"
```
Where as the LangChain format:
```
from langchain_community.chat_models import ChatOllama
from langchain.prompts import ChatPromptTemplate
chat = ChatOllama(model='mixtral')
chat_resp = (ChatPromptTemplate.from_messages(messages_lc) | chat_tmp ).invoke({},{'callbacks': [ConsoleCallbackHandler()]})
```
I then copied the tokens sent to the LLM (using the ConsoleCallbackHandler) into ```tokens``` and decoded them:
```
tokenizer.decode(tokens)
```
To receive this:
```
" [INST] <<SYS>> You are a chef <</SYS>>\n[INST] What is your favourite condiment? [/INST]\nWell, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!\n[INST] Do you have mayonnaise recipes? [/INST] [/INST]
```
### Description
I'm trying to use LangChain with ChatOllama.
Output tokens decoded were:
```
" [INST] <<SYS>> You are a chef <</SYS>>\n[INST] What is your favourite condiment? [/INST]\nWell, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!\n[INST] Do you have mayonnaise recipes? [/INST] [/INST]
```
Where the proper format is:
```
"<s>[INST] <<SYS>>\nYou are a chef\n<</SYS>>\n\nWhat is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen! </s><s>[INST] Do you have mayonnaise recipes? [/INST]"
```
### System Info
langchain==0.0.350
langchain-community==0.0.3
langchain-core==0.1.0
Python 3.11.4
MacOS Sonoma
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async | {
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- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The bug was found by trying to install [jupyter-ai](https://github.com/jupyterlab/jupyter-ai) via conda-forge.
```install -c conda-forge jupyter_ai```
Basically, there is an incompatibility with the latest version of langchain and geopandas.
### Description
Based on the bug report posted on the jupyter-ai github https://github.com/jupyterlab/jupyter-ai/issues/557#issue-2062998660 , it looks like langchain enforces a version of geopandas below 0.14. Could this be relaxed to allow for >0.14? There are many features of modern geopandas that make GIS workflows so much easier, and the current enforcement version prevents langchain-dependent repositories from making full use of GIS-adjacent stacks.
### System Info
The incompatibility arose while using an M1 apple silicon laptop.
### 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 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16152 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16152/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16152/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16152/events | https://github.com/langchain-ai/langchain/pull/16152 | 2,086,707,299 | PR_kwDOIPDwls5kVysM | 16,152 | docs: change parallel doc name | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16151 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16151/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16151/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16151/events | https://github.com/langchain-ai/langchain/issues/16151 | 2,086,661,037 | I_kwDOIPDwls58X-ut | 16,151 | Dependency issues with Spark toolkit examples | {
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] | open | false | null | [] | null | 1 | 2024-01-17T17:37:23 | 2024-01-17T17:43:36 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
pip install langchain langchain-community langchain-core langchain-experimental
```
```
from langchain.agents import create_spark_sql_agent
from langchain_community.agent_toolkits import SparkSQLToolkit
from langchain_community.utilities.spark_sql import SparkSQL
from langchain_openai import ChatOpenAI
from pyspark.sql import SparkSession
```
### Description
I get errors related to dependicies when trying to follow the Spark toolkit examples from the documentation.
