docs: fix broken source links after repo restructure

docs/query/drift_search.md

@@ -4,7 +4,7 @@
 
 GraphRAG is a technique that uses large language models (LLMs) to create knowledge graphs and summaries from unstructured text documents and leverages them to improve retrieval-augmented generation (RAG) operations on private datasets. It offers comprehensive global overviews of large, private troves of unstructured text documents while also enabling exploration of detailed, localized information. By using LLMs to create comprehensive knowledge graphs that connect and describe entities and relationships contained in those documents, GraphRAG leverages semantic structuring of the data to generate responses to a wide variety of complex user queries.
 
-DRIFT search (Dynamic Reasoning and Inference with Flexible Traversal) builds upon Microsoft’s GraphRAG technique, combining characteristics of both global and local search to generate detailed responses in a method that balances computational costs with quality outcomes using our [drift search](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/drift_search/) method.
+DRIFT search (Dynamic Reasoning and Inference with Flexible Traversal) builds upon Microsoft’s GraphRAG technique, combining characteristics of both global and local search to generate detailed responses in a method that balances computational costs with quality outcomes using our [drift search](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/drift_search/) method.
 
 ## Methodology
 
@@ -19,7 +19,7 @@ DRIFT Search introduces a new approach to local search queries by including comm
 
 ## Configuration
 
-Below are the key parameters of the [DRIFTSearch class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/drift_search/search.py):
+Below are the key parameters of the [DRIFTSearch class](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/drift_search/search.py):
 
 * `model`: Language model chat completion object to be used for response generation
 - `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main/graphrag/query/structured_search/drift_search/drift_context.py) object to be used for preparing context data from community reports and query information

docs/query/global_search.md

@@ -4,7 +4,7 @@
 
 Baseline RAG struggles with queries that require aggregation of information across the dataset to compose an answer. Queries such as “What are the top 5 themes in the data?” perform terribly because baseline RAG relies on a vector search of semantically similar text content within the dataset. There is nothing in the query to direct it to the correct information.
 
-However, with GraphRAG we can answer such questions, because the structure of the LLM-generated knowledge graph tells us about the structure (and thus themes) of the dataset as a whole. This allows the private dataset to be organized into meaningful semantic clusters that are pre-summarized. Using our [global search](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/global_search/) method, the LLM uses these clusters to summarize these themes when responding to a user query.
+However, with GraphRAG we can answer such questions, because the structure of the LLM-generated knowledge graph tells us about the structure (and thus themes) of the dataset as a whole. This allows the private dataset to be organized into meaningful semantic clusters that are pre-summarized. Using our [global search](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/global_search/) method, the LLM uses these clusters to summarize these themes when responding to a user query.
 
 ## Methodology
 
@@ -52,19 +52,19 @@ The quality of the global search’s response can be heavily influenced by the l
 
 ## Configuration
 
-Below are the key parameters of the [GlobalSearch class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/global_search/search.py):
+Below are the key parameters of the [GlobalSearch class](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/global_search/search.py):
 
 * `model`: Language model chat completion object to be used for response generation
-* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/global_search/community_context.py) object to be used for preparing context data from community reports
-* `map_system_prompt`: prompt template used in the `map` stage. Default template can be found at [map_system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/global_search_map_system_prompt.py)
-* `reduce_system_prompt`: prompt template used in the `reduce` stage, default template can be found at [reduce_system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/global_search_reduce_system_prompt.py)
+* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py) object to be used for preparing context data from community reports
+* `map_system_prompt`: prompt template used in the `map` stage. Default template can be found at [map_system_prompt](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/prompts/query/global_search_map_system_prompt.py)
+* `reduce_system_prompt`: prompt template used in the `reduce` stage, default template can be found at [reduce_system_prompt](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/prompts/query/global_search_reduce_system_prompt.py)
 * `response_type`: free-form text describing the desired response type and format (e.g., `Multiple Paragraphs`, `Multi-Page Report`)
 * `allow_general_knowledge`: setting this to True will include additional instructions to the `reduce_system_prompt` to prompt the LLM to incorporate relevant real-world knowledge outside of the dataset. Note that this may increase hallucinations, but can be useful for certain scenarios. Default is False
-*`general_knowledge_inclusion_prompt`: instruction to add to the `reduce_system_prompt` if `allow_general_knowledge` is enabled. Default instruction can be found at [general_knowledge_instruction](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/global_search_knowledge_system_prompt.py)
+*`general_knowledge_inclusion_prompt`: instruction to add to the `reduce_system_prompt` if `allow_general_knowledge` is enabled. Default instruction can be found at [general_knowledge_instruction](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/prompts/query/global_search_knowledge_system_prompt.py)
 * `max_data_tokens`: token budget for the context data
 * `map_llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call at the `map` stage
 * `reduce_llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to passed to the LLM call at the `reduce` stage
-* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/global_search/community_context.py) object when building context window for the `map` stage.
+* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py) object when building context window for the `map` stage.
 * `concurrent_coroutines`: controls the degree of parallelism in the `map` stage.
 * `callbacks`: optional callback functions, can be used to provide custom event handlers for LLM's completion streaming events
 

