| Name | llama-index-readers-azcognitive-search JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | llama-index readers azcognitive_search integration |
| upload_time | 2024-08-22 05:50:25 |
| maintainer | mrcabellom |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Azure Cognitive Search Loader
```bash
pip install llama-index-readers-azcognitive-search
```
The AzCognitiveSearchReader Loader returns a set of texts corresponding to documents retrieved from specific index of Azure Cognitive Search.
The user initializes the loader with credentials (service name and key) and the index name.
## Usage
Here's an example usage of the AzCognitiveSearchReader.
```python
from llama_index.readers.azcognitive_search import AzCognitiveSearchReader
reader = AzCognitiveSearchReader(
"<Azure_Cognitive_Search_NAME>",
"<Azure_Cognitive_Search_KEY>",
"<Index_name>",
)
query_sample = ""
documents = reader.load_data(
query="<search_term>",
content_field="<content_field_name>",
filter="<azure_search_filter>",
)
```
## Usage in combination with langchain
```python
from llama_index.core import VectorStoreIndex, download_loader
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.agents import Tool, AgentExecutor, load_tools, initialize_agent
from llama_index.readers.azcognitive_search import AzCognitiveSearchReader
az_loader = AzCognitiveSearchReader(
COGNITIVE_SEARCH_SERVICE_NAME, COGNITIVE_SEARCH_KEY, INDEX_NAME
)
documents = az_loader.load_data(query, field_name)
index = VectorStoreIndex.from_documents(
documents, service_context=service_context
)
tools = [
Tool(
name="Azure cognitive search index",
func=lambda q: index.query(q),
description=f"Useful when you want answer questions about the text on azure cognitive search.",
),
]
memory = ConversationBufferMemory(memory_key="chat_history")
agent_chain = initialize_agent(
tools, llm, agent="zero-shot-react-description", memory=memory
)
result = agent_chain.run(input="How can I contact with my health insurance?")
```
This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).
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"description": "# Azure Cognitive Search Loader\n\n```bash\npip install llama-index-readers-azcognitive-search\n```\n\nThe AzCognitiveSearchReader Loader returns a set of texts corresponding to documents retrieved from specific index of Azure Cognitive Search.\nThe user initializes the loader with credentials (service name and key) and the index name.\n\n## Usage\n\nHere's an example usage of the AzCognitiveSearchReader.\n\n```python\nfrom llama_index.readers.azcognitive_search import AzCognitiveSearchReader\n\nreader = AzCognitiveSearchReader(\n \"<Azure_Cognitive_Search_NAME>\",\n \"<Azure_Cognitive_Search_KEY>\",\n \"<Index_name>\",\n)\n\n\nquery_sample = \"\"\ndocuments = reader.load_data(\n query=\"<search_term>\",\n content_field=\"<content_field_name>\",\n filter=\"<azure_search_filter>\",\n)\n```\n\n## Usage in combination with langchain\n\n```python\nfrom llama_index.core import VectorStoreIndex, download_loader\nfrom langchain.chains.conversation.memory import ConversationBufferMemory\nfrom langchain.agents import Tool, AgentExecutor, load_tools, initialize_agent\n\nfrom llama_index.readers.azcognitive_search import AzCognitiveSearchReader\n\naz_loader = AzCognitiveSearchReader(\n COGNITIVE_SEARCH_SERVICE_NAME, COGNITIVE_SEARCH_KEY, INDEX_NAME\n)\n\ndocuments = az_loader.load_data(query, field_name)\n\nindex = VectorStoreIndex.from_documents(\n documents, service_context=service_context\n)\n\ntools = [\n Tool(\n name=\"Azure cognitive search index\",\n func=lambda q: index.query(q),\n description=f\"Useful when you want answer questions about the text on azure cognitive search.\",\n ),\n]\nmemory = ConversationBufferMemory(memory_key=\"chat_history\")\nagent_chain = initialize_agent(\n tools, llm, agent=\"zero-shot-react-description\", memory=memory\n)\n\nresult = agent_chain.run(input=\"How can I contact with my health insurance?\")\n```\n\nThis loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).\n",
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