For example:
https://python.langchain.com/docs/integrations/toolkits/spark_sql
```
ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/databricks/python/lib/python3.10/site-packages/typing_extensions.py)
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
File <command-2000110168240505>, line 1
----> 1 from langchain.agents import create_spark_sql_agent
2 from langchain_community.agent_toolkits import SparkSQLToolkit
3 from langchain_community.utilities.spark_sql import SparkSQL
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-b2a0b64a-b43b-40f8-a165-ff3aac48e3b4/lib/python3.10/site-packages/langchain/agents/__init__.py:34
31 from pathlib import Path
32 from typing import Any
---> 34 from langchain_community.agent_toolkits import (
35 create_json_agent,
36 create_openapi_agent,
37 create_pbi_agent,
38 create_pbi_chat_agent,
39 create_spark_sql_agent,
40 create_sql_agent,
41 )
42 from langchain_core._api.path import as_import_path
44 from langchain.agents.agent import (
45 Agent,
```
### System Info
langchain==0.1.1
langchain-community==0.0.13
langchain-core==0.1.11
langchain-experimental==0.0.49
Python 3.10
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16150 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16150/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16150/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16150/events | https://github.com/langchain-ai/langchain/issues/16150 | 2,086,621,372 | I_kwDOIPDwls58X1C8 | 16,150 | Inference Component Name header is required (SageMaker RedShift Flan T5) | {
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] | open | false | null | [] | null | 1 | 2024-01-17T17:11:42 | 2024-01-17T17:20:15 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
llm = SagemakerEndpoint(
endpoint_name=endpoint_name,
endpoint_kwargs={ "CustomAttributes": "accept_eula=true,InferenceComponentName="+inference_component_name },
region_name = region_name,
credentials_profile_name = profile_name,
model_kwargs = parameters,
content_handler = content_handler,
)
print(str( llm.endpoint_kwargs ))
db = SQLDatabase.from_uri(REDSHIFT_ENDPOINT)
db_chain = SQLDatabaseSequentialChain.from_llm(
llm, db, verbose=True, use_query_checker=True
)
try:
results = db_chain.run(QUESTION_01)
print(results)
except (ProgrammingError, ValueError, DataError) as exc:
print(f"\n\n{exc}")
### Description
I am using a Sagemaker Flan T5 Xl model to ask model and is ok,
but when try to use langchain to query RedShift DB.
I have this error.
----> " Error raised by inference endpoint: An error occurred (ValidationError) when calling the InvokeEndpoint operation: Inference Component Name header is required for endpoints to which you plan to deploy inference components. Please include Inference Component Name header or consider using SageMaker models."
### System Info
model jumpstart-dft-hf-text2text-flan-t5 xxl
Python 3.11.6
boto3 1.34.19
langchain 0.1.0
langchain-community 0.0.12
langchain-core 0.1.10
langchain-experimental 0.0.49
sentence-transformers 2.2.2
SQLAlchemy 1.4.48
### 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 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16149 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16149/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16149/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16149/events | https://github.com/langchain-ai/langchain/pull/16149 | 2,086,455,919 | PR_kwDOIPDwls5kU70D | 16,149 | Update gradient.ipynb | {
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] | closed | false | null | [] | null | 1 | 2024-01-17T15:39:56 | 2024-01-17T16:48:24 | 2024-01-17T16:48:24 | CONTRIBUTOR | null | Enviroment -> Environment
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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,
- **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` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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- Adding unpack operation to "or" and "and" filter for pgvector retriever. # | {
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] | closed | false | null | [] | null | 2 | 2024-01-17T15:18:55 | 2024-01-17T17:19:16 | 2024-01-17T17:19:15 | CONTRIBUTOR | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The following code:
```
from langchain.vectorstores import PGVector
filter = {
"source": {
"or": [{"contains": "xlsx"}, {"contains": "pdf"}]
}
}
vector_db = PGVector(connection_string=connection_string, embedding_function=embeddings)
vector_db.as_retriever(search_type="similarity", search_kwargs={"k": 5, "filter": filter})
```
won't work because the list is not unpacked when passed to
filter_by_metadata = sqlalchemy.or_(or_clauses) in https://github.com/langchain-ai/langchain/blob/e5cf1e2414a22c7465f0f47bd661ece183e88924/libs/community/langchain_community/vectorstores/pgvector.py#L548
The code throws the error
`ArgumentError: SQL expression for WHERE/HAVING role expected, got [, ].`
### Description
* I am trying to filter documents based on metadata during retrieval
* I want to pass a list to filter metadata via an "or" statement.