docs/query/local_search.md

@@ -2,7 +2,7 @@
 
 ## Entity-based Reasoning
 
-The [local search](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/) method combines structured data from the knowledge graph with unstructured data from the input documents to augment the LLM context with relevant entity information at query time. It is well-suited for answering questions that require an understanding of specific entities mentioned in the input documents (e.g., “What are the healing properties of chamomile?”).
+The [local search](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/local_search/) method combines structured data from the knowledge graph with unstructured data from the input documents to augment the LLM context with relevant entity information at query time. It is well-suited for answering questions that require an understanding of specific entities mentioned in the input documents (e.g., “What are the healing properties of chamomile?”).
 
 ## Methodology
 
@@ -46,14 +46,14 @@ Given a user query and, optionally, the conversation history, the local search m
 
 ## Configuration
 
-Below are the key parameters of the [LocalSearch class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/search.py):
+Below are the key parameters of the [LocalSearch class](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/local_search/search.py):
 
 * `model`: Language model chat completion object to be used for response generation
-* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects
-* `system_prompt`: prompt template used to generate the search response. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/local_search_system_prompt.py)
+* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects
+* `system_prompt`: prompt template used to generate the search response. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/prompts/query/local_search_system_prompt.py)
 * `response_type`: free-form text describing the desired response type and format (e.g., `Multiple Paragraphs`, `Multi-Page Report`)
 * `llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call
-* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object when building context for the search prompt
+* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py) object when building context for the search prompt
 * `callbacks`: optional callback functions, can be used to provide custom event handlers for LLM's completion streaming events
 
 ## How to Use

docs/query/question_generation.md

@@ -2,20 +2,20 @@
 
 ## Entity-based Question Generation
 
-The [question generation](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/) method combines structured data from the knowledge graph with unstructured data from the input documents to generate candidate questions related to specific entities.
+The [question generation](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/question_gen/) method combines structured data from the knowledge graph with unstructured data from the input documents to generate candidate questions related to specific entities.
 
 ## Methodology
 Given a list of prior user questions, the question generation method uses the same context-building approach employed in [local search](local_search.md) to extract and prioritize relevant structured and unstructured data, including entities, relationships, covariates, community reports and raw text chunks. These data records are then fitted into a single LLM prompt to generate candidate follow-up questions that represent the most important or urgent information content or themes in the data.
 
 ## Configuration
 
-Below are the key parameters of the [Question Generation class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/local_gen.py):
+Below are the key parameters of the [Question Generation class](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/question_gen/local_gen.py):
 
 * `model`: Language model chat completion object to be used for response generation
-* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects, using the same context builder class as in local search
-* `system_prompt`: prompt template used to generate candidate questions. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/question_gen_system_prompt.py)
+* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects, using the same context builder class as in local search
+* `system_prompt`: prompt template used to generate candidate questions. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/prompts/query/question_gen_system_prompt.py)
 * `llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call
-* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object when building context for the question generation prompt
+* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py) object when building context for the question generation prompt
 * `callbacks`: optional callback functions, can be used to provide custom event handlers for LLM's completion streaming events
 
 ## How to Use