* I think it should be `filter_by_metadata = sqlalchemy.or_(*or_clauses)` in https://github.com/langchain-ai/langchain/blob/e5cf1e2414a22c7465f0f47bd661ece183e88924/libs/community/langchain_community/vectorstores/pgvector.py#L548
### System Info
langchain==0.0.353
langchain-community==0.0.13
langchain-core==0.1.11
Python 3.10.13
### 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 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16145 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16145/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16145/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16145/events | https://github.com/langchain-ai/langchain/issues/16145 | 2,086,351,099 | I_kwDOIPDwls58WzD7 | 16,145 | DOC: No documentation about QAGenerationChain in langchain.evaluation.qa | {
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] | open | false | null | [] | null | 1 | 2024-01-17T14:46:57 | 2024-01-17T14:53:36 | null | NONE | null | ### Issue with current documentation:
This is no documentation about QAGenerationChain in langchain.evaluation.qa
I used `from langchain.evaluation.qa import QAGenerateChain` to run evaluation using LLM-generated examples.
I tried to find documentation regarding the QAGenerateChain and found none.
Even in [api.python.langchain.com/](https://api.python.langchain.com/en/latest/search.html?q=QAGenerationChain#) site there is documentation regarding `langchain.chains.qa_generation.base.QAGenerationChain` but nothing about `from langchain.evaluation.qa import QAGenerateChain`
### Idea or request for content:
I believe there is more work that needs to be done. Please fill a documentation or a use case of `QAGenerateChain` | {
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- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
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https://api.github.com/repos/langchain-ai/langchain/issues/16140 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16140/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16140/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16140/events | https://github.com/langchain-ai/langchain/issues/16140 | 2,086,101,220 | I_kwDOIPDwls58V2Dk | 16,140 | i want to add OutputFixingParser in LCEL chain | {
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] | open | false | null | [] | null | 1 | 2024-01-17T12:35:21 | 2024-01-17T12:43:19 | null | NONE | null | ### Feature request
like
from langchain.output_parsers import OutputFixingParser
fix_parser = OutputFixingParser.from_llm(llm, parser)
chain= chat_template | llm | parser |fix_parser
### Motivation
I think OutputFixing should be in the process of generating content in a specific format.
### Your contribution
I'm sorry. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16138 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16138/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16138/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16138/events | https://github.com/langchain-ai/langchain/pull/16138 | 2,086,064,746 | PR_kwDOIPDwls5kTnbU | 16,138 | Add neo4j timeout and value sanitization option | {
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The value sanitization removes all lists that are larger than 128 elements. The idea here is to remove embedding properties from results. | {
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* Added Astra DB env vars in langchain package, used for cache tests
* Added conftest.py to load env vars in langchain_community IT
* Added .env.example in langchain_community IT
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"name": "area: vector store",
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] | open | false | null | [] | null | 3 | 2024-01-17T10:43:29 | 2024-01-17T11:37:29 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The chain is defined as:
```python
retriever_chain = create_retriever_chain(llm, retriever, use_chat_history)
_get_k_or_less_documents = partial(get_k_or_less_documents, k=k)
context = (
RunnableMap(
{
"question": lambda x: x["question"],
"memory": memory.load_memory_variables,
}
)
| RunnableMap(
{
"context": (
retriever_chain
| _get_k_or_less_documents
| reorder_documents
| format_docs
),
"question": lambda x: x["question"],
"chat_history": lambda x: x["memory"]["chat_history"],
}
)
)
prompt = ChatPromptTemplate.from_messages(
messages=[
("system", SYSTEM_ANSWER_QUESTION_TEMPLATE),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
response_synthesizer = prompt | llm
response_chain = context | response_synthesizer
```
### Description
I am trying to get back source documents used for my RAG application. However, I cannot find any ways to retrieve the document. I am using `chain.astream` to stream through my chain. The relevant code and chains are given below.
```python
async def generate_response():
input = {"question": data.message}
response = ""
async for token in chain.astream(input=input):
yield token.content
response += token.content
memory.save_context(input, {"output": response})
```
The chain is defined as:
```python
retriever_chain = create_retriever_chain(llm, retriever, use_chat_history)
_get_k_or_less_documents = partial(get_k_or_less_documents, k=k)
context = (
RunnableMap(
{
"question": lambda x: x["question"],
"memory": memory.load_memory_variables,
}
)
| RunnableMap(
{
"context": (
retriever_chain
| _get_k_or_less_documents
| reorder_documents
| format_docs
),
"question": lambda x: x["question"],
"chat_history": lambda x: x["memory"]["chat_history"],
}
)
)
prompt = ChatPromptTemplate.from_messages(
messages=[
("system", SYSTEM_ANSWER_QUESTION_TEMPLATE),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
response_synthesizer = prompt | llm
response_chain = context | response_synthesizer
```
Any kind of help is highly appreciated.
### System Info
langchain 0.1.0
langchain-community 0.0.11
langchain-core 0.1.9
langchain-openai 0.0.2
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16135 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16135/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16135/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16135/events | https://github.com/langchain-ai/langchain/issues/16135 | 2,085,861,615 | I_kwDOIPDwls58U7jv | 16,135 | ElasticsearchStore does not accept proxy | {
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] | open | false | null | [] | null | 1 | 2024-01-17T10:17:26 | 2024-01-17T10:19:47 | null | NONE | null | ### Feature request
We should be able to pass some extra parameters to `ElasticsearchStore` in form of kwargs or predefined parameters.
For example, to allow usage of `RequestsHttpNode` that takes `https_proxy` / `http_proxy` / `no_proxy` env variables.
### Motivation
In some cases, all the external calls to internet/ELK (on cloud) must use a proxy, else it will be rejected by the firewall of the company.
The default behavior of `Elasticsearch` class from https://github.com/elastic/elasticsearch-py does not take by default proxy env variable and you need to set a `node_class` to `RequestsHttpNode`
### Your contribution
Instead of calling `ElasticsearchStore` from langchain, I duplicated locally the file and hardcoded in the `Elasticsearch` init the `node_class`.
``` python
from elastic_transport import RequestsHttpNodes
# https://github.com/langchain-ai/langchain/blob/e5cf1e2414a22c7465f0f47bd661ece183e88924/libs/community/langchain_community/vectorstores/elasticsearch.py#L593
es_client = elasticsearch.Elasticsearch(
**connection_params,
node_class=RequestsHttpNode,
headers={"user-agent": ElasticsearchStore.get_user_agent()},
)
```
I am sure there's a better way and more global way to pass parameters to `Elasticsearch()` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16134 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16134/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16134/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16134/events | https://github.com/langchain-ai/langchain/issues/16134 | 2,085,829,150 | I_kwDOIPDwls58Uzoe | 16,134 | How is multiquery retreiver implemented with RAG? | {
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] | open | false | null | [] | null | 1 | 2024-01-17T10:01:04 | 2024-01-17T10:21:37 | null | NONE | null | <peek>
```
below is my code :-
def regenerate_custom_prompt(new_project_qa,chroma_db_path,query,name,not_uuid):
check = query.lower()
relevant_document = new_project_qa.get_relevant_documents(query=query)
context_text="\n\n---\n\n".join([doc.page_content for doc in relevant_document])
# print(context_text,"context_text")
user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid)
greetings = ['hi', 'hello', 'hey', 'hui', 'hiiii', 'hii', 'hiii', 'heyyy']
if check in greetings:
custom_prompt_template = f"""
Just simply reply with "Hello {name}! How can I assist you today?"
"""
elif check not in greetings and user_experience_inst.custom_prompt:
custom_prompt_template = f"""Answer the question based only on following context: ```{context_text} ```
You are a chatbot designed to provide answers to User's Questions:```{check}```, delimited by triple backticks.
Generate your answer to match the user's requirements: {user_experience_inst.custom_prompt}
If you encounter a question for which you don't know the answer, please respond with 'I don't know' and refrain from making up an answer.
- Before saying 'I don't know,' please re-verify your vector store to ensure the answer is not present in the database.
Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, feel free to ask for clarification.
User's Question: ```{check}```
AI Answer:"""
else:
# Create the custom prompt template
custom_prompt_template = """Generate your response exclusively from the provided context: {context_text}. You function as a chatbot specializing in delivering detailed answers to the User's Question: ```{check} ```, enclosed within triple backticks.
Generate your answer in points in the following format:
1. Point no 1
1.1 Its subpoint in details
1.2 More information if needed.
2. Point no 2
2.1 Its subpoint in details
2.2 More information if needed.
…
N. Another main point.
If you encounter a question for which you don't know the answer based on the predefined points, please respond with 'I don't know' and refrain from making up an answer.
However, if the answer is not present in the predefined points, then Provide comprehensive information related to the user's query.
Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, you can ask for clarification.
User's Question: ```{check} ```
AI Answer:"""
# Create the PromptTemplate
custom_prompt = ChatPromptTemplate.from_template(custom_prompt_template)
formatted_prompt = custom_prompt.format(context_text=context_text, check=check)
print(formatted_prompt,"-------------------------------")
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
llm = ChatOpenAI(model="gpt-3.5-turbo-16k",temperature=0.1)
### mmr stands for Maximum marginal relevance
retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={"k":2})
qa = MultiQueryRetriever.from_llm(llm=llm, retriever=retriever)
return qa
</peek> | {
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] | open | false | null | [] | null | 1 | 2024-01-17T09:39:10 | 2024-01-17T17:12:08 | null | CONTRIBUTOR | null | Replace this entire comment with:
- **Description:** allow user to define tVector length in PGVector when creating the embedding store, this allows for later indexing
- **Issue:** #16132
- **Dependencies:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/16132 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16132/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16132/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16132/events | https://github.com/langchain-ai/langchain/issues/16132 | 2,085,775,436 | I_kwDOIPDwls58UmhM | 16,132 | Implement vector length definition at init time in PGVector for indexing | {
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] | open | false | null | [] | null | 1 | 2024-01-17T09:31:57 | 2024-01-17T09:34:24 | null | CONTRIBUTOR | null | ### Feature request
Allow manual definition of embedding column length in PGVector
### Motivation
Right now embeddings stores in PGVector are initialised with:
`embedding: Vector = sqlalchemy.Column(Vector(None))`
This won't allow indexing of the DB ([https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/howto-optimize-performance-pgvector](url))
### Your contribution
I can open a PR shortly to fix it | {
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] | open | false | null | [] | null | 1 | 2024-01-17T09:28:12 | 2024-01-17T09:35:57 | null | NONE | null | ### Issue with current documentation:
https://python.langchain.com/docs/langgraph
The paragraph under "Examples":
>
> Modifications
>
> We also have a lot of examples highlighting how to slightly modify the base chat agent executor. These all build off the [getting started notebook](https://python.langchain.com/docs/examples/chat_agent_executor_with_function_calling/base.ipynb) so it is recommended you start with that first.
The link is not working.
### Idea or request for content:
_No response_ | {
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] | open | false | null | [] | null | 1 | 2024-01-17T09:09:24 | 2024-01-17T09:18:55 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
db = SQLDatabase.from_uri(
'vertica+vertica_python://user:pwd@x.x.x.x:5433/verticadst',
schema='myschema'
)
```
### Description
When I use the above snippet to create an SQLDatabase object for vertica, I get the below error:
> sqlalchemy.exc.ProgrammingError: (vertica_python.errors.MissingSchema) Severity: ERROR, Message: Schema "pg_catalog" does not exist
Which makes sense because Vertica does not have pg_catalog schema, but how can I create the object without the reflect part where it tries to read from pg_catalog
### System Info
sqlalchemy-vertica-python~=0.6.3
langchain==0.1.0
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | open | false | null | [] | null | 1 | 2024-01-17T06:51:10 | 2024-01-17T07:03:05 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The following code follows the example from [langchain docs](https://python.langchain.com/docs/use_cases/extraction#option-2-parsing).
```python
class Person(BaseModel):
person_name: str
person_height: int
person_hair_color: str
dog_breed: Optional[str]
dog_name: Optional[str]
class People(BaseModel):
"""Identifying information about all people in a text."""
people: Sequence[Person]
query = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde. Alex has a poodle named Rex."""
# Set up a parser + inject instructions into the prompt template
parser = PydanticOutputParser(pydantic_object=People)
prompt = PromptTemplate(
template="Follow the given instructions and perform the task mentioned in the context of given information. \n{format_instructions}\n Context is:\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": parser.get_format_instructions()},
)
input_to_model=prompt.format_prompt(query=query)
print(input_to_model.to_string())
# output=FLAN.generate_text(input_to_model.to_string())
output=LLAMA.generate_text(input_to_model.to_string())
print(output)
parser.parse(output)
```
### Description
* I'm trying to use PydanticOutputParser for structured output.
* Incase of an optional field missing from JSON, pydanticoutput parser raises a validation error saying missing a required field
Output from model:
```json
```
{
"people": [
{
"person_name": "Alex",
"person_height": 5,
"person_hair_color": "blonde",
"dog_breed": "poodle",
"dog_name": "Rex"
},
{
"person_name": "Claudia",
"person_height": 6,
"person_hair_color": "brunette"
}
]
}
```
```
Complete Error:
```json
{
"name": "ValidationError",
"message": "2 validation errors for People\npeople.1.dog_breed\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing\npeople.1.dog_name\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing",
"stack": "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValidationError\u001b[0m Traceback (most recent call last)\nCell \u001b[1;32mIn[70], line 33\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;66;03m# output=FLAN.generate_text(input_to_model.to_string())\u001b[39;00m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28mprint\u001b[39m(output)\n\u001b[1;32m---> 33\u001b[0m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;66;03m# print(parser.get_format_instructions())\u001b[39;00m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\langchain\\output_parsers\\pydantic.py:30\u001b[0m, in \u001b[0;36mPydanticOutputParser.parse\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m 28\u001b[0m json_str \u001b[38;5;241m=\u001b[39m match\u001b[38;5;241m.\u001b[39mgroup()\n\u001b[0;32m 29\u001b[0m json_object \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(json_str, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m---> 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpydantic_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_object\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (json\u001b[38;5;241m.\u001b[39mJSONDecodeError, ValidationError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 33\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpydantic_object\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\typing_extensions.py:2499\u001b[0m, in \u001b[0;36mdeprecated.__call__.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 2496\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(arg)\n\u001b[0;32m 2497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 2498\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(msg, category\u001b[38;5;241m=\u001b[39mcategory, stacklevel\u001b[38;5;241m=\u001b[39mstacklevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m-> 2499\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marg\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\pydantic\\main.py:1027\u001b[0m, in \u001b[0;36mBaseModel.parse_obj\u001b[1;34m(cls, obj)\u001b[0m\n\u001b[0;32m 1021\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m 1022\u001b[0m \u001b[38;5;129m@typing_extensions\u001b[39m\u001b[38;5;241m.\u001b[39mdeprecated(\n\u001b[0;32m 1023\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe `parse_obj` method is deprecated; use `model_validate` instead.\u001b[39m\u001b[38;5;124m'\u001b[39m, category\u001b[38;5;241m=\u001b[39mPydanticDeprecatedSince20\n\u001b[0;32m 1024\u001b[0m )\n\u001b[0;32m 1025\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_obj\u001b[39m(\u001b[38;5;28mcls\u001b[39m: \u001b[38;5;28mtype\u001b[39m[Model], obj: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Model: \u001b[38;5;66;03m# noqa: D102\u001b[39;00m\n\u001b[0;32m 1026\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe `parse_obj` method is deprecated; use `model_validate` instead.\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[1;32m-> 1027\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\pydantic\\main.py:503\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[1;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[0;32m 502\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_attributes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\n\u001b[0;32m 505\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\n\u001b[1;31mValidationError\u001b[0m: 2 validation errors for People\npeople.1.dog_breed\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing\npeople.1.dog_name\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing"
}
```
### System Info
Langchain:
```
langchain==0.1.0
langchain-community==0.0.10
langchain-core==0.1.8
```
platform:
```
Windows 11 Enterprise Version 10.0.22621 Build 22621
```
python :
```
Python 3.11.7
```
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | closed | false | null | [] | null | 1 | 2024-01-17T06:29:53 | 2024-01-17T17:11:17 | 2024-01-17T17:11:16 | CONTRIBUTOR | null | **Description**: This PR fixes an error in the documentation for Azure Cosmos DB Integration.
**Issue**: The correct way to import `AzureCosmosDBVectorSearch` is
```python
from langchain_community.vectorstores.azure_cosmos_db import (
AzureCosmosDBVectorSearch,
)
```
While the [documentation](https://python.langchain.com/docs/integrations/vectorstores/azure_cosmos_db) states it to be
```python
from langchain_community.vectorstores.azure_cosmos_db_vector_search import (
AzureCosmosDBVectorSearch,
CosmosDBSimilarityType,
)
```
As you can see in [azure_cosmos_db.py](https://github.com/langchain-ai/langchain/blob/c323742f4fda30e8bef5381f41f66192afb2b9d2/libs/langchain/langchain/vectorstores/azure_cosmos_db.py#L1C45-L2)
**Dependencies:**: None
**Twitter handle**: None
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https://api.github.com/repos/langchain-ai/langchain/issues/16121 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16121/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16121/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16121/events | https://github.com/langchain-ai/langchain/issues/16121 | 2,085,484,831 | I_kwDOIPDwls58Tfkf | 16,121 | Why can't test117 be retrieved by vector and provide replies for reference to achieve long-term memory? | {
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] | open | false | null | [] | null | 1 | 2024-01-17T06:29:33 | 2024-01-17T06:37:50 | null | NONE | null | ### Issue with current documentation:
import os
from langchain_community.chat_models import ChatOpenAI
from langchain.schema import Document
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Qdrant
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from qdrant_client import QdrantClient, models
os.environ['OPENAI_API_KEY'] = "mykey"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
client = QdrantClient(host="192.168.10.31", port=6333)
collection_name = "test117"
client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE),
optimizers_config=models.OptimizersConfigDiff(memmap_threshold=20000),
)
vectorstore = Qdrant(
client=client,
embedding=embeddings,
location=":memory:",
collection_name=collection_name,
)
llm = ChatOpenAI(
temperature=0.7,
model="gpt-3.5-turbo",
max_tokens=100
)
memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory, verbose=True,)
collection_info = client.get_collection(collection_name)
while True:
chat_history = memory.load_memory_variables({})
question = input('ask：')
question_vector = embeddings.embed_query(question)[0]
search_results = client.search(
collection_name=collection_name,
query_vector=models.NamedSparseVector(
name=collection_name,
vector=models.SparseVector(
indices=question_vector.indices,
values=question_vector.values,
),
),
limit=1,
)
if search_results and search_results[0].payload:
response_vector = search_results[0].payload.vector
response_str = vectorstore.retrieve_text(response_vector)
else:
response = conversation.predict(input=f"Q: {question}\nA:")
response_str = response.choices[0].text.strip()
response_vector = embeddings.embed_query(response_str)[0]
vectorstore.add_documents([Document(vector=response_vector, text=response_str)])
print("respones：", response_str)
memory.add_memory_variables({"input": question, "output": response_str})
if question.lower() == 'bye':
break
### Idea or request for content:
Why can't test117 be retrieved by vector and provide replies for reference to achieve long-term memory?
Is it that the retriever is not written well? | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16120 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16120/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16120/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16120/events | https://github.com/langchain-ai/langchain/pull/16120 | 2,085,467,940 | PR_kwDOIPDwls5kRlrm | 16,120 | Add Tuple support for messages placeholder | {
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} | [] | open | false | null | [] | null | 1 | 2024-01-17T06:16:51 | 2024-01-17T17:16:15 | null | COLLABORATOR | null | It's a bit annoying to work with LangChain messages on the frontend, which is why we [did things like this in chat langchain](https://github.com/langchain-ai/chat-langchain/blob/592d588a1bfe35fe03ed33ac8ba36afd9452bb6d/chain.py#L148). It would be convenient if the messages placeholder also accepted the same tuple format as the prompt template, so you could just pass the role and content values around.
Maybe not as useful in a real memory setup, but simplifies the user code a bit in some situations. | {